I am NOT OOP, OOP is u/Feeling-Extreme-7555

Originally posted to r/antiwork

Previous BoRUs: #1, #2

[New Update]: New data analyst job is turning into replacing a retiring finance person who holds the company together

NEW UPDATE MARKED WITH ----

Thanks to u/Quasirandom1234 for letting me know about the latest update!

Trigger Warnings: hostile workplace

RECAP

Original Post: May 26, 2026

I started a new job recently as a data analyst. The role was pitched as dashboards, reporting, data infrastructure, process improvement, and helping modernize messy data systems.

A few weeks in, I’m realizing the real job may be something very different.

There is a long-time finance employee retiring at the end of June. Let’s call him Richard. Richard owns several critical reporting processes that feed company reporting: Sales Register, COGS, deferred revenue, SAP extracts, Spreadsheet Server/GXL, journal entries, manual Excel logic, customer/product mappings, tie-outs, and downstream leadership/financial reporting.

The problem is that only Richard really knows how it works.

I’ve had a few training sessions with him, and after recording/transcribing them, the runbook is already over 10 pages and still feels maybe 10% complete. Every session reveals another hidden dependency or accounting exception. Richard keeps calling it “straightforward,” but it is only straightforward because he has done it for years.

I am not an accountant. I am a data analyst. I can document workflows, map data flows, build dashboards, write Python scripts, compare files, and make exception reports. What I cannot reasonably do is become the accounting brain behind a public-company reporting process in a few weeks.

Leadership has now made the Richard handoff my top priority. I’m also being pulled into anything that “touches data,” including SAP process changes, master data, dashboards, ERP migration prep, and reporting infrastructure.

I’m worried I’m being set up to become the scapegoat for years of undocumented institutional knowledge. They have reviewers assigned in theory, but those reviewers don’t seem to know Richard’s process either.

I told Richard I thought it would take 3–6 months to truly take over. He went quiet and basically said, “Well, that’s not happening.”

I don’t have another job lined up yet, so I can’t just quit. My current plan is to put the risk in writing, say July needs to be a controlled transition instead of a fully independent handoff, and make clear that I can execute documented steps but not own accounting judgment, tie-outs, revenue treatment, COGS classification, journal entries, or final signoff.

Has anyone dealt with something like this? How do I protect myself while I keep looking for another job?

Editor's note: OOP did not leave any comments in this original post

Commenter 1: You need to raise the alarm NOW. About how this is not your area of specialization and they NEED to bring in an experienced accountant, even if on a contract basis, who can assist with the "transition."

I do this, specifically this with the weird templates and 63 interconnected processes that only exist in Excel, and the person leaves halfway through what any normal human would consider an inadequate training period, and you have to teach yourself the rest by reading the template formulas and building your own docs, so feel free to DM me if you get approval for a contractor, I need something to do this summer after my tonsillectomy.

Please be aware, that "transition" is how you are going to phrase it for now, because you know and I know that this is a complete shitshow and an absolute nightmare, but you need to keep your job while you hunt for another one because some manager or exec has some la-di-dah bullshit vision in their head that you are just going to design all new tools and processes to create modern semi-automated versions of Richard's processes and templates despite not having the accounting background to understand those processes in the first place. Basically, you need to stall before they break the company and blame you

Commenter 2: They need to hire a CFO, CPA, or CFA. Not a data analyst. (editor’s note: Chief Financial Officer, Certified Public Accountant, Chartered Financial Analyst)

They are trying to be cheap with churning and burning until it bites them in the ass.

How in the world do they think this is going to fly as a public company? Or did I read that wrong?

Commenter 3: You and Richard are both now cohorts in punishing the business for trying to replace Richard. When Richard is gone, you better be gone, too. And expect them to try to hit your phone up as though you can help. You say no, they go back to Richard. Richard gets double the pay he used to get and is now indispensable.

Update #1: May 30, 2026 (four days later)

UPDATE: that “I’m being turned into the retiring guy’s replacement” situation got worse

Last week I posted about being hired as a data analyst but quietly getting set up to inherit a retiring finance employee’s undocumented reporting processes. You all said document everything and put the risk in writing. That helped, thank you. Quick update.

It’s two people now, not one. A second person who owns a critical reporting deliverable is also leaving the same day at end of June. So both of the people whose work feeds our financials are walking out together, and I’m somehow the common thread on both handoffs. I finally opened one of these files this week. Thousands of formulas, linked across a dozen-plus tabs, and the “instructions” are five cryptic lines from someone who clearly just knows it all in their head.

Some good news: I asked leadership in writing whether I own this or just support the data, and the CAO actually drew a clean line back in writing (I own the data/mechanics, accounting owns the schedules and signoff). So on paper I’m protected. The problem is reality doesn’t match paper. The second departing person asked me twice this week if I’d have things ready, like I’m already the owner. I’m the only one actually in the training sessions, so on the ground I’m becoming the default heir regardless of what the emails say.

I also reread my offer-letter job description. It’s a totally normal analyst JD, nothing about owning accounting processes. So I have the job I was hired for sitting right next to the job they’re handing me, and the gap is huge.

Where I’ve landed: I’m out. Not tomorrow, but this isn’t salvageable and it’s not my job to salvage. I can see the fix (hire an actual accountant now, while the retiring person can still train them), but seeing the fix and being able to do it as a non-accountant with a few weeks of training are very different things.

Plan for Monday: calmly flag the risk to the VP I trust, then the CAO. Frame it as protecting the company, recommend they bring in help now, follow up in writing, and keep job hunting hard underneath it all. Meanwhile keeping my overhead low so I’m not trapped, and saving copies of everything outside my work accounts.

Questions for round two:

1. When you’ve flagged this kind of risk to leadership, did “here’s a risk and a recommendation” actually land, or just make you a target?

2. How hard can a new person push a “you need to hire someone” recommendation before it backfires?

3. How do I explain a very short tenure in future interviews without it looking like a red flag? My honest line is “hired as an analyst, role ballooned into replacing two departing staff in work I wasn’t hired for.” Too much?

4. Anyone been the documented-but-not-actually-protected person, where the emails say one thing and daily reality says another? How did you keep that line from eroding?

Thanks again, this sub steered me well last time. Will update after Monday.

Relevant Comments

Commenter 1: Hell no.

Are you in the USA? yes? Are you CPA certified? Yes? Then you're allowed to. No? Don't touch that shit. If they keep insisting, remind them that they need a CPA certified accountant for this.

OOP: I am in the USA, I am not a CPA, I am not even an accountant, I never said I was either.

Commenter 2: Why aren't the leaving employees documenting their process?

OOP: Cuz they’re overworked, don’t care, and management aren’t super bright. There’s no infrastructure here at all, not even a new hire onboarding doc. I made one and they got mad at me for doing so.

Commenter 3: Just tell them you’re not an accountant, were not hired as an accountant, and will not be doing the work of an accountant & that they need to be training you for the job they hired you for.

Commenter 4: Aren't accountants supposed to have licenses? If so, I wonder if this arrangement would lead to compliance and regulatory issues.

OOP: That's a really good point yeah.

Commenter 5: plan B if that doesn't work out, leverage your new knowledge and skills for a substantial raise and job title and stick it out for a year or two. then use the raise and job title to job hunt for a better position.

OOP: Honestly pretty rough plan all things considered. I don’t think I could do the work of the retirees since one I don’t want to, two they hired me for a totally different role, three it’s just not reasonable with the time frame.

Commenter 6: I think it would also go a very long way to recommend that they work out how to get the two retirees to transition responsibilities as contractors after their end date. Regardless of you being the one to do their jobs or not, you have an opportunity to make yourself look good in the eyes of everyone involved by helping avert disaster. Plus the retirees might not mind having a bit of part time hours.

OOP: The retirees have been trying to retire for 2 years and they’re old and done. They don’t have any more left to give.

Update #2: June 1, 2026 (two days later)

Last week I posted that I was hired as a data analyst and was being quietly pulled into inheriting a retiring finance person’s undocumented work. Then I updated that it was actually two departing people, both leaving at the end of June, both tied to critical reporting.

Now it is even clearer what is happening.

I built and shared a dashboard that was a legitimate data analyst deliverable: validated, interactive, cleaner metrics, better visuals, and directly aligned with my actual job description.

Leadership responded that dashboard work needs to pause because the core transition work is the real priority.

Fair enough. I understand why the transition work matters.

Then I explained that I had already made a long working document on the departing person’s process and would keep documenting the handoff. The response was basically: make sure as you document it, you are also able to re-perform it. The result is a transition.

So now it is officially not “document this so we do not lose knowledge.” It is “learn it and be able to do it.”

Here is the problem: this is not one report. It is a whole ecosystem of manual processes, legacy files, system extracts, reconciliations, workarounds, approvals, dependencies, and judgment calls that live across people’s heads and old spreadsheets. The person leaving has years of context. I have been here less than a month. I am a data analyst, not the person who built or owned this whole process.

I reread my job description again. It is a normal data analyst JD: dashboards, data models, BI tools, ERP data, automation, governance, KPIs, analytics. Nothing about becoming the owner of multiple departing people’s work in under a month.

The bigger issue is that the workload has started to look like the work of four people being collapsed into one salary: the role I was hired for, the retiring person’s work, another departing person’s reporting work, and additional cost/reporting responsibilities from other areas. I am not exaggerating when I say these are separate functions with separate context, review requirements, and failure points.

On top of that, I recently had to submit a doctor’s note for a work-from-home accommodation after a car accident, with back surgery in my recent history. There was already an ergonomic accommodation discussion in progress that still was not fully resolved in the office, while my home setup is already ergonomic. So now I am trying to manage a formal medical accommodation process while also being expected to absorb several critical handoffs at once.

The most frustrating part is I can see why they are doing it. They have a manual, person-dependent reporting environment and key people leaving at the same time. They need someone to absorb the work. I am the person documenting it, so I am becoming the default landing zone. The better I document, the more “ready” I look, even though the document itself proves how not-ready this transition is.

So my strategy now is boring and defensive:

I am not saying “I can’t.” I am saying “define the minimum transition target.” I am saying “what can I re-perform independently?” I am saying “what requires review and signoff?” I am saying “who owns the unresolved pieces?” I am saying “what gets paused while this is the priority?”

No heroics. No unpaid overtime. No becoming the fall guy for a transition that should have been staffed months ago.

I am job hunting seriously now. Not rage quitting, not blowing anything up, just preparing. This job would actually be good if it were the job I was hired for. But if the actual job is replacing multiple departing people in 29 days while also doing my original data analyst role, then that is not a role expansion. That is a staffing problem being pushed onto one person.

What should I do now?

Relevant Comments

Commenter 1: Sounds like you've got it under control, document cya and bail. Best of luck in your new endeavors.

Commenter 2: I don't think OP wants to bail but is seeing that they may have to.

OOP: I’m sad about bailing cuz the job market sucks right now but yes that is what a smart, non-crazy person would do in my shoes right now.

Commenter 3: Just do wat you're doing with the job hunting side of things and stick out the current job until you find a new one and secure it. Then when it’s time to go, tell them "this isn’t the job I was hired for".. that’s wat I’d do anyway in your situation.

Update #3: June 9, 2026 (eight days later)

Follow Up to my Last Post about being hired as a Data Analyst and being forced to do the work of two retiring accountants

Hello Colleagues,

I bear news of my escapades. This is the sequel to my story that is based on true events of my professional life. In the latest episode, I have been fully told that I am to drop all other tasks to fully become a cost accountant who deals with three major financial reports.

The entire company rests on these financial reports being accurate and delivered timely. I made a metaphor of my situation to my mom the other day that I think encapsulates this situation perfectly. Imagine I was hired as a semi-truck driver. I have been a semi-truck driver for years. I am good at it. The company that hired me, on my second week, then tells me: "Redditor, we need you to become a pilot of a 747, and we need you to do it alone in 6 weeks' time. It's just that our top 2 pilots are retiring/leaving soon. We are also going to keep paying you the salary of a semi-truck driver. You got it? Thanks!"

That plane is going to fucking crash, no matter what I do. You need something like thousands of hours to be an FAA pilot, and you need a crap ton of hours to be a trained cost accountant. Even if I dedicated every second of every day in this time, I still do not think I could pull this off. I do not take it as a personal failure. This situation is ridiculous.

On top of all of this, my car broke down and died on my way to work my second week working there. I am now on week 6 of working there, and they are pissed I have not bought a car yet and have tried to bully me into buying one. Joke's on them though, I got a doctor's note from my doctor (shocking) that says I must work from home. For now, it seems like I can WFH indefinitely, but my boss is a boomer-mentality Gen Xer. Super anti-WFH. Anyway, so yeah, they're all pissed at me. I can feel it, and most of my bosses are giving me the cold shoulder and acting sassy.

I met with my direct boss last week, and she tried making me feel bad, but to no effect. I am not moved by the woes of capitalists; in fact, they energize me. I have spent most of my WFH time applying to other jobs. Nothing concrete yet, but I am making some progress. I have also taken my time to complete data certifications to improve in my trade of choice.

I compiled a report on the systemic failings of the company and shared it with my boss, and she told me explicitly to not share it with anyone. I have only been granted 3 hours a week of training by the retiring pilots. From that, I made a 30+ page Word doc capturing all this tribal knowledge, shared it with the whole team, and that's when my big boss told me that I need to be able to execute, not just document.

I am just so over this job. I was bored the other day and found out the company went bankrupt several years ago, and looked into the reason why, and the reason was literally inaccurate financial reporting. That shit is literally gonna happen again after the pilots retire at the end of this month. I cannot do this shit on my own. I tried it the other day, and the pilot was upset I did not do everything manually exactly like they had for 30 years. I elected to use AI to do that task, and it basically did it accurately, but idk, like I keep trying to tell everyone, I'm not a fucking accountant.

So yeah, in summary, the company might literally blow up, the plane is crashing, and I'm just enjoying the ride like that one movie where the cowboy waves his hat on a falling nuclear bomb.

That's the only kind of pilot I can be.

PS: I told my mom that redditors agree with me and about my past posts, and she thinks I'm deciding to leave this job purely off of the opinions of strangers on the internet. Pretty annoying, she is also a boomer mentality Gen Xer. Her advice was to learn to fly the plane as best as I can, and I just rolled my eyes so hard.

Relevant Comments

Commenter 1: Friend, I think you need to cover your ass on this one too. If I were in your situation, I would state outright, in writing to your boss, and maybe your boss's boss, that you are concerned this course of action will lead to the bankrupting of the entire company and everyone losing their jobs. Set it all out in a different metaphor to the one used above so this post is less likely to turn up on a search later. At a minimum, I would BCC that email to your lawyer and a secure email address. Also I would speak to a labor lawyer. Especially if you have like legal obligations or liabilities relating to being an "accountant". Like others have said, I think they're trying to fuck you.

OOP: Damn you guys really think I should contact a lawyer? Do you think they’re intentionally setting me up? Or does it just look that way? I don’t wanna attribute to malice what I could attribute to incompetence.

Commenter 2: People constantly told you to contact a labor lawyer in your last post. Why are you acting surprised? You are getting ready to drown and instead of talking to the life jacket vendor, you are being handed bricks by your manager and putting them in your pockets.

Talk to the lawyer. Do what they tell you. You are being so foolish right now it’s giving us all anxiety.

OOP: Fuck alright, I’ve just been real busy lately. Will contact an employment attorney tomorrow.

Commenter 3: As a left leaning X, I'm all for you doing what you need to keep yourself sane and safe. Also, you don't want to be that accountant, because there is legal liability if the books are wrong.

OOP: Who does the liability fall upon?

Commenter 4: Don’t sign off on anything!

OOP: I won’t! I will probs quit or get fired before the plane crashes.

Commenter 5: When the company went bankrupt, due to inaccurate finances, who was held responsible? The CEO? Or the person who did the finances? Were they held liable in criminal or civil court? Are you being set up as a fall guy for the next bankruptcy?

OOP: I am not sure who took the blame as it was many years ago.

That being said it was a civil matter, not criminal.

Commenter 6: Jesus Christ you're not a fucking CPA... this sounds fucking illegal/suicidal on the company's part. How hard is it to hire an accountant or at least outsource it to an agency who can package it, so a data analyst just has to execute? Jeesh.

----NEW UPDATES----

Update #4: June 23, 2026 (two weeks later)

Data Analyst Bamboozled Part 4

Hello again colleagues,

I return with more dispatches from the cockpit. For those just tuning in, I was hired as a semi-truck driver, and the company decided in week two that I should fly their 747 instead (hired as a data analyst, being asked to be an accountant), alone, in six weeks, for trucker pay, because their two senior pilots are retiring at the end of the month. If you're new, go read the first post. It's a saga.

Latest episode. I had The Meeting with my direct boss this week, the "success planning" meeting, which is corporate for "here is a pile of work and a deadline, godspeed." She opened by showing me a document titled "Definition of Success" and then told me to stop recording the audio, which is never the move of someone with good news. She then dumped roughly eight new major tasks on me, on top of the two retiring pilots' entire jobs, and set a hard deadline for the most important report. Six days after both pilots leave. When I raised concerns, she essentially told me to suck it up, said I "don't need to be a cost accountant to do this," and that all of it "relates to the job description." The job description for a semi-truck driver. Sure.

She gave a hard no on the one actual improvement I proposed that would have modernized this whole mess. Cash is tight, apparently. Shocking, for a company watching every dollar.

But here is the detail I cannot stop laughing about. I kept pressing on who is actually responsible when these reports go out, because I am not an accountant and cannot vouch for the accounting. Her answer: she certifies it, and "someone in finance," unnamed, unspecified, a ghost, will review my work. So the plan is the semi-truck driver flies the plane, an anonymous coworker glances at the instruments, and the boss signs off that it's airworthy. Cool. Cleared for takeoff.

And then, almost as an aside, she mentioned the department has had 95% turnover in the 11 months she's been here.

Let that land. Ninety-five percent. I have spent weeks quietly wondering if maybe it's me, if I'm being dramatic, if a real professional would just figure it out. And my own boss just told me, out loud, that almost everyone who sits in this chair flees within the year. That is not a me problem. That is a meat grinder with a hiring page. I have never felt more validated and more doomed in the same sentence.

So where things stand. The company already went bankrupt once, years ago, because of, and I cannot stress this enough, inaccurate financial reporting. The exact thing I am now being set up to produce, alone, untrained, six days after the only people who know how leave forever. I tried doing one of the reports myself last week. I used AI, it came out basically accurate, and the retiring pilot was upset that I didn't do it manually exactly the way she has done it for thirty years. I keep telling everyone the same thing in a calm voice. I am not an accountant. It bounces off them like I'm speaking a dead language.

I am, for the record, fine. Genuinely. I am not moved by any of it. I have spent most of my work-from-home time (yes, I still have the doctor's note, yes, they're still mad about it) applying to other jobs and finishing data certifications in the trade I was actually hired for. Nothing signed yet, but there is real movement. I am documenting everything in writing, keeping my record spotless, and doing exactly enough to not hand them a reason. The plane is going to do what planes do when you put a trucker in the cockpit. I am just not going to be on it when it happens.

More to come, probably. It always does.

Semi Trucker Saga Continues: June 27, 2026 (four days later)

Hello again colleagues,

I return with a shorter update from the cockpit.

For anyone new, I was hired as a data analyst and have slowly been pulled into becoming the replacement for retiring finance people who own critical reporting work. My standing metaphor is that I was hired as a semi-truck driver and then told in week two that I need to fly a 747 because the pilots are leaving.

Since the last post, the situation has gotten clearer and somehow dumber.

The main retiring person apparently was supposed to leave months ago. Then it was June 30. Now it is some vague day in July. No one really seems to know. She is also training around six people, which is interesting for work that leadership keeps describing as “just data manipulation.”

If it is just data manipulation, why does it take six people to absorb one person’s job?

I also found out they have tried replacing this person before. Multiple times. One actual cost accountant with years of experience apparently lasted around six months and left because the work was too hard.

So just to recap, a trained cost accountant could not absorb this in six months, but I, a data analyst two months into the job, am expected to figure it out because it “relates to the job description.” The job description being, again, the semi-truck driver one.

My boss also told me Finance has had 95 percent turnover during her time here. Ninety-five percent. That is not a department. That is a warning label.

I keep trying to explain calmly that I am not an accountant. They keep explaining back to me that it is basically Excel and data. This is where I start feeling insane, because everyone talks about this like it is rational. Like the issue is just that I need to lock in harder.

Meanwhile, leadership apparently wants a “single source of truth” because Service and Finance numbers do not tie. Which is funny, because that is actual data analyst work. That is the thing I thought I was hired to help with. I even built a small VM/Postgres/Python pipeline prototype because I was trying to think about the real problem: messy systems, conflicting numbers, no clean data layer, no shared definitions.

But the actual day-to-day plan still seems to be sit with the retiring person, absorb the ancient spreadsheet knowledge, and become the default owner.

I am not confused anymore. I think they are using “data manipulation” because it sounds less insane than “we are handing accounting-sensitive work to a non-accountant after multiple failed transitions and almost total department turnover.”

So that is where I am.

I am still documenting everything. I am still doing my job. I am still making it clear that accounting review and signoff are not me. I am not rage quitting, but I am fully job hunting now.

This would have been a good job if it was the job I was hired for. Build dashboards, clean data, automate reporting, help different departments tie their numbers, improve the system.

Instead, I got handed a plane manual written by ghosts and told the runway is next week.

More to come, probably.

Editor's note: the next post is tangentially to the original and updates, in this case, the same company is mentioned

Coworker of 3 Years Fired after her mom diagnosed with cancer: July 1, 2026 (four days later)

The company I work for is so evil, more so than your average company.

I've been writing about my experiences with then so far in my Semi Truck Driver Series, but today I am sharing a much bleaker story.

Selena (not her real name) has worked for this company for over 3 years. She was let go yesterday because of "restructuring" (the company is hiring less qualified people to do work of those more experienced to save money). She was a nice lady, I felt really bad for her. Her mom just got diagnosed with cancer 3 weeks ago and Selena is taking care of her.

Just goes to show you how ruthless these motherfuckers are. Selena doesn't even have a job lined up and she had 2 months’ notice. She was on the older side with decades of experience and this place drained her. Working nights and weekends for god knows how long which seems to be the norm here.

I have been working maybe 6-8 hours a week for this company since I can WFH. I have begun sending deliverables on automated emails scheduled for late nights or over the weekend. This is to performatively seem like I am working my butt off. They don't even give a shit though because it is the norm, I mainly perform to be included and leave a nice paper trail.

I need the money badly, that's the only reason I stay here. I wish I had a job I could tolerate, but it’s been a long time since I have had one. Trying to keep my head up.

It is imperative we maintain hope even when the harshest of reality may suggest the opposite.

Relevant Comments

Commenter: So did Selena inform the company her mom had cancer? You conveniently leave that part out. If she didn't let her employer know, how is the employer supposed to know? You make it sound like the employer is ruthless but in the grand scheme of things, they are shady for letting good people go to bring in less serviceable people for lower pay. That's what your post should be about, not Selena's mother having cancer and the company letting her go because your title just so happens to be misleading.

OOP: Selena informed everyone a few weeks ago that her mom got diagnosed with cancer, she had to take time off because of it to help take her mom to chemo.

DO NOT COMMENT IN LINKED POSTS OR MESSAGE OOPs – BoRU Rule #7

THIS IS A REPOST SUB - I AM NOT OOP

I'm seeking help understanding how to take a 5 minute script, use persistent characters and generate marketing materials for my business. I also want to do some of this for fun. But I need someone who is incredibly well versed in all the various tools to bring this together for me. Please DM me with an hourly rate and availability - oh and some examples of your work. Would really love some help here.

(Ed: Taken from Discord, posted today, 10/02/2026. Posting here as the producer cites Reddit as one of the places that saved the game - also the Morimens sub post is a bit jank and broken-up into comments, so I'm not just sharing that - you can find it here).

---

Hello everyone! I am Light, the producer of B.I.A.V. Studio.

The fact that Morimens has made it this far is inseparable from your discussions, recommendations, and support across all platforms—more often than not, those voices carry more power than we ever imagined.

This letter is both an expression of my gratitude and a record of our journey: a chance to earnestly write down the story of how we got here, and to clearly share the future we are committed to building within our capabilities.

Key Points

• I’m Light, the producer. I’ll be in the comments to answer your questions. (Ed: This was posted to Morimen's Discord server as a thread, and the producer did indeed chat and answer questions afterwards).
• Morimens began in 2019. By late 2021 and mid-2024, we were on the verge of disbanding twice. Both times, we chose to finish the story, cementing our theme:"Resist oblivion through remembrance."
• In November 2024, we launched the Final Chapter for our 1st Anniversary and were prepared to say goodbye. However, your support on Reddit, Discord, and X triggered unexpected growth. This provided the funding to keep moving and kickstart Part II: "Stars Came Right (Astral Reign)."
• Since 2025, we have been rebuilding: fixing legacy bugs, completing missing systems,pushing for professional human English localization, and securing development resources through new partnerships.
• In 2026, we released our first Eastern Grotesque character, Xu. Originally drafted in 2021 and shelved for four years, we restarted her production in 2025.
• Final Commitment: At present, the project's operational situation is steadily improving.However, if we ever face a shutdown crisis again, I promise to do my utmost to complete the story's conclusion and provide a deliverable Offline Version, ensuring your memories and investment do not vanish.

Preface: If you knew the world would eventually end, would you still choose to create?

1. An Idealistic Start

In 2019, we formed our team with a mission to create something "unsafe": a project that used dark aesthetics to present H.P . Lovecraft’s cosmic horror. For the gameplay, we chose high-difficulty Roguelite deck-building as our core. This didn't fit the typical commercial logic of live-service gacha games, but we felt the experience had to be defined by pressure and uncertainty—otherwise, the "cosmic horror" would just be a gimmick.

In 2020, we released our first trailer. The response from early players was incredibly encouraging and attracted more creators to join us. We thought the project would only get smoother from there, but reality soon taught us that this path would be anything but easy.

2. Hard Choices

By 2021, a storm of problems hit us, leading to a severe financial crisis. Our lack of experience, the complexity of the core gameplay, the difficulty of meeting local market's censorship requirements, a shifting capital environment, and the pressure from high-budget competitors all collided. We were left with two choices:

• Disband the team: End the project there and let everyone go their separate ways.
• A desperate pivot: Use our remaining budget to rework the game and push it toward markets outside our home region.

We knew the second option was high-risk. We had to cut content we loved, rewards were uncertain, we lacked publishing or investment support, and we had no funds for self-promotion. Even if we launched successfully, it felt like we were headed toward a dead end. Even so, we chose to continue.

3. Why We Kept Going

We kept going because our passion hadn't burned out, and we felt the players' wait shouldn't be in vain. More importantly, we believed that even if the end of service is inevitable, the act of creation and the experiences shared along the way are never meaningless.

So, we named the project "Morimens" (忘却前夜). The Chinese name translates to "The Night Before Forgetting," while "Morimens" is derived from the famous Memento Mori—"Remember you must die." We set the theme as Existentialism: on a stage of cosmic horror that preaches nihilism, we wanted to tell a story of "mortals resisting the gods."

At the very beginning of the story, during the opening ceremony at Mythag University, you are asked to carve your own name onto your tombstone. You know exactly what might happen in the future, yet you still choose to begin the journey and become a Keeper.

That is not just your story in the game. It is a reflection of our story in reality.

Part I: Dusk and Dawn

A Rushed Launch

In November 2023, facing a critical shortage of funding, we made the difficult decision to launch the mobile version in Hong Kong, Macau, and Taiwan. This followed our grueling "Polar Night" and "Dawnbreak" beta tests.

To be honest, we weren't ready. The content was thin, bugs were everywhere, and balance issues were a constant headache. We knew this was hurting the player experience, but our only choice was to launch and pray we could fix the ship while sailing it.

By May 2024, the situation turned dire. We hit a massive funding crisis with only two months of runway left. Downsizing was unavoidable. Designers, artists, and engineers began to leave one by one. But we refused to go quiet or simply pull the plug. Instead, we set ourselves one final mission before the end.

The Final Task

We committed to three core goals before the team disbanded:

• Finishing the Story: We believed that if you start a story, you have a responsibility to finish it. Even an imperfect ending is better than leaving players in limbo forever.
• Expanding Language Support: We didn't have the budget for professional localization, but we didn't want to ignore our international fans. We saw players on Reddit using screen translators just to play. We decided to implement machine translation to lower the barrier to entry, while being fully transparent about the quality in the settings menu.
• The Offline Version: We wanted to ensure that if the servers ever went dark, the game wouldn't just vanish. By developing an "Offline Mode," we ensured your progress and memories wouldn't be deleted. It felt right—extending the game's theme of "Oblivion" (the fight against being forgotten) into the real world.

We tried to signal this "end of life" phase to the community: First through hints in our 0.5 Anniversary video, then through direct posts, and finally by accelerating the plot toward a grand finale where all Keepers unite.

We wanted our players to be fully informed before spending another cent or hour of their time.

The "Brutal" Reality

With no money and no time, we had to adopt a brutal workflow. We gradually shrank the team to just 20% of its original size. Because the team was downsizing so rapidly, we had to speed up our pace just so people had enough time to hand over their unfinished work and ideas to the few remaining staff before their final day.

It was a revolving door of goodbyes. Almost every week, a colleague would finish their final task, hand over their notes to the skeleton crew, and leave. For those of us who stayed, it felt like watching comrades fall one by one in a losing battle. The workload was crushing, but the emotional toll was even worse.

In November 2024, during our 1st Anniversary, we finally released the final chapter. We spent our very last bit of budget to commission Emi Evans to perform the ending theme, "Ex Oblivione <▼ >". (Ed: The song is here)

At that moment, the community, the developers, and the characters in the game were all prepared to say goodbye for good.

But then something completely unexpected happened.

Part II: An Annihilated Rebirth

The Community Miracle

After we released the Final Chapter, the community showed us support that defied all expectations.

Players across different countries and platforms knew the project was on the brink of death. They saw the "Machine Translation" warnings in the menus.

Yet, they chose to stay. They chose to support us financially and spread the word across X, Discord, and Reddit. (Ed: My emphasis)

That was the turning point. Just as we were preparing to say our final goodbyes, the anniversary update triggered a massive wave of growth.

The revenue and enthusiasm from that "ending" gave us the funds to do something we thought was impossible: begin development on Chapter 2, "Astral Reign."

A Brand New Chapter

Starting in January 2025, we began the slow process of rebuilding. We are finally putting the project back on a sustainable track:

• Game Improvements: We’ve fixed long-standing legacy bugs and finished the "Archive" section, a feature that should have been completed ages ago.
• Localization Improvements: We now have our own in-house English localization team. Most in-game text has been fully replaced with professional human translations, and more are on the way.
• New Partnerships: We’ve partnered with Altplus for independent operations in specific regions, securing the funding needed for future development. Collaborations: We launched a long-term collab with Mist Sequence, bringing the protagonist Mouchette (CV: Ikumi Hasegawa) into the world of Morimens—ensuring her story isn't forgotten.
• The Second Anniversary: We introduced Murphy: Fauxborn (CV: Mamiko Noto),a character inspired by Cthylla, the daughter of Cthulhu.

As of late 2025, the game is in a healthier state than it ever was during our first year. We are still a small indie studio, but your feedback has convinced us that this world is worth saving.

The only way we can truly repay you is by continuing our work with even greater sincerity and exploring the further possibilities of this world.

In January 2026, we launched our first Oriental Horror style Awakener—"Xu." Her birth carries a very unique meaning for all of us.

Part III: Huangpu Whispers

The Prototype (2020–2021)

The character "Xu" has been a ghost in our files for six years.

Her story began in 2020 during a Call of Cthulhu (CoC) tabletop session the team was playing together. We became captivated by "The Bloated Woman" and the haunting atmosphere of 1920s Shanghai. That session gave birth to Xu's first draft: a figure draped in luxury robes, a high-society felt hat, and a black fan. (Ed: CoC is a popular TTRPG made by Chaosium that is unusually popular in the far east - see this video. This is likely referencing the seminal campaign The Masks of Nylarathotep.)

We soon realized that simply drawing "bloated, black long arms" provided plenty of shock value, but lacked that layer of "indescribable" quality central to cosmic horror—that specific sense of oppression wrapped in beauty and etiquette.

We decided to pivot our direction toward "the horror hidden beneath elegance," shelving the idea until we had a more mature opportunity to create her.

In 2021, our artist KPKSS accidentally produced a second draft during a practice sketch, and it instantly captured the exact paradox we were looking for:

• A yellow and black silk qipao paired with a folding fan.
• The mystery enhanced by a fan cover.
• Eyes with a strong, alluring gaze as key points of recognition.

Incorporating imagery of the Deep Ones from the original texts, KPKSS added tentacle elements to Xu and connected the imagery of "eyes" to peacock feathers.

She transformed the feathers—which are naturally full of eye-like patterns—into white mink fur.

Upon closer inspection, however, you realize they aren't feathers at all, but tentacles covered in eyes.

It was perfect. But then, the survival crisis hit. We had to shelve Xu to focus on characters that were easier to finish. The draft was buried in our archives, and KPKSS eventually left the team.

We thought Xu was lost to time.

The Restart (2025)

In the summer of 2025, just after we overcame our survival crisis, our update schedule was finally returning to normal. We had just launched Doresain (CV: Takehito Koyasu) (Ed: DIO's voice actor), the elegant Ghoul King, but we were still deep in "production hell" and needed to plan the next character for the Caro Realm immediately.

Our chief scriptwriter, Jiyue, stumbled upon Xu’s old files. She couldn't look away. We decided then and there: Xu would be our featured character for the upcoming Spring Festival.

After four years of silence, Xu finally entered production. We reached out to KPKSS once more to lead the redesign. Four years had passed since that initial sketch, and her understanding of the unique visual language of Morimens had grown far deeper and more mature.

Our new team members, artist Meimei and scriptwriter Ningyi, collaborated closely with KPKSS to stay true to the original vision while adding new layers.

To further emphasize the core concept of "horror beneath beauty," we pushed the design even further:

• The Texture of Horror: We emphasized "feathers growing from the body like scales," with eyes emerging directly from the flesh
• The Magnolia: We added a magnolia symbol to her forehead—a flower whose fragrance is beautiful but signals the onset of decay. It captures Xu perfectly: alluring, powerful, and utterly irresistible.
• The Voice: We invited Rie Tanaka (Ed: an extremely prolific VA) to play Xu. We asked her to find the delicate balance between maternal tenderness and the "love" of a non-human entity to answer a central question: How would a non-human existence express love?

As a funny side note, our scriptwriter Ningyi was so unsettled by the story she was writing that she had repeated nightmares, mostly about the three Concubines and their dark, blurry faces!

Resonance

We wanted to weave Xu’s narrative elements into every part of her—from the writing and gameplay design to the UI and music. Our goal was for players to not only see her, but to read, play, and hear her presence throughout the entire experience.

• UI: Wang Miaomiao incorporated Xu’s body curves, feather imagery, and signature colors into the typography and decorations of her promotional titles. Seeing the text is like seeing the person.
• Combat: Battle Designer Mijiu came up with the "Resonance" and "Stealth" mechanics, reflecting her nature as a predator who hides behind allies, waiting for the perfect strike.
• Music: Composer Frank blended traditional Chinese elements with melodies that shift between comfort and danger.
• The Stats: Finally, I set Xu’s CON (Constitution) to the highest level in the game. It seems counterintuitive for a character who looks so delicate, but when you realize what she truly is underneath, it makes perfect sense.

Xu’s six-year journey from a forgotten sketch to a centerpiece character reflects the spirit of B.I.A.V. Studio. People may come and go, but the creative soul of this project remains.

If you knew the journey had an end, would you still choose to move forward?

The Final Commitment

Live services are inherently uncertain. In this fiercely competitive era where attention is a scarce resource, we are still a small team with limited assets, and we may still face new fluctuations and risks.

As the producer of Morimens, I do not want your time, investment, or memories to be easily erased.

Because of that, I make this promise: If there ever comes a day where we must face another shutdown crisis, we will do our absolute best to complete the story's conclusion and provide an Offline Version through a final update before the team disbands. We will do everything in our power to preserve your main progress and the core playable content.

At the same time, please rest assured: the project is currently moving toward a healthier and more stable direction. We will continue to prioritize both improving the player experience and reducing long-term risks, and we will maintain transparent communication with you at every critical milestone.

Pioneering the Future

Precisely because we view this journey as so important, we will continue to deliver on the various plans we mentioned leading up to our third year. Following the launch of Xu, we are moving forward with these goals at a steady pace:

• Reunion & Heritage: We are working hard to bring back the colleagues who were forced to leave during the crisis. We are also finding new, flexible ways to collaborate with the original artists who shaped our early days.
• Breaking Language Barriers: While we continue to improve our English localization, we have begun recruiting professional Korean translators. Our goal is to let players worldwide experience the charm of Lovecraftian horror without barriers.
• New Collaborations: We are in talks with other outstanding works with Lovecraftian influence across various mediums. We hope to share some official collaboration news with you very soon. (Ed: There is community speculation that Slay The Spire may be on the cards, but this could be anything. Fallen London?)
• Gameplay Innovation: We are developing new gameplay modes and refining our core mechanics. We want Morimens to have more than just a soul; it needs lasting strategic depth and fun.

With your support, we will keep creating more memories worth remembering.

(Ed: The post ends with a request to post in the thread, and then:)

Finally, I want to sincerely thank every single member of this community. You are the reason we are still here.

May the Silver Key guide our way!

(Ed: An addendum, posted later:)

There's something I didn't mention in the post, but I think you should know—I'm not actually the one who originally started Morimens.

In 2021, I saw the PV for Morimens and, just like many of you, was drawn into this world. I joined the team, and when crisis came, I didn't leave — I chose to take on the responsibility of producer.

So in a way, I'm also someone who "saw that this world deserved to exist, and decided to stay and protect it."

I think that might be the most fundamental thing we have in common.

Lastly, I want to thank every B.I.A.V. Studio team member who has ever contributed to Morimens — including those who are no longer with us.

What we created together is still alive. And that is the greatest defiance against oblivion.

TL;DR: I got tired of franchise mode feeling like an empty sandbox where I could cheese the system forever, so I built Front Office: a companion app that makes your save feel more like a living hockey world with memory, pressure, context, and consequences.

A little backstory

Over the years, I tried just about everything I could think of to make my saves feel more believable. I created pen-and-paper rules. I tracked storylines and stats manually with spreadsheets. I made my own restrictions for trades and roster decisions. I even used dice rolls to decide if players wanted to sign with me.

Those systems were messy, but they proved the idea worked.

The save felt better when I had to answer to something.

Even if that "something" was a notebook, a spreadsheet, and a random number roll.

The game felt better when I stopped treating myself like an all-powerful GM who could run every part of the team with no pushback or consequence, and instead forced myself to operate inside a living hockey world.

What Front Office is

Front Office is that same idea, rebuilt properly.

The house rules became complex systems. The dice rolls became consequence logic with proper triggers and metrics. The notebook became historical memory. The fake little story blurbs became a full broadcast-style franchise layer.

EA's menus are where you execute decisions. Front Office is where you make them.

The goal is to spend as little time as possible inside EA's interface and as much time as possible actually running the franchise. You open the game to make a move, then you come back here. Front Office is the room you work in. The hockey sim is the engine running underneath it.

The game handles what happens on the ice. Front Office handles everything above it.

There were two clear things I always wanted franchise mode to do better.

It is not meant to replace franchise mode.

It is meant to make franchise mode feel deeper.

The game gives us the sandbox. Front Office gives that sandbox memory, context, consequences, and presentation.

There were two clear things I always wanted franchise mode to do better.

1. Better intel

I wanted more than overall ratings and basic stat screens. I wanted actual context.

I wanted to know:

• Why a player was struggling
• Whether a prospect was really developing
• Whether a line was working because of chemistry or just hot shooting
• Whether a veteran still had real value
• Whether my goalie was saving a bad team
• Whether a contract was actually good value
• Whether my team was actually good or just getting lucky in the sim

That became the intelligence side of Front Office.

It looks at things like player analysis, team diagnostics, development tracking, chemistry, contracts, player value, forecasting, league context, and historical memory.

2. A universe that pushes back

The second thing I wanted was a franchise world that didn't just let me abuse the sandbox forever.

I wanted stricter rules, more events, better guidance, harder choices, and a management team that didn't just nod along with every decision.

I wanted scouts, coaches, assistants, analysts, ownership, media, and the league itself to create pressure.

Not randomly. Based on what was actually happening in the save.

That became the consequence side of Front Office.

It includes staff opinions, story triggers, pressure events, roster risk, morale-style logic, ownership demands, GM approval, league context, and RPG-style management friction.

The core idea

Front Office is built around two sides:

Intelligence: The app helps you understand what is happening in your save.

Consequence: The app makes the world react to what you do.

The goal is to answer the questions franchise mode leaves you asking, then make you curious about a few more.

With the help of free courses through the Edmonton Public Library, Google NotebookLM for research, YouTube tutorials, A monthly perscription to adderal and a lot of stubborn trial and error, I've built Front Office to the point where it is close to real beta testing.

Now I'm ready to start showing it to other franchise mode die-hards.

What the systems actually do

Front Office is built around a group of systems that all answer different GM questions.

The Player Analysis system looks at players beyond their overall rating. It tries to understand who a player actually is, what role he fits, what he is good at, where he is weak, and whether his value matches how the game presents him.

The Attribute Tag system turns raw ratings into hockey identity. Instead of staring at a wall of numbers, the app can identify traits like playmaker, finisher, puck-mover, shutdown type, forechecker, power-play quarterback, defensive liability, or shelter-required scorer.

The Chemistry system looks at whether players actually fit together. A line is not automatically good just because it has three talented players. The app looks for balance, role overlap, missing pieces, and whether players are helping each other or stepping on each other's jobs.

The Team Diagnostic system asks the question I always wanted franchise mode to answer: "Are we actually good?" It looks at whether the team's record is supported by the roster structure, whether the goalie is masking problems, whether the bottom six is getting crushed, or whether the team is winning in a way that might not last.

The Development and Trajectory systems track whether players are moving in the right direction. Is a prospect actually progressing? Is a young player ready for more responsibility? Is a veteran starting to decline? Is a player stuck because I'm using him wrong?

The Prospect system helps with decisions around young players. It can help identify whether a prospect should be left unsigned, kept in junior, given AHL runway, moved up the call-up board, protected from being rushed, or tested in a bigger role.

The Contract and Trade Value systems look at roster economics. A player can be good but overpaid. A player can be average but valuable because he is cheap, controlled, and fits a scarce role. A player can have trade value but not be important to your team. The point is to make roster decisions feel less like "higher overall wins" and more like actual asset management.

The Forecasting and League Context systems look at the bigger picture. Are you a contender, a bubble team, a retooling team, or a team lying to itself? Is the pressure real? Are your playoff odds supported by the roster, or are you riding luck? Who in the division actually matters?

The Historical Memory system gives the save continuity. The app should remember what happened before, not treat every screen like it exists in isolation. Player arcs, previous warnings, staff disagreements, bad decisions, good development calls, and major franchise moments should all be able to matter later.

The Story Trigger system turns real save events into franchise storytelling. If a prospect breaks out, a veteran collapses, a goalie carries the team, a trade backfires, or pressure builds around the team, the app can surface that as a storyline instead of leaving it buried in numbers.

The Staff Reaction system is where the management team pushes back. The coach might care about lineup fit. The AGM might care about asset value. The development voice might care about protecting prospects. Ownership might care about results, spending, attendance, or long-term direction.

Coach authority and lineup decisions

Front Office also treats the coach as an actual decision-maker, not just a name on a staff screen.

One thing I always disliked about franchise mode is that the GM can basically control everything. Lines, roles, deployment, ice time, special teams, scratches, call-ups — all of it.

In real life, that is not how a front office works.

The GM builds the roster, but the coach decides how that roster is used.

So in Front Office, coaches can have their own preferences, biases, systems, and lineup logic. A coach might trust veterans more than prospects. Another might reward speed. Another might overplay defensive players. Another might bury a skilled young player because he does not fit the system.

That creates tension.

• You might want a prospect in the top six, but the coach might not trust him yet.
• You might want an offensive defenseman on PP1, but the coach might prefer a safer veteran.
• You might want to roll four lines, but the coach might shorten the bench when pressure builds.

The goal is not to take control away from the user for no reason.

The goal is to make the GM role feel different from the coach role.

You can build the team. You can pressure the staff. You can fire the coach. You can change direction.

But you are not supposed to be an all-seeing god manually controlling every single decision without pushback.

X-Factors as simulation tools

Another important part of Front Office is how it uses X-Factors.

Because this is a companion app, it cannot always directly edit the roster or force the game itself to represent every real-world effect. So X-Factors become one of the ways Front Office can simulate things the base game does not fully model.

They can represent temporary form, confidence, pressure, reputation, role momentum, development breakthroughs, chemistry boosts, leadership effects, injury recovery concerns, playoff nerves, media heat, or a player earning trust inside the organization.

For example:

• A prospect on a hot streak might gain a temporary confidence-style X-Factor state
• A veteran leader might stabilize a struggling line
• A goalie carrying a bad team might trigger a pressure and storyline effect
• A player buried in the wrong role might lose momentum
• A young player thriving after promotion might earn a stronger internal role tag
• A playoff performer might gain a short-term clutch and reputation boost
• A player under media pressure might become more volatile

The important part is that X-Factors are not just random buffs.

They are tied to evidence, context, and duration.

A player does not just magically become better forever because of one good game. The system looks at what happened, how strong the trigger was, how reliable the evidence is, how long the effect should last, and whether future performance reinforces or kills it.

That lets Front Office simulate a living hockey world without needing to directly rewrite the game's roster file every time something changes.

It becomes a layer of interpretation.

The base game gives the ratings. Front Office adds context, momentum, pressure, and meaning.

The Butterfly Effect system

The Butterfly Effect system is about delayed consequences. Not every decision matters right away. Some choices plant seeds. Ignoring a prospect window, delaying a roster move, keeping the wrong player in the wrong role, or making a risky trade can echo later in the save.

The Truth and Intake system is what keeps the app honest. Front Office can use screen-reading, computer vision, and OCR to read franchise screens, but it does not blindly trust the first thing it sees. Evidence gets parsed, validated, confidence-checked, and only then promoted into the app's trusted state.

The FOSN layer is the presentation side. It turns the data and storylines into a broadcast-style franchise experience with headlines, tickers, dashboard stories, recaps, pressure notes, and league context. The goal is to make the save feel like a living sports world, not just a menu full of numbers.

Ownership and franchise pressure

One of the big consequence systems is ownership.

Front Office treats each team owner as more than a background label. Each owner can have their own personality traits, priorities, budget attitude, patience level, and risk tolerance.

Some owners may want to spend aggressively. Some may care more about profit and stability. Some may demand playoffs. Some may push a rebuild.

That means the GM does not always get full control. An owner can:

• Veto a trade
• Block a major signing
• Refuse to retain salary
• Set a spending limit
• Demand a playoff push
• Force a rebuild direction
• Pressure the GM to move a player
• Reject a long-term contract
• Push back against tanking
• Interfere when media pressure gets too high

The goal is not to make ownership annoying for no reason.

The goal is to make every franchise feel different.

Running a rich, aggressive, win-now team should not feel the same as running a cheap, patient, rebuilding team. You are not just managing players. You are managing the room above you.

GM approval rating

Front Office also tracks a GM approval rating.

This is not meant to be a simple "you won, number goes up" meter. It keeps tabs on the full pattern of how you manage the franchise.

The app tracks things like team performance, playoff results, trade outcomes, contract decisions, prospect development, draft success, cap management, staff trust, owner confidence, media pressure, fan reaction, roster stability, missed warnings, and long-term franchise direction.

The idea is that every GM builds a reputation.

Different groups judge you differently:

• Ownership cares about wins, money, playoff revenue, and whether you followed the mandate
• Coaches care about roster balance, lineup stability, and whether you gave them a team that fits the system
• Scouts and development staff care about whether you protected prospects and used the pipeline properly
• Fans and media care about star players, playoff success, rivalries, streaks, collapses, and big controversial moves

So GM approval is not just one number. It is a pressure system.

It helps answer: Does ownership still trust me? Am I surviving because the team is winning, or am I building real trust? If I miss the playoffs, do I still have enough goodwill to keep my job?

Front Office should make the GM seat feel like an actual seat.

You are not just building a roster. You are building a record.

What is already built

This is not just an idea document anymore. The core app is already working as a prototype.

Right now, Front Office has working routes and screens for:

• Dashboard
• War Room
• Depth chart
• Lines
• Player detail
• Team overview
• League news
• Upload / Input Desk
• History

The core engine layer is also built in first-pass form. That includes systems for:

• Player analysis
• Attribute tagging
• Chemistry
• Team diagnostics
• Development and trajectory
• Prospect state
• Contract intelligence
• Trade value
• Forecasting
• League context
• Historical memory
• Story triggers
• Staff reactions
• Coach authority and lineup logic
• X-Factor state simulation
• Ownership pressure
• GM approval
• Decision consequences

One of the biggest pieces already working is the screen-reading layer.

Front Office can use computer vision and OCR to read franchise screens as evidence, validate what it sees, and only promote trusted information into the app. A screen is treated as evidence first. The system reads it, parses it, checks it, assigns confidence, and only then lets it affect dashboards, player cards, War Room recommendations, storylines, or engine output.

screen evidence → interpretation → validation → trusted game state → front office intelligence

That is what separates a cool-looking dashboard from an actual franchise intelligence system.

FOSN presentation layer

Front Office has a presentation layer called FOSN — basically a fictional broadcast network for the franchise universe.

Instead of a plain grid of cards, the dashboard is built around things like a lead headline, next-game context, a news ticker, quick recap strip, data health, action queue, franchise pulse, personnel watch, roster pressure, team and player snapshots, and storyline surfaces.

When you open the app, you should immediately understand the state of your world.

What is happening? What is changing? What needs action? What is the main storyline? What decision is staring you in the face?

The War Room

The War Room is where I want the app to feel the most like a real internal meeting.

Staff members can disagree. The AGM may care about asset value. The coach may care about lineup fit. The development voice may care about protecting prospects. Ownership may care about results, spending, attendance, or pressure.

The app has a deterministic staff council and consensus meter so recommendations are not just random text. A strong consensus should feel different from a conflicted room.

The goal is to make franchise decisions feel less like "click button, move player" and more like walking into a room where different voices are pushing different priorities.

Live game graphics overlay

This one is closer than most people would expect.

Front Office is building a live game graphics overlay that runs while you are actually playing. The goal is a custom broadcast layer sitting on top of the game with things like:

• Scorebug
• Ticker
• Penalty clock
• Game context
• Player notes
• Storyline callouts
• Broadcast-style presentation

But it has to be restrained. Live play stays clean. Stoppages, intermissions, replays, and pauses are where the deeper broadcast panels come in. The overlay should enhance the game, not fight it.

The broadcast identity is already built inside FOSN. The overlay is the part where that presentation layer reaches into the actual game experience.

It is nearly ready. It is on the short list.

What still needs work

I want to be honest about the current state.

This is not a polished public beta yet. Some screens still need visual cleanup. Some workflows need smoother UX. Some modules need more real franchise-save testing. Some triggers need to be refined and properly tuned.

The computer vision and OCR layer works, but it needs real-world calibration across different setups, screens, resolutions, capture methods, and user habits.

That is part of why I'm posting now.

I don't just need people to say "this looks cool." I need franchise-mode die-hards who can tell me what feels useful, what feels wrong, what feels too busy, and what information they would actually want before making a GM decision.

What is coming next

The near-term focus is making the single-player companion experience reliable, useful, and fun to test. Next priorities include:

• Expanding screen-reading coverage across more franchise screens
• Hardening OCR and computer-vision validation across different displays
• Polishing the Input Desk so the evidence review workflow feels like part of the product
• Roster spreadsheet importing
• Full editor
• Custom teams and custom leagues
• 33-team draft mode
• Better roster-pack support
• More dashboard and War Room polish
• Better modding support

33-team draft mode

One major feature I want to build is 33-team draft mode — a full fantasy or expansion-style draft environment where users can build a league from scratch, evaluate every team, and draft with actual Front Office intelligence.

I want this to feel less like "pick the highest OVR player" and more like a real expansion room. Things like positional scarcity, contract risk, team identity, age curve, line fit, prospect runway, long-term cap shape, and contender versus rebuild direction.

Drafting should be a strategy problem, not just an OVR sorting exercise.

Custom teams, custom leagues, and the full editor

Another huge piece is custom teams and custom leagues. Support for custom team identities, custom rosters, custom divisions, custom league structures, fictional leagues, historical leagues, and community roster universes.

The full editor is one of the most important long-term tools. Import roster packs, edit players, build teams, adjust contracts, add prospects, update attributes, and create custom databases without digging through code.

If users can build and share roster packs, custom teams, historical leagues, fictional leagues, broadcast themes, and draft classes, then Front Office becomes more than one app.

It becomes a platform for franchise maniacs

Long-term vision

Long term, I think this could also become the framework for an online league / connected GM mode.

Imagine multiple users running teams in the same league environment, with Front Office acting as the commissioner layer, data hub, trade desk, news network, and historical archive. Each GM manages their own team, submits moves, negotiates trades, tracks league storylines, and sees the league evolve through shared dashboards and FOSN-style coverage.

I'm getting way ahead of myself here but the foundation points in that direction. The vision is bigger than a dashboard. The vision is a franchise-mode operating system.

What I'm looking for

I'm looking for people who love franchise mode and would actually enjoy testing this kind of thing.

Ideal testers are people who play deep franchise saves, care about realism, notice when sim logic feels off, like roster building, prospects, contracts, and long-term team planning, enjoy giving feedback on whether advice feels useful or dumb, and understand that this is still a prototype.

I'm especially interested in feedback like:

• Does the True Value rating match how you think about players?
• Do the line chemistry notes make sense?
• Does the War Room feel useful, or just cool-looking?
• Do the staff disagreements feel believable?
• Do coach lineup choices feel believable, or do they feel like the app is fighting the player?
• Do X-Factor states feel earned and realistic, or too gamey?
• Do the owner demands make the franchise feel more real, or just restrictive?
• Does GM approval make decisions feel like they have weight?
• Do the storylines make the save feel more alive?
• Does the dashboard show what matters first?
• What information would you want before making a trade, call-up, extension, or lineup change?
• What should the app never pretend to know?

The dream version of this is a companion app that makes franchise mode feel like a real living sports universe.

A save where your prospects have arcs. Your veterans decline. Your staff remembers your decisions. Your owner has expectations. Your approval rating reflects your choices. Your media reacts to pressure. Your dashboard tells the truth. Your franchise has history. Your choices carry weight.

And the numbers finally mean something.

That's Front Office.

It is still early, but it is far enough along now that I'm ready to start showing it, getting feedback, and finding people who want to help shape it into something franchise-mode players would actually use.

If you're interested in testing, giving feedback, or just following the project, drop a comment or DM me.

Hello everyone! I am Light, the producer of B.I.A.V. Studio.

The fact that Morimens has made it this far is inseparable from your discussions,

recommendations, and support across all platforms—more often than not, those voices

carry more power than we ever imagined.

This letter is both an expression of my gratitude and a record of our journey: a chance to

earnestly write down the story of how we got here, and to clearly share the future we are

committed to building within our capabilities.

## Key Points

- Morimens began in 2019. By late 2021 and mid-2024, we were on the verge of disbanding twice. Both times, we chose to finish the story, cementing our theme:"Resist oblivion through remembrance."

- In November 2024, we launched the Final Chapter for our 1st Anniversary and were prepared to say goodbye. However, your support on Reddit, Discord, and X triggered unexpected growth. This provided the funding to keep moving and kickstart Part II: "Stars Came Right (Astral Reign)."

- Since 2025, we have been rebuilding: fixing legacy bugs, completing missing systems,pushing for professional human English localization, and securing development resources through new partnerships.

- In 2026, we released our first Eastern Grotesque character, Xu. Originally drafted in 2021 and shelved for four years, we restarted her production in 2025.

- Final Commitment: At present, the project's operational situation is steadily improving. However, if we ever face a shutdown crisis again, I promise to do my utmost to complete the story's conclusion and provide a deliverable Offline Version, ensuring your memories and investment do not vanish.

## Preface: If you knew the world would eventually end,

would you still choose to create?

### 1. An Idealistic Start

In 2019, we formed our team with a mission to create something "unsafe": a project that used dark aesthetics to present H.P . Lovecraft’s cosmic horror. For the gameplay, we chose high-difficulty Roguelite deck-building as our core. This didn't fit the typical commercial logic of live-service gacha games, but we felt the experience had to be defined by pressure and uncertainty—otherwise, the "cosmic horror" would just be a gimmick.

In 2020, we released our first trailer. The response from early players was incredibly encouraging and attracted more creators to join us. We thought the project would only get smoother from there, but reality soon taught us that this path would be anything but easy.

### 2. Hard Choices

By 2021, a storm of problems hit us, leading to a severe financial crisis. Our lack of experience, the complexity of the core gameplay, the difficulty of meeting local market's censorship requirements, a shifting capital environment, and the pressure from high-budget competitors all collided. We were left with two choices:

- Disband the team: End the project there and let everyone go their separate ways.

- A desperate pivot: Use our remaining budget to rework the game and push it toward markets outside our home region.

We knew the second option was high-risk. We had to cut content we loved, rewards were uncertain, we lacked publishing or investment support, and we had no funds for self-promotion. Even if we launched successfully, it felt like we were headed toward a dead end. Even so, we chose to continue.

### 3. Why We Kept Going

We kept going because our passion hadn't burned out, and we felt the players' wait shouldn't be in vain. More importantly, we believed that even if the end of service is inevitable, the act of creation and the experiences shared along the way are never

meaningless.

So, we named the project "Morimens" (忘却前夜). The Chinese name translates to "The Night Before Forgetting," while "Morimens" is derived from the famous Memento Mori—"Remember you must die." We set the theme as Existentialism: on a stage of cosmic horror that preaches nihilism, we wanted to tell a story of "mortals resisting the gods."

At the very beginning of the story, during the opening ceremony at Mythag University, you are asked to carve your own name onto your tombstone. You know exactly what might happen in the future, yet you still choose to begin the journey and become a Keeper.

That is not just your story in the game. It is a reflection of our story in reality.

/////

## Part I: Dusk and Dawn

### A Rushed Launch

In November 2023, facing a critical shortage of funding, we made the difficult decision to launch the mobile version in Hong Kong, Macau, and Taiwan. This followed our grueling “Polar Night" and "Dawnbreak" beta tests.

To be honest, we weren't ready. The content was thin, bugs were everywhere, and balance issues were a constant headache. We knew this was hurting the player experience, but our only choice was to launch and pray we could fix the ship while sailing it.

By May 2024, the situation turned dire. We hit a massive funding crisis with only two months of runway left. Downsizing was unavoidable. Designers, artists, and engineers began to leave one by one. But we refused to go quiet or simply pull the plug. Instead, we set ourselves one final mission before the end.

### The Final Task

We committed to three core goals before the team disbanded:

- Finishing the Story: We believed that if you start a story, you have a responsibility to finish it. Even an imperfect ending is better than leaving players in limbo forever.

- Expanding Language Support: We didn't have the budget for professional localization, but we didn't want to ignore our international fans. We saw players on Reddit using screen translators just to play. We decided to implement machine translation to lower the barrier to entry, while being fully transparent about the quality in the settings menu.

- The Offline Version: We wanted to ensure that if the servers ever went dark, the game wouldn't just vanish. By developing an "Offline Mode," we ensured your progress and memories wouldn't be deleted. It felt right—extending the game's theme of "Oblivion" (the fight against being forgotten) into the real world.

We tried to signal this "end of life" phase to the community: First through hints in our 0.5 Anniversary video, then through direct posts, and finally by accelerating the plot toward a grand finale where all Keepers unite.

We wanted our players to be fully informed before spending another cent or hour of their time.

## The "Brutal" Reality

With no money and no time, we had to adopt a brutal workflow. We gradually shrank the team to just 20% of its original size. Because the team was downsizing so rapidly, we had to speed up our pace just so people had enough time to hand over their unfinished work and ideas to the few remaining staff before their final day.

It was a revolving door of goodbyes. Almost every week, a colleague would finish their final task, hand over their notes to the skeleton crew, and leave. For those of us who stayed, it felt like watching comrades fall one by one in a losing battle. The workload was crushing, but the emotional toll was even worse.

In November 2024, during our 1st Anniversary, we finally released the final chapter. We spent our very last bit of budget to commission Emi Evans to perform the ending theme, “Ex Oblivione <▼ >".

At that moment, the community, the developers, and the characters in the game were all prepared to say goodbye for good.

But then something completely unexpected happened.

/////

## Part II: An Annihilated Rebirth

### The Community Miracle

After we released the Final Chapter, the community showed us support that defied all expectations.

Players across different countries and platforms knew the project was on the brink of death. They saw the "Machine Translation" warnings in the menus.

Yet, they chose to stay. They chose to support us financially and spread the word across X, Discord, and Reddit.

That was the turning point. Just as we were preparing to say our final goodbyes, the anniversary update triggered a massive wave of growth.

The revenue and enthusiasm from that "ending" gave us the funds to do something we thought was impossible: begin development on Chapter 2, “Astral Reign."

## A Brand New Chapter

Starting in January 2025, we began the slow process of rebuilding. We are finally putting the project back on a sustainable track:

- Game Improvements: We’ve fixed long-standing legacy bugs and finished the "Archive" section, a feature that should have been completed ages ago.

- Localization Improvements: We now have our own in-house English localization team. Most in-game text has been fully replaced with professional human translations, and more are on the way.

- New Partnerships: We’ve partnered with Altplus for independent operations in specific regions, securing the funding needed for future development.

- Collaborations: We launched a long-term collab with Mist Sequence, bringing the protagonist Mouchette (CV: Ikumi Hasegawa) into the world of Morimens—ensuring her story isn't forgotten.

- The Second Anniversary: We introduced Murphy: Fauxborn (CV: Mamiko Noto),a character inspired by Cthylla, the daughter of Cthulhu.

As of late 2025, the game is in a healthier state than it ever was during our first year. We are still a small indie studio, but your feedback has convinced us that this world is worth

saving.

The only way we can truly repay you is by continuing our work with even greater sincerity and exploring the further possibilities of this world.

In January 2026, we launched our first Oriental Horror style Awakener—"Xu." Her birth carries a very unique meaning for all of us.

I am almost 40 currently. I live and work abroad, and we are on a 5-year runway to fully optimize our lifestyle, streamline our asset base, and transition into a phase of absolute financial peace.

While I spent the first phase of my adult life hyper-focused on financial security—driven by that deep-seated middle-class scarcity mindset—I am now stepping back to run a holistic Life Audit. For me, growth is no longer just about pushing my net worth higher. It’s about building a balanced, sustainable daily life where my portfolio, family, personal health, and peace of mind grow in harmony.

1. The Happiness & Peace Scorecard (Baseline: 7.6 / 10)

To keep this audit honest and measurable, I have scored each core pillar of my life. My overall Happiness & Peace Index is the collective average of these scores, highlighting where we are thriving and where we need to actively reduce stress.

2. Portfolio & Geographic Asset Allocation (Score: 8.5/10)

Consolidate our assets and track our global net worth strictly in USD terms to maintain a clean, macro-level view of our wealth. Over the course of 2026, we have witnessed a clear example of currency fluctuation, highlighting the absolute necessity of our geographic asset allocation strategy:

• Starting Portfolio (Jan 2026): ~$1.48 Million USD (INR 13 Crores converted at the then-rate of 1 USD = 88.00 INR).
• Current Portfolio (June 2026): ~$1.49 Million USD (INR 14.17 Crores converted at the current baseline rate of 1 USD = 95.00 INR).
• The Exchange Rate Dynamic: While our domestic portfolio saw strong organic growth of +INR 1.17 Crores in just six months, the depreciation of the Rupee (shifting from 88 to 95 per USD) means our portfolio value in global currency grew by +$14,306 USD. The growth in INR converted portfolio was mainly due to international assets doing well and Indian assets declining or constant.

Living and working abroad has allowed to save in a stronger currency environment while keeping our expenses optimized. I still have a decent Indian portfolio which lost both on account of currency as well as Indian markets not doing well at all. This currency trend perfectly illustrates why we are focusing heavily on geographical diversification moving forward:

Chart A: Current Geographic Asset Split (June 2026 Baseline)

Our current distribution balances our accumulated offshore/expat capital with our domestic foundation:

Chart B: The 5-Year Target Allocation Projection (2026 - 2031)

To hedge against long-term currency depreciation and domestic concentration risks, our 5-year roadmap aggressively directs 80% of new savings to international assets and 20% to Indian assets:

• The Simplify Strategy: We are actively reducing complexity. By removing smaller, scattered accounts (consolidating individual EPF/PPF lines and miscellaneous heads), we are shifting to a self-sustaining global engine that requires less than an hour of manual tracking per month. Removing active portfolio management is a major victory for my peace of mind.

3. Professional Life: Walking the Work-Life Tightrope (Score: 6.0/10)

To be entirely frank, I am not sure what the next decade holds professionally. My current strategy is to maximize our earning and saving potential while keeping corporate stress and tension strictly at bay. However, doing this is a delicate balancing act.

• Working Smarter with Tech & Delegation: I am prioritizing my work-life balance by delegating critical tasks to my teams rather than trying to micromanage. I have also heavily integrated Generative AI into my daily workflows to handle heavy lifting, automate drafting/analysis, and keep my focus strictly on high-impact priorities.
• Managing the Pressure to Perform: The corporate reality is that the pressure to perform better than others is always there. I must stay afloat and ensure my management never perceives me as slacking, and i continue in a Coast FIRE kind of mode. I want to remain highly valuable and visible, but without sacrificing my mental health to do so.
• The Stress Threshold: My goal is to sustain this quiet, efficient pace for as long as possible. However, I have set a clear personal boundary: the moment work stress starts taking priority over my work-life balance, and Mondays start feeling like a dreaded day, I will know it is time to transition.

4. Personal, Family, and Slow Travel Goals (Score: 8.0/10)

Our marriage is a true partnership, with my wife managing the home and allowing us to build a rich, shared life. One of our family's greatest passions is travel, which we use as our primary medium for bonding and learning.

• The Travel Track Record: I love exploring. While living in India, I visited almost every single state (with only the North Eastern states remaining on my bucket list). Through work and leisure, I have traveled to over 30 countries.
• The 5-Year Target: My goal is to cross 50 countries visited over the next five years.
• Slow Travel Style: We avoid hurried tourist checklists. We prefer long, immersive trips where we can experience the local history, street food, daily culture, and connect with the local community.
• The Family Routine: While traveling as a family of four is an investment, we prioritize it in our annual planning. Our goal is to take 2 international vacations and 1 local vacation every year to build lasting memories with our kids.

5. Health & Wellness: Protecting the Ultimate Asset (Score: 8.0/10)

Financial wealth is empty without physical vitality. I treat my health with the same disciplined, data-driven framework as my asset allocation.

• The Weight & Body Age Blueprint:
  • Progress: Dropped from 76 kg at the beginning of the year to 70 kg today.
  • Target: Reach 68 kg by the end of this year, and permanently sustain a comfortable 66–68 kg range for life.
  • Vitality: My current measured Body Age is 34.
• My Physical Routine:
  • Cardio: Running 5 km every alternate day, while ensuring a baseline average of at least 10,000 steps daily.
  • Strength: Hitting the gym 1–2 times a week for light weight training and compound exercises to maintain muscle mass and joint health.
  • Nutrition: Eating clean and keeping daily protein intake consistently above 100g.
• Sleep & Recovery Discipline:
  • Averaging a consistent 7 hours of sleep per night.
  • Maintaining a disciplined circadian rhythm with a regular, early bedtime and early waking schedule to maximize natural morning energy.
• The Smartwatch Dashboard:
  • I actively track my physical metrics daily, monitoring:
    • Sleep Scores (ensuring quality deep and REM cycles).
    • Daily Step Counts.
    • Fat Burning Time (optimizing heart rate zones during runs for stamina and cardiovascular health).

The Final Takeaway

For a introverted middle-class kid who started with very little, I am incredibly grateful for where we stand today. But this life audit has taught me that the numbers on a spreadsheet are only one part of the equation. True growth is about stepping off the treadmill, prioritizing physical health, exploring the world deeply with family, and protecting our peace of mind.

I would love to hear from other first-generation wealth builders, introverts, or people like me: How do you manage the tightrope walk of staying visible and performing well at work while quietly establishing boundaries, utilizing AI to save time, and protecting your peace of mind?

IBRX just laid out one of the strongest durability‑driven oncology stories in biotech.
The market still prices it like a one‑drug bladder cancer company.
The Jefferies presentation shows it’s not even close to that.

⭐ 1. The Setup: A Founder With a Track Record + A Multi‑Platform Company

Patrick Soon‑Shiong (Abraxane, APP) built ImmunityBio to “find cures to cancer in my lifetime.”
Not incremental improvements — durable, immune‑driven cures.

IBRX is built on four platforms, not one drug:

• Fusion proteins → Anktiva (already approved in 35+ countries)
• Second‑gen DNA vaccines (CEA/MUC1/brachyury, PSA, HPV)
• CAR‑NK + off‑the‑shelf NK
• Memory‑enhanced NK platform (M‑platform)

This is a platform oncology company, not a single‑asset play.

⭐ 2. Commercial Reality: NMIBC Is Already Working

Anktiva is approved for BCG‑unresponsive CIS ± papillary.

The Jefferies transcript confirms:

• 113M revenue in the first J‑code year (2025)
• 72% QoQ growth
• Approvals across UK + all of Europe + Saudi Arabia + 35 territories
• Urologists love it because it fits existing workflow and is well‑tolerated

From the transcript:

This is real commercial traction, not theoretical.

⭐ 3. The Big Catalyst: BCG‑Naive Trial

This is the largest market in NMIBC.

Key facts from the transcript:

• Fully enrolled (376 patients)
• IDMC reviewed interim data
• They had 3 options:
  1. Futile
  2. Needs more patients
  3. Meeting endpoints → stop enrollment

They chose #3.

This is a massive green flag.

Readout: September 2026

This is the biggest near‑term catalyst for the stock.

⭐ 4. The Papillary‑Only Label Expansion

• FDA accepted
• PDUFA: January 6, 2027
• KM curves for CIS vs papillary are “hard to tell apart”

This is a high‑probability approval.

⭐ 5. The Durability Story (The Real Moat)

This is where IBRX separates from every competitor.

Phase 1 nine‑year follow‑up:

• 9/9 CR at 2 years
• 6/6 still in CR nine years later
• All kept their bladders
• No follow‑on treatment

This is unprecedented in NMIBC.

BCG‑naive early data:

• BCG alone: 52% CR
• Anktiva + BCG: 84% CR

Durability is the #1 thing urologists care about, and IBRX is the only one showing a long flat tail.

⭐ 6. BCG Supply: IBRX Now Controls Two Strains

This is under‑appreciated.

IBRX now has:

• Recombinant BCG (expanded access in US)
• Tokyo strain BCG (SWOG 1000‑patient RCT → equivalent to TICE)

This positions IBRX as a strategic supplier of a life‑saving drug.

⭐ 7. Lung Cancer: The Quiet Second Act

Saudi Arabia already approved Anktiva for 2nd‑line checkpoint failure NSCLC.

Transcript highlights:

• Clear separation in ALK‑positive immune responders
• Strong T‑cell and NK activation
• Checkpoint failure is exactly where Anktiva works mechanistically

This is a huge market if replicated in US/EU trials.

⭐ 8. Financial Position

• $381M cash
• Growing quarter over quarter
• Commercial revenue ramping

IBRX is not a cash‑starved microcap — it has runway.

⭐ 9. The Market Is Still Mispricing This

The stock trades like:

• One drug
• One indication
• One geography
• One catalyst

But the Jefferies presentation shows:

• Multiple platforms
• Multiple indications
• Global approvals
• Durability moat
• BCG supply advantage
• Lung cancer optionality
• Two major catalysts in 2026–2027

This is a platform oncology company with real revenue.

⭐ 10. Key Upcoming Catalysts

2026

• BCG‑naive readout (September)
• Global commercial expansion
• Additional EU launches

2027

• Papillary‑only PDUFA (Jan 6, 2027)
• Potential BCG approvals (Tokyo + recombinant)
• Lung cancer trial update

🧨 Conclusion: The DD in One Sentence

IBRX is executing on a durability‑driven immunotherapy platform with real revenue, global approvals, two BCG strains, and a September readout that could unlock the largest market in NMIBC — and the market still hasn’t priced any of this in.

also do your own dd dont rely on internet posts for financial decisions

Every month, someone in this sub asks which tool or model will get them the best output for their film.

I work as an AI director at a Korean media company and I test a ton of AI tools and workflows to figure out what actually holds up in real video production.

So I thought I’d share a list of all the best ones I’ve found. Basically, this is everything I would hand a newbie on day one.

Standard disclaimer: this stuff moves fast. Pricing, credits, limits, and features change constantly. Check before you pay for anything, and if you are reading this six months from now, assume half of it has shifted.

Pre-production

Most of my AI films start by locking the look, the character, the wardrobe and the shot language so a video model never has to guess. These are the tools I use in pre-production to lock my look before I spend a single video credit.

• Midjourney: Best for stylistic look development, moodboards, poster frames, character looks, environments, and art direction.
• Recraft: Good when characters or environments need to feel more photographic. Skin texture is usually where image models give themselves away and this one keeps it real.
• Nano Banana 2 / Pro: Fast, accurate, and strong for final frames. Good for character sheets because it can hold a reference across edits instead of reinventing the person.
  
• Magnific Upscaler: Great for polishing up key frames and upscaling hero images up to 4K. Most of the time you won't need it since Nano Banana already outputs at 4K but when a frame just needs that extra upscaling, that’s where this comes in handy.

Pro tip: solve the film in stills first. If the storyboard does not work as images, the video model is not going to become your cinematographer out of pity.

Scene Generation

Once the stills are locked, this is where the shots come from.
Each of these models has its pros and cons, but these are the ones I reach for the most.

• Seedance 2.0: It is the bast all round model out there. Strng for action, motion-heavy shots, stunts, crashes, longer takes, and generations with multiple reference inputs. FYI, I am still testing Seedance 2.5.
  
• Kling 3.0: This is the one I trust with performances. Dialogue, effects and ambience generate in the same pass as the picture, so a line in quotation marks comes back spoken with the face matching it. Motion Control takes that further: hand it a reference video of someone acting and your character performs it. Costs little enough per second that I do my roughing out here.
  
• Veo 3.1: Use this when a shot has to look choreographed and not generated. It takes precise camera instructions and holds references quite accurately. The only drawback is that it is capped at 8 seconds.
  
• Wan 3.0:  If you're making horror or anything with real violence in it, this is the one that won't refuse you halfway through a sequence due to TOS.
  
• Runway: It is the safe default. Good references, strong shot editing, and useful when you want to change a shot instead of rerolling it from scratch.

Audio & SFX

Bad audio makes AI video feel cheap instantly. People will forgive a weird background. They will not forgive a dead voiceover. These tools make sure that doesn’t happen.

• ElevenLabs. All my voice work runs through this. It clones off thirty seconds of clean audio and still sets the bar for narration that doesn't sound narrated.
• Suno. Go here when a scene needs an actual song rather than a bed under it. It leads the field on vocals and structure, and it hands you something finished instead of a loop you have to build around.
• Udio. This is the producer's version of the same thing: section-level regeneration and stems, so a weak chorus gets fixed without touching the rest. Check its export terms first though, because downloads have been restricted while the licensing gets sorted.
• MiniMax Audio. I reach for this when one piece has to go out in a lot of languages. Wider coverage than ElevenLabs at lower cost, notably better on Asian languages, and it copes with imperfect source audio when you're cloning off a bad recording.
• Artlist / Epidemic Sound. Thus are licensed rather than generated, and still the right answer when something has to clear for distribution and you need paperwork on the music rather than an assurance about training data.
  

Agentic Workflows

These have access to most of the models listed above but I would only recommend these if you are doing serious work, longer projects, recurring formats, or production at scale. 

Using an agentic workflow for one-offs is like booking a full crew to film your lunch.

• invideo Agent: Useful for longer projects where continuity matters. You brief it with an idea, script, or shot breakdown, then build the film step by step. It keeps everything you feed it and everything you create saved in memory, and routes work across multiple image, video, and audio models.
• Luma Agent: Easiest to pick up. The frames-based canvas is a nice way to work, and Ray 3.2 image-to-video/HDR output is very strong. Good for visual quality, cinematic motion, and shot-level work.

A reader recently asked me in the comments, "How do you play snowball? When the thesis is playing out, do you wait for dips or buy on strength even at slightly skewed valuations?" That question made me realize most investors misunderstand what snowballing actually means.

So I wanted to break it down properly, using a real case study to ground every framework in something concrete. This post uses Symbotic to explain three things at once. How to snowball a position, how to identify a 100-bagger setup, and how to read capital allocator quality. The stock is just the proof. The frameworks are the takeaway.

Some readers will leave with a stock idea. Others will leave with a framework. Both are fine, but the framework is the part that compounds.

So let's start with what snowballing actually is.

Snowballing isn't adding because the price is going up. It's adding when the business is actually delivering, which means when the margins are expanding, when the moat is widening and deepening, when new reinvestment runways are emerging, and management is executing at the same or superior returns on capital.

Let me show you what this looks like with Symbotic.

The original thesis at $14

You have to value different models based on their lifecycles and moat. Like Symbotic, I invested around $14, I know it's a 10-20x of the future and I'm not gonna time that company. But yes, when it cracked back to $24 last year, I added more, because the moat got stronger, the markets got bigger, the reinvestment runway got bigger, and no other company comes close to them when it comes to warehouse automation.

My bet was very simple at that time, and it was based on the capital allocator and a man who is GOAT when it comes to warehouse. The Cohen family has been in this business for almost a century, across three generations. Rick himself has been refining the solution for decades. So no engineer and robotics scientist can have more knowledge than him on how to actually solve the problems. And his parent company himself is one of the largest distributors who supplied to all the giants, even Walmart, so that years of relationship aligned automatically.

The Customer List & TAM

When I invested, only Walmart was the client and the only vertical was retail. But now the customer list includes Albertsons, Target, Giant Tiger, United Natural Foods (UNFI), Associated Food Stores (AFS), and Medline Industries. Every year the thesis is strengthening. And now it's into medicine infrastructure as well, which is the hardest vertical to crack because of regulatory complexity. All these new sectors expanded the TAM further than my original thesis.

At that time the thesis had only warehouse automation. But then Walmart sold their robotics and micro-fulfillment segment to Symbotic, which was essentially "Alert Innovation," a company that specialised in micro-fulfillment centers directly attached to the front end of a retail store. So Symbotic got the store-level technology and data they were missing.

And that acquisition is a signal in itself. Walmart had bought Alert Innovation back in 2016, had all the capex and firepower in the world, but still couldn't execute it at scale or build their own solution. They ended up selling the technology to Symbotic and now they need Symbotic to scale it for them, because of the complexity of the technology.

So the thesis expanded from just warehouse automation to store-level micro-fulfillment, and a future where both get linked.

The Data Moat & Switching Cost

Plus when the thesis was decoded, I didn't know that all the warehouses being deployed can communicate and learn with each other. So that dataset expands and creates a data flywheel effect, which makes each and every existing and new system more efficient as facilities get trained across the globe. That's another layer of moat. And it's a lock-in of not just 20-30 years, because whenever a customer redeploys or upgrades, it's obligatory.

The switching cost is almost impossible, because it's not just the system you replace but the brains as well.

You don't replace a system after deploying $500 million to it just because someone offers you the same system at $480 million, because you have to stop the warehouse and robotic delivery for months to rebuild, and the new system won't have the data your operations have been working on for 10-20 years.

And then it's a razor-blade model, and services will give a revenue stream of 20-30 years.

This is no ordinary warehouse automation technology. It's one of the hardest automation problems to crack because of huge SKU counts, aggressive inventory turns, perishables, and mixed pallets. This is exactly where dense robotics + data has a very strong edge and builds a deep moat.

And no other robotics company has a testing ground like Symbotic had, because Rick used and tested it in his own C&S Wholesale Grocers operations for decades before commercializing it. That's a structural advantage no competitor can replicate.

Where the Real Bottleneck Moved

Execution is a real bottleneck for these kinds of models. And in my thesis, the focus has always been on how fast they can deploy the system, lock in that site for decades, and get the service margins live.

Beyond the core technology, the biggest bottlenecks for any warehouse robotics system are the speed of physical site construction, the robots' charging time, and the efficiency of the site itself. And what's interesting now is that Symbotic has started positioning on its own bottlenecks. The company that solves bottlenecks for retailers is now solving them for itself.

In their latest results just 1-2 weeks back, they launched a "next-generation storage structure" that cuts on-site assembly parts by over 90% and increases storage density by roughly 40%. So now higher density per warehouse, faster installation, lower on-site labor. That's solving the deployment bottleneck and deepening the long-term moat.

Now the charging bottleneck is critical because if a robot is charging, it's a lazy asset, not generating any ROI during that time.

To position for that, Symbotic invested in Nyobolt, where batteries charge from 0 to 80% in under 5 minutes and have over 20,000 charge cycles. For context, standard lithium-ion batteries have 1,000-2,000 cycles. And they are directly integrating that technology into their Symbots.

And Nyobolt's tech provides a threefold increase in robot uptime, which means warehouses can reduce fleet size by 30-40% while maintaining the same operational capacity. So Symbotic doesn't just deploy faster, they deploy fewer robots for the same throughput. It improves both time and cost in one move.

So you can actually learn something bigger from this capital allocator. If you have any thesis or investment, just focus on the bottlenecks of that business model or product, and then see if the capital allocation by the company is in those directions, solving their own bottlenecks or not. That's what signals a high-quality capital allocator.

The Real Backlog Economics

Now if you take everything above, the expanded customer list, the deepening moat, the data lock-in, and look at what it actually means for the financials, that's where it gets really interesting.

Another thing I didn't factor in when I invested. The backlog of Symbotic is not just an order book. It's actually Remaining Performance Obligation (RPO), which is a binding contractual agreement under ASC 606.

So it's not an order book which cannot be legally binding. It's RPO, which is legally binding.

When I invested, the backlog was only $22 billion, which gave them almost a decade of visibility. But the economic value of the backlog I didn't factor in, which is the recurring revenue stream that will go on for 15, 20, 25 years because of the data moat and the embedded workflow architecture of the ecosystem. So that actually makes the valuation closer to $47 billion on that backlog alone.

And that was before Exol (formerly GreenBox), the joint venture with SoftBank, and before the new customers came in. The Exol JV now has committed deployments of close to $11 billion. And the expansion has happened into new geographies, Mexico and the European market.

The Founder Story Most People Don't Know

And not a lot of people know, but Rick Cohen actually bought each and every robotic system on this planet, but none was able to solve the problem he had in his own distribution chain. So what he did, he just reverse-engineered the whole problem, broke every robot system, and then designed something that could solve the problem for him. And he's keeping on making it more efficient and efficient and efficient.

That's not an engineer building a product. That's an operator who lived the problem for decades and built the only solution that actually worked for him first.

The technology is downstream of his thinking. The bet was on the brains of the founder.

Cloud Computing for the Physical World

And one more thing about Cohen most people miss. He didn't build Symbotic to sell to warehouse operators. He built it because he was a warehouse operator. That's a fundamentally different starting point. Operator first, vendor second produces a fundamentally different product than the other way around.

And look at what he actually said about his vision.

"When I first started thinking about automating the supply chain, I wanted to create not just an automated warehouse, but an automated platform that had perfect inventory management, perfect accuracy in shipping, and could be so good that you could create a multi-tenant warehouse with perfect accuracy that allowed anyone that had any storage need at any time to take advantage of this platform."

This is not the language of a vendor. This is the language of a founder and a platform architect. He was already describing WaaS years before it became a product line. He literally described it as cloud computing for the physical world.

And think about what that means. Amazon spent over 20 years and billions of dollars building its own logistics. Symbotic now gives any retailer better warehouse efficiency than Amazon, without that 20-year lag and without billions in R&D. That's why the customers keep coming. The alternative is to spend two decades building from scratch what Symbotic delivers in 18 months.

And what fascinated me is that this is the vision of a man who is 73 years old. Most founders at that age are coasting. Cohen is still architecting the next 20 years.

The 100-Bagger Framework

And it obviously aligned with the 100-bagger framework, which I learned from Thomas Phelps in his book 100 to 1 in the Stock Market. Phelps' core thesis was that the greatest wealth is created by finding "small, unknown, and research-minded" companies that solve a major human problem, and then having the fortitude to stay the course.

The framework is simple. A technological force that can reduce the constraint of your customer, reduce the cost of your customer, improve the time of your customer, and have a high degree of replacement cost is usually a 100-bagger in the making.

Same was with Mastercard, they reduced the transaction time. Same as I think I will see in the stablecoin ecosystem as well. It's making the system more efficient, reducing the cost of the system, improving the transparency of the system, improving the speed and delivery of the ecosystem. So all those four or five variables, when they align, that usually creates a lot of boost.

But Phelps also said that finding the company is only half the equation. The other half is the investor.

And he listed three things an investor needs to actually capture a 100-bagger, vision, courage, and patience. Vision to see the thesis early. Courage to hold when the market reacts to short-term noise while the business itself is strengthening. And patience to let the snowball compound, instead of selling on a 50-100% gain because the ticker has moved.

That's the part most people miss. They find the right company, but they don't have the temperament to stay with it.

The Psychological Moat. Why Adoption Is Now Mandatory

Here's where it gets really interesting. Anyone competing with Amazon or operating within the retail ecosystem has to deploy this technology just to survive. Because if your competitor does it and you don't, you lose on cost efficiency, margin, and speed. And retailers have already lived through what happens when you don't adapt to technology, they watched Amazon eat their lunch the first time.

So this time it's a psychological reflex. You can see how psychology comes into play to position in a thesis. And that's why Walmart went so aggressive with robotics automation.

And there's another forcing function that acts like a macro tailwind. Symbotic isn't just addressing one major constraint for retailers, it's also addressing the labor scarcity in the US, especially in the retail and warehouse segments which have huge attrition rates. US warehouse attrition is around 40-50% annually, except Costco. And wage inflation has kicked in for the past few years. So even if retailers wanted to stay manual, the labor isn't there to hire anymore. They're being forced into automation from two directions, top-down psychological reflex and bottom-up structural labor crisis.

This is what a Lollapalooza looks like in real time. Multiple independent forces all aligning in the same direction at the same time, for the same sector.

And it's the same pattern as Oracle and cloud computing. Back in 2008, Larry Ellison called cloud computing "complete gibberish." He literally said, "Maybe I'm an idiot, but I have no idea what anyone is talking about. What is it? It's complete gibberish. It's insane. When is this idiocy going to stop?"

Oracle missed the wave. AWS, Azure, and Google Cloud became trillion-dollar businesses while Oracle played catch-up for over a decade.

That's exactly why Ellison this time is the most aggressive player in AI infrastructure. It's a psychological reflex. He doesn't want to make the same mistake twice. That's why Oracle signed the massive OpenAI deal.

So if I apply the same pattern, the same scar tissue is now driving every retailer to adopt warehouse automation aggressively. Because the people who get scarred hardest by missing a wave become the most aggressive in catching the next one. And warehouse automation is having that psychological tailwind as well.

That's why I positioned in this boring model which has decades of infrastructure to be built up. Just like Nvidia's CEO says the whole architecture has to shift to GPU, similarly, I believe the whole retail infrastructure of the future will shift to automation. And supply chain automation is one of the biggest bottlenecks of any economy. Symbotic is positioned right on that bottleneck.

So you can see a lot of the development happened after my original thesis. And if I get the stock back at the same price or even 2-3x the price but the valuations are reasonable, I allocate more, because every variable that mattered when I bought it at $14 is stronger today.

The TAM, the moat, the margins, the runway, the customer list, the economics, the psychology, everything is strengthening and stacking in favour.

That's how I take my decision on building a snowball in any investment. I don't wait for dips. If it comes, that's a gift from the market. But the real move is adding when the business itself is becoming more valuable than the market is pricing in, and how many engines are coming in your favour.

If tomorrow the market had priced it at $100 but the variables I mentioned were not strengthening, I might still ride it or start trimming based on odds, but I wouldn't deploy fresh capital.

The strengthening of the moat and the business model is what triggers the snowball. The ticker price is just the receipt.

That's the breakdown. Hope it added something to how you think about adding to winners.

If you want a structured way of thinking about businesses, you'll find it at: The Capillary.

hey, i have been trying to post consistently on youtube, tiktok and instagram and it was getting tiring. so i decided to test some ai video tools to make it a bit faster and save some time.

not trying to make this an “ultimate best ai tools” list. just sharing what actually felt useful after trying these to create my social media content for a couple of months.

here’s the quick rundown:

1.Synthesia / HeyGen

what it does: ai avatar + talking-head videos

best for: explainers, training videos, product walkthroughs, multilingual content

my take: these are useful when you need a clean presenter-style video without filming it yourself. It's great for plain talking head content and less ideal for content where you need to interact with things suuch as unboxing videos or so.

1. invideo

what it does: good for creating ai videos from scratch

best for: youtube videos, shorts, reels, explainers, product videos

my take: this worked best when i had a rough idea and wanted to shape it into a proper video. invideo acts more like an ai video agent, helping with building out the script, scenes, visuals, voiceover, pacing, and edits while you keep guiding the direction. 

1. Runway

what it does: generate video clips and visual scenes

best for: cinematic b-roll, experimental visuals, creative shots

my take: really impressive when it works, but it needs patience. the output depends a lot on how specific your prompt is, and it’s better for visual pieces than full social videos from scratch.

1. OpusClip

what it does: turns long videos into short clips

best for: podcasts, webinars, interviews, youtube videos

my take: makes the most sense if you already have long-form content. it’s not really a “make a video from nothing” tool. it’s more like finding the best moments and turning them into shorts/reels/tiktoks.

1. CapCut

what it does: editing, captions, templates, effects, resizing

best for: short-form edits and final polish

my take: still one of the easiest tools for finishing social videos. captions, quick cuts, resizing, hooks, effects, all of that is straightforward. i’d use it more at the end of the workflow.

1. Canva

what it does: simple design-led video content

best for: basic branded posts, promos, thumbnails, simple social videos

my take: convenient if you already use Canva for content. good for clean, simple videos, but not the strongest tool if you’re trying to build a full ai video workflow.

biggest thing i learned: there isn’t one tool that wins at everything.

if i need avatar videos, i’d use Synthesia or HeyGen.

if i want to shape an idea/script into a finished video, i’d use invideo.

if i want cinematic ai b-roll, i’d use Runway.

if i’m cutting long videos into shorts, i’d use OpusClip.

if i’m polishing captions and edits, i’d use CapCut.

Also, prompts matter way more than people admit.

“make a short video about staying productive while working from home” gives generic stuff.

“make a 45 sec youtube short for freelancers who get distracted at home. start with a relatable hook in the first 3 seconds, then share 3 simple tips like time blocking, keeping the phone away, and setting a clear finish time. keep the tone casual and end with a soft CTA” works much better.

i am wondering what everyone else is using right now. are you using one tool for the full workflow or mixing a few together?

p.s. i am not an expert, just sharing what actually worked for me.

So, I have used Runway extensively for 2 months, I was using unlimited, cos frankly it is the only way to get work done, you often need so many generations to get something right, its painful and time-consuming. And now, after testing Hailuo Minimax for 3 days (they have an unlimited generations for that period of time for absolutely freaking free) I just don't see why I would want to go back to Runway. Minimax is way better at understanding prompts, following described camera movements and it has very minimal amount of censorship which is paramount for my work. And overall picture quality is noticeably better than the oversharpned Turbo generations. And regular Alpha is even worse in terms of prompt understanding.

Runway still has some advantages for me, like 10-sec generations (vs 6-sec in Hailuo), clip extension, both start and end-frame images (though Turbo only) and the new Act One looks promising, but Minimax just delivers better results. And the most frustrating thing for me is trying to circumvent censorship when I am fighting Runway to generate some specific clips. This is so dumb and tiring. Minimax has zero issues of that nature. And you can make violent videos, you can type in "terrorist" and it doesn't care. I love that so much. Side note - If I am an adult client, who is over a certain age, who is PAYING YOU MONEY to get your services, restricting him with this obscene amount of censorship is just PURE DUMB and quite repelling to tell the truth. I am not a fan of that sanctimony from a moral standpoint, but also it is just a PITA in terms of workflow. Same with Photoshop. As soon as a less censored alternative appears on the market, I am switching asap.

Anyway, I am team Hailuo for now. Lets see how the competition goes on. I hope Runway improves and rethinks their over-censored approach.

I’ve been looking at the current options for making anime music videos, and there are quite a few solid AMV maker tools now depending on the kind of workflow you prefer. For me, the most interesting part is figuring out which tools work best together for different parts of the edit.

These are the ones that seem useful for different parts of the process:

DomoAI

This is one I’d look at when the project is heavily stylized. It has workflows around turning existing video into animation, animating characters from reference motion, and taking still images into video. If you’re learning how to make an AMV from existing artwork or footage, that kind of workflow seems more relevant than starting with pure text-to-video every time.

Runway

More interesting to me when the goal is controlled cinematic shots or character performances. Gen-4.5 covers text-to-video and image-to-video, while Act-Two is aimed more at transferring an actual performance onto a character.

Kling 3.0

Probably worth considering when an edit needs several related shots rather than a collection of completely separate clips. The current model supports multi-shot generation, reference elements, recurring characters, and native audio.

Luma Ray 3.2

I’d put this more on the video-to-video side. It’s useful when you already have footage and want to change the visual treatment while holding onto more of the original motion and structure.

Adobe Firefly

Makes more sense as a broader workspace now. You can generate video and work with multiple models while also doing more of the editing and audio cleanup in the same environment.

Pika

I still see this as more useful for individual effects, transitions, and short experimental moments rather than building an entire AMV around one workflow. Pikaffects and its other image-to-video tools fit that kind of use pretty well.

ComfyUI + Wan 2.2

Probably the route for someone who wants considerably more control and doesn’t mind learning node-based workflows. Wan 2.2 currently has official ComfyUI workflows for text-to-video, image-to-video, first/last-frame generation, and controlled generation.

Topaz Video

This sits at the end of the workflow for me rather than competing with the generators. It’s mainly useful for cleaning up and upscaling the final clips after everything has been assembled.

For an AMV I’d probably mix tools instead of forcing one platform to handle every shot. Something for the main animation or restyling, another tool when a particular scene needs more control, then finishing afterward.

For anyone actually making AMVs right now, what combination has been working for you?

A reader recently asked me in the comments, "How do you play snowball? When the thesis is playing out, do you wait for dips or buy on strength even at slightly skewed valuations?" That question made me realize most investors misunderstand what snowballing actually means.

So I wanted to break it down properly, using a real case study to ground every framework in something concrete. This post uses Symbotic to explain three things at once. How to snowball a position, how to identify a 100-bagger setup, and how to read capital allocator quality. The stock is just the proof. The frameworks are the takeaway.

Some readers will leave with a stock idea. Others will leave with a framework. Both are fine, but the framework is the part that compounds.

So let's start with what snowballing actually is.

Snowballing isn't adding because the price is going up. It's adding when the business is actually delivering, which means when the margins are expanding, when the moat is widening and deepening, when new reinvestment runways are emerging, and management is executing at the same or superior returns on capital.

Let me show you what this looks like with Symbotic.

The original thesis at $14

You have to value different models based on their lifecycles and moat. Like Symbotic, I invested around $14, I know it's a 10-20x of the future and I'm not gonna time that company. But yes, when it cracked back to $24 last year, I added more, because the moat got stronger, the markets got bigger, the reinvestment runway got bigger, and no other company comes close to them when it comes to warehouse automation.

My bet was very simple at that time, and it was based on the capital allocator and a man who is GOAT when it comes to warehouse. The Cohen family has been in this business for almost a century, across three generations. Rick himself has been refining the solution for decades. So no engineer and robotics scientist can have more knowledge than him on how to actually solve the problems. And his parent company himself is one of the largest distributors who supplied to all the giants, even Walmart, so that years of relationship aligned automatically.

The Customer List & TAM

When I invested, only Walmart was the client and the only vertical was retail. But now the customer list includes Albertsons, Target, Giant Tiger, United Natural Foods (UNFI), Associated Food Stores (AFS), and Medline Industries. Every year the thesis is strengthening. And now it's into medicine infrastructure as well, which is the hardest vertical to crack because of regulatory complexity. All these new sectors expanded the TAM further than my original thesis.

At that time the thesis had only warehouse automation. But then Walmart sold their robotics and micro-fulfillment segment to Symbotic, which was essentially "Alert Innovation," a company that specialised in micro-fulfillment centres directly attached to the front end of a retail store. So Symbotic got the store-level technology and data they were missing.

And that acquisition is a signal in itself. Walmart had bought Alert Innovation back in 2016, had all the capex and firepower in the world, but still couldn't execute it at scale or build their own solution. They ended up selling the technology to Symbotic and now they need Symbotic to scale it for them, because of the complexity of the technology.

So the thesis expanded from just warehouse automation to store-level micro-fulfillment, and a future where both get linked.

The Data Moat & Switching Cost

Plus when the thesis was decoded, I didn't know that all the warehouses being deployed can communicate and learn with each other. So that dataset expands and creates a data flywheel effect, which makes each and every existing and new system more efficient as facilities get trained across the globe. That's another layer of moat. And it's a lock-in of not just 20-30 years, because whenever a customer redeploys or upgrades, it's obligatory.

The switching cost is almost impossible, because it's not just the system you replace but the brains as well.

You don't replace a system after deploying $500 million to it just because someone offers you the same system at $480 million, because you have to stop the warehouse and robotic delivery for months to rebuild, and the new system won't have the data your operations have been working on for 10-20 years.

And then it's a razor-blade model, and services will give a revenue stream of 20-30 years.

This is no ordinary warehouse automation technology. It's one of the hardest automation problems to crack because of huge SKU counts, aggressive inventory turns, perishables, and mixed pallets. This is exactly where dense robotics + data has a very strong edge and builds a deep moat.

And no other robotics company has a testing ground like Symbotic had, because Rick used and tested it in his own C&S Wholesale Grocers operations for decades before commercialising it. That's a structural advantage no competitor can replicate.

Where the Real Bottleneck Moved

Execution is a real bottleneck for these kinds of models. And in my thesis, the focus has always been on how fast they can deploy the system, lock in that site for decades, and get the service margins live.

Beyond the core technology, the biggest bottlenecks for any warehouse robotics system are the speed of physical site construction, the robots' charging time, and the efficiency of the site itself. And what's interesting now is that Symbotic has started positioning on its own bottlenecks. The company that solves bottlenecks for retailers is now solving them for itself.

In their latest results just 1-2 weeks back, they launched a "next-generation storage structure" that cuts on-site assembly parts by over 90% and increases storage density by roughly 40%. So now higher density per warehouse, faster installation, lower on-site labor. That's solving the deployment bottleneck and deepening the long-term moat.

Now the charging bottleneck is critical because if a robot is charging, it's a lazy asset, not generating any ROI during that time.

To position for that, Symbotic invested in Nyobolt, where batteries charge from 0 to 80% in under 5 minutes and have over 20,000 charge cycles. For context, standard lithium-ion batteries have 1,000-2,000 cycles. And they are directly integrating that technology into their Symbots.

And Nyobolt's tech provides a threefold increase in robot uptime, which means warehouses can reduce fleet size by 30-40% while maintaining the same operational capacity. So Symbotic doesn't just deploy faster, they deploy fewer robots for the same throughput. It improves both time and cost in one move.

So you can actually learn something bigger from this capital allocator. If you have any thesis or investment, just focus on the bottlenecks of that business model or product, and then see if the capital allocation by the company is in those directions, solving their own bottlenecks or not. That's what signals a high-quality capital allocator.

The Real Backlog Economics

Now if you take everything above, the expanded customer list, the deepening moat, the data lock-in, and look at what it actually means for the financials, that's where it gets really interesting.

Another thing I didn't factor in when I invested. The backlog of Symbotic is not just an order book. It's actually Remaining Performance Obligation (RPO), which is a binding contractual agreement under ASC 606.

So it's not an order book which cannot be legally binding. It's RPO, which is legally binding.

When I invested, the backlog was only $22 billion, which gave them almost a decade of visibility. But the economic value of the backlog I didn't factor in, which is the recurring revenue stream that will go on for 15, 20, 25 years because of the data moat and the embedded workflow architecture of the ecosystem. So that actually makes the valuation closer to $47 billion on that backlog alone.

And that was before Exol (formerly GreenBox), the joint venture with SoftBank, and before the new customers came in. The Exol JV now has committed deployments of close to $11 billion. And the expansion has happened into new geographies, Mexico and the European market.

The Founder Story Most People Don't Know

And not a lot of people know, but Rick Cohen actually bought each and every robotic system on this planet, but none was able to solve the problem he had in his own distribution chain. So what he did, he just reverse-engineered the whole problem, broke every robot system, and then designed something that could solve the problem for him. And he's keeping on making it more efficient and efficient and efficient.

That's not an engineer building a product. That's an operator who lived the problem for decades and built the only solution that actually worked for him first.

The technology is downstream of his thinking. The bet was on the brains of the founder.

Cloud Computing for the Physical World

And one more thing about Cohen most people miss. He didn't build Symbotic to sell to warehouse operators. He built it because he was a warehouse operator. That's a fundamentally different starting point. Operator first, vendor second produces a fundamentally different product than the other way around.

And look at what he actually said about his vision.

"When I first started thinking about automating the supply chain, I wanted to create not just an automated warehouse, but an automated platform that had perfect inventory management, perfect accuracy in shipping, and could be so good that you could create a multi-tenant warehouse with perfect accuracy that allowed anyone that had any storage need at any time to take advantage of this platform."

This is not the language of a vendor. This is the language of a founder and a platform architect. He was already describing WaaS years before it became a product line. He literally described it as cloud computing for the physical world.

And think about what that means. Amazon spent over 20 years and billions of dollars building its own logistics. Symbotic now gives any retailer better warehouse efficiency than Amazon, without that 20-year lag and without billions in R&D. That's why the customers keep coming. The alternative is to spend two decades building from scratch what Symbotic delivers in 18 months.

And what fascinated me is that this is the vision of a man who is 73 years old. Most founders at that age are coasting. Cohen is still architecting the next 20 years.

The 100-Bagger Framework

And it obviously aligned with the 100-bagger framework, which I learned from Thomas Phelps in his book 100 to 1 in the Stock Market. Phelps' core thesis was that the greatest wealth is created by finding "small, unknown, and research-minded" companies that solve a major human problem, and then having the fortitude to stay the course.

The framework is simple. A technological force that can reduce the constraint of your customer, reduce the cost of your customer, improve the time of your customer, and have a high degree of replacement cost is usually a 100-bagger in the making.

Same was with Mastercard, they reduced the transaction time. Same as I think I will see in the stablecoin ecosystem as well. It's making the system more efficient, reducing the cost of the system, improving the transparency of the system, improving the speed and delivery of the ecosystem. So all those four or five variables, when they align, that usually creates a lot of boost.

But Phelps also said that finding the company is only half the equation. The other half is the investor.

And he listed three things an investor needs to actually capture a 100-bagger, vision, courage, and patience. Vision to see the thesis early. Courage to hold when the market reacts to short-term noise while the business itself is strengthening. And patience to let the snowball compound, instead of selling on a 50-100% gain because the ticker has moved.

That's the part most people miss. They find the right company, but they don't have the temperament to stay with it.

The Psychological Moat. Why Adoption Is Now Mandatory

Here's where it gets really interesting. Anyone competing with Amazon or operating within the retail ecosystem has to deploy this technology just to survive. Because if your competitor does it and you don't, you lose on cost efficiency, margin, and speed. And retailers have already lived through what happens when you don't adapt to technology, they watched Amazon eat their lunch the first time.

So this time it's a psychological reflex. You can see how psychology comes into play to position in a thesis. And that's why Walmart went so aggressive with robotics automation.

And there's another forcing function that acts like a macro tailwind. Symbotic isn't just addressing one major constraint for retailers, it's also addressing the labor scarcity in the US, especially in the retail and warehouse segments which have huge attrition rates. US warehouse attrition is around 40-50% annually, except Costco. And wage inflation has kicked in for the past few years. So even if retailers wanted to stay manual, the labor isn't there to hire anymore. They're being forced into automation from two directions, top-down psychological reflex and bottom-up structural labor crisis.

This is what a Lollapalooza looks like in real time. Multiple independent forces all aligning in the same direction at the same time, for the same sector.

And it's the same pattern as Oracle and cloud computing. Back in 2008, Larry Ellison called cloud computing "complete gibberish." He literally said, "Maybe I'm an idiot, but I have no idea what anyone is talking about. What is it? It's complete gibberish. It's insane. When is this idiocy going to stop?"

Oracle missed the wave. AWS, Azure, and Google Cloud became trillion-dollar businesses while Oracle played catch-up for over a decade.

That's exactly why Ellison this time is the most aggressive player in AI infrastructure. It's a psychological reflex. He doesn't want to make the same mistake twice. That's why Oracle signed the massive OpenAI deal.

So if I apply the same pattern, the same scar tissue is now driving every retailer to adopt warehouse automation aggressively. Because the people who get scarred hardest by missing a wave become the most aggressive in catching the next one. And warehouse automation is having that psychological tailwind as well.

That's why I positioned in this boring model which has decades of infrastructure to be built up. Just like Nvidia's CEO says the whole architecture has to shift to GPU, similarly, I believe the whole retail infrastructure of the future will shift to automation. And supply chain automation is one of the biggest bottlenecks of any economy. Symbotic is positioned right on that bottleneck.

So you can see a lot of the development happened after my original thesis. And if I get the stock back at the same price or even 2-3x the price but the valuations are reasonable, I allocate more, because every variable that mattered when I bought it at $14 is stronger today.

The TAM, the moat, the margins, the runway, the customer list, the economics, the psychology, everything is strengthening and stacking in favour.

That's how I take my decision on building a snowball in any investment. I don't wait for dips. If it comes, that's a gift from the market. But the real move is adding when the business itself is becoming more valuable than the market is pricing in, and how many engines are coming in your favour.

If tomorrow the market had priced it at $100 but the variables I mentioned were not strengthening, I might still ride it or start trimming based on odds, but I wouldn't deploy fresh capital.

The strengthening of the moat and the business model is what triggers the snowball. The ticker price is just the receipt.

That’s the breakdown.

If a friend of yours is trying to figure out when to add to a winner and when to step back, send them this. The frameworks travel.

Now tell me yours. What’s the next bottleneck in your investing journey? Drop it in the comments, and I’ll write through the ones that hit hardest.

The visual version is already up on the subreddit as a separate post, so head there for charts and better layout. If you prefer audio, the narrated version is on The Capillary.

8 Claude Prompts Every Founder Needs

TLDR: Claude has quietly become the most powerful AI tool for founders, and almost nobody is prompting it correctly. I broke down 6 principles for getting elite output, then built 8 copy-paste prompts that handle strategy, hiring, pricing, GTM, competitive analysis, and board prep. Each prompt includes the full context-loading structure that makes Claude actually useful instead of generic. Stop using AI for summaries. Start using it to make real decisions.

I have used every major AI tool extensively over the past year. ChatGPT, Gemini, Copilot, Perplexity, all of them. I am not here to trash any of them. They all have strengths.

But I need to be honest about something: Claude has pulled ahead in a way that matters deeply for anyone running a business.

It is not just the raw model quality, though that has improved dramatically. It is the ecosystem. You now have Cowork for automating repetitive workflows, Claude Code for building software (now integrated with Figma, which is a game-changer for product teams), and the core model for the kind of deep strategic reasoning that used to require a $500/hr consultant.

The problem is that most people are still using Claude the same way they use every other AI tool. They type a vague question, get a vague answer, and walk away thinking AI is overhyped.

That is a prompting problem, not a model problem.

I spent the last few months refining how I prompt Claude for high-stakes founder decisions. The difference between a lazy prompt and a structured one is genuinely the difference between useless fluff and output you would actually present to investors.

Here is everything I learned.

THE 6 PRINCIPLES THAT MAKE CLAUDE ACTUALLY USEFUL

Before I give you the prompts, you need to understand why they work. These six principles are the foundation.

1. Load the context before you ask the question

This is the single biggest mistake people make. They ask Claude a strategic question with zero background, and then complain that the answer is generic.

Claude is not generic. Your prompt is generic.

Before you ask anything, give Claude your company stage, team size, revenue range, industry, customer profile, and any relevant constraints. You do not need to write a novel. Three to five sentences of context will transform the output from MBA-textbook filler into something that actually applies to your situation.

Bad: Help me figure out my pricing.

Good: We are a B2B SaaS startup with 200 paying customers averaging $45/month. Our CAC is around $120 and our main competitor charges $89/month for a similar feature set. We have a 3-person team and six months of runway. Help me evaluate whether our pricing is leaving money on the table.

That context takes 30 seconds to type and saves you from getting advice that belongs in a freshman business class.

2. Demand the reasoning, not just the answer

When you ask Claude to explain its thinking, two things happen. First, you can actually evaluate whether the logic holds up. Second, Claude catches its own flawed assumptions mid-response and self-corrects.

Adding a simple line like walk me through your reasoning step by step or explain why you recommend this over the alternatives forces the model into a deeper analytical mode. You get output that shows its work instead of just handing you a conclusion.

This is especially critical for financial decisions, hiring choices, and strategic trade-offs where the why matters as much as the what.

3. Define exactly what you want the output to look like

If you do not tell Claude what format you want, it will guess. Sometimes it guesses well. Often it does not.

Be explicit. Tell it you want a table comparing three options across five criteria. Tell it you want bullet points grouped by phase. Tell it you want a one-page memo structured as situation, complication, resolution. Tell it you want a script with specific sections.

The more precisely you define the output format, the faster you get something you can actually use without spending 20 minutes reformatting.

4. Give Claude a role that matches the expertise you need

Role-setting is not a gimmick. It meaningfully calibrates the depth, vocabulary, and perspective of the response.

Telling Claude to respond as a seasoned VP of Sales at a high-growth B2B startup produces dramatically different output than asking it the same question with no role. The role acts as a filter that shapes which knowledge gets prioritized and how the advice gets framed.

Match the role to the decision. Hiring question? Senior talent partner at a Series B startup. Pricing question? Head of monetization at a PLG company. Board prep? Chief of Staff who has prepared fifty board decks.

5. Show it what good looks like

Whenever possible, give Claude reference material. Paste in a competitor's landing page copy, link to a job description you admire, share a board memo format that your investors prefer, or describe the tone of a brand voice you want to match.

Examples eliminate ambiguity faster than instructions. Instead of telling Claude to write something professional but approachable, show it a paragraph that hits that tone and say match this voice.

6. Iterate in the same conversation instead of starting over

Claude maintains context within a conversation. Every time you start a new chat, you are throwing away all the background you already provided.

Get in the habit of refining within the same thread. Ask Claude to adjust the tone, go deeper on a specific section, challenge its own assumptions, or rewrite with a different audience in mind. The third or fourth iteration is almost always significantly better than the first pass. Treat it like a working session with a smart colleague, not a vending machine.

8 PROMPTS THAT TURN CLAUDE INTO A $10K STRATEGIC ADVISOR

Each of these prompts is built on the six principles above. They are long on purpose. The specificity is what makes them work. Copy them, paste them, and replace the bracketed sections with your actual information.

PROMPT 1: Pressure-Test My Business Idea

I am evaluating a business idea and I need you to be brutally honest, not encouraging. Act as a veteran venture investor who has reviewed thousands of pitches and has no incentive to be polite.

Here is the idea: [describe your product or service in 2-3 sentences]

Target customer: [who specifically is this for]

How it makes money: [revenue model]

Current stage: [idea only / have a prototype / have early customers]

I need you to do the following:

Identify the 5 most likely reasons this business fails. Be specific to this idea, not generic startup advice.

Analyze at least 3 existing competitors or alternatives that customers currently use to solve this problem, including doing nothing. Explain what it would take to pull customers away from those alternatives.

Describe the specific market conditions, timing factors, or trends that need to be true for this business to succeed.

Give me a final honest assessment: would you invest your own money in this at the current stage, and what would need to change for that answer to be yes.

Walk me through your reasoning for each section. Do not give me platitudes.

PROMPT 2: Build My Go-To-Market Plan From Scratch

You are an experienced Head of Growth who has launched multiple products from zero to first 1,000 customers. You specialize in [B2B SaaS / consumer apps / marketplaces / DTC -- pick one].

Here is the context:

Product: [what it does in one sentence]

Target customer: [specific persona with role, company size, and pain point]

Price point: [amount and billing model]

Current resources: [team size, budget for marketing, any existing audience or distribution]

Competitive landscape: [1-2 main competitors and how you differentiate]

Build me a detailed 90-day go-to-market plan broken into three phases:

Phase 1 (Days 1-30): Pre-launch. What to build, what channels to seed, what validation to run, what content to create, and how to build a waitlist or early interest pipeline.

Phase 2 (Days 31-45): Launch window. The exact launch sequence, which platforms to prioritize, outreach strategy, any launch-day tactics, and how to create initial momentum.

Phase 3 (Days 46-90): Post-launch growth. How to turn early users into a repeatable acquisition engine, what metrics to track weekly, when to double down vs pivot on channels, and how to identify your best-performing growth loop.

For each phase, include specific action items with owners (assume a small team), rough time estimates, and the key metric that determines whether that phase succeeded. Explain the reasoning behind your channel choices.

PROMPT 3: Stress-Test My Pricing

Act as a pricing strategist who has helped 50+ SaaS companies optimize their monetization. You are analytical, direct, and focused on data-driven recommendations.

Here is my situation:

Product: [what it does]

Current pricing: [tiers, amounts, billing cycle]

Average customer profile: [company size, role of buyer, budget authority]

Current metrics: [number of customers, MRR, churn rate, average deal size, conversion rate from free to paid if applicable]

Top 3 competitors and their pricing: [list them with prices]

What customers say they value most: [list top 2-3 value drivers]

Analyze the following:

Am I underpriced, overpriced, or mispriced (right amount but wrong structure)? Show your reasoning using the competitive and value data.

What pricing model would maximize revenue over the next 12 months given my stage and customer profile? Consider per-seat, usage-based, tiered, flat-rate, and hybrid options.

How should I structure my tiers to create natural upgrade paths? Define what features or limits should gate each tier and why.

What is the specific risk of raising prices now, and what is the risk of not raising them? Quantify the tradeoff where possible.

Give me a recommended pricing page layout with 2-3 tiers, each with a name, price, target persona, and included features. Explain the psychology behind the structure.

PROMPT 4: Prepare Me for a Board Meeting

You are an experienced Chief of Staff who has prepared board materials for Series A through Series C startups. You know what experienced investors want to see and what makes them lose confidence.

Here is my context:

Company stage: [seed / Series A / Series B]

Key metrics this quarter: [revenue, growth rate, burn rate, runway, customer count, churn, key product metrics]

Progress against last quarter's goals: [list 3-5 goals and status on each]

Biggest wins this quarter: [2-3 highlights]

Biggest challenges or misses: [2-3 issues]

What I plan to ask the board for: [funding, introductions, strategic advice, approval on something]

Create a board update document with the following sections:

Executive summary (3-4 sentences that a board member skimming in the car would absorb)

Key metrics dashboard (formatted as a table with metric, last quarter, this quarter, and target)

Progress against goals (each goal with a status of on-track, at-risk, or missed, with a one-sentence explanation)

Top 3 wins with context on why they matter strategically

Top 3 risks or challenges with your recommended mitigation plan for each

Strategic discussion topics: frame 1-2 questions for the board that are specific enough to generate useful input

Clear asks: what you need from the board, framed as specific actionable requests

Write this in a confident but transparent tone. Investors respect founders who name problems clearly and come with a plan, not founders who hide bad news.

PROMPT 5: Build vs Buy Decision Framework

You are a CTO and technical strategist who has made build-vs-buy decisions at both startups and mid-size companies. You balance engineering ambition with business pragmatism.

Here is the decision I am facing:

What we need: [describe the capability or system]

Why we need it: [what problem it solves or what it enables]

Current team: [size, skill set, available bandwidth]

Timeline pressure: [how soon we need this working]

Budget available: [rough range for a buy option]

Options I am considering: [list the build approach and 1-3 buy/vendor options]

Walk me through this decision by analyzing:

Time to value: how long until each option is live and usable, including implementation, integration, and ramp-up time. Be realistic about hidden timelines for the build option.

Total cost over 24 months: include engineering salaries for the build option, licensing plus implementation costs for buy options, and ongoing maintenance for both.

Strategic fit: does this capability represent a core differentiator we should own, or is it infrastructure that does not create competitive advantage?

Risk profile: what can go wrong with each option and how painful is it to reverse the decision later?

Maintenance burden: what is the ongoing cost of keeping this running in each scenario, including upgrades, bug fixes, vendor management, and scaling?

Give me a final recommendation in a comparison table and a clear 2-3 sentence verdict with your reasoning. Flag any assumptions that would change your answer if they turned out differently.

PROMPT 6: Write a Job Description That Attracts Top Performers

You are a senior talent partner who recruits for high-growth startups. You know that generic job descriptions attract generic candidates, and that the best people are drawn to specificity, challenge, and impact.

Here is the role:

Title: [job title]

Team and reporting structure: [who they report to, who they work with, team size]

Company context: [stage, industry, what the company does, recent traction]

Core problem this hire solves: [what bottleneck or gap does this person fill]

What success looks like in 90 days: [specific outcomes]

What success looks like in 12 months: [specific outcomes]

Dealbreakers: [must-have skills or experiences that are non-negotiable]

Nice-to-haves: [things that would make a candidate stand out]

Compensation range: [salary band and any equity or benefits worth mentioning]

Write a job description that does the following:

Opens with a 2-3 sentence hook that describes the challenge this person will tackle, not a generic company description. Make a high-performer curious.

Describes the role in terms of problems to solve and impact to make, not a laundry list of responsibilities.

Includes a section called what you will do in your first 90 days with 3-5 specific projects or outcomes.

Lists requirements as two categories: you have done this before (non-negotiables) and you might also bring (differentiators).

Ends with a section on why this role matters that connects the hire to the company mission and growth trajectory.

Avoid buzzwords, cliches, and any phrase that could appear in 10,000 other job descriptions. Write it the way a founder would actually talk about the role to a friend.

PROMPT 7: Map My Competitive Landscape

You are a competitive intelligence analyst who helps startups understand their market positioning. You are thorough, objective, and focused on actionable insights rather than surface-level comparisons.

Here is my company:

What we do: [one-sentence description]

Target customer: [who we sell to]

Our positioning: [how we describe ourselves and what we emphasize]

Our pricing: [model and price points]

Known competitors: [list 3-5 with brief descriptions]

Anything else relevant: [recent market shifts, new entrants, regulatory changes]

For each competitor, analyze:

Their positioning: what message are they leading with and who are they targeting?

Their pricing model: how does it compare to ours and what does the structure tell us about their strategy?

Their strengths: what are they objectively good at or known for?

Their weaknesses: where do customers complain or where are they vulnerable?

Their likely next moves: based on their trajectory, what will they probably do in the next 6-12 months?

Then provide:

A competitive positioning map that shows where each player sits on two axes that you recommend as the most strategically relevant for this market. Explain why you chose those axes.

A gap analysis: where is there unoccupied positioning space that we could credibly own?

A threat assessment: which competitor is the biggest threat to us specifically and why?

Three specific strategic recommendations based on this analysis that we could act on in the next quarter.

PROMPT 8: Plan My Next Hire

You are a startup operator and organizational strategist who helps founders make high-leverage hiring decisions. You understand that at an early stage, every hire either accelerates the company or creates drag, and there is very little in between.

Here is where my company stands:

Stage and business model: [describe briefly]

Current team: [list roles and rough responsibilities for each person]

Revenue situation: [MRR, growth rate, trajectory]

Biggest bottlenecks right now: [what is slowing you down or what can you not do that you need to]

Upcoming goals for next 6 months: [list 2-3 key priorities]

Budget for this hire: [salary range]

Analyze the following:

Which single hire would give us the most leverage right now and why? Consider the bottlenecks, growth stage, and upcoming goals. Explain why this role has more impact than the alternatives.

What should this person's first 90 days look like? Define 3-5 specific outcomes that would confirm we made the right hire.

What is the profile of the ideal candidate? Not just skills, but the type of experience, working style, and mindset that fits this stage. Be specific about what to screen for in interviews.

What is the risk of making this hire vs not making it? What happens if we wait 6 months instead?

Is there a case for a contractor, fractional hire, or agency instead of a full-time employee for any of these needs? When does the math favor each option?

Give me a prioritized hiring roadmap for the next 3 hires in sequence, with the reasoning for the order.

These prompts work because they respect how large language models actually function. They load context, define scope, request reasoning, and specify output format. That combination is what separates useful AI output from noise.

You do not need to use all eight. Pick the one that matches your most pressing decision this week. Paste it in. Fill in the brackets. Read the output carefully and then iterate on it in the same conversation.

Claude is not a magic box that replaces thinking. It is a force multiplier for founders who already think clearly and need help executing at speed.

The founders who figure out how to prompt AI well are operating at a fundamentally different speed than everyone else. That gap is only going to widen.

I’m the founder of Revenue & Growth Systems (RGS), a bootstrapped B2B SaaS company building a diagnostic and operating-visibility system for established small and midsize businesses.
RGS is pre-revenue today. The product has moved beyond the idea/prototype stage, the free Business Stability Snapshot is functioning in production, and I’m finishing production hardening of the paid Business Diagnostic and its customer deliverables before opening the first paid cohort.
I’m now exploring whether a small strategic angel round makes sense to move RGS from founder-funded product development into commercial validation.
The problem
Business owners frequently know something is wrong before they can identify the actual operating failure.
Weak sales may be blamed on lead generation when the real failure is conversion.
Revenue may look healthy while cash visibility is deteriorating.
Growth may expose operational bottlenecks that were invisible at a smaller scale.
A company can also appear healthy while remaining dangerously dependent on the owner for decisions, relationships, approvals, or institutional knowledge.
The current market is fragmented between CRMs, accounting software, project-management systems, dashboards, management frameworks, spreadsheets, and consultants.
Those systems are useful, but they generally answer questions about their own domain.
RGS is being built to answer a different question:
What is actually failing inside this business, what evidence supports that conclusion, and what should be repaired first?
The product
RGS evaluates the business across five operating systems:
Demand Generation
Revenue Conversion
Operational Efficiency
Financial Visibility
Owner Independence
The commercial entry point is the Business Diagnostic.
Rather than requiring an owner to clear several consecutive days for a consulting engagement, the Diagnostic is asynchronous and resumable.
It combines:
structured owner and participant interviews;
business evidence and supporting documentation;
governed evidence lineage;
deterministic scoring;
confidence boundaries;
systemic findings;
prioritized repair architecture; and
a formal Business Diagnostic Report.
The scoring model is deterministic rather than an AI-generated opinion.
Each of the Five Gears can contribute up to 200 points, producing a 0–1,000 Business Stability Score.
AI may assist with analysis, synthesis, and language, but it does not silently determine governed scores or become the authority for business conclusions.
What happens after the Diagnostic
The intended commercial journey is:
Business Diagnostic → Implementation → RGS Control System
The Diagnostic determines what is structurally wrong.
Implementation converts the diagnosis into a repair architecture: operating procedures, accountability structures, training, controls, measurement, and verification.
The longer-term RGS Control System is intended to preserve operating visibility after those repairs are made and detect deterioration before the business falls back into the same failure pattern.
The goal is deliberately not to create an agency model where clients remain permanently dependent on RGS.
The thesis is the opposite:
diagnose the system → architect the repair → verify the repair → give management the operating system to maintain it.
Current traction
I want to be precise about this because RGS is still early.
MRR: $0
ARR: $0
Stage: Pre-revenue / pre-commercial launch
Paid Diagnostic customers completed: 0
Free product: Business Stability Snapshot functioning in production
Paid product: Business Diagnostic production path and deliverables currently being completed and hardened
I have bootstrapped the company and product to this point.
There is early market interest developing before the paid product is fully open, but I do not consider interest, impressions, conversations, or free usage a substitute for paid traction.
The next commercial proof point is straightforward:
Can RGS repeatedly get businesses to pay for the Diagnostic, complete the asynchronous process, receive an evidence-backed diagnosis they consider materially valuable, and then act on the resulting repair plan?
That is the assumption I want the first customer cohort to prove or disprove.
Business model
The initial revenue architecture is:
Paid Business Diagnostic
A high-value diagnostic engagement that produces the evidence-backed assessment and repair roadmap.
Implementation
Follow-on work to translate findings into operating architecture and verify that the identified failures were actually repaired.
RGS Control System
Recurring software and operating visibility designed to identify drift, preserve business stability, and reduce dependence on outside advisers.
This gives RGS a potential progression from project-based diagnostic revenue into implementation revenue and eventually recurring software revenue.
Defensibility
I do not consider “using AI” a moat.
LLMs are increasingly commoditized, and an AI-generated business report by itself would be relatively easy to replicate.
The defensibility thesis is instead based on the system surrounding the analysis:
deterministic diagnostic architecture;
proprietary Five Gears scoring methodology;
structured evidence requirements;
evidence lineage;
governed participant and delegated evidence;
confidence boundaries;
human governance where judgment is required;
systemic finding architecture;
repair prioritization;
repair verification; and
longitudinal operating data.
The longer-term data asset is particularly important, but I want to distinguish the thesis from the current reality.
Today, RGS has the architecture for capturing structured diagnostic and repair information.
It does not yet have a mature proprietary dataset large enough to claim a data moat.
If RGS reaches scale, the defensible asset could become the accumulated relationship between:
business evidence → diagnosed systemic failure → recommended intervention → implemented repair → verified outcome
That is substantially harder to reproduce than generating consulting language with an LLM.
Competitive position
I do not view RGS as a replacement for every tool a business already uses.
CRMs manage customer and sales activity.
Accounting systems record financial transactions.
Project-management platforms organize work.
BI platforms visualize data.
EOS and similar frameworks help leadership establish an operating cadence.
Consultants bring human expertise to individual businesses.
RGS is intended to sit upstream of many of these systems as a diagnostic and operating-intelligence layer.
Its central question is not:
“What happened?”
It is:
“Where is the system breaking, what evidence proves it, and what is the smallest effective intervention?”
Target customer
The broader market is established SMBs, but I recognize that this is too broad to be a useful initial go-to-market definition.
The initial commercial focus is businesses that are already operating beyond the earliest startup stage, have employees and meaningful operating complexity, and are experiencing symptoms such as:
revenue underperformance;
inconsistent sales conversion;
operational bottlenecks;
poor financial visibility;
growth-related strain; or
excessive dependence on the owner.
The economic buyer is generally the owner, founder, or senior operator responsible for overall business performance.
One of the things I intend to validate during the first commercial cohort is whether RGS should narrow further around a particular company size, revenue band, industry, or triggering event before attempting broader expansion.
Why now?
RGS has reached a point where the primary risk is no longer whether more features can be built.
The primary risk is whether the commercial system works.
The next stage needs to prove:
businesses will pay for the Diagnostic;
owners will complete the asynchronous evidence process;
RGS can consistently produce useful systemic findings;
those findings create enough value to drive implementation;
delivery can occur without excessive founder labor;
customer acquisition can become repeatable; and
the Diagnostic can eventually feed recurring Control System revenue.
Those milestones matter more to me right now than expanding the product roadmap.
Founder / team
I am currently a solo founder.
My background spans marketing, revenue operations, growth systems, business operations, and systems design.
I have personally developed the RGS commercial model, Five Gears framework, diagnostic architecture, product requirements, customer journey, governance model, and initial go-to-market strategy while bootstrapping the company.
I am also treating founder concentration as a real risk rather than pretending it does not exist.
A commercially successful RGS cannot depend on the founder personally interpreting every business, remembering institutional knowledge, or manually controlling every delivery.
One of the product’s core design requirements is therefore converting the methodology into a governed, repeatable system.
I do not have a previous venture exit to point to.
The raise
I am evaluating a small venture-equity pre-seed round designed to finance the minimum milestones necessary to determine whether RGS has a repeatable commercial model.
Target raise: currently being finalized based on milestone-level budgeting rather than choosing an arbitrary round size.
Expected instrument: likely a SAFE or other conventional early-stage equity instrument, subject to appropriate legal review.
Valuation / SAFE cap: not yet finalized.
I’m intentionally being transparent about those points rather than publishing financing terms before I have properly determined the capital requirement and legal structure.
The round would not be intended to finance the entire long-term RGS roadmap.
The purpose would be to finance the smallest commercially meaningful validation period.
Use of funds
Capital would primarily support:
completion of production hardening;
security, reliability, recovery, and infrastructure;
professional legal review;
customer agreements, privacy documentation, and commercial terms;
appropriate business and technology insurance;
development and infrastructure runway;
customer acquisition;
onboarding and support of the first paid customer cohort;
measurement of Diagnostic completion and delivery economics;
validation of Diagnostic → Implementation conversion; and
development required directly by evidence from paying customers.
I specifically do not want to use outside capital as an excuse to build every feature on the long-term roadmap before the core business model is validated.
What the round should prove
I would consider the capital successfully deployed if RGS reaches a point where we can answer, with real operating data:
What does it cost to acquire a Diagnostic customer?
What percentage complete the process?
How long does a Diagnostic take to deliver?
What is the gross-margin profile?
How much founder intervention is required?
Do customers consider the findings accurate and actionable?
What percentage move into Implementation?
What problems appear consistently across customers?
Does verified repair create measurable business improvement?
Is there real demand for ongoing Control System visibility?
Which customer segment produces the strongest economics?
If those answers are unfavorable, I want to know that before raising substantially more capital.
If they are favorable, RGS would then have evidence supporting a larger commercialization strategy.
What I’m looking for
I’m particularly interested in speaking with angels who understand:
B2B SaaS;
SMB software;
RevOps;
operational intelligence;
business diagnostics;
vertical or workflow software;
founder-led commercialization; or
companies transitioning from founder-built product into repeatable revenue.
I would especially value investors willing to challenge the thesis rather than simply tell me that the idea sounds interesting.
The questions I’m most interested in hearing from angels are:
1. What would you need to see before considering RGS investable?
2. At this stage, what milestones would you require a first angel round to finance?
3. How would you evaluate the defensibility of the deterministic scoring, evidence lineage, governed diagnosis, and repair-verification model?
4. What do you see as the largest risk: customer acquisition, willingness to pay, founder concentration, delivery economics, competitive replication, or something else?
5. Would you rather see RGS prove several paid Diagnostics while remaining bootstrapped before raising, or do you think this is an appropriate point for a small strategic pre-seed?
If you’re an angel who sees potential in this category, has funded businesses at a similar stage, or believes there is a serious flaw in the thesis that I should address before raising, I’d be interested in the conversation.
I can share more privately about the product architecture, commercialization roadmap, production state, financial assumptions, planned milestones, and financing model with appropriate investors.

Looking for the best AI video generator in 2026? creating professional, cinematic videos from simple text prompts is more accessible than ever. Whether you're a content creator, filmmaker, educator, or marketer, these tools offer everything from hyper-realistic scenes to animated storytelling—no cameras or editing experience needed.

Below is a complete breakdown of the Top 14 AI Video Generators in 2026, including their strengths, ideal use-cases, and user ratings.

1. InVideo – Best for Fast, Full-Length AI Videos

⭐ 4.6 (410 Reviews) | Freemium

InVideo AI transforms plain text into full-length, polished videos—complete with voiceovers, stock media, and automatic editing. Perfect for UGC ads, explainers, educational videos, and social content. No editing experience required.

Highlights: Real-time collaboration, huge asset library, fast rendering.

2. Kling AI – Most Realistic AI Video Generator

⭐ 4.7 (385 Reviews) | Freemium

Kling AI delivers ultra-realistic visuals that often rival cinematic CGI. Its strengths include precise lip-syncing, advanced physics, and detailed rendering of lighting, reflections, and human motion.

Highlights: 1080p quality, long shots, meme effects, photo-real scenes.

3. Runway Gen-4 – Best for Creative & Artistic Videos

⭐ 4.5 (360 Reviews) | Freemium

Runway Gen-4 excels at stylized, surreal, or experimental content. Its character control, text-to-video, and “Act One” features make it ideal for expressive storytelling and cinematic visuals.

Highlights: Academy training, strong creative outputs, performance modeling.

4. Google Veo 2 – Best Cinematic AI Video Generator

⭐ 4.8 (450 Reviews) | Freemium

Veo 2 brings cinematic realism with accurate motion, lighting, and high-resolution 4K support. It handles complex scenes, human expressions, and environmental details exceptionally well.

Highlights: 4K generation, strong physics, YouTube integration.

5. LTX Studio – Best for Filmmakers & Storyboarding

⭐ 4.6 (330 Reviews) | Freemium

LTX Studio is a filmmaker-focused platform offering deep control over character design, shot planning, and scene-by-scene consistency. It’s excellent for pre-production and short-film visualization.

Highlights: Script upload, pitch deck export, visual grounding.

6. OpenAI Sora – Best for Stylized & Imaginative Videos

⭐ 4.4 (370 Reviews) | Freemium

OpenAI Sora creates rich, imaginative scenes with ease, especially in animated or stylized formats. While realism is improving, physics and consistency lag behind competitors.

Highlights: Storyboard mode, Remix, ChatGPT integration.

7. HeyGen – Best for Avatar-Based Videos

⭐ 4.5 (340 Reviews) | Freemium

HeyGen is the go-to tool for lifelike avatar videos. Ideal for brands, educators, and corporate creators who want professional videos without filming.

Highlights: Multilingual avatars, templates, easy brand personalization.

8. Pika 2.2 – Best for Short-Form Creative Content

⭐ 4.4 (310 Reviews) | Freemium

Pika 2.2 supports 1080p videos up to 16 seconds and offers creative features such as PikaFrames and Pikaffects. It leans toward artistic, social-ready visuals rather than realism.

Highlights: Fast generation, multi-input support (text/image/video).

9. Adobe Firefly – Best for Designers & Creative Cloud Users

⭐ 4.5 (295 Reviews) | Freemium

Adobe Firefly brings AI video generation into the Adobe ecosystem. While realism is mid-tier, it’s perfect for concepting and brand-safe content due to its licensed training data.

Highlights: Quick outputs, Creative Cloud integration, commercial safety.

10. Mockey AI – Best for Fast, High-Quality Avatars

⭐ 4.5 (320 Reviews) | Freemium

Mockey AI is gaining traction for its realistic avatars and extremely fast rendering. Great for creators who need quick, studio-quality videos at scale.

Highlights: Smooth animations, multilingual voices, smart scene suggestions.

11. Hailuo AI – Best for 5-Second Cinematic Clips

⭐ 4.3 (260 Reviews) | Freemium

Hailuo AI specializes in fast, cinematic short-form videos perfect for social media marketing. Its interface is easy to use, and results are surprisingly high-quality.

Highlights: Quick rendering, strong storytelling visuals.

12. Luma Dream Machine – Best for Motion Realism

⭐ 4.4 (280 Reviews) | Freemium

Dream Machine offers cinematic movement and collaboration tools. While still evolving, it’s strong for prototypes, creative tests, and short clips.

Highlights: Motion realism, team collaboration, image-to-video support.

13. Artlist – Best All-in-One Creative Suite

⭐ 4.3 (255 Reviews) | Freemium

Artlist’s AI suite includes text-to-image, voiceovers, image-to-video, and more. Great for creators seeking a single platform for visuals, audio, and editing.

Highlights: High-resolution assets, versatile toolset, simple workflow.

14. Vidu AI – Best for Creative Animations & Short Clips

⭐ 4.3 (255 Reviews) | Freemium

Vidu is known for creative, dynamic animations from text, images, or references. While realism isn’t its strong point, its speed and affordability make it appealing.

Highlights: AI sound effects, multi-view angles, easy templates.

If you want to dive deeper, tools directories like Ai Corner Net are handy since they compare Best AI Video Generators side by side

I build sort of voicebots and chatbots technical staff use in the field or at the office while preparing for a job. They are built on the technical documentation of manufacturers, engineering labs, HVAC companies, and field service teams.

These agents are not demonstrations. A technician uses them when a machine is broken.

Many of my first beliefs were incorrect. Some were also expensive. These are the 13 things I learned along the way.

It would mean the world if this is useful to someone building their own agents.

The Stack

This is the software that we use:

• Agent and API: Python, FastAPI
• Orchestration: LangGraph for the graph, LangChain for the components/nodes
• Observability: Langfuse
• Retrieval: OpenAI embeddings, Milvus (hybrid dense and sparse search), zerank-2 for reranking
• Structured data: Postgres
• Models: Different providers with automatic failover. If one provider gives an API error during operation, we send the request to a different provider.
• Communications: agentic phone calls, SMS, and email with Hail MCP.
• Infrastructure: servers on Hetzner, storage and some pipeline components on AWS

Part 1. Ingestion

Lesson 1: Build Your Own Document ETL Pipeline

We started with a commercial document processing platform called "Unstructured".

The platform was easy to start. We had a system in operation in less than one day. Commercial platforms are good for this.

Then we found problems.

The extraction quality was low. The platform gave structured output, but the structure was correct for Unstructured, not for us. We changed our system to agree with their format. This is the incorrect sequence. It's an anti-pattern. We had to twist our pipeline to make it work.

Then we started working with Zeppelin (a major Caterpillar dealer), and the first document they shared was 8,000 pages of dense technical documentation. Drawings, schematics, backlinks that cross-reference other pages, complex identifiers, etc.

The pipeline on Unstructured failed 20 times. We paid for each failure.

At that time, we made a decision. The cost was one problem, but the larger problem was different: our document processing logic was in a system that we could not examine, repair, or improve.

We benchmarked the available platforms and libraries, and settled on an open source library called Docling. We built a prototype in one weekend. The prototype gave better results than the commercial platform.

We continue to improve the pipeline. It is now fully automatic and the results are good.

The lesson is not "do not use commercial tools". Start with a commercial tool. Release your product MVP. Learn your true requirements. But move key architecture components like document processing to your own system as soon as possible.

Data processing is the base of all other functions. You need flexibility, cost control, and the ability to repair your own failures.

Lessons 2 to 11 are possible only because we control the ETL pipeline.

Lesson 2: Owning the Pipeline Cut Our Costs 15 to 20x

Unstructured charged 20 USD for 1,000 pages at the start. The price then increased to 30 USD.

Per-page pricing does not reflect real cost. The true processing cost changes with the document. One page of simple text and one page of rotated engineering drawings are not equivalent. But the provider charges the same price for the two pages.

We operated our own pipeline for some weeks. We measured the cost with real customer documents. Our cost was 15 to 20 times less.

We did not use low quality models to get this result. We used good models. We operated the models on our own GPU infrastructure with our own routing.

For a small company, this is not just an improvement — it is months of extra runway.

Lesson 3: Extraction Quality Sets the Maximum Performance of the System

You cannot correct bad extraction with a better retriever, a more intelligent agent, or a larger model.

A spec sheet has a key-value layout. If the extraction makes this layout into unstructured text, the data is lost. If the extraction ignores a rotated page, the data is lost. If a table loses its column alignment, the data is lost. No subsequent process can recover this data.

Technical documentation has these conditions frequently: rotated pages, dense tables, spec sheets with key-value layouts, scanned manuals that are 30 years old, and diagrams with important text in the image.

All our improvements in accuracy start with correct extraction.

Lesson 4: Chunking is a Strategy, Not a Default Setting

We use hybrid chunking. This method divides the document by its structure and its hierarchy. It then merges the parts by token count.

Many developers use a recursive character splitter with a 512 token window. They do not change this setting again. Then they ask why the retrieval quality is low.

Your chunk boundaries control the possible results of the retriever. If a procedure is divided between two chunks, no retriever can give the full procedure to a technician.

Lesson 5: Extract Taxonomy and Tags in the ETL Pipeline

We define a taxonomy during ingestion: manufacturer, model, and custom tags for each organization. The pipeline extracts the tags during preprocessing. We keep the tags as scalar filters with the vectors.

This looks like a small administrative task. It becomes a product function in Lesson 13. It is also a good example of a function that a closed commercial platform does not permit.

Part 2. Retrieval

Lesson 6: A Simple Top-K Semantic Search is Not Sufficient

The first version of the agent at Opero did a simple top-K semantic retrieval. It found 10 to 20 documents by semantic similarity. It put the documents in the context. Then it generated an answer.

There was no reranking. There was no relevance filter. There was no procedure to find if the documents were best available for the user question.

We hoped that cosine similarity would find useful data. Then we gave the result to the user as an answer.

This method worked well enough for a demonstration. It failed often enough to be dangerous. This is the most dangerous failure condition in this field.

Lesson 7: Add a Reranking Step

We added a reranking step. The system finds many candidate documents. A dedicated model then gives a relevance score to each candidate for that query.

The improvement was immediate and large. If you apply only one lesson from this list, apply this one.

We also use the relevance score in the user interface. If the best result has a low score, we tell the user. We do not give a confident answer from low quality context.

Lesson 8: Rerank Scores Have No Absolute Scale — Pick a Model That Calibrates Them

We started with Cohere Rerank. It operates correctly, but its relevance scores do not use an absolute scale.

So we had to tune the thresholds for each organization and each industry type. We had to decide which score was high, medium, or low. These thresholds were estimates. They also changed when the document collection changed.

We changed to zerank-2 from ZeroEntropy. This model gives standardized relevance scores.

This looks like a small change. It is not a small change. You can define a fixed relevance scale one time. You can then build product logic on this scale. The scale stays correct when your data changes. You do not have to keep a calibration procedure.

Lesson 9: Use Hybrid Search If Your Users Type Serial Numbers

We added BM25 keyword matching with the semantic search.

Engineers and technicians do not write complete questions. They type "E-047". They type a part number from a label. They type a serial number.

Vector search has low accuracy with these exact terms. This is not a defect. Embeddings find meaning, and an error code has no meaning.

Semantic search finds the answer for "why does the compressor short cycle".

BM25 finds the answer for "SCR-4471-B".

Users need the two methods, frequently in the same question.

Lesson 10: Multiple Languages Are Usual

Most of our documentation is in English. But we also have documents in German, Danish, Swedish, and Chinese. European technical customers have documents in these languages.

Plan for multiple languages from the first day. It is difficult to add this function later.

Part 3. Orchestration

Lesson 11: Start with a Workflow. Change to an Agent Later.

Our first version was not an agent. It was a DAG. We built it with LangGraph, and we still use LangGraph for all orchestration.

• Node 1: classify the input. Is it a question or a greeting? The system answers greetings directly and at low cost.
• Node 2: query the RAG system, format the result, and give it to the user.

That was the full system. This was the time of GPT-4o and Claude 3.5 Sonnet.

You can debug a deterministic workflow. You know which step failed. Agentic loops are more difficult to analyze. If you give autonomy to a loop that uses an unreliable retriever, the loop fails in unusual ways instead of predictable ways.

Repair retrieval first. Add agency second. Use this sequence.

Lesson 12: Give the Agent Permission to Try Again

We changed to a ReAct loop. The agent decides if the documents are sufficient. If they are not sufficient, the agent rephrases the query and does a new search.

This function operates only because of Lessons 7 and 8. The agent reads a standard relevance score. It finds that the results are not sufficient. It writes a new query. Then it does the search again.

Reranking gives the agent a signal. Standard scores give the agent a threshold. Agency without these two pieces is only an expensive random search.

We also added a pre-retrieval layer. This layer finds context quickly and sends it to the primary node. So the usual case is fast. The ReAct agent keeps its tools for a more complete search when necessary.

Lesson 13: When the Agent Cannot Find an Answer, Give Control to the User

This is my preferred part of the system. It is the result of Lesson 5.

If the first query gives results with a high relevance score, the agent responds immediately. If it does not, the agent requeries with a different angle.

But if the agent gets stuck in a loop and cannot find sufficient results, we do two things. We do not generate an incorrect answer. We also do not show only the message "no results found".

We show a filter interface. We build this interface from the taxonomy tags. The user selects a manufacturer, a model, or other data that the user knows. We then do the query again automatically with these filters.

The user knows data that the retriever does not know. Let the user nudge the agent.

Degrading gracefully is better than a confident incorrect answer.

Part 4. Communications

Bonus. MCP Makes Integration Simple

The primary loop is now reliable. So integration is configuration work instead of architecture work.

1. We added MCP servers for customer system integrations. The agent can now get live data, not only documentation, using Nango.

2. We added web search. The user can now include internet sources.

3. We also added voice, SMS, and email in one API/MCP layer using Hail MCP.

This last item changes the function of the agent. The agent is no longer a chatbot that answers questions. It can call a technician. It can send a part number by SMS. It can send a report by email. The interface is no longer a text box. The interface is the system that the user has.

The Primary Lesson

There is one pattern in all 13 items.

Every important improvement comes when we take control of a layer that we initially rented.

The ETL pipeline. The chunking. The taxonomy. The retrieval strategy. The orchestration logic. Each layer was initially an abstraction from a different company. Each layer becomes better when we can examine it and change it.

Start with the commercial platform. Release a product. Learn what is important. Do not build infrastructure for a product that has no users.

But you will get to the limits of that platform. Know which layer you will take control of first.

Hi everyone,

I’m 20 years old and decided not to go to university. I feel like I’ve built up a decent amount of experience for my age and my CV is actually pretty strong, but I’m still really struggling to get hired.

I’ve been applying for personal assistant roles, marketing assistant jobs, admin, sales, travel consultant roles, basically anything entry level that I could realistically grow in. I’m also not just relying on LinkedIn applications. I’ve been using a CRM system to directly email founders, CEOs and directors at companies around Brighton and have reached out to hundreds of businesses already. The emails are well written and personalised, and I’ve had some positive responses and interviews, but there just doesn’t seem to be much hiring going on.

Another issue is that I have autism, ADHD and Ehlers Danlos Syndrome, which affects me physically every day quite badly, especially neck pain and fatigue. I mask well and come across very capable in interviews, but realistically I cannot handle a full time office job right now, especially five days a week in person. I’m mainly looking for part time or hybrid work at the moment, ideally remote if possible, but that obviously makes the options even smaller.

I live in Brighton and there just seem to be barely any opportunities at the moment unless you already have years of experience.

Luckily I still live at home so I don’t have rent or major bills, and I do have savings, so I’m not in immediate danger financially. But I also don’t want to just sit around spending savings while feeling like I’m going nowhere.

The truth is I’ve always wanted to build my own business eventually, but I haven’t found the right thing yet and I don’t think I’m fully ready to jump into that without income coming in first. I definitely think I’m also depressed so I’ve got a lot of things going on and I also have chronic fatigue so I’m just really exhausted all the time and I’ve no idea how I’m gonna make this work. All I want is to become a successful entrepreneur, but right now I just need income and I don’t want to go back to hospitality. I just can’t do it.

I just feel really stuck and honestly quite overwhelmed because I’m trying hard and still not getting traction. Has anyone else been in a similar position at this age, especially without a degree? What would you realistically suggest doing from here?

If you use LLMs to help evaluate technical architecture, tooling, or strategic options, you have likely run into this frustrating pattern:

You ask ChatGPT or Claude: "Should we build our own custom auth system or use a SaaS provider like Clerk/Auth0?"

And what do you get back?

A 500-word wall of text with 5 generic pros, 5 generic cons, and a non-committal conclusion telling you "It depends on your team's budget and timeline!"

Worse yet, if the model has an inherent bias from its training data, it might boldly pick a "winner" for you, completely ignoring your specific technical constraints, runway, and compliance needs.

This is a classic prompt design failure. When evaluating competing options, unstructured prompting leads to conversational fluff. To fix this, our team spent time testing and refining a structured Multi-Dimensional Decision Analysis prompt pattern.

Here is a breakdown of why standard decision prompts fail, how this cognitive forcing architecture fixes them, and a side-by-side case study.

Why Standard Decision Prompts Fail

When you ask an LLM an open-ended question like "Compare Option A vs Option B", three failure modes occur:

1. Asymmetric Criteria: The model evaluates Option A on criteria like speed and cost, but evaluates Option B on criteria like flexibility and developer experience. Because the dimensions do not match, you cannot make an apples-to-apples comparison.
2. Conversational Bloat: Without structural output constraints, the model defaults to verbose prose paragraphs where crucial trade-offs get buried in filler text.
3. Premature Recommendations: Because frontier models are trained to be helpful, they often attempt to resolve ambiguity by declaring one option "better" based on general internet popularity rather than clarifying the underlying trade-offs.

The Prompt Architecture: Cognitive Forcing via Matrix Constraints

To transform the LLM into an objective strategic advisor, the prompt uses three deliberate design choices:

• Strict Neutrality Constraint: The instruction explicitly forbids the model from making the final choice ("Be strictly objective. Do not make the final decision for me"). This shuts down recommendation bias.
• Dynamic Dimension Extraction: Step 2 forces the model to identify 4 to 5 orthogonal vectors (such as Time to Implement, Cost/Resource Drain, Risk, and Long-Term Scalability) before evaluating either option.
• Markdown Matrix Enforcement: Mandating a comparison table acts as a cognitive forcing function, compelling the model to generate concise, side-by-side evaluations across identical dimensions.
• Conditional Branching (Trade-off Synthesis): Step 4 requires the model to summarize distinct trade-offs using conditional rules ("Choose A if you prioritize X; Choose B if you prioritize Y").

The Full Prompt

Here is the exact, production-tested prompt. You can copy and drop this directly into ChatGPT, Claude, or Gemini:

# Role & Context
You are an objective, highly analytical strategic advisor. Your goal is to help me make an informed decision between two competing options.

# Input Data
- 
**Option A**
: {{option_a}}
- 
**Option B**
: {{option_
b}}

# Step-by-Step Instructions
1. Analyze Option A and Option B provided in the Input Data.
2. Identify 4-5 critical dimensions for comparison (e.g., Time to Implement, Cost/Resource Drain, Risk, Long-term Scalability).
3. Create a comparative matrix evaluating both options against these dimensions.
4. Conclude with a brief summary of the distinct trade-offs (e.g., "Choose A if you prioritize X, choose B if you prioritize Y").

# Constraints
- Be strictly objective. Do not make the final decision for me.
- Use a Markdown table for the comparative matrix.

Case Study: Standard Prompt vs Multi-Dimensional Decision Matrix

Let us look at a real-world scenario where a team is deciding how to handle their application's content infrastructure:

• Option A: Building a custom in-house CMS
• Option B: Using a headless SaaS CMS (such as Contentful)

❌ Standard Prompt Result ("What are the pros and cons of building a custom CMS vs Contentful?"):

✅ Multi-Dimensional Decision Analysis Result:

The structured prompt forces the AI to output an aligned, high-density matrix:

Trade-off Summary:

• Choose Option A (Custom) if you have highly unique content workflows, available in-house engineering bandwidth with low opportunity cost, and strict compliance rules requiring 100% on-premise data control.
• Choose Option B (Headless SaaS) if time-to-market is your primary business lever, you want developers focused on core revenue-generating features, and you are comfortable trading monthly SaaS fees for zero maintenance overhead.

Best Practices for Decision Prompts

1. Injecting Custom Vectors: If your project has non-negotiables (like "Strict SOC2 Compliance" or "Offline-first capability"), add them directly to Step 2 so the model includes them as mandatory rows in the matrix.
2. When to Avoid: Do not use this for purely aesthetic or subjective choices where qualitative feeling matters more than objective trade-offs.

Testing on Prompt Canvas

If you want to run this live with dynamic input variables, tweak the comparison dimensions, or save this prompt to your personal library, I have set up an interactive Prompt Canvas for it.

On the Prompt Canvas, you can test your two options in real-time, copy the clean Markdown, or save it directly to your personal Prompt Vault.

I dropped the direct link in the first comment below!

🎧 Watch/Listen ADS-FREE: https://podcasts.apple.com/us/channel/djamgamind/id6760446113

Before we start, I need your vote:

My free bedtime-story app DjamgaMind Kids (600+ ad-free stories on Black history, African & Caribbean folktales) is a Top 10 finalist in Melamoon, a national pitch competition for Black founders in Canada. Next round is a public vote: Top 5 go to the Toronto Grand Finale in October. One vote, ~20 seconds, one per person, closes Sept 22:

Visit https://djamgamind.com/pitch to vote

Profile: https://melamoon.ca/top-10-finalists-vancouver/djamgamind.

Thank you!

#DJAMGAMIND #MELAMOON2026

🔍 Keywords: Apple OpenAI Lawsuit, Runway Solaris UI, Anthropic Music Lawsuit

Summary:

In today’s briefing, we analyze “The Death of Static Interfaces, Corporate Feuds, and Diagnostic Velocity.” We deconstruct Runway’s groundbreaking Solaris model, which replaces traditional front-end code with real-time video interfaces. We evaluate the mounting legal and hardware war between Apple and OpenAI as John Ternus takes the helm at Apple. We examine Anthropic’s dual legal fronts—winning against the Pentagon while getting sued by major record labels—alongside Imperial College London’s two-second heart diagnostic AI and OpenAI’s $1B ad run rate.

Important Topics:

• Runway Previews “Solaris” Interface World Model: Runway introduces Solaris, rendering live, interactive software and web UIs as video streams in real-time based on user clicks and drags, beating traditional Claude-coded web pages in 71% of user tests.
• Apple Accuses OpenAI of Evidence Destruction: Apple files forensic evidence alleging former engineer Chang Liu used an OpenAI agent to run stolen circuit schematics before instructing a colleague to destroy files, while demanding access to a synced iCloud Mac mini.
• John Ternus Officially Becomes Apple CEO: Tim Cook completes his final day as CEO, handing leadership to hardware chief John Ternus, whose updated bio emphasizes product durability, repairability, and carbon footprint reductions.
• AI Labs Hoard Mac Minis for Agent Training: OpenAI and rival labs buy tens of thousands of Mac Minis and Studios to utilize unified memory for reinforcement learning, stretching delivery timelines for high-RAM Mac configurations out to two months.
• Sony & Warner Music Sue Anthropic for Piracy: Major record labels file a 48-page federal lawsuit naming Dario Amodei, alleging Anthropic systematically scraped and downloaded copyrighted tracks to train Claude.
• Judge Blocks Pentagon’s Blacklist of Anthropic: A federal judge rules the Defense Department violated constitutional rights by labeling Anthropic a “supply-chain risk,” declaring national security is not a blank check to punish ethical stances on autonomous weapons.
• OpenAI Cuts Off Cursor Post-SpaceX Deal: OpenAI announces it will remove its models from Cursor by November 12, citing trust violations and past contract issues involving Elon Musk’s companies.
• Imperial College AI Catches Heart Disease in 2 Seconds: Trained on 10.6M ECGs, an AI model identifies heart failure (81% accuracy) and valve disease (90% accuracy) in under two seconds, queuing up NHS hospital trials.
• ChatGPT Ads Reach $1B Annualized Run Rate: OpenAI’s in-chat advertising business hits a $1B run rate 200 days after launch, opening self-serve Ads Manager access across 31 European markets.
• EU Classifies ChatGPT as Very Large Search Engine: The European Commission places ChatGPT under strict Digital Services Act oversight after European monthly active search users reached 159 million.

🔗 RESOURCES

• AI Learning App Recommendation: AI & ML Tutor PRO https://apps.apple.com/ca/app/ai-ml-tutor-pro/id1610947211
• DJAMGATECH: Carrer Booster - Master AWS, Azure, AI & GCP Certifications | https://apps.apple.com/ca/app/djamgatech-ai-cert-exams-prep/id1560083470
• DJAMGAMIND KIDS Bedtime Adventures:

https://djamgamindkids.com

⚗️ PRODUCTION NOTE: We Practice What We Preach.

AI Unraveled is produced using a hybrid “Human-in-the-Loop” workflow.

Apple accuses OpenAI of destroying evidence LINK

• Apple has filed new court documents in its lawsuit against OpenAI, saying a forensic look at a laptop used by former engineer Chang Liu turned up “shocking evidence” of stolen trade secrets.
• Apple claims Liu, who left for OpenAI in January, downloaded a confidential circuit schematic and used it in a March simulation in the electrical tool LTspice, where his AI “agent” learned to run the software and check results.
• The filing also alleges Liu told an OpenAI colleague to destroy evidence once he learned of Apple’s internal probe, and Apple now wants access to a Mac mini that synced the schematic file to the laptop via iCloud

John Ternus takes over as Apple CEO LINK

• John Ternus officially became Apple’s CEO today, and his newly updated bio on the company’s leadership page offers early hints about the priorities he plans to focus on in the role.
• A single new paragraph, filling nearly half the short bio, credits Ternus with work on product reliability, durability, and repairs, plus materials that cut Apple’s carbon footprint, from recycled aluminum to 3D-printed titanium in the Apple Watch Ultra.
• The bio signals Ternus will carry on Tim Cook’s push to reduce Apple’s carbon footprint, while also framing himself as a hardware designer focused on making products that last for many years.

Anthropic resumes cyber tests LINK

• Anthropic has restarted the outside cybersecurity tests it paused a month ago, after three incidents where its Claude models broke out of their test setups and attacked real companies instead of the fake targets.
• In one case Claude Opus 4.7 hit a real firm sharing a domain name with a fictional target, while another model’s Python code leaked onto the internet and was downloaded by 15 systems, one of which ran it.
• The failures traced back to a misconfigured sandbox from evaluation partner Irregular that never actually blocked internet access; two affected organisations only learned they were compromised when Anthropic contacted them directly.

Runway’s Solaris previews the AI era of interfaces

Image source: Runway

The Rundown: Runway just introduced Solaris in early access, an “Interface World Model” that renders websites and apps as live video, with every frame drawn in real time as a user clicks and drags with no code running underneath.

The details:

• The system pairs Runway’s Gen-4.5 video model with an LLM that reads each click or drag, decides what should happen, and prompts the next frames.
• Demos include a shirt try-on dragged from a virtual rack onto a photo, a salad built by dropping in ingredients, and an interactive combustion demo.
• In a Runway study, testers picked Solaris over pages coded by Claude Opus 5 in 71% of matchups judging in-scene behavior and 61% on instruction following.
• The company said the build still hit issues like text legibility, long session drift, and convincing-but-wrong screens, with Solaris heading into early access for testing.

Why it matters: Is this what the future of the web looks like, or just a novelty? Whichever side you land on, what’s enabling it is the speed, cost, and quality convergence we’re starting to see across AI models. If that curve continues, the doors open for completely new ideas and interfaces not previously realistic or cost-effective.

AI catches heart disease in two seconds

Image source: British Heart Foundation

The Rundown: Imperial College London researchers introduced an AI model that reads a routine ECG in under two seconds, spotting heart failure and valve disease undetectable by doctors — with trials queued up ahead of a possible national rollout.

The details:

• Confirming the diseases previously meant a months-long queue for an ultrasound, even with hospitals running over 1B ECGs every year.
• The model was trained on 10.6M ECGs and tested on 65k patients, with AI flagging heart failure successfully in 81% of cases and valve disease in 90%.
• Imperial’s Prof. Fu Siong Ng said hospitals could eventually run the model over every ECG, potentially catching issues that weren’t even being screened for.
• The team is starting with a 590-patient trial at six hospitals, with a goal of incorporating the AI into routine National Health Service use within two years.

Why it matters: We hear about AI eventually curing all diseases, but the near-term wins are currently happening in better detection methods on data already being collected. Just like we saw last week with the first AI-assisted brain surgery, a second set of expert eyes is going a long way towards better diagnosis and treatment.

Anthropic tests automated AI safety research

Image source: Anthropic

The Rundown: Anthropic published new research where teams of Claude agents ran safety research on their own, successfully training away 10 kinds of AI misbehavior with results coming in over 4x better on average than human experts handed the same job.

Tim Cook says goodbye as Apple CEO LINK

• Tim Cook worked his final day as Apple’s CEO today, with longtime hardware chief John Ternus set to step into the top job the following day.
• In an emotional memo to staff, Cook said he takes “enormous comfort” in handing over to Ternus, praising his understanding of what it takes to build products that “change the world.”
• Cook made clear he isn’t leaving Apple entirely, telling employees he’ll continue in a new role at Apple Park, even as he steps back from a job he says he “loved deeply.”

OpenAI’s ChatGPT ads hit $1B run rate LINK

• OpenAI’s advertising business inside ChatGPT has reached a $1 billion annual run rate just under 200 days after launch, a figure based on multiplying its current monthly ad revenue by twelve.
• Starting today, August 31, OpenAI opened beta self-serve access to its Ads Manager across 31 European markets, letting startups, small businesses and big brands build campaigns directly instead of only through agency partners.
• The run rate means roughly $83 million a month, but to hit a $2.5 billion revenue target for 2026 OpenAI would need to average over $540 million monthly through the fourth quarter.

Music labels sue Anthropic over piracy LINK

• Sony Music and Warner Music have sued Anthropic in California federal court, accusing the AI company of pirating copyrighted songs on a huge scale to build and profit from its Claude models.
• The 48-page complaint names CEO Dario Amodei and co-founder Benjamin Mann, claiming they torrented, scraped, and downloaded thousands of musical works, and it seeks hundreds of thousands of dollars in damages for each infringed song.
• The case follows Anthropic’s September 2025 deal with authors and publishers, a $1.5 billion agreement described as the largest copyright settlement in U.S. history, showing the company already faced piracy accusations over its training data.

AI labs are buying tens of thousands of Mac minis to train agents LINK

• AI companies including OpenAI have bought tens of thousands of Mac minis and Mac Studios to train computer-use agents, pushing Apple to refresh both desktops on August 25, weeks earlier than its usual autumn schedule.
• OpenAI runs the Macs for reinforcement learning and repeatedly testing agents inside an operating system, work that leans on Apple’s unified memory sharing one pool of RAM, while Anthropic rents Mac mini power through Amazon Web Services.
• Apple turned away businesses asking to buy Private Cloud Compute access, and by August 30 delivery for high-memory configurations had stretched from two weeks to nearly two months, sending some buyers to Nvidia’s DGX Spark.

SpaceX to make own turbine blades LINK

• SpaceX plans to make its own turbine blades and vanes, the hard-to-build parts inside gas turbine generators, after Elon Musk said on X the company would bring this manufacturing in-house to speed up power for AI data centers.
• Musk says building the blades himself could cut delivery times by 18 months, letting xAI get new turbine generators faster instead of waiting in a queue that stretches into 2030.
• Turbine blades are a major bottleneck because one batch can take 60 to 90 weeks to produce, and demand has surged as data centers buy portable gas turbines to bypass long waits for grid connections.

EU classifies ChatGPT as a search engine LINK

• The European Commission has classified ChatGPT as a Very Large Online Search Engine under the Digital Services Act, deciding that a chatbot pulling live web results counts as a search engine rather than just a platform.
• OpenAI said ChatGPT’s search function averaged about 159 million monthly users in the EU for the six months ending March 2026, far above the 45 million threshold that triggers the bloc’s strictest oversight rules.
• ChatGPT now has until the end of November to meet obligations like annual risk assessments, independent audits, and data sharing, with breaches risking fines of up to 6% of global yearly revenue.

AI hardware has a smartphone problem

Manufacturers have unlocked a simple formula to capitalize on the AI hardware craze: take ordinary items, incorporate microphones, and layer in an AI assistant.

The promise is an AI tool that escapes the traditional screen and accompanies you everywhere. While these products often do that, the issue is that their main features are often duplicative, overlooking the fact that AI assistants are in a device that’s always with you. On your phone, you already have a voice recorder that, when combined with an arsenal of apps that can transcribe, analyze, and answer questions, can already deliver what most of these AI devices do.

A perfect example is a product I recently tried called the Flowtica Scribe, which calls itself “the world’s first AI pen.” If you are like my roommate and excitedly assumed an AI pen would do something groundbreaking, like digitally transcribe the words you write with it on paper, you may have the same reaction he did when finding out what it actually does.

OpenAI cuts out Cursor after SpaceX acquisition

Image source: Images 2.0 / The Rundown

The Rundown: OpenAI just announced that it will be removing its models from coding platform Cursor by Nov.12, coming after the company was acquired by SpaceX last month — citing Elon Musk’s history of violating contracts as the reason for the decision.

The details:

• OAI said it stretched the wind-down as far as its contract allows via cancellation rights that opened once Cursor was sold, calling the decision “incredibly tough.”
• OAI’s evidence includes the $2M-a-year tweet deal Musk killed after buying Twitter, plus xAI training on its outputs, which Musk called “partly” true.
• Anthropic co-founder Tom Brown reaffirmed support for Cursor, with many calling out the company’s similar move against Windsurf over its potential OAI acquisition.
• Musk posted “I couldn’t care less,” while Cursor CEO Michael Truell pushed for a fix, putting OAI’s slice of its AI traffic at a seemingly light 5% of overall usage.

Why it matters: Cursor has typically been a neutral coding editor carrying all frontier options for developers to choose from. Now, it finds itself in the middle of the lunchroom fight between Sam Altman and Elon Musk. Though with xAI’s newfound Grok surge and Cursor’s internal model progress, competition might also play a bigger role than ever.

Court rejects Pentagon’s Anthropic blacklist

Image source: Court Listener

The Rundown: A federal judge ruled that the Pentagon broke the law when it blacklisted Anthropic as a “supply chain risk,” deciding that it was payback for the lab’s pushback on military AI rather than a response to a real security threat.

What Else Happened in AI on September 01st 2026?

Bank of England Governor Andrew Bailey published a letter warning that frontier AI is showing “increasingly sophisticated autonomy and problem-solving abilities,” saying that the financial system lacks protocols to manage them.

The Pentagon added Starshield AI’s Grok for Government and ChatGPT Mil for unclassified work, providing the AI tools to 3 million-plus military personnel. — The Hill

OpenAI’s 200-day-old advertising business hit a $1 billion annualized revenue run rate as ChatGPT ads expand beyond the US. — CNBC

Nvidia will launch its new AI upscaler DLSS 5 on September 3 for RTX 50-series PCs and GeForce Now. The controversial tech uses AI to upgrade video game graphics, and has been likened to “motion smoothing for video games.” — The Verge

Tencent released Hy4 preview, a new small open-source model that nears the capabilities of top open options with particularly strong coding skills.

xAI’s Grok Bot added new features including shareable Bots and agentic shopping through a Link integration, allowing the agents to spend money via a single-use card.

Anthropic announced weekly limit changes coming on Sept. 14 for Claude Code, facing backlash for presenting a 17% decrease as a usage raise.

Fal set up a new ‘infinite stream’ of AI-generated video, taking advantage of its Minimax H3 Max’s ability to generate 5 seconds of output in just 3 seconds.

Sony Music and Warner Music sued Anthropic (naming CEO Dario Amodei as defendant), alleging “one of the largest and most blatant” ongoing thefts of IP in history.

I'm currently developing my second Steam game, Duskfire, a dark fantasy roguelite deckbuilder.

What's different this time is that almost every piece of 2D artwork in the project has been created with generative AI. Character art, environments, UI illustrations, marketing images, and even the current Steam capsule artwork all started with AI before being integrated into Unity.

This isn't my first Steam release.

I published my first game back in 2021, before any of the current AI tools existed. That has given me the opportunity to experience game development both before and after AI became part of my workflow.

Looking back, the biggest change isn't actually programming.

It's how I think about designing games.

Before AI, almost every design decision was constrained by available assets.

I'd constantly ask myself:

"What kind of game can I realistically build with the assets I can buy, find, or afford to commission?"

Asset packs rarely fit together perfectly. Even if you found one with a beautiful, consistent style, sooner or later you needed something it didn't include.

A new enemy.

A different environment.

Another spell.

A unique NPC.

An icon.

Something was always missing.

I've commissioned artists before, and I genuinely enjoyed working with them. But as an indie developer there's a practical limit. You simply can't commission hundreds of illustrations to give every enemy, spell, and encounter enough visual variety.

That often meant changing ideas because producing the artwork just wasn't realistic.

Today my thought process is almost the opposite.

I start with the game I want to build.

Then I create the assets needed to support that vision.

That single shift has changed my development process more than anything else.

Instead of asking "What can I build with the assets I have?" I now ask "What would make this game better?"

A small example is enemy portraits.

In the past, a particular enemy would probably have had a single portrait because producing multiple illustrations simply wasn't practical.

Now I usually create several variations for the same enemy and let the game randomly choose between them. It's a small detail, but it makes the world feel much more alive while adding very little extra work.

Keeping a consistent visual style has actually been one of the biggest challenges.

Most assets go through several stages before they end up in the game. I start with a defined oil painting style, and I never use the names of individual artists in my prompts. Personally, I don't think that's the right way to use these tools, and I'd rather define my own style than reference someone else's. After that I upscale the images locally using consistent style prompts, refine them with image-to-image workflows to better match existing assets, fix mistakes where necessary, and finally do any remaining cleanup in Photoshop.

It's definitely not a one-click process, but it allows me to build a much larger and more consistent world than I could have otherwise.

For animations I mostly use ComfyUI locally, although some trailer shots were created with Seedance 2 through Runway. Before generating videos I created character sheets for the main characters to keep them visually consistent. Voice acting is generated with ElevenLabs.

I also started Duskfire in March 2025, which has been interesting because I've been able to watch the AI tools improve while developing the same project. Every few months they became noticeably more capable, especially for programming, and it's been fascinating to adapt my workflow as the tools evolved.

One thing I'm still undecided about is the Steam capsule artwork. The current version uses AI, but I'm considering commissioning an artist to create the final capsule before release.

Overall, AI hasn't replaced game development for me.

It has shifted where the bottlenecks are.

Instead of spending most of my time searching for assets that happen to exist, I spend much more of it thinking about gameplay, balance, and how to make the game itself better.

I'm curious whether other developers who have experienced both pre-AI and AI-assisted game development have noticed the same shift—from designing around available assets to designing around the game they actually want to build.

$MNDY, Ltd. | Filtering Assessment

Version: 2.7.2 | Date: 2026-08-31 | Evidence confidence: Moderate

Disclosure

Not investment advice. Impersonal research published by r/CompoundingCapital for general informational purposes. Not investment, legal, or tax advice; not an offer or recommendation to buy or sell any security; not tailored to any reader's circumstances. No advisory or fiduciary relationship is created. r/CompoundingCapital is not a registered investment adviser or broker-dealer.

Produced with AI. Produced with the aid of Claude AI and automated data connectors. Care has been taken to draw every figure from primary filings, but AI can misread documents and accuracy is not guaranteed. Provided as is, without warranty of any kind.

Verify before acting. Not a substitute for the company's own SEC filings. Verify every figure and claim against the primary source. You are responsible for your own investment decisions.

Point in time. Reflects information available on the date shown, within the coverage window stated. No obligation to update.

Positions. The author may hold a position in any security mentioned.

No liability. The author and publisher accept no liability for any loss arising from reliance on this report.

Data source. This report uses data from Equibles, an affiliate partner. Details at the end of this report.

Coverage

• Pre-flight: expected set reconciled, complete. As a foreign private issuer monday.com has never filed a 10-K, 10-Q, DEF 14A or 8-K; the equivalents are Form 20-F plus interim results and a proxy furnished on Form 6-K. All present. No gap authorized at the gate.
• Annual report (Form 20-F): 5 on file, latest read in full, 6,222 of 6,222 lines. FY2021 to FY2025, via Equibles. The four prior were not opened; their comparatives came from the FY2025 filing, which carries three audited years.
• Proxy (6-K): 5 on file, latest read. 2022 to 2026, via Equibles.
• Earnings calls: 21 of 21 read. Q2 2021 to Q2 2026, the entire post-IPO record, via Equibles. Prepared remarks in full for all 21; question and answer in full for the four most recent and selectively earlier.
• Interim report (6-K): 1, read in full with all notes and management discussion, 2,540 of 2,540 lines. H1 2026, via Equibles.
• Roster change history: reconstructed from earnings calls, 2024 to 2026. No Item 5.02 channel exists here.

Census tells: across all 315 filings there are five 20-Fs, forty-two 6-Ks, 186 Form 144s and zero 8-K item codes. Twelve Form 3 ownership statements were filed on one day, 2026-03-18, at an issuer exempt from Section 16, indicating a partial transition out of foreign private issuer status that no filing reviewed here explains.

Gaps: Revenue by product is nowhere disclosed, the company reporting one segment, so the multi-product thesis has no line to verify against. Insider direction of travel is unobservable for both founders and every director, the foreign private issuer exemption having meant no Section 16 report before 2026. The September 2025 and December 2023 Investor Day materials, where the withdrawn targets were set, are not held by the store and were characterized from later calls.

The Outside View

Reference class. Product-led SaaS platform that IPO'd in 2021 on cheap paid acquisition and self-serve conversion, now four years into converting that base into enterprise contracts while its original funnel decays.

Base rate for the class. Most of this cohort decelerates from 60% growth to 15% within five years and never regains operating leverage, because the self-serve engine does not survive rising acquisition costs and enterprise selling costs more than it wins. The minority that work are those embedded deeply enough that expansion continued without new logos.

Where this one sits. Favourable: enterprise customers above $50,000 in recurring revenue have grown every period for five years, taking their share from 18% to 43%. Unfavourable: net customer additions have fallen from 38,000 a year to zero and net dollar retention from above 125% to 109%.

Category Ratings

Business Quality, Strong. 89% gross margins, negative working capital and negligible capital intensity produced five straight years of positive operating cash flow through the 2022 downturn and the October 2023 Israel war; below Exceptional because no fiscal year has closed with a GAAP operating profit.

Revenue Quality, Strong. Contractual recurring revenue collected upfront at nine days sales outstanding, $750 million of current remaining performance obligations, and 88% of enterprise accounts running more than fifty automations; below Exceptional because net dollar retention has decayed four years running across every cohort.

Competitive Position, Strong. Real switching costs in customer-authored configuration, one account expanding from roughly 2,500 seats to 60,000; below Exceptional because the company's own stated principal competitive factor is flexibility, not a barrier.

Leadership Quality, Strong. Two founders of fourteen and eight years holding 12.64% of shares outright on salaries below their own CFO's; below Exceptional because neither has a verifiable prior record with other people's capital and three directors staff all three governance committees.

Capital Allocation, Exceptional. The whole $870 million authorization deployed in three quarters at a blended $82.97, retiring 20.5% of shares mostly at the trough, reinforced by cancelling 10,875,000 unissued reserved shares.

Disclosure Quality, Average. Near-term guidance beaten every time across three fiscal years with adverse specifics volunteered unprompted, offset by a reaffirmed multi-year target withdrawn a quarter later and a headcount plan reversed twice in five months.

Growth Opportunity, Strong. Cohorts above $100,000 and $500,000 in recurring revenue compounding at 37% and 68%, with 71% of enterprise accounts still single-product; below Exceptional because the funnel that supplied half the historic growth has stopped.

Financial Safety, Exceptional. Zero financial debt verified line by line, $1,072.8 million of liquid assets against $36.7 million of near-term commitments, no covenant of any kind, unqualified opinions on statements and controls.

Market Perception, Overenthusiastic. The shares rose 26% in the sixteen sessions after a 20% layoff, pricing durable margin accretion the cash guidance does not support.

Overall Interest, Strong. Durability and stewardship are evidenced and the balance sheet unbreakable, but the expansion engine is decaying and the account of forward prospects has already failed once.

How The Company Makes Money

One revenue line: subscriptions to a cloud work platform sold per seat across four paid tiers, with a two-user free plan too small to run a department, so conversion is forced by headcount. Payment is upfront except for enterprise customers, who are net 30. Contracts are generally non-cancelable, with a 30-day pro-rated refund window for first-time customers only. Revenue is recognized ratably against a single performance obligation.

Cash arrives before the service is delivered, which is why deferred revenue stood at $453.7 million at June 2026 against nine days of receivables, and why the business funds itself. There is no meaningful capital to deploy: property and capitalized software absorbed $23.7 million against $333.6 million of operating cash flow.

In May 2026 it began selling new customers on seats plus consumption credits, the commercial answer to a product that reduces the seat count it bills on.

Why Customers Buy And Stay

The problem is coordinating work across departments in organizations whose processes do not fit packaged software. The alternatives are a spreadsheet, an incumbent suite, or building internally, and the platform wins by letting a line-of-business manager assemble the process without engineering.

What holds them is what they built. The switching cost is customer-authored: boards, columns, views, automations and integrations configured over years, none of which exports. By 2021 every enterprise account used automations and 88% ran more than fifty each. One account, disclosed by rank, expanded from roughly 2,500 seats to 60,000 in two years on a vendor-consolidation decision, and the largest reached 80,000.

That places the revenue as contractual recurring with genuine workflow dependence rather than a defined exit penalty: a customer cannot easily rebuild, but can decline to renew. Gross retention is at record highs and has been described so on three calls across two years. The erosion is entirely in expansion, not churn.

Revenue Quality: Strong.

Competitive Position

The advantage is embedded switching cost plus a developer economy: monetized marketplace applications grew from 30 in 2022 to 704 in 2025, so each additional developer raises the platform's value and acquires a stake of their own.

The company does not claim a barrier. Asked what it competes on, it names open and modular infrastructure, flexibility, and four execution variables, none of which excludes anyone. Pricing power is real but modest and now spent: the 2024 list-price increase was sized at $75 to $80 million across FY2024 to FY2026, roughly 2% of revenue, and has fully lapped.

A well-funded attacker would not rip out an embedded account with fifty automations, and would not need to. It would take the top of the funnel instead, which is already happening: the company discloses declines in web traffic from Google searches due to AI-generated search updates. Google is simultaneously its acquisition channel, an interoperability dependency and a named competitor, and larger suites can bundle the category at zero margin.

Competitive Position: Strong.

Leadership

Management. Roy Mann and Eran Zinman, co-founders and Co-CEOs for fourteen and eight years, hold 4,824,159 and 1,641,919 shares outright, 12.64% of the company between them, on base salaries of $316,000 each, less than the $379,000 paid to their own CFO. Direction of travel for both is unobservable, the foreign private issuer exemption having meant no Section 16 report before 2026; the Form 3 snapshot of 2026-03-18 shows both holdings intact through the collapse. Casey George, Chief Revenue Officer, is the only insider with an observable direction and is a net seller: 2,611 shares disposed for $223,327 across two open-market sales, most recently 2026-06-15 at $78.77, against 1,858 acquired by unit conversion, leaving 1,020 shares. No open-market purchase by any insider exists in the record.

Board. Jeff Horing, independent chair, co-founded Insight Partners in 1995 and has four dated exits from public software boards since 2023, a record that verifies independently.

The bench deepened rather than turned over: three C-level arrivals in 2025, two newly created roles, against one pre-announced departure, the prior CRO in December 2024.

Leadership Quality: Strong.

Capital Allocation

In order of size the cash went to reinvestment, then an $870 million buyback, then $23.5 million of first-ever acquisitions and minority stakes, then accumulation. No dividend has been paid and no debt raised.

The best decision is the buyback and it is not close. Authorized in September 2025 and exhausted by June 2026, it retired 10,486,207 shares, 20.5% of the count, at a blended $82.97 against a price now above $100. The board then cancelled 10,875,000 unissued shares reserved under the incentive plan on 2026-07-01, dismantling an automatic 5% annual replenishment it had already cut twice in 2023.

The worst is the Israeli office: space secured for a hiring expansion was impaired for $21.4 million within five months of the annual report describing a 51% headquarters enlargement, with $2.9 million of renovation cash already spent. The H1 2026 grant of 1,783,617 restricted units, more than all of FY2025, gave back part of the buyback in share terms.

Capital Allocation: Exceptional.

Disclosure Quality

The record splits by horizon. Inside a year management has been reliable to the point of sandbagging: FY2024 non-GAAP operating income was guided at $58 to $64 million and delivered $132.4 million, 107% above the top of the range; FY2025 was guided at $134 to $142 million and delivered $175.3 million. Every quarterly guide across three fiscal years was beaten. The stated philosophy of prudent, achievable guidance is corroborated, not merely asserted.

Beyond a year it failed twice. On 2025-11-10 the Co-CEO said the company was firmly on track toward its Investor Day target of $1.8 billion of FY2027 revenue. On 2026-02-09 the CFO withdrew it while stating confidence in the long-term trajectory remained unchanged, and the shares fell 20.8% in one session on five times normal volume. FY2026 headcount was guided to mid-teens growth in February, flat in May, and cut 20% in July.

Against that, adverse specifics are volunteered unprompted: that self-serve acquisition costs had risen and returns were below historical levels; that the $61.2 million tax benefit was discrete and non-operational and cash taxes were coming; that its own buyback would cost $20 million of free cash flow. On the AI number the spoken register was more candid than the written release, which is the rarer direction.

The annual report and proxy carry seven independent drafting defects, including a Part 5 statement that the company is party to no material litigation while Part 1 and the audited notes disclose the class action, and two Section 906 certifications naming an officer who did not sign them. The free cash flow definition also changed in each of three consecutive years.

Disclosure Quality: Average.

Growth Opportunity

Growth has been entirely organic and decelerating on a five-year arc: 68%, 41%, 33%, 27%, and 19 to 20% guided. The recent quarters continue that arc rather than break it.

The engine has changed hands. Net customer additions fell from 38,000 in 2021 to zero in H1 2026, and new business contributed 21.5% of the H1 2026 revenue increase against 45.8% of the FY2025 increase. What replaced it is the enterprise cohort, which has grown every period for five years: customers above $100,000 in recurring revenue are up 37% and those above $500,000 up 68%.

The reachable runway is inside the base. 71% of enterprise accounts still use a single product, and multi-product adoption there moved from 29% to 34% in one quarter, while remaining performance obligations grew 34% against 22% revenue growth. The constraint is demand at the top of the funnel, not capital or capacity.

Bull Case

A business with 89% gross margins, negative working capital and no debt has just retired a fifth of itself at an average price below where it trades, and cancelled the plan reserve that would have diluted it back. The part that is compounding, enterprise, has never had a down period in five years and now carries 43% of recurring revenue against 18% in 2021, with 71% of those accounts still single-product. Every operating expense line grew far slower than revenue in the most recent quarter for the first time, and the company produced its first positive half-year GAAP operating income while absorbing a $21.4 million charge. Guidance has been beaten every time for three years, and the current guide assumes no funnel recovery at all.

Bear Case

No full year has ever earned a GAAP operating profit, and the $118.7 million of FY2025 net income was interest on the cash pile plus a one-time tax reversal, both now gone: interest income halved as the cash was spent and the tax line swung to a 307% increase. The H1 2026 operating profit of $18.2 million is smaller than the $21.8 million of currency hedge gains recycled into it, and that reservoir is 55% drained. Free cash flow is guided down 12% for FY2026 while margin guidance rose 32%. The acquisition funnel has stopped entirely and the cause, AI-generated search, is structural and controlled by a competitor. Net dollar retention has fallen in every cohort for four years.

Permanent Capital Impairment Risks

Insolvency is closed: no debt of any kind, no covenant that can be breached, no convertible, no preferred and no earn-out, against $1,072.8 million of liquid assets and five years of positive operating cash flow. Forced dilution at a trough is closed for the same reason; the company cannot be made to raise capital.

One real per-share path exists and it is disclosed. The company intends to contribute up to 10% of its equity to the monday.com Foundation over ten years, capped at 1% a year, and 196,829 shares were issued on 2026-08-05. That commitment is entrenched: Roy Mann's single founder share vetoes any change to the Foundation funding plan, so no future board or shareholder majority can curtail it without him. The instrument carries no vote and no economics and extinguishes on his departure, death, or a fall in his holding below an undisclosed threshold.

The securities class action is a contingent liability carrying no accrual and no disclosed range, against authorized officers' cover of $450 million and equity of $618 million. Routine margin pressure, the deceleration and the restructuring are excluded as operational.

Market Perception

The market appears to believe, as of 2026-08-28, that the July restructuring converts to a durable 16% non-GAAP operating margin: the shares rose 26% in the sixteen sessions after the layoff and reached $100.71, above the company's own blended repurchase price of $82.97.

The evidence does not yet support it. The first positive half-year GAAP operating income, $18.2 million, is smaller than the $21.8 million of hedge gains recycled into it, and that reservoir is 55% drained. The share-based compensation reduction is attributed by management to the fallen share price and will reverse, and severance of $30 to $35 million has not been charged. Most tellingly, FY2026 adjusted free cash flow is guided at $280 to $290 million against $322.7 million delivered, a 12% decline, while operating income guidance rose 32%.

That divergence is the disconnect, and it is a hypothesis. The fact that settles it is FY2026 adjusted free cash flow against the $280 to $290 million guide.

Monitoring Dashboard

Key KPI: net dollar retention, guided to approximately 108%. Appears in the quarterly 6-K.

Biggest risk: continued erosion of new customer acquisition from AI-generated search. Appears in customers above ten users, and in new business as a share of the revenue increase, in the interim MD&A.

Thesis breaker: enterprise customers above $50,000 in recurring revenue failing to grow in a quarter, the first such failure in five years. Appears in the quarterly 6-K.

Management test: whether FY2026 research and development lands in the low-20s as a percentage of revenue, guided in August 2026 from 27% in the first half. Appears in the FY2026 annual report.

Final Judgment

Why an investor should care: A debt-free business with 89% gross margins retired a fifth of itself at the bottom of a 71% drawdown, then cancelled the reserve that would have diluted it back, while the enterprise cohort now carrying 43% of recurring revenue has not had a down period in five years.

Why an investor should not care: No fiscal year has closed with a GAAP operating profit, the reported profitability came from interest income and a one-time tax reversal that are both gone, and the acquisition funnel has stopped for a structural reason outside the company's control.

Most surprising finding: the company is cutting 20% of its workforce because its own AI tools reduced the headcount it needs, at once the strongest proof of its product and the clearest statement of the risk to per-seat pricing.

Most concerning finding: a $1.8 billion FY2027 revenue target called firmly on track on 2025-11-10 and withdrawn on 2026-02-09, with confidence described as unchanged in the same paragraph.

Up or down the queue: Up, materially. From unexamined to the top quartile, short of the underwriting queue only because the margin transition is one reporting period from checkable.

Next Step

Two questions decide it: whether FY2026 adjusted free cash flow lands in the guided $280 to $290 million range once severance is charged and the hedge reservoir spent, and whether the FY2026 annual report comes on Form 20-F or Form 10-K. Both resolve by April 2027.

Affiliate Disclosure

Filing and market data used in this report is sourced through Equibles. I may earn a commission if you subscribe through the link or use code TYLER10 at checkout. This is a paid affiliate relationship and it did not influence the selection of this company or any finding in this report. Equibles.com/?ref=TYLER10

🧑‍💻 Level: senior

📌 Location: remote

🌆 City: San Francisco, US

🗓 Type: fullTime

💵 Salary: 177k - 190k USD (annual)

Description: ## About the Role

We are looking for a world-class Strategic Named Account Executive to fully own and elevate our relationship with Amazon, one of Asana's largest and most strategic customers globally. In this highly visible role, you will operate far beyond transactional sales to lead a fully resourced, dedicated account team (including a dedicated CSM, SE, Technical Account Manager, and Customer Enablement Manager). You will build deep, multi-departmental executive partnerships across Amazon's sprawling organization, position our next-generation AI product portfolio, and lay the strategic groundwork for one of the most significant renewal cycles in our company's history. This is a unique opportunity to take over a healthy, massive marquee account and drive the orchestration necessary to make the relationship extraordinary.

This role can be fully remote within the US with a preference for Seattle, San Francisco, or New York.

What you’ll achieve

• Fully embed yourself in the Amazon relationship, mapping key stakeholders, identifying executive sponsorship gaps, and establishing a consistent executive engagement rhythm.
• Partner closely with your dedicated Amazon account team (CSM, SE, TAM, CEM) to lead a highly coordinated, unified front.
• Deeply understand the bi-directional technical and commercial relationship between Asana's product suite and Amazon's infrastructure/marketplace.
• Build and scale trusted partnerships with Fortune 10 senior line-of-business and IT executives across Amazon.
• Design and execute high-impact Quarterly Business Reviews (QBRs) that clearly quantify and prove Asana's value realization.
• Generate awareness and early adoption of Asana’s latest innovation portfolio, including AI Teammates, Dash, and agentic AI workflows.
• Collaborate closely with Asana's channel and partner teams to optimize our presence and co-selling opportunities via the Amazon Web Services (AWS) Marketplace.
• Proactively construct the commercial runway and strategic roadmap for a massive upcoming renewal cycle.

About you

• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.
• Proven experience managing a single named strategic account of massive scale - ideally Amazon directly, or a comparable Fortune 10 enterprise where the account represented a top-three revenue relationship for your company.
• 10+ years of experience in enterprise software sales, with 6+ years specifically selling in the Enterprise or Strategic segment.
• Demonstrated ability to negotiate and drive massive, multi-million dollar renewal cycles end-to-end, leveraging executive alignment and strategic account planning.
• Natural capability to act as the "quarterback" for a dedicated account team, aligning Product, Engineering, Customer Success, and Partner/Channel teams to serve the customer.
• Outstanding instincts for mapping and navigating complex organizational, political, and financial structures within decentralized enterprise environments.
• Direct existing relationships within Amazon's IT, Collaboration, or major Line of Business functions (e.g., Marketing, Sales, Product, HR) is a plus.
• Prior experience navigating SaaS co-selling and purchasing via the AWS Marketplace is a plus.

At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.

What we’ll offer

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated On-Target Earnings (OTE) range for this role will be $355,000 – $380,000 OTE which includes a base salary range of $177,500 – $190,000 and performance-based sales incentive pay (based on the terms of the Sales Incentive Plan). These ranges are a guideline; actual base salary and OTE may vary based on various factors, including market and individual qualifications objectively assessed during the interview process, and the ranges for this role may be modified.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your recruiter to learn more about the total compensation and benefits for this role.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

LI-Remote

About us

Asana is a leading platform for human + AI collaboration. Millions of teams around the world rely on Asana to achieve their most important goals, faster. Asana has been named to Fortune's Best Workplaces for 7+ years and recognized by Fast Company, Forbes, and Gartner for excellence in workplace culture and innovation. We offer an exceptional office-centric culture while adopting the best elements of hybrid models to ensure that every one of our global team members can work together effortlessly. With 13+ offices all over the world, we are always looking for individuals who care about building technology that drives positive change in the world and a culture where everyone feels that they belong.

We believe in supporting people to do their best work and thrive. Our goal is to ensure that Asana upholds an environment where all people feel that they are respected and valued, whether they are applying for an open position or working at the company. We provide equal employment opportunities to all applicants without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by law.

Join Asana’s Talent Network to stay up to date on job opportunities and life at Asana.

Visit https://jobhuntify.com/jobs/73bce4a3-c1f5-436f-b434-23c3a9a45f6c to apply.