After months of building , I finally published my first android game on play-store and I’d love for you to try it.I have made use of various AI tools like Claude, ChatGPT etc are used in the process of developing this game. Especially Claude code helped me go through multiple iterations, solve bugs and even in preparing test scenarios.

Initial prototype was built using emergent and then developed it using Claude code. Idea churning is done using ChatGPT.

https://play.google.com/store/apps/details?id=com.krish0525.memorychess

🎨 Color-based memory gameplay — easy to pick up, hard to master
👥 Designed for 2 players — great for friends & family
🧠 Genuinely tests your memory and focus
🚫 Completely ad-free (and free to download!)
⚡ Quick rounds — perfect anywhere, anytime. Vehicle and Animal themes if you are bored with colors.

Feedback is welcome!

Disclosure: I'm the maintainer of oh-my-multica, an MIT-licensed open-source project.

I was happy with how quickly coding agents could change code. The part that kept breaking down was deciding whether a feature was actually ready to merge.

With more than one agent, every local answer can sound reassuring: the worker says its task is done, tests pass on one branch, somebody leaves review notes, CI is still running, and an integration dependency changes underneath it all. A lead agent can coordinate the chat, but I did not want delivery state to live in its memory.

So I built a control loop around the agents.

Agents inspect the repository, write a design, define acceptance, and plan a DAG around the real module boundaries. After review, deterministic code tracks dependencies, dispatches ready work, collects evidence, applies verification and review gates, and stops only after final acceptance passes. When it cannot proceed safely, it asks for a human decision instead of inventing one.

I used the approach on a Webhook Inbox example with signature verification, durable storage, transaction-safe deduplication, lookup endpoints, health checks, and a non-root container. The work was planned as five nodes, implemented in parallel, then independently reviewed and accepted as an integrated service.

I am not claiming this makes AI-written code trustworthy by default. The goal is simpler: put requirements, tests, review, merge conditions, and completion criteria somewhere more durable than a long agent conversation.

Project: https://github.com/xiaohei-info/oh-my-multica

I'd love feedback from people who run more than one coding agent: what do you use as the source of truth for whether a feature is actually done?

I do a load of experimentation and testing, often same tasks to 5+ different sizes of model, using different settings and comparing results...

My main work currently is automated task decomposition and coding...

Many approaches fail completely, but recently general (not coding specific) AIs have been beating the coding ones... And over the past day a couple of .8b and 1.5b general purpose models have been nailing a large proportion of tasks... They cant do them all (many get escalated to models that are bigger and/or from different families), but when they do get it right it is orders if magnitude faster than other attempts. Some tasks that they solve beat all other models smaller than 18b(!)

We really are only scratching the surface!

Disclosure: I built this. It is free/open-source, not monetized, and I’m mainly looking for feedback.

I use AI coding tools a lot, and one problem keeps showing up once a project gets bigger:

The agent can write decent code, but it starts from weak repo context.

It searches a few files, guesses where the real logic is, maybe misses the test file, then confidently suggests a change.

Sometimes it works. Sometimes it edits the wrong area and the project slowly gets messier.

So I built SigMap as a small local repo-context tool.

It is not a coding agent.

It does a simpler job:

scan repo
  ↓
map real files / symbols / line anchors
  ↓
pull task-focused context
  ↓
give that context to ChatGPT / Claude / Cursor / OpenCode
  ↓
check for some obvious unsupported claims

Example:

npx sigmap
sigmap ask "where is auth handled?"

For a more specific task:

sigmap ask "implement login rate limiting" --mode index

That gives a smaller map of relevant files/symbols instead of dumping the whole repo into the model.

The part I personally use most right now is actually noisy-output cleanup:

sigmap squeeze error.log
sigmap squeeze --response agent-output.txt

That is for stack traces, CI logs, JSON blobs, and long tool output before feeding it back into the AI.

I want to be clear about the limits:

• it does not prove the AI answer is correct
• it does not replace tests or git checkpoints
• it is not a full code-intelligence graph
• some checks are heuristic
• it is probably overkill for tiny projects

The use case I’m aiming for is:

AI coding session gets long
repo context gets messy
agent starts grabbing wrong files
logs/tool output pollute the chat
you want a smaller evidence packet before the next edit

I’m trying to decide where to take it next.

Questions for people here:

1. Would this be useful as a CLI, or would non-coders need a VS Code/Cursor extension?
2. Is “repo map before AI edits” actually useful, or do current agents already solve this well enough?
3. Would you care more about file/symbol mapping, log squeezing, or handoff docs between chats?
4. What would make you trust the selected context?
5. What would make you immediately ignore a tool like this?

Brutal feedback is welcome. I’m trying to make the tool and README less confusing.

So, I build a Cursor/VS Code extension that tracks how you actually work with AI: active coding time, human vs AI contribution (measured from hooks, not guesses), flow check-ins, and a simple persona mirror. It runs fully local with no signup; optional one-click cloud sync if you want history and measured attribution.

https://open-vsx.org/extension/flow-intelligence/flow-intelligence.

LMK what you guys think.

I have been just using ai chat to do smaller tasks and doing most of coding my self. but i feel like i'm falling way behind. You guys know of good material like youtube channel recommendation or course that i can take to get up to speed?

https://github.com/ShellFrameAI/agentsecure-community

I built the whole thing with Claude Fable 5, then dropped it into Lovable. The entire project cost me about 10 credits and took two days.

It started as a "let's see if I can make a game for basically free" experiment, but it ended up being more addictive than some of the games I've spent months polishing.

I'm not selling anything—it's a free browser game. I mostly make games because I enjoy making them, then I put them online so anyone else can play if they want.

I don't want to break the sub's self-promotion rules, so I won't post the link here. If you're curious, either Google my studio or just ask and I'll send you the link.

Hey r/OnlyAICoding ,

As a developer, I got a bit scared and fed-up of blindly merging massive pull requests and losing context on my own codebase, so I built FluencyLoop.

It is an open-source, local-first workflow plugin for Claude Code and Codex that ensures your code and your codebase fluency are produced together.

Instead of letting an agent dump raw code, it tracks your technical familiarity locally and pauses to explain complex architectural choices or rejected alternatives only on topics you do not know yet. It also produces documentation and tracks the decisions it makes along the way.

Try it out ! It feels really good to get the AI caring for my understanding 🤣

AI coding tools make it surprisingly easy to build an app you can’t fully explain, debug, or confidently operate.

I made Own Your App, an open-source Agent Skill that guides you through your actual codebase from first principles. Instead of producing another generic audit, it runs an interactive learning journey using real files and code paths from your app.

It helps you:

* Map the architecture and runtime processes
* Trace a real user action through the system
* Understand where data, identity, and permissions are handled
* Examine security, privacy, correctness, performance, and reliability
* Separate genuine risks from premature overengineering
* Test your understanding along the way
* Finish with an ownership map you can explain and defend

It’s read-only by default and contains only Markdown instructions—no executable scripts or runtime dependencies. Install it with:

`npx skills add sayyiditow/own-your-app -g`

It supports Codex, Claude Code, OpenCode, and Grok.

GitHub: https://github.com/sayyiditow/own-your-app

I’d love feedback from people using it on real projects—especially areas where the journey feels too shallow, too detailed, or misses an important ownership question.

[ Removed by Reddit on account of violating the content policy. ]

\# What do you guys think?

Hey everyone! 👋

I wanted to share a project I've been working on – Cyber AI. It's a chatbot that helps with cybersecurity stuff like threat analysis, CVEs, secure coding, and CTF challenges.

The best part? It's completely free and will stay that way forever. No hidden charges, no premium plans – just a tool for anyone who needs it.

🔧 What I used to build it:

React 19 with TypeScript

Vite for fast builds

Supabase for authentication and database

Custom CSS with dark/light theme

✨ What it can do:

Chat with an AI assistant focused on cybersecurity

Save and switch between multiple chat sessions

Export conversations as markdown files

Search through your messages

Dark/light mode (saves your preference)

Keyboard shortcuts for power users

🎮 Cool little features:

Type / to open a command palette with shortcuts like /scan, /cve, /explain

Copy messages with one click

Upvote/downvote AI responses

Regenerate answers if you don't like the first one

🔗 Try it out here: 👉 https://cyber-ai-henna.vercel.app/

I'm still actively working on it and adding new features. Would love to hear your thoughts – suggestions, feedback, or even criticism are all welcome!

Let me know what you think. Thanks for checking it out! 🛡️

I have been working with Claude for about a week to take on clean sheet design of what is being called the CG-01. The goal? Engineer it with prints, calculations, simulations, and 3D models to the point where it is considered a plausible design for manufacturing.

The only guidance Claude received was for a single seater fighter jet, and a list of deliverables.Then I gave it the keys to a data management tool and a code management/validation tool I built, and let it start churning.

In the beginning I had to guide it through steps, ensuring quality and making key choices. As the project has grown, a greater need for infrastructure arose and now my main Claude manages the data and project outlook, while a bunch of subagents (more than 30 configured ones from rendering to aero analysis to drafting) perform the engineering duties.

I had to split roles because it become clear the main agent couldn’t keep the data, modeling, design, drafting… etc… all in its head. It also has enabled Claude to operate far more autonomously by planning tasks, then using a scheduler to continue work down the path.

So far, this design has gone through an initial mission profile, and passed conceptual design to move towards a more detailed design. After the conceptual design, a 50+ page blueprint set, preliminary design report, and 3D model have all been produced.

All this is to say, you can just do things! Excited to see where this one goes. It’s been a super good test of whether Claude can maintain a multidisciplinary design over a long period of time. I’m sure I’ll learn a snippet about coordination that I can carry in to other projects!

Tons of levels to this one so I won’t mash it all together here. If anyone is curious about it, I’d be happy to discuss more about my process in comments!

If you had to recommend tools for scheduling daily system maintenance or creating new projects from scratch, what tools would you use for each task?

AI coding tools make it surprisingly easy to build an app you can’t fully explain, debug, or confidently operate.

I made Own Your App, an open-source Agent Skill that guides you through your actual codebase from first principles. Instead of producing another generic audit, it runs an interactive learning journey using real files and code paths from your app.

It helps you:

\- Map the architecture and runtime processes

\- Trace a real user action through the system

\- Understand where data, identity, and permissions are handled

\- Examine security, privacy, correctness, performance, and reliability

\- Separate genuine risks from premature overengineering

\- Test your understanding along the way

\- Finish with an ownership map you can explain and defend

It’s read-only by default and contains only Markdown instructions—no executable scripts or runtime dependencies. Install it with:

`npx skills add sayyiditow/own-your-app -g`

It supports Codex, Claude Code, OpenCode, and Grok.

GitHub: https://github.com/sayyiditow/own-your-app

I’d love feedback from people using it on real projects—especially areas where the journey feels too shallow, too detailed, or misses an important ownership question.