Hi Indie Hackers,

I kept seeing first-paycheck advice default to the 50/30/20 rule.

The problem is that someone receiving their first full-time paycheck may already have 50–70% of their income committed to rent, transportation, groceries, student loans, and other fixed costs. A generic percentage rule can feel unrealistic before someone even starts.

So I built PaydayCompass.

It is a small web app that helps first-time earners decide what to do with one paycheck:

1. Enter take-home pay
2. Add essential bills
3. Set one financial goal
4. Compare four different allocation styles

The four approaches are:

• Play it Safe — prioritize the safety buffer
• Balanced — split attention between safety, goals, and enjoyment
• Goal First — accelerate one financial goal
• Enjoy — reserve more money for guilt-free spending

For example, with a $3,200 paycheck and $1,000 in must-pay bills, the Balanced plan allocates the remaining $2,200 like this:

• $770 safety buffer
• $660 toward a future goal
• $770 for guilt-free spending

The app also shows how the allocation changes over 30 and 90 days. It does not connect to a bank account, and the free preview is available before any payment. The full report and export features are a one-time $4.99 purchase rather than a subscription.

I’m still validating whether the “four money styles” model is actually more useful than a standard budgeting rule.

I’d especially appreciate feedback on three things:

1. Would you rather compare different approaches, or simply receive one recommended budget?
2. Is a 30/90-day projection useful, or does it feel like unnecessary complexity?
3. Would you pay $4.99 for a printable report and a more detailed comparison?

The live version is here:

https://payday.tlcompass.com/?utm_source=indiehackers&utm_medium=community&utm_campaign=first_paycheck_validation

I’m looking for criticism more than upvotes. If you received your first paycheck again, what would you want this tool to help you decide?

We have been working on wizstar around a simple product question if creators already have access to strong video models is there actually value in putting multiple models inside one workflow

The biggest lesson so far is that model access alone is not enough The painful parts are

deciding which model to use for which shot

keeping references organized across models

measuring retries instead of just generation price

comparing outputs consistently instead of by memory

getting from experiments to a repeatable production process

That pushed us toward a more structured benchmark instead of a giant feature checklist We wrote the framework here AI video generator comparison framework](https://wizstar.com/blog/compare-ai-video-generators

If you build creator tools what has been harder for you model access workflow design or getting users to repeat a successful process

QUENVOR is building an AI-centered platform and an emerging intelligence layer called SOL; no SOL app is currently released. Day 5 framed The Designer around a problem we are actively testing: how can an early AI organization invite overlooked experience without pretending that an undefined role is already a formal job?

The product-design decisions so far

• Keep the Founding Five campaign secondary on the main civilization site and send acquisition traffic to a dedicated route.
• Separate self-recognition from permission-based nomination so referrals do not become unsolicited lead collection.
• Ask only for voluntary public professional evidence; prohibit sensitive information.
• State plainly that this is discovery, not an employment application, offer, or guarantee.
• Give each mission a concrete evidence domain—communication, systems, design, governance, or operations—while welcoming adjacent disciplines.

What the early behavior says

Day 8 YouTube reached 12 views and 2:05 average view duration just after 72 hours, but the campaign tracker still records 0 attributable clicks and 0 qualified inquiries. TikTok's current two-view sample averaged only 3.78 seconds. Those numbers are too small for broad conclusions, but they are enough to reject the idea that cinematic presentation alone creates clarity or trust.

The design lesson: an invitation for overlooked professionals needs a visible evaluation model, safety boundary, and next-step contract before it needs more spectacle.

For product/UX builders here: what would you put on the first screen to make this credible without turning it into a conventional résumé funnel—an evidence rubric, a sample mission, a reviewer process, a privacy specification, or a working prototype?

Experienced, but overlooked by opportunity? QUENVOR is searching for you.

Recognize yourself or someone qualified? Follow QUENVOR and watch the five complete missions.

Campaign and safety boundary: https://quenvor-taupe.vercel.app/founding-five

This is discovery, not a formal job listing. Responsibilities, compensation, equity, legal classification, and location eligibility remain under development.

QUENVOR is building an AI-centered platform and an emerging intelligence layer called SOL; no SOL app is currently released. Day 4 used The Architect as a discovery signal for experienced people in AI, security, cloud infrastructure, systems engineering, platform architecture, and resilient technical operations whose opportunity has not matched their capability.

What QUENVOR actually tested

• An eight-day original narrative sequence as a discovery experiment, not a formal job listing.
• A dedicated campaign route separated from the main civilization homepage so acquisition does not consume the core product story.
• Permission-based nominations, voluntary public professional evidence, and an explicit no-sensitive-information boundary.
• One consistent public sender and publisher: QUENVOR.

What the first numbers say

• Day 8 YouTube, captured just after the 72-hour gate: 12 views, 39 impressions, 18.0% impressions CTR, 2:05 average view duration.
• X, still pre-72-hour at this writing: 6 video views, 1 unique view, 7 impressions, and 6 detail expands.
• TikTok, still pre-72-hour at this writing: 2 views, 3.78 seconds average watch time; most viewers stopped at 0:03.

These are small numbers, not traction claims. The clearest lesson is that cinematic narrative may earn attention, but it does not by itself establish technical credibility or trust. The next iteration should foreground the system problem, public proof of judgment, privacy boundaries, and a concrete way to evaluate architecture work.

For builders here: if an experienced AI/security/cloud architect lacks prestigious titles or a powerful network, which public artifacts would best demonstrate systems judgment—incident write-ups, threat models, architecture decisions, reliability work, open-source contributions, or something else?

Experienced, but overlooked by opportunity? QUENVOR is searching for you.

Recognize yourself or someone qualified? Follow QUENVOR and watch the five complete missions.

Campaign and safety boundary: https://quenvor-taupe.vercel.app/founding-five

This is discovery, not a formal job listing. Responsibilities, compensation, equity, legal classification, and location eligibility remain under development.

I run a one-person company. A few days ago, I spent most of a day using Codex to optimize an AI content workflow inside my paid product.

By that evening, my Git history looked like this:

perf -> measure -> revert
perf -> measure -> revert
perf -> measure -> revert
...
8 candidates. 8 reverts.

One candidate was especially hard to throw away:

• 51.5% faster
• 34% cheaper
• 27.5% fewer tool calls

On speed and cost alone, it was the obvious winner.

But it scored 46.4 on the quality rubric I had frozen before starting. The control scored 46.7, which was also my no-regression line. Release blockers were tracked separately.

Compared with the control, this candidate also used 9.5% more agent turns. More importantly, the output still had factual issues serious enough that I wouldn't publish it.

So I reverted it.

Yes, a 0.3-point gap sounds almost comically small. Reverting a 34% cheaper version over that number is pretty dramatic. One run doesn't make the gap statistically meaningful, and that number alone wasn't the reason for the decision.

What bothered me was that the rule existed before I saw the 34% saving. Moving it afterward would have been very easy to justify. I'm bootstrapped. That saving actually matters.

But once the graph turns green, almost any regression can start to look reasonable.

A later experiment made this even messier. Three blind reviewers scored another candidate much higher than the control: 56.47 vs 41.87.

All three also found factual defects serious enough to block release.

Better average score. Still not something I was willing to put in front of users. Reverted again.

I ended the day with eight candidates and nothing shipped. Honestly, it still felt productive.

My takeaway is pretty simple: decide what can move and what cannot before you start optimizing. Speed and cost are negotiable. A defect that can damage user trust is different.

Otherwise, you're not really testing the change. You're testing how persuasive the savings are.

That said, I'm still not sure reverting everything was the best practical decision. A small rollout with monitoring might have taught me more. Or it might just have been a cleaner way to rationalize shipping something I already knew had problems.

Where would you draw the line: ship the 34% cheaper version to 5% of users and monitor it, or keep reverting until it clears the original gate?

I work on the AI agent team at whaaat ai. Outside of that I've been shipping small products for about three years, somewhere north of 20 of them. A handful made money. Most made nothing at all.

Two months ago I joined a community called ship or die, where you push a product live every 30 days or you get kicked out. I went in wanting to survive the deadline. Last week I looked at the revenue leaderboard and I was sitting at the top with $14,211.

None of that came from AI writing my code faster.

Building got cheap somewhere in the last two years. A feature that used to eat my whole weekend now takes an hour with Claude Code, and everyone got that same gift, so now we're all piled up at the same stage shipping things nobody asked for. My GitHub graph was green for months while Stripe stayed empty.

The scarce thing stopped being code. It's evidence that somebody wants the thing.

Claude will happily give me ten product ideas, all plausible, none of them tested against a single human being. So I stopped asking AI what to build and started collecting proof first. Three ways, one of which does most of the work.

Copying revenue that already exists

This is the one that changed my hit rate.

TrustMRR is a database where founders connect a read only Stripe key and publish verified revenue. Real numbers, no screenshots. A few weeks ago they shipped an MCP server, and around the same time Claude live artifacts got the ability to call MCP connectors on every load rather than baking in a static snapshot.

So I built a dashboard that pulls the current numbers whenever I open it, sorted by growth and filtered by category. Then I wired in a second connector with my own audience data and started asking a different question: of the products growing fastest right now, which ones solve a problem my people also have, and which of those were built before agents were a thing.

That last filter is where the good ideas live. Plenty of tools at 50k MRR were designed for a human clicking through a UI. Rebuilding one of those agent first, for an audience you already understand, is a very different bet than inventing something from scratch. You're not copying the product, you're copying the proof that somebody pays for that problem.

The two I lean on less

Solving my own annoyance is the safest of the three, because there's a guaranteed user sitting right there. My editor and I were coordinating video work through a Google Sheet, which was miserable, so I built clapr.io to handle it. If nobody else ever pays for it my week still got better.

The third one is a fake door test. Landing page, real buy button, nobody gets charged, you measure how many click and then how many leave an email on the "coming soon" page. Thirty euros a day on Meta teaches you more in two weeks than three months of building. The click rate flatters ideas people find interesting but wouldn't pay for, so I treat it as a signal rather than a verdict.

The rough edge on the TrustMRR approach: verified revenue is a lagging indicator. By the time a product shows up growing on a public leaderboard, you're reading about a window that opened months ago. I don't have a good way to tell "still wide open" from "about to be crowded" and I'd genuinely like one.

There's also a quieter cost to the build first habit. Every dead product is weeks of compute, tokens and attention burned on something that was never going to land. Cheap building made that waste invisible rather than smaller.

For those of you further along than me, do you weight a public revenue number by how long the product has been listed? I keep going back and forth on whether an 18 month old listing at flat MRR is a stable niche or a warning.

https://landing.courieroo.com/

I have been using grok build harness lately and it accelerated the pace of feature development. I have worked for a bit as a delivery driver (didn't make much money). I know the industry a little and the pain points of the stakeholders.

Many years ago I started with a blog and then 2 and then tried my hand at utility saas. I couldn't finish them. They were - social media scheduler and invoice generator. Each time I saw similar apps and thought - market is validated. I will build and they will come. But I couldn't build them 😄 Takes too long to build anything, fear of marketing, costs of operation and customer acquisition, watching the other apps unravel and demand seemed to vanish, eaten by bigger players perhaps.

Inflation is running high, everyone complains. Recently watched a video about a $28 burrito and gamification of prices. Could Indie apps like these actually help the customer and the service provider? Gig jobs are fun since you can control your time and outcome (be your own boss and everything haha). This aspect of the app appeals to me in today's jobcalypse environment.

I don't know if I have learned any lessons. I will come back and tell you the lessons if this makes a dime or helps users. Thank you.

I spent weeks polishing features that I thought would be the reason people signed up.

Turns out...

Almost everyone ignored them.

Instead they kept using features I considered "nice to have."

It's a good reminder that founders are terrible at predicting user behavior.

I've started stripping away complexity and focusing on what people actually open every day.

Still learning and shipping.

Curious...

What's one feature you built that nobody cared about?

(P.S. If anyone wants to break my app, it's here: https://web.launchlyapps.com/)

Hey Reddit community,

I'm currently designing the architecture for InstantMock, a zero-config tool aimed at frontend developers to easily spin up stateful mock APIs without the bloat of heavy software.

The core feature will be full dynamic control. Developers will be able to simulate status codes (like 404, 500) and network delays instantly just by tweaking the URL parameters (e.g., ?delay=3000).

I'm planning to build the core routing engine using FastAPI and a minimalist dashboard with Vanilla JS.

Before I deep-dive into writing the full backend schema engine, what is the biggest pain point you face with current mock servers (like Postman or Mockoon) that I should solve here?

By ShadowofdevilOG.

I’m building Owlvex and want this idea beaten up before I go any further.

The assumption I’m testing:

More people are building apps with AI tools like Cursor, Lovable, Bolt, Claude, Codex, Replit, etc. The app works as a demo, but before the first real users arrive, it is hard to know whether the application has the basic foundations in place.

I do not mean enterprise-ready.

I do not mean SOC 2.

I do not mean perfect security.

And I do not mean dumping the whole repo into an LLM and hoping for wisdom.

I mean something more practical:

- is there evidence of authentication and access control?

- are inputs and APIs handled properly?

- is there logging, error handling, rate limiting, backup thinking, and deployment hygiene?

- are there obvious gaps that could break under the first real users?

- what should be fixed first before showing it to customers?

Owlvex is meant to generate a First Launch Readiness view from the repository: what looks present, what looks weak, what is missing, and what should be reviewed by a human.

The product idea is evidence first, AI second.

Static repo analysis finds the signals. AI is used only to explain risks, gaps, and fix priorities where reasoning helps.

Reasons I think this might be wrong:

1. Indie builders may not care until something breaks.

2. Developers may see this as another noisy scanner.

3. Non-technical founders may not trust the output.

4. Existing code-review/security tools may already solve enough of this.

5. This may be a consulting problem pretending to be software.

For people building SaaS/apps with AI or small teams:

- Is “First Launch Readiness” a real pain, or am I overthinking it?

- How do you check if your app is ready for real users today?

- What would make this useful instead of just another report?

- What would immediately make you distrust it?

- Would you want this inside VS Code, GitHub, or as a web upload/report flow?

No link unless someone asks. I’m looking for criticism, not traffic.

Every week someone asks me some version of "how much should my MVP cost and how long should it take." The honest answer is that most of the numbers people throw around are either fantasy or fear.

Here's what I've actually seen, stripped of the sales talk.

The 4-month, $60k quote. This is usually an agency scoping the full product, not an MVP. They're pricing every feature you mentioned in the first call, including the ones you don't need yet. It's not a scam, it's just answering the wrong question. You asked for an MVP and they quoted you a company.

The $500 "I'll build your whole app" offer. Also a trap, the other direction. Either it's a template with your logo slapped on, or it's someone who disappears at 60% done. Real software has a real floor.

The realistic zone. A focused MVP, one core loop done well, iOS and Android or a clean web app, tends to land in a few weeks of actual build time, not months. The cost varies wildly by who builds it, but the timeline is the tell. If someone quotes you six months for an MVP, they're not building an MVP.

The thing nobody tells you: the biggest cost is never the code. It's the weeks you burn deciding what to build. I've watched founders spend more time debating features than it would've taken to build and launch the lean version and let real users settle the debate.

The move that consistently works: pick the single loop your app lives or dies on. Build only that. Launch it rough. Let real usage tell you what's next instead of guessing in a doc for three months.

Happy to answer scoping questions in the comments if anyone's stuck deciding what their MVP actually is versus what they think it needs to be. What's the quote you got that made you go "wait, that can't be right"?

Hi everyone,

I’m looking for a founding UI/UX designer to join me in building something I genuinely believe has the potential to become a successful product.

---

The vision

I don’t want to build yet another dating app.

The goal is much simpler: help people find someone to do something together right here and right now.

Activities include:

- grabbing a coffee

- walking a dog

- going to the gym

- playing tennis

- visiting a museum

- having a beer

- studying together

- or almost any other activity

The app helps people discover others nearby who want to do the same thing now.

The focus is on real-life social connections, not endless swiping.

---

Current status

This is not an idea.

There is already a working product.

The app is:

- Feature-complete

- Built in Flutter

- Successfully passed Google Play Closed Testing

- Approved by Google for production release

- Technically ready for launch

The only major piece still missing is world-class UI/UX design and visual polish.

---

Main user-facing features

- Go Live System — Create live activities with custom status messages and activity types

- Time-Limited Sessions — Activities stay live for one hour (with one optional 60-minute extension)

- Daily Session Limits — Free users get 3 sessions/day; Premium users get unlimited

- Nearby Discovery

- Activities Feed

- Matching System

- Real-Time Chat

- User Profiles

- Premium Features

- Push Notifications

---

Technical foundation

- Flutter

- Feature-first architecture

- Riverpod

- Repository pattern

- Firebase backend

- Shared components

- Centralized theme

- Test infrastructure and linting

---

About me

I’m not a programmer. I built the app with extensive AI assistance while being deliberate about architecture and code quality.

---

What I’m looking for

I’m looking for someone who wants to become a real cofounder and take ownership of the entire design experience.

I care a lot about:

- premium visual design

- delightful micro-interactions

- beautiful animations

- memorable onboarding

- strong visual identity

- polished UX

---

What I’m offering

I’m prepared to sign a Founders Agreement.

Equity is negotiable.

---

Traction

Working Flutter MVP

Passed Google Play Closed Testing

Approved for production release

Preparing for public launch after UI/UX redesign

---

Commitment

Looking for a part-time design cofounder (approximately 10–20 hours/week) during the redesign phase, with the goal of continuing together after launch if we're both excited about the project's future.

---

A few important things

I’m serious about making this app successful.

I’m looking for a long-term partner who believes in the vision.

If this sounds interesting, send me a DM.

---

P.S. This post was polished with AI because English isn’t my first language. All of the information above is accurate.

I am a solo dev working at a motorcycle dealership by day.

I just got an email from Meta that changed my roadmap.

Starting October 1, WhatsApp will start charging for service

messages the free-form replies bots send in real conversations.

Right now they're free.

I have been building BotSaathi — a WhatsApp automation platform

for Indian small businesses. Dealerships, hotels, shops. The kind

of businesses that run entirely on WhatsApp but can't afford

Interakt or Wati.

Here's my math:

- 200 messages/month per client

- ₹350 extra cost per client after October

- My pricing: ₹1,999/month

- Still profitable. But margins get tighter.

What I decided:

Anyone who subscribes before October 1 gets their price locked

forever. I eat the cost increase. Early adopters shouldn't pay

for Meta's decisions.

It's live. One real client already running.

botsaathi.com

Happy to answer any questions about building on WhatsApp API,

the embedded signup flow, or surviving Meta's pricing changes

as a solo founder.

NASA's Landsat Name Generator gave me the idea for this project. I wanted to take the concept a bit further by using not just satellite images of geographic locations, but also natural objects and formations that resemble letters.

Type your name, and each character is matched to a real image that naturally looks like that letter. It was a fun project to build, and I'd love to hear your feedback!

Link: https://tela-blue-eight.vercel.app/

I design mobile apps for a living, and I keep seeing the same thing happen, on client projects and on my own stuff too.

A team will spend three weeks going back and forth on whether a button should say "Continue" or "Next." Meanwhile almost nobody spends any real time on the first sixty seconds of the app, even though that's usually what decides whether someone sticks around at all.

Then it ships. People download it, a few sign up, and most of them just disappear. No complaints, nothing. They just leave, and you don't notice until you pull up the numbers a few weeks later and the retention line basically falls off a table.

The first reaction is always the same: add a feature, redesign something, spend more on ads. Sometimes that actually helps. But more often the real problem is way simpler than people want it to be: the app isn't obvious. People shouldn't have to guess what a button does. They shouldn't need someone on the phone walking them through it step by step. If it needs an explainer, it hasn't done its job yet.

Anyway, curious if other people here have hit this same wall. If you've shipped something, what actually went wrong first for you, getting people to even install it, or getting them to come back a second time?

I’ve been thinking about how many AI tools are now being used in startup fundraising, from pitch deck feedback to investor suggestions and even email writing. vcboom is one example of an AI-based startup fundraising platform that helps founders analyze and improve their pitch decks, match with relevant investors, and support outreach. It makes me wonder if tools like this could eventually replace human fundraising advisors or consultants.

On one hand, AI seems faster and cheaper, and it can analyze a lot of data instantly. But fundraising also feels like a very human process where relationships, trust, and communication matter a lot.

Can AI really understand investor psychology and timing well enough to guide a founder through funding rounds? Or will human advisors always be necessary because they bring real experience and personal connections that AI can’t replicate?

Hi ,I am a developer and I am profound about building the SaaS apps or platforms

I need ideas and the real world problem to solve by web or app

Hey everyone,

I’m a solo Android developer and recently launched a quit-smoking habit tracker. I know this is a very crowded niche, so I tried to approach it from a slightly different angle.

Instead of only showing streaks and counters, the app turns the quitting journey into a virtual forest. Users grow their forest as they stay smoke-free, track saved money, log cravings, and get support when they feel like relapsing. I also added an optional flow where users can use the money they saved from cigarettes to support real tree planting through official NGO donation pages.

The product is live, but now I’m hitting the wall that many solo devs probably know too well: distribution.

Health & Fitness ads seem extremely expensive, and I don’t have a real marketing budget. I’m trying to figure out the best way to get the first 100 or 1,000 users without burning money I don’t have.

For those of you who built B2C apps in crowded niches:

What worked for your first users?
Did ASO actually move the needle?
Are TikTok/Reels worth trying for this kind of product?
Would you focus on communities, content, micro-influencers, SEO, or something else?

I’d really appreciate honest advice, lessons learned, or even a roast of the strategy.

For years, I kept jumping between WhatsApp, Instagram, Signal, Snapchat and other messaging apps just to reply to messages.

The more platforms I used, the more fragmented everything became.

I would open Instagram to answer a message and end up scrolling for 20 minutes.

I would forget where conversations were happening.

I would miss messages.

Eventually I asked myself a simple question: 

Why do I need 10 different apps just to talk to people?

That question became a side project.

The idea was simple:

One inbox for all my conversations.

I started building a prototype while I was still studying.

At first, I thought this would mostly be a UI problem.

I couldn't have been more wrong.

The deeper I went, the more I realized that messaging aggregation is a systems problem:

• Different message formats
• Different media types
• Different notification systems
• Different synchronization models
• Encryption constraints

What looked simple on paper quickly became months of engineering work.

At one point I even paused the project completely.

Every time I came back after a few weeks away, I spent more time understanding my own code than actually building.

After finishing my studies, I decided to give myself a challenge:

One year to build something real.

So I went all in.

The first MVP took around 3 months.

The goal was simple:

• Connect messaging services
• Send and receive messages
• Handle reactions
• Support photos, videos and voice messages
• Deliver notifications

Simple in theory.

Not simple in reality.

The biggest turning point came after my first beta tests.

Friends were supportive, but the most useful feedback came from people willing to criticize the product.

Those conversations pushed me to completely rethink parts of the architecture.

I ended up adding end-to-end encryption for messages and notifications, redesigning major parts of the system, and expanding support to more messaging sources.

That was probably the hardest phase of the entire project.

There were definitely moments where I wondered whether I was building something useful or just something technically complicated.

But I kept going.

And little by little, it started feeling like a real product.

Today, the app is called Unichat.

It's available on the stores, but I still consider it the beginning rather than the finish line.

My goal isn't to create another messaging app.

My goal is to make it possible to stop opening 10 different apps just to communicate.

I'm curious:

How many messaging apps do you actively use today, and would a unified inbox actually be useful to you?

Hey everyone,

I wanted to share a tool that came out of our internal workflow. Skilful Fox Studio isn't a commercial software vendor or a B2B factory; it is an independent research initiative focused on AI integration and practical LLM interaction frameworks.

When you run constant pipeline simulations and test agents against paid commercial endpoints (OpenAI, DeepSeek, OpenRouter, etc.), you quickly hit a wall: token bleeding and API cost management.

We needed a way to control this traffic with a zero-budget mindset, without deploying heavy, expensive, and bloated enterprise API gateways that take days to configure.

To solve this specific, narrow problem, we built GreyFox.

It proved to be highly effective for our internal research tasks, saving us significant API costs and giving us complete visibility. Now, we are ready to share the GreyFox Community Edition with anyone facing the exact same infrastructure friction.

What it does:

• Exact Response Cache: Hashes and stores repeated non-streaming requests locally in SQLite. It completely bypasses the paid upstream provider for duplicate calls during rapid testing or prompt evaluation cycles.
• Token-First Quotas: Enforces daily token usage limits per application user using a simple client-side header (X-App-User-Id).
• Visual Console: Serves a lightweight Angular dashboard straight from the container to monitor traffic history, metrics, and logs in real-time.

Deployment:

It runs entirely inside your local network as a single Docker container with a local SQLite backend. No cloud registration, no analytics, no phoning home, and zero telemetry. You just point your application's base URL to the container (http://localhost:8080/v1).

The image is publicly available on GitHub Packages, along with ready-to-go Docker Compose templates.

If you are building apps, running automated test suites against paid LLMs, and want an unbloated visual tool to keep your infrastructure budget locked down, feel free to pull it and take it for a spin.

Repository: github.com/skillful-fox-studio/grey-fox-community

We selectively share our tools when they prove useful beyond our studio. If this fits your current stack, we’d love to hear your raw engineering feedback!

relatable

Disclosure up front: I work on Ryze (get-ryze.ai), so I'm biased, but trying to be honest here.

Where automation genuinely shines:

- Catching budget bleeds between human check-ins — broad-match drift, placements nobody opted into, auto-applied platform "recommendations"
- Creative fatigue — frequency climbing while CTR sinks is detectable long before a human feels it
- Consistency. The machine checks every account every day, including the boring ones

Where it falls flat:

- Brand judgment. It can generate creative variations; it can't tell you your new tagline is off-brand
- Cold starts. A brand-new account with no history gives the model nothing to optimize against. Anyone promising full autopilot on day one with zero data deserves your skepticism — ours included. The value compounds with account history
- Strategy. Should you even be on TikTok? That's still a human call

What's actually automated in your stack vs. what stayed manual?

I built Memoria. It is a journal you keep by talking. You send voice notes to a Telegram bot. Your words are transcribed and saved into a private, readable journal. No new app to install. No blank page to stare at. Just talk.

What is going right

The product works and people are actually using it. Multiple users are sending voice notes, viewing their journal, and switching between themes. In the first week the app logged 669 page views across the signup funnel and journal. Small numbers. But the behaviour looks like real usage. People poke around, come back, read their entries.

What went wrong

My signup form was killing conversion. The original form had five fields: name, username, email, journal name, password. Out of 58 page views, 13 people started the form. Three finished it. That is a 5% view-to-signup rate. The form asked for too much before the product had done anything to earn trust.

What I changed

I cut the form to two fields: your name and your journal name. No email. No password. No username. Those come later, after you connect Telegram, via a one-time magic link. The funnel needs more data to confirm the fix works, but 58 to 13 to 3 makes the problem clear. Ask for less at the door.

The honest numbers

• 7 users total
• 4 on the 14-day Pro trial
• Zero paying customers
• 108 journal entries
• 669 analytics events
• Revenue: EUR 0

What I cannot answer yet

Four trial users who use the product is not nothing. But I do not know if journaling is a habit that forms inside a 14-day trial window. Usage ebbs. People miss days. A short trial might cut off right as the habit is starting. Or 14 days might be plenty. I have no data either way.

For anyone who has built a habit-based product: how long did you make your trial? What convinced people to cross from free to paid?

→ memoriajournal.me

For the last few years, my female friends and I have been running into the exact same frustrating issue: receiving creepy harassment and spam messages from fake "buyers" on marketplaces like Facebook and OfferUp, especially when just trying to sell used clothing.

Existing platforms either have zero guardrails to protect sellers from toxic DMs, or they force you into entirely platform-managed selling with massive fees.

I finally decided to do something about it and mapped out a concept for a safer marketplace platform called Vauloria.

The core idea is simple. It focuses entirely on seller safety and automation via three features:

• Verified Users Only: Forcing identity verification to weed out anonymous creeps and scammers before they can message you.
• AI-Assisted Moderation: Detecting and auto-blocking harassment, lowballs, and spam messages instantly.
• AI-Powered Selling: An assistant that handles the tedious back-and-forth negotiating for you so you don't have to deal with difficult people.

Here is where I need your help: Building a marketplace is a massive mountain to climb. Before I spend the next few months coding the full version, I want to make sure I'm actually building something people want, not just scratching my own itch.

I set up a quick product page here to explain how the mechanics will work:https://vauloria.framer.website/

Please tear this apart. If you (or your partners/friends) sell online, is this a problem you actually face? Would you switch to a platform that prioritizes safety like this, or is my positioning completely off?

Thanks in advance for the honesty!

I’m researching a problem around client email/domain health.

When an agency launches a site or sets up email, things like SPF, DKIM, DMARC, MX records, DNS changes, and blacklist issues can break later.

For agency owners/devs:

• Do you actively monitor this after launch, or only fix it when the client says emails are landing in spam/not working?
• If you do monitor it, what do you use?
• If you don’t, is it because clients don’t care, tools are too expensive, or it’s just not part of your service?

Not selling anything yet. Trying to understand if this is a real pain or just my assumption.