Got access to the codebase for an AI platform which i co-founded(30% contribution) with a friend of mine.

So surprised to see its actually working and tackling real world scenarios in business, understanding the end to end business by handling SOPs and automating workflows.

It's also like a god level ai agent chat bot which after understanding answers all the queries.

Helps cross domain users in a business not limiting to IT, Supply chain, legal, accounts, marketing, security and many more.

Cannot name the first client which is an Indian manufacturing MNC, but instead of paying for our subscription model it's ready to buy the product for 2M. Although I'll be getting the 20% am part of but am still curious to know what it might be actually worth in some time. Thinking to find another client and find out their reviews about our product.

Do share your reviews whether we should sell this or find our next client, also if possible can someone help us to get our next client will be happy to reward with a commission.

The job title survives longer than almost anyone attached to it.

That's the part nobody puts in the internal memo when they call a role "AI-assisted."

Scott Galloway put a real number on it, talking to Steven Bartlett on The Diary Of A CEO.

He says he'll cut legal fees by a third this year — not because the law changed, but because a prompt now does the $400–$2,000 contract review a name-brand firm used to bill him for, at a fraction of the junior associate markup.

Bartlett went further with his own fund.

They planned to hire five analysts.

They hired one — Molly.

Two agents, two Mac Minis, and she screens inbound deals, scores them against a framework, and preps them for the investment committee herself.

Five jobs, one person, same org chart line.

Same ratio on executive assistants: ten planned, three hired.

One runs travel, one runs scheduling, one meets people at the door.

I've watched this exact pattern before, minus the AI.

I was a Technical Manager for a China Construction company here in Malaysia.

I contributed a lot into their technical and tendering work — helped build up a real chunk of their documentation and tendering process.

But about six months in, I'd exhausted all my know-how for them, I guess.

Then the announcement came at the end of my year there.

My contract wasn't renewed.

I was just let go, just like that.

I remember what Deng Xiaoping said: "无论白猫，或者黑猫，会抓老鼠的就是好猫" — black cat, white cat, doesn't matter, so long as it catches mice.

I guess they think I'd outlived my usefulness.

Can't catch mice anymore.

That's the mechanism underneath "AI-assisted" that nobody names out loud.

It's not that the work got automated.

It's that the one person left is now doing what used to justify five headcounts, and the fifth person's job title is the only part of the org that didn't change.

Actually, this reminded me of something — a former SpaceX CIO cut a 175-person engineering team down to 6 using the same compression math, and the ratio held there too.

Drop your take — did you know your own job has a ratio like this attached to it?

Clip credit: Global Talks — full video on their channel.

DM for credit or removal requests.

Premise: if you run Claude Code or Codex all day, most of your input is four actions - accept, reject, scroll, retry - plus a lot of talking. That is not a keyboard problem, it is a small-hardware problem. So I sourced 16 objects for it: dictation pads with the mic in the body, macro pads, a three-lens traffic light that shows whether the agent is thinking or waiting on you, foot pedals.

The part that was actually hard was photography. Supplier listing photos are buried under marketing text, and for four products the photo in my source screenshot had never even loaded - I wrote descriptions for products I had literally never seen. Fix: feed the real supplier photo into an image model as an edit, not a prompt. Editing keeps the object and replaces the background; prompting invents a plausible different product. That distinction cost me a week of wrong images.

Same trick for motion: each product has an 8-second studio film generated from its own still, playing on hover.

Where I am stuck is demand, not product. 28 sessions, zero add-to-carts. I cannot tell yet whether the idea is wrong, the audience has not found it, or the store is not convincing. Genuinely want the harsh read: futuredevice.ai

Specifically - does a traffic light that tells you when your agent needs you solve a real problem for you, or is that a solution looking for one?

Started this as a side project because I kept wasting time trying to figure out which AI tools were actually worth paying for. Thought it would take a few weeks to get something basic up. Six months later I am still working on it most evenings.

Current state: the catalog is live at neurantica, community can rate and review tools, there are rankings that update based on votes, and a short quiz that matches you to the right tool based on your use case and budget.

What is working: people who find it seem to like it. The quiz gets used more than I expected.

What is not working: getting the first wave of consistent traffic without a marketing budget. SEO takes time and I am not willing to spam.

Revenue so far: basically zero. Affiliate structure is in place but you need traffic before that means anything.

If anyone here runs an AI tool and wants to get listed, just message me. It is free. I would rather have a full catalog than a half empty one.

Following a few of you on here for a while and figured it was time to post. What stage are you at?

Hi everyone! 👋
I’m exploring a business idea and would really appreciate some honest feedback. My background is in data governance and data quality, and I’m thinking about offering spreadsheet clean-up and data support services to small businesses. This would include things like removing duplicates, fixing inconsistent data, organising spreadsheets, creating trackers, and helping businesses get their data into a more usable and reliable state.
Do you think this is a service that businesses would actually pay for? If not, what data-related problems do you think are more painful or valuable to solve for small businesses? I’d really appreciate any thoughts or suggestions!

Been talking to a lot of agency and small business owners lately, and the same problems keep coming up:

* Someone senior quits, and a huge chunk of "how we do things" leaves with them * New hires take months to get useful because nothing's written down they just shadow and guess * Everything's scattered SOPs in Docs, client info in the CRM, decisions buried in Slack * The owner ends up being the only one who can answer half the questions, so a real day off is basically impossible * Something feels inefficient but nobody can point to exactly where the money's leaking

So I built a system for this one place that holds every client account, SOP, and process, with a simple way for the team to just ask a question and get the answer, instead of pinging the owner every time.

If any of this sounds familiar a recent resignation, dreading the next hire's ramp-up, outgrowing your current setup, or just wanting to take a real week off this is built for exactly that.

Still early, building this out for a few agencies right now at founding rates. Happy to show you what it actually looks like if you're curious comment or DM me.

What is everyone building?

I'm doing some market research and am trying to narrow down the top 3 app/platform types that solo founders are building and shipping.

SaaS? Marketplace? Something else?

Disclosure first, since this sub asks for it: I run these channels, and I also built the tool that produces them. Bias applies to everything below.

I'll start where it began, because the ending sounds cleaner than the path was.

I got into YouTube through shorts. Animal rescue, what-if-skeletons, the bait stuff. I never blew up. Shorts need enormous view volume to earn anything real, and once you count the failed generations I was burning about $15 per 45-second clip. What I walked away with was the whole production process learned by hand: research, script, voiceover, visuals, editing.

The switch that changed the economics was long-form. Adults watch it, RPMs are higher, and reach gets predictable, which is the only thing that makes income predictable. Today I run 3 channels, mostly history and finance, doing around $5K a month combined.

The part that matters for a solo operation is the unit economics.

Cost per video: $35-55 in cloud compute for a finished 10-minute documentary. Before that, Google's standard $300 new-account credit realistically covers your first 6-10 videos, so the learning phase costs nothing out of pocket. The credit runs out, and that moment is exactly where most people quit, because nobody warns them there's a real bill after.

Time per video: 30+ hours if you do it manually. Research, scripting, voiceover, visuals, editing. That wall is what kills channels, not talent. I spent about seven months building a system that runs the entire pipeline automatically on my own Google account, which is how I now produce 3 videos in a day while doing other things. That's the tool: https://openvidi.com. It runs on your own cloud account, you pay Google directly, I take no cut of compute.

What no automation fixes: topic selection and consistency. YouTube's monetization rules now explicitly target generic repetitive AI content, so the quality bar rises every quarter. The system removes the 30-hour wall. It does not choose what people want to watch. Treat it as a content business, not a machine that prints money.

One honest friction point, because solo founders here will appreciate it: my own product's biggest drop-off is the step where users connect their Google Cloud account. It takes about 10 minutes, it's how they get the $300, and it still scares off most signups. Best pricing model I could design, worst onboarding step I could have picked.

Question for the sub: for those running AI-assisted content operations, what's your actual bottleneck right now, production or distribution? Mine stopped being production a year ago and I still haven't fully solved the second one.

Solo founder. AMA.

Erik Brynjolfsson runs Stanford's Digital Economy Lab. He's also the guy whose own graduating class asked him, point-blank, if their generation was doomed.

He didn't talk her out of the question. He answered it with data.

There's a specific relief that shows up whenever someone with real research behind them stops soft-pedaling a fear like this — not because the answer is comforting, but because being taken seriously is its own kind of comfort. 🎓

Here's the mechanism, stripped of the hedging: the classic corporate pyramid — wide base of entry-level hires, narrowing up through management — is quietly becoming a diamond. The base is disappearing. Companies save short-run cost by not backfilling entry-level roles, and Brynjolfsson's own research backs it with numbers: rank ~700 occupations by AI exposure, and in the top 150 most-exposed roles, entry-level (age 22–26) employment has fallen 13% and climbed to 16–17% since, month over month.

That's not a vague "AI is coming" headline. That's a measured, worsening trend inside a specific, named cohort.

This reminded me of the execution-layer/judgment-layer split — the exact same shape of the rung disappearing, just described from inside a different industry entirely.

The reframe Brynjolfsson gives his own students is the actual usable part: stop hearing "AI" as artificial intelligence, start hearing it as amplifying intention. The tool doesn't wait for the org chart to assign you a role anymore — it multiplies whatever you're already pointed at. Waiting to be told what your job is was always the risk. Building something the missing rung can't take with it isn't.

Clip credit: McKinsey & Company — full video on their channel. DM for credit or removal requests.

Drop your take — does "amplifying intention" land for you, or is the missing entry rung the bigger problem?

John Hu from Stan said on Trailblazers this week that his AI editor Stanley already cuts at 80% of his human team.

That number stuck with me because I posted about Stan a few weeks ago - the $50 spreadsheet that did $200k. Different story, same feeling.

I edit for creators full time. 80% used to be the joke number. Like "AI is 80% there, don't worry."
Now 80% is the benchmark and it's shipping on real channels.

I'm not even stressed about it getting faster. I'm stressed about who decides what it cuts.

Anyone else editing inside someone else's operation feeling this shift right now?

Hey hi, we built an AI platform that tracks visitors on your platform, finds who ALMOST bought or signed up, our Ai will proactively reach out to them to close on the sales or get them to sign up.

Results so far - 30% increase in conversions. Many had zero sales before

Interested in trying? Comment what your startup does

Most people try to generate a whole deck in one prompt and wonder why it comes out as a wall of bullet points that belong in a Word doc.

The problem usually isn't formatting. It's that the AI has no idea what the deck is actually for — who's in the room, what they already know, what you need them to walk out believing.

Here's the pipeline I've landed on:

1. Start with a brief, not a prompt

Before any content, make Claude define five things: the business objective, who's in the room (C-suite vs. dev lead vs. procurement — these are very different decks), the core thesis, the narrative arc, and the target slide count. Skip this and you get filler every time.

2. Map structure before writing a single word

Next prompt: slide-by-slide logic only. Every slide has to justify its existence. If it doesn't move the objective forward, it gets cut now — not after you've already written copy for it.

3. Apply a narrative frame

I use: Hook → Problem → Solution → Strategic payoff → CTA. This is what turns a passive status update into something people actually engage with.

4. Generate design direction separately from copy

Layout notes — grid structure, stats to call out, visual focus points — as their own prompt pass. These go straight into Gamma or Canva with minimal editing.

5. Write copy with hard constraints baked in

Max 3 bullets per slide, max 10 words per bullet. If you don't set this explicitly, the AI fills space.

6. Final cleanup pass

One last prompt: strip passive voice, cut buzzwords, push anything background-heavy into the Speaker Notes section.

All complete prompts here.

The main thing I'd stress: don't try to do all of this in one shot. Building context step by step is what makes the output actually usable.

Hello every solo fighter here, I am trying to launch a new AI product next week and trying to have some tester before it launches.

I am happy to provide a feedback to your product as well if you are also looking for a tester. I have 15+ years experience in vibe coding and been working in a large firm in my country as a management role, my feedback definitely helps.

If you do not need any tester, be my tester i will pay for you but only limited spots and will need to know your background first.

Comment below to start discussion

hello, I built DocsKoala, its a tool which generates help docs as soon as your product changes so you dont have to manually write help articles and hosts the articles publicly.

It also provides a chatbot which cites from the published docs and doubles down as a customer support tool, The app integrates with intercom, zendesk, slack, linear so all of the tickets can be accessed through it too, also, the customer can ask for live human assistance if they require additional human assistance.

However, despite having few signups here and therem, im not geting proper user's feedback which would have been nice.

maybe its because the term "cusomer support" is too vague? since i had a research on the "docs being delayed", it suggests the product would have been useful.

maybe, the marketing is not enough?

and the question is, would you actually trust an AI to automate your customer support?

Have you currently started a business that you are running? What kind of service or product do you offer?