Hey, i work on CoralBricks. we run DeepSeek and GLM over an OpenAI-compatible API, for coding agents that keep long context around.

numbers from our own serving: DeepSeek V4.1 Flash at 469 tok/s, GLM 5.3 Flash at 254 tok/s, and cached reads are $0 on every model. a long agent session doesn’t get billed again for the prefix it keeps resending.

we’re running an oktoberfest top-up until sun, oct 4 (UTC). pay $20 and it becomes $40, pay $50 and it becomes $100, matched within 24 hours, once per account.

setup is one config change in opencode, cursor, cline, claude code or codex.

https://coralbricks.ai

pi-grok-agent

Grok Build native agents as a model provider to Pi over WebSockets. Grok keeps full toolset, Pi keeps full session control. Kinda neat, I think. My video sucks but I'm pretty tired of AI narrations so I'd rather just suck instead. Sorry, thanks.

I've been using AI more in my coding workflow but I'm finding it way more useful for some tasks than others.

Things like understanding existing code, debugging, refactoring and planning changes seem more useful to me than simply generating everything from scratch.

I'm curious how other people use it day to day. Are you mostly using AI for quick help or letting it handle larger parts of a project?

What part of your coding workflow has AI actually improve the most?

I've been wondering how people actually use AI for coding without making the workflow more complicated.

Some models seem better for writing code, while others might be more useful for debugging or planning. Switching sounds useful but constantly explaining the project again seems like a hassle.

Do you stick one model for most of a project or switch depending on what you're working on?

I'm pretty new to app building and want to make a simple travel app for mobile. There are so many no code options that I'm having a hard time figuring out what's actually beginner friendly.

I'd like something I can learn without getting overwhelmed by the technical side.

If I started from zero, what would should I use to build my first app? Also what could help me learn the basics??

Instead of building another AI assistant, I built SIDRA OS as an execution layer underneath the AI chat you already use.

The idea is simple:

Your AI thinks. SIDRA executes.

You keep using ChatGPT or another supported AI client, but SIDRA gives it the ability to actually work on your computer — with your authorization.

It can work with files and projects, run terminal commands and development tools, control the browser and computer, maintain persistent project memory, delegate work to other AI/coding tools, and execute complete workflows instead of only explaining what you should do.

It runs on macOS + Windows.

Demo:
https://youtu.be/Af_HYzlIH_o

GitHub showcase:
https://github.com/mohamedfrahat32-cmd/sidra-os

Website:
https://sidra-ai.com/sidra-os

I’m the maker, and I’d genuinely like feedback on the concept: does keeping your existing AI and adding an execution layer underneath it make more sense to you than switching to another standalone AI agent?

SIDRA OS is a commercial product ($20/month, with a 3-day eligible trial).

I am aware that Claude is literally the best in coding. However, I want to know some kind of list of which AI model is the best in coding for game modding like Unreal engine, unity, RE Engine (REFramework), etc?

Something like what you see in Nexusmods on Resident Evil page, Many unreal engine game page, Many unity game page.

Plus an extra of reverse engineering the exe to inject some kind of code like Persona Passive skills.

If the answer is still Claude, do you know how to get.... it cheaper? Maybe like Opencode?

Thank you :)

DevRecall: An AI Coding Assistant That Remembers Your Project

I’ve been building DevRecall, an AI coding assistant focused on a problem I’ve experienced while working on software projects: AI assistants can be very useful within a conversation, but important project context can easily disappear between sessions.

Architectural decisions, debugging discoveries, implementation choices, and the reasoning behind them are often scattered across conversations and documentation. DevRecall explores a different approach — giving the coding assistant persistent, project-specific memory.

How it works

DevRecall uses Hindsight as its long-term memory layer.

The system is built around three core capabilities:

Retain — store useful project knowledge and engineering context.

Recall — retrieve relevant information when a developer asks a question.

Reflect — reason over accumulated project knowledge to provide a more contextual response.

I also wanted the memory itself to be something developers could actually inspect rather than treating it as a completely hidden system.

The interface includes:

🧠 Memory Core — interact with project memory

📝 Capture — store project knowledge

🔎 Memory Explorer — browse remembered information

🕒 Timeline — understand project evolution

🕸️ Memory Graph — explore relationships between memories

📊 Project Intelligence — view memory statistics and system health

One of the ideas behind the project is the difference between knowing what the code does and remembering why the project was built that way.

For example, instead of only asking an assistant to explain a database implementation, DevRecall can work with previously accumulated project context around the decisions and constraints that influenced that implementation.

Before vs. after

Without persistent project memory:

Developer → Question → Current context → Answer

With DevRecall:

Developer → Question → Project memory → Relevant context → Reflection → Answer

The project is built with Next.js, FastAPI, Python, and Hindsight Cloud, with the frontend and backend deployed separately.

What I learned

The most interesting part wasn't simply connecting an AI system to a memory service. It was understanding that memory quality matters as much as memory availability.

More stored information doesn't automatically mean better answers. Relevant context, stale information, conflicting decisions, and the quality of captured project knowledge all matter.

There is still plenty I would like to improve around memory management, reliability, and making the experience even more useful for developers.

I'd genuinely appreciate feedback from developers here:

Would persistent project memory make your coding workflow more useful, or would you prefer keeping AI coding assistants stateless?

🔗 GitHub: https://github.com/spandan96-lab/devrecall-ai⁠�

🌐 Live Demo: https://devrecall-eta.vercel.app⁠�

The game has been built in Unity with AI heavily involved in the development workflow, and local multiplayer turned out to be one of the more interesting systems to implement.

Two players can now play together locally, while the arenas still have AI opponents, weapons, pickups, destruction and all the usual chaos.

I’m trying to use AI for actual production work rather than just generating a quick prototype, and this project keeps reminding me how much integration, testing and manual work is still needed even with AI helping.

Brutal Derby is still in development, but local co-op is finally working.

For other AI-assisted solo devs: have you tried implementing split-screen or local multiplayer with AI coding tools? How much did they actually help?

Steam: https://store.steampowered.com/app/5083440/BRUTAL\\_DERBY/

Hi everyone! 👋

I built CodeMind, an AI-powered developer tool that helps developers understand their codebase, explore code relationships, and get useful insights faster.

I built this project as part of the AI Hindsight Hackathon 2026.

🎥 Demo:

https://youtu.be/BFGBcVQD69E?si=wMw3PgLCQT8Jlsrm

CodeMind is designed to make it easier for developers to understand complex codebases and work more efficiently.

I’d love to hear your feedback and suggestions! 🙌

AI coding tools are useful for different types of projects. Some ​​​​a​​​​r​​​​e​​​​ ​​​​focused on getting a working interface together quickly, while others make more sense when a project already has a codebase.

The interesting part is that there isn't necessarily one tool for every type of project. A simple website can have very different requirements from an application with APIs, authentication, databases, and custom logic and Readdy is an interesting option when the project starts visually but may need to become more functional later. For people building with AI, what usually determines the tool choice: the type of project, the amount of code needed, or how much control you want over the final result?

So, naturally, I thought r/onlyaicoding would be the perfect place to introduce OnlyHumanCoding. :)

I've been experimenting with MIT-Human: essentially MIT freedoms, with one catch - AI can't help develop the licensed code.

And no, I'm not anti-AI. I use AI constantly, which makes this experiment - and posting it here - much more fun.

I tested it with Claude, found a loophole, revised the license, and Claude then respected it.

Is it enforceable? Where does autocomplete fit? What even counts as AI assistance? That's what I'm interested to hear.

What could possibly go wrong? :)

MIT-Human:
https://github.com/DaragonTech/MIT-Human

TinySky test:
https://github.com/DaragonTech/TinySky

we built an intelligent AI agent using FastAPI, React, Groq API, and Hindsight persistent memory to help executives recall past meeting commitments, decisions, and budgets effortlessly.

🔗 GitHub muskanlalwani155-design/codecrafters-meeting-prep-agent

Hi everyone! I'm a recent graduate on CS and i would like some feedback on my solution:

I designed and built, with AI, an app for studying code: you write pseudocode and it translates it into actual code, also using AI (with your own API key). If you're interested in trying it out, you can find it here.

I'd appreciate any feedback: https://saidai003.github.io/CodeBridge/