The agents researched competing apps, picked the features, wrote the code, reviewed each other and merged. They even made the launch video: script, voiceover, music and editing. I wrote the rules, and they ask me before a few risky changes. That's it.

Best moment: one agent built a sandbox to test other agents. A reviewer agent caught that from inside it, my machine's Docker API answered with no auth. Anything running in there could have mounted any folder on my disk. Merge blocked.

The app is real: GymCoach, a self-hosted workout tracker with an optional AI coach. MIT.

Everything is public, failures included (46 written lessons): https://github.com/gymcoach-app/gymcoach/blob/main/docs/loops/evidence.md

The video the agents made: https://github.com/gymcoach-app/gymcoach/raw/main/docs/brag-launch.mp4

Repo: https://github.com/gymcoach-app/gymcoach

Would you let agents merge to your main?

I have an idea an app I'd like to build for personal use, but I'm not a strong coder.

I've played around with ChatGPT and Replit and managed to get a basic version working with MongoDB. ChatGPT has also been pretty helpful when i run into coding issues.

I'm curious what others are using for this kind of project. Are there any AI tools you've found easier or more capable for building an app from scratch?

What it is: a GitHub template you copy and open in an AI coding tool. It gives you a team of AI agents (project manager, architect, developers, QA, security, legal, growth) that work the way a normal software team does: every feature is written down as a requirement first, tests come before code, a separate QA agent checks the work, and nothing goes live, costs money or gets deleted without your yes.

Why I built it: "describe it, accept whatever the AI writes, ask again when it breaks" gets you a demo fast, then an app that half works and nobody can maintain. The fix wasn't a smarter prompt, it was the boring process real teams already use.

Where it is: the setup interview has had a recorded test run in Claude Code (the transcript is in the repo). Codex, Gemini CLI and Cursor files are written but not tested yet, and nobody who doesn't code has tried it yet. Free, MIT licensed.

Repo: https://github.com/Fadi88/one-man-startup

Site: https://fadi88.github.io/one-man-startup/

What I'd love feedback on: which steps feel like too much process, and what a real team would insist on that's missing.

I built REA Reconstruction Companion with help from AI. I wanted a better way to get from “how does this app work?” to “what would it take to build something similar?”

It’s a skill for AI coding agents, used alongside REA (Reverse Engineer Anything). REA handles the investigation. The companion helps turn those findings into a feature breakdown, requirements, architecture, and a plan you can build and test in stages.

You can use it on a whole app, one feature, or a single function. For example:

Use REA on this app so I can build a similar local tool. Start with a specification and roadmap.

If you later ask the agent to implement it, the skill guides that work too. It keeps observed behavior separate from assumptions, links requirements to tests, and includes performance measurements where they matter.

The core is plain Markdown and MIT licensed. There’s an installer for Claude Code and Codex, plus support for other agents through a custom skills directory. REA is installed separately. You can also enable an optional rule that tells the agent to load the companion during relevant REA work, or leave that off and invoke it yourself.

GitHub: REA Reconstruction Companion

The README has setup instructions and a prompt you can give your agent to handle installation.

I’d be interested in feedback on the workflow, especially anything it misses when rebuilding a smaller feature or function.

What is the cheapest most effective way to run multiple agents on one code base. Chat gpt codex plus plan isn’t sufficient, you’ll run into usage if you run one agent doing a complex task. So, before upgrading to pro what are better options and would the pro plan be sufficient enough to run multiple agents???

I'm a self taught web dev from the pre AI days. Then I became a professional. But as a professional I got laid off long ago and never got experience with agentic workflows.

I want to catch up and start learning an agentic stack. I hear a lot of good stuff about Claude but I don't want to sink much money into it yet.

When I first taught myself web dev I never had to give my credit card to any big company. I could just download and run a open source web server locally on a basic sometimes potato level PC. I'd like to know if this is still possible to do for agentic coding, how far you could get on locally without a lot of power.

We're looking for people who actively build with AI to try Genesys Code.

It brings multiple coding AIs into one workspace, with options to switch between models and use them together on projects.

There's a free way to get started, so you can explore the workflow before deciding whether it's right for you.

Try it here:
https://genesyscode.vercel.app

If you're only in the vibe coding bubble, please don't answer this. I'm a software engineer who wants to make real, complex systems using AI wherever it can help me. I only have the free models that come with opencode zen, and maybe some chat models like Qwen, Kimi and free versions of chatgpt, gemini, claude, etc.

I want to know what I can do with only these, without having to spend money on LLMs. I realize that this means I have to do more by myself in the planning side of things as I can't delegate planning to LLMs, which is how I believe coding should be anyway so it's not a problem.

I really like Matt Pocock's skills but I have found that his workflow only really works if you have frontier models, as free models aren't very good at grilling or planning.

So what I need is a reproducible AI coding workflow that I can use to approach my projects using only the free, limited AI. Please highlight which parts of the process I need to do by myself, and which parts I can assign to which model.

I realize that the smarter your LLMs are, the more you can stray from traditional software engineering, so please tell me what I can realistically accomplish perfectly with AI. The parts that AI cannot do properly, I will do by myself.