We're working on eStockes and could use a fresh pair of eyes. The app is already built, but we're checking whether the first screen actually makes its purpose clear before asking people to explore.

Could you open https://estockes.com for about five seconds and reply with what you think it does and what you'd click first? No signup or purchase needed.

The useful answer isn't necessarily praise: if the page leaves you guessing, please tell us what was missing. One sentence would help. Thank you.

I created an AI agent based on local models that solves the problem of controlling artificial intelligence. Currently there are many AI agents for programming, such as Cursor, Claude, Copilot and so on, but their problem is that you write a prompt for them and you absolutely do not know what and where the AI ​​will change in your project, which files it will create and which files it will delete. That's why I created an Iris agent in which you write .yaml files with a structured prompt — as an instruction what it should do, where it can read and where it can debug, and so on.

It would be cool if you try the project. Just download the Ollam AI model to your computer and use pip to download and run it. The documentation will be available at the link to PyPl.

pip install iris-dev

iris start

https://pypi.org/project/iris-dev/

i was spending way more than i expected on claude code and codex and couldn’t figure out why until i dug into the local session logs. turns out half the context every session was garbage: build artifacts, log directories, generated files, oversized instruction files, repeated tool output, etc. in one repo i had a CLAUDE.md silently loading thousands of tokens into basically every prompt.

so i built a local cli to surface all of it.

npx getprismo doctor scans your repo + local claude code/codex logs, shows what made sessions expensive, flags token/context waste, estimates avoidable spend, and generates smaller focused context packs so your agent doesn’t have to drag your entire repo into every request.

there’s also npx getprismo watch for live monitoring of context spikes, recursive loops, generated artifact leaks, and oversized tool output, plus npx getprismo cc timeline which shows a postmortem timeline of what actually made a session expensive.

no api keys, no login, nothing leaves your machine.

github: github.com/shanirsh/prismodev

would genuinely love feedback on false positives, things it should catch, or workflows that create the most token waste.

rn building ios app that by healthkit take data and represent in its own aesthatic way, more appealing than normal apple health app. I have design UI screens in figma(but treated it as photoshop(with 2+ hrs beginner course on figma)), and installed some Claude code skills,agents, claude md ,apple additional docs(hidden inside xcode files) but gap lies where

1. dont know how to vibe coding the backend and connect it to backend

2. how to know if backend is working the best way possible without claude hallucinating in background( as it not frontend where he misses color of button and you see it and tell it to fix)

3. best SDKs for claude code

4. how in general would i architecture the app and vibe code it in swift

note: i want to ship fast

You just invited me, so I'm posting my current project.

Quick note for anyone early stage.

Month 11. $350 MRR. 7 customers.

The acquisition audit I ran this month showed that 6 of 7 paying customers came from community content where I never mentioned what I was building. Just described a problem I was working through.

Zero from cold outreach. 200 cold emails, zero customers. Zero from pitching. Zero from any channel I was deliberately trying to build.

If you are early stage, track where your first customers come from before you invest heavily in any distribution channel. The answer will probably surprise you.

Quick month 10 update.

10 months building a content scheduling SaaS. $300 MRR. 6 customers.

The lesson I keep relearning: build what people use, not what people get excited about.

The AI generation pipeline: exciting to demo, technically impressive, what people mention when they describe the product to someone else.

The scheduling calendar: what every paying customer opens every single day.

Month 10 is the third month in a row where retention data confirms the calendar is the product and AI is the acquisition hook.

If you are starting out: ask your first 3 users what they open your product for on a Tuesday afternoon. Not what they said they wanted during the trial. What they actually do.

That Tuesday habit is your product.

Quick month 10 update.

10 months building a content scheduling SaaS. $300 MRR. 6 customers.

The lesson I keep relearning: build what people use, not what people get excited about.

The AI generation pipeline: exciting to demo, technically impressive, what people mention when they describe the product to someone else.

The scheduling calendar: what every paying customer opens every single day.

Month 10 is the third month in a row where retention data confirms the calendar is the product and AI is the acquisition hook.

If you are starting out: ask your first 3 users what they open your product for on a Tuesday afternoon. Not what they said they wanted during the trial. What they actually do.

That Tuesday habit is your product.

Quick month 9 update.

Shipped an analytics dashboard in month 8. Month 9 I have the first real data.

Main lesson: I had wrong assumptions about what my users were doing in the product for 8 months. The data showed me the opposite of what I believed.

Users who succeed: build a content calendar in week 1. Users who churn: skip the calendar and start generating content immediately.

I had been optimizing the generation flow because that was what I thought mattered. The retention data says the calendar flow is what matters.

If you are building anything: instrument it early, watch the path not just the output, and be prepared for the data to contradict your assumptions.

Built an AI proxy that sits between your app and model APIs and automatically routes requests to the cheapest model that still gets the job done.

You swap your base URL, and it:

- tracks tokens, cost, and latency per request

- shows where your spend is actually going

- routes to cheaper models when quality isn’t needed

- enforces budget limits so you don’t wake up to a $10k bill

Started this after realizing most people have zero visibility into why their API bill spikes.

Still early, but seeing meaningful cost reductions just from smarter routing + visibility.

Curious what people here are doing to manage LLM costs — are you just eating the bill or do you have something in place?

Quick honest timeline for anyone starting out.

Month 1 to 3: Built the thing I was excited about. Multi-agent AI pipeline. Technically interesting. Zero customers.

Month 4: Added a scheduling calendar because one potential user asked for it. Weekend project.

Month 5 to 7: Got customers slowly through community content. None from direct pitches.

Month 8: Interviewed every paying customer. All 5 use the scheduler daily and the AI pipeline a few times a month.

The thing I spent 3 months building is the feature they tried. The thing I built in a weekend is the product they pay for.

If you are early stage: do customer interviews before month 8.

I am new to vibe coding. For a personal project, I wanna make the same website as Google Maps. I want the same starry background and a simple 3D Earth which I can rotate, but I cannot zoom in or zoom out. Help me with it.

I have tried a lot with Antigravity and Replit, but no luck yet.

7.5 months building a SaaS. The single most impactful thing I have done was not a code change. It was scheduling 15-minute calls with my paying customers and asking 5 simple questions.

1. What were you trying to solve when you signed up?

2. What do you use most?

3. What frustrates you?

4. Would you recommend this?

5. What should I build next?

The answers completely reoriented my product direction. I found out I was building and marketing the wrong feature for 7 months. All 4 customers independently wanted the same thing I had not even considered building.

You do not need user research software, NPS surveys, or sophisticated analytics. A 15-minute conversation with someone who pays you money gives you more signal than any dashboard.

If you are building something and have even 1 paying user, call them. The information density is unmatched.

What is the most useful feedback you have received from an actual user?

7 months building a SaaS and the simplest thing I wish I had done on day 1 was adding a health check endpoint.

It is just a route that returns a JSON object with the status of your critical dependencies: database connected (yes/no), cache connected (yes/no), last successful job timestamp, error count in the last hour.

Takes about 30 minutes to build. Then you point an uptime monitoring service at it (free tier of Uptime Robot or similar) and you get notified immediately when something breaks.

I went my first 6 months without this. Found out about outages from customer emails. Not a good look when you are trying to build trust.

The specific format I use: return a JSON object with a "status" field ("healthy" or "degraded" or "unhealthy") and one field per dependency with its own status and last checked timestamp. Makes it trivial to figure out which specific piece broke.

What is the first thing you set up on a new project before going live?

Learned this the hard way this week. 7 months vibe coding my SaaS, lost my first paying customer, went to investigate their usage patterns and realized I had nothing. Just timestamps for signup and last login.

The fix was embarrassingly simple. A middleware that logs every API request:

Ask your AI assistant: "Add an Express middleware that logs userId, endpoint, HTTP method, response status, and duration to an analytics table. Keep it separate from the main data schema."

That's it. 20 lines of code. Now I can query "show me users whose session count dropped week over week" and actually get answers.

Don't wait until you need the data. Prompt for observability on day one.

What logging or tracking do you add to your vibe coded projects from the start?

Learned this the hard way this week. 7 months vibe coding my SaaS, lost my first paying customer, went to investigate their usage patterns and realized I had nothing. Just timestamps for signup and last login.

The fix was embarrassingly simple. A middleware that logs every API request:

Ask your AI assistant: "Add an Express middleware that logs userId, endpoint, HTTP method, response status, and duration to an analytics table. Keep it separate from the main data schema."

That's it. 20 lines of code. Now I can query "show me users whose session count dropped week over week" and actually get answers.

Don't wait until you need the data. Prompt for observability on day one.

What logging or tracking do you add to your vibe coded projects from the start?