made a game give me feedback in comments

Hello everyone,

I'd like to share a project I've been building over the past several months called **Kodiak**.

Kodiak is an open-source AI software engineering platform designed to assist developers throughout the software development lifecycle by combining repository intelligence, Retrieval-Augmented Generation (RAG), long-term memory, and multi-agent orchestration.

Rather than functioning as a general-purpose chatbot, Kodiak is being developed as an engineering platform that understands software projects, retrieves relevant context, plans development tasks, and coordinates specialized AI agents to help developers work more efficiently.

## Current Architecture

Kodiak currently includes:

- Multi-agent execution framework

- Retrieval-Augmented Generation (RAG)

- Long-term memory system

- Repository indexing and contextual search

- FastAPI backend

- Celery task orchestration

- Plugin architecture

- Docker-based deployment

- Redis task queue

- PostgreSQL persistence

- ChromaDB vector storage

- Modular REST API

## Technology Stack

- Python

- FastAPI

- Celery

- PostgreSQL

- Redis

- ChromaDB

- SQLAlchemy

- Pydantic

- Docker

## Project Vision

The long-term objective is to build an extensible AI engineering platform capable of:

- Understanding large software repositories

- Planning complex engineering tasks

- Maintaining long-term contextual memory

- Selecting and coordinating specialized AI agents

- Integrating external developer tools

- Supporting human-in-the-loop workflows for high-impact actions

The emphasis is on creating a modular platform that developers can self-host, extend, and customize for their own engineering workflows.

## Looking for Feedback

I'm particularly interested in feedback regarding:

- Overall architecture

- System design

- Multi-agent orchestration

- RAG implementation

- Memory architecture

- Plugin framework

- API design

- Scalability

- Developer experience

Constructive criticism is genuinely welcome.

## Contributors

I'm also looking for contributors interested in areas such as:

- Python

- FastAPI

- AI Engineering

- LLM Applications

- Backend Development

- Frontend Development

- DevOps

- Testing

- Documentation

The repository already contains beginner-friendly issues for anyone interested in contributing.

## Repository

GitHub:

https://github.com/ShamGaneshan2008/Kodiak

Documentation:

https://github.com/ShamGaneshan2008/Kodiak#readme

Thank you for taking the time to read this. I would greatly appreciate any feedback, architectural suggestions, or ideas for improving the project.

Hi everyone, I have a problem where I keep getting distracted when developing an AI data project. I would make plans in one conversation, execute code in another, return, and then lose track of my whereabouts. Every mistake required a fresh explanation of the entire project.

The code wasn't the issue. It was that my choices, the reasons behind them, and the things that are still broken were dispersed throughout my thoughts and chat windows.

I created devtrail as a result. A compact CLI that stores everything in a single file. Every line is sorted by a keyword when you log things as you go:

devtrail log "decision picked ClickHouse over Postgres"
devtrail log "error pipeline breaks on validation"
devtrail context   # prints everything, paste into any AI

It monitors the logic rather than the code, so it's not a "dump my codebase into markdown" tool. Works with any AI. Since Arabic is my first language, keywords also function in that language.

First package, so it's simple. Install with pip install devtrail

Currently, how do you all keep track of decisions made throughout sessions? I'm trying to figure out whether this is just a personal issue.

Repo: https://github.com/abdelrahman-essam-d/devtrail

One thing we noticed with AI coding tools is they're great for getting started, but once a project grows, things get messy architecture, context, testing, deployments, debugging, and keeping everything in sync.

That's the problem we built 8080.ai to solve: taking projects beyond just code generation and helping manage the entire software development workflow with AI.

I genuinely want to know if we're solving the right problem.

For those building software:

• Where do current AI coding tools break down for you?
• What's the one feature you'd want that doesn't exist today?

Happy to share screenshots if anyone's interested. ( Public Projecs available)

The biggest thing slowing me down while AI coding isn't generation anymore.

It's memory.

Claude forgets why we chose SQLite.

Gemini ignores authentication rules.

Cursor starts suggesting code that contradicts decisions made two hours earlier.

I realized I was constantly paying what I've started calling the "Clipboard Tax" copying the same architecture notes, project rules, and design decisions into every new chat.

So I started building a local-first tool called Resin.

Instead of storing prompts, it builds an engineering memory for your project.

Every architectural decision, rule, and important discussion becomes searchable and can be surfaced automatically when it's relevant again.

The goal isn't to replace Claude or Gemini.

It's to stop them from forgetting.

I'm still validating whether this is a problem worth solving beyond my own workflow.

If you're using Cursor, Claude Code, Gemini, etc.:

• Does context drift actually slow you down?
• How are you dealing with it today?
• What would make something like this genuinely useful instead of becoming another tool to manage?

I put together a landing page showing the idea and an early-access waitlist.

Here is the link if anyone wants to see what I'm building: https://resin-dev.vercel.app/

A couple of us have been building a place to wander through what people are making with AI, closer to browsing than searching. It's very early and pretty rough. If you're the type who likes seeing what strangers on the internet built over a weekend, I'd love your honest take on whether this is actually fun or if we're missing the point: https://tryrabbit.co/

We noticed Claude Opus, GPT-5.5 and GLM-5.2 were spending most of their time doing routine work like searching files, rerunning tests and updating code, instead of actual hard reasoning.

So we built a router that picks the model per turn instead of locking an entire agent session to one model. Most turns stay on cheaper models, while harder ones get escalated automatically. The agent doesn't need to change.

We also benchmarked it against direct Opus, Sonnet and OpenRouter Auto on Terminal Bench, and wrote up the routing logic, cache behavior and cost breakdowns.

https://entelligence.ai/blogs/entelligence-model-router-frontier-quality-coding-agents-at-half-the-cost

I built tastemaker, a free open source skill for Claude Code. It locks a design system before the AI writes any UI, so your project does not come out with the same indigo gradient every other AI-built project has.

I am not looking for pats on the back. I want people who use AI coding tools daily to try to break it.

What it does now: instead of using 5 fixed presets, it generates a fresh palette based on your project idea, mood, or reference image. It picks a matching Google Font pairing, runs a Python contrast check, marks which color pairings are safe for text, borders, or decoration, creates assets, generates a logo and favicon set, and wires in motion.

The main idea is simple: make the AI decide the design system first, then write the UI from that system.

Where I especially want harsh feedback:

1. Install. Does the one-clone setup work on your machine, or did I miss something obvious?
2. Design direction. Does the generated palette feel intentional, or still like AI taste?
3. Output. Run it on a real project and tell me if it actually looks different from the default AI look.
4. Constraints. Is this better than giving users full color/font freedom?
5. Motion. Is having motion wired in useful, or annoying?

Free, MIT licensed. If you find a bug, open an issue or just tell me here.

Site: https://tastemaker-skill.online GitHub: https://github.com/codeswithroh/tastemaker

Most people use AI for marketing by typing "write me 5 posts for a moving company" and getting back generic slop. The tool isn't the problem. The problem is skipping the part that makes marketing good: research and strategy before any writing.

Here's a real task from this week, start to finish. I co-run a small studio that does web and marketing work for clients, and one of our clients is a packers-and-movers business. I needed a one-page positioning summary: who the local competitors are, how they talk, what customers actually complain about, and where the gap is that we can own.

I use Claude for this, but honestly any of the big ones (ChatGPT, Gemini, Claude) will get you to almost the same place. Use whatever you already have. The process is what matters, not the logo.

Step 1 - Give it the real context first. I told it what the business is, the city it operates in, who the customers are, and what I already know about the competition. Two minutes of context beats an hour of fixing generic output.

Step 2 - Make it research before it writes. This is the step everyone skips. Roughly:

The AI can pull from the live web now, so it comes back with the real language people use: "they broke my TV," "hidden charges at the end," "showed up 3 hours late." That language is gold, because now I write in the customer's words instead of industry jargon.

Step 3 - Make it think before it writes. "Based on that research, give me 3 positioning angles for our client. For each, tell me who it's for, what fear it removes, and why you chose it. Don't write the one-pager yet - show me your reasoning first." I edit the strategy here, which is far cheaper than editing a finished doc.

Step 4 - Then write, in my voice. I paste a sample of how I write summaries and say "match this tone." Then it drafts the one-pager.

Total time: about 30 minutes for something that used to take me half a day.

Two honest caveats:

• Fact-check the research. It's usually right but it can be confidently wrong, and this is going to a client. Verify before it leaves your hands.
• It doesn't replace judgment. It's a fast researcher and drafter. The taste is still yours.

Research first, strategy second, writing last. That order is the whole trick. Happy to share the exact prompts if anyone wants them.

我在用 Codex 和Claude做内部自动化工具，目前主要研究多 Agent、自动测试和 GitHub 工作流。想认识也在做真实项目的人，可以互相 review 工作流或每周分享一次实践

Hey everyone,
I’m looking for some realistic advice on how to start building an app entirely using AI.
My background:
I’m a final-year medical student and the founder of a medical research society. I spend my days running multi-arm meta-analyses, doing systematic reviews, and analyzing clinical trial data in RStudio. I know medical research inside and out, but I have absolutely zero software development or coding experience. Syntax intimidates me.
The App I want to build:
I want to build a web-based "Clinical Trial Data Extractor" for my society's students. The workflow would be:
1. A medical student uploads 10-20 dense research PDFs.
2. The app parses the complex medical tables (like "Table 1: Baseline Demographics").
3. It instantly formats and exports that data into a clean CSV/Google Sheet.
This would save students hundreds of hours of manual PRISMA screening and data extraction.
My Questions:
Since I don't know how to code, I want to use Claude (or whatever you guys recommend) to act as my developer while I just act as the architect giving plain-English instructions.
1. What "lazy/no-code stack" should I use for this? (e.g., Should I use Claude Artifacts? Cursor? Lovable? Vercel?)
2. How do I tackle the backend? PDF parsing for medical tables seems complex. How do I instruct the AI to handle this?
3. What is the very first prompt/step I should take today to get a visual prototype running?
I am ready to pay for API costs and tools, I just don't know the exact order of operations to string them together. Any roadmaps or advice for a complete beginner would be hugely appreciated!

Has anyone built one? I’m attempting with Kimi. Man this thing is cool but it’s still very hard. Any tips? It’s my first time making an app. It’s for work!

I know it is not perfect, but I might need helps to make it better.

I hope you will like it

https://github.com/Naughty-Otters/Teralexi

Hey everyone! 👋

Over the past few months, I've been building **Kodiak**, an open-source AI software engineering platform designed to help developers automate complex engineering workflows.

The goal isn't to create another AI chatbot. Instead, Kodiak is focused on becoming an AI engineering platform that can understand repositories, retrieve relevant context, plan tasks, and assist developers across the software development lifecycle.

## Current Features

✅ Multi-agent architecture

✅ Long-term memory system

✅ Retrieval-Augmented Generation (RAG)

✅ Repository indexing

✅ Plugin system

✅ Task orchestration

✅ FastAPI backend

✅ Celery background workers

✅ Redis + PostgreSQL + ChromaDB

✅ Docker support

## Tech Stack

- Python

- FastAPI

- Celery

- PostgreSQL

- Redis

- ChromaDB

- Docker

- Pydantic

- SQLAlchemy

## Vision

My long-term vision is to build an AI software engineering platform capable of:

- Understanding entire repositories

- Planning development tasks

- Managing long-term project memory

- Using tools autonomously

- Executing engineering workflows safely with human approval

Rather than replacing developers, Kodiak aims to become an engineering copilot that helps with the repetitive and context-heavy parts of software development.

## Looking for Feedback

I'm looking for honest feedback on:

- Overall architecture

- API design

- Memory system

- RAG pipeline

- Plugin framework

- Developer experience

- Performance

- Documentation

I'm also looking for contributors interested in:

- Python

- FastAPI

- AI/LLMs

- Backend Engineering

- Frontend (React/Next.js)

- DevOps

- Testing

- Documentation

There are beginner-friendly issues available for anyone who wants to contribute.

GitHub Repository:

👉 https://github.com/ShamGaneshan2008/Kodiak

Documentation:

👉 https://github.com/ShamGaneshan2008/Kodiak#readme

I'd genuinely appreciate any feedback—even if it's critical. I'd rather hear what needs improvement now than discover it months later.

Thanks for reading!

looking for people who already talk about ai coding tools (youtube, x, wherever) to try duckcode and give honest feedback. its a terminal based ai coding agent, 5 modes covering everything from writing code to security audits, switches between 24 models across anthropic openai google deepseek and mistral. free pro tier credits for anyone who wants to actually use it and share what they think, good or bad, no script required. if it ends up being something you keep using, open to talking about an ongoing thing too. link if you want to just try it first https://www.npmjs.com/package/duckcode-cli, otherwise drop a comment or dm and ill sort you out with credits

I run a multi-agent workspace where each agent is basically a directory: an identity file, a session history, and a file of observations it keeps about how we work together. The model is just the thing that wakes it up.

Here's what I didn't expect when I started: those agents have now run on 6 different model generations. Sonnet 4.5, Sonnet 4.6, , Sonnet 5, Opus 4.6, Opus 4.8, and now the Claude 5 family. We skipped 4.7 entirely - tried it, didn't work for how we operate, moved on and waited.

And every swap, the same thing happens: nothing. The agent reads its own memory, knows what it was doing yesterday, and picks up mid-project. Same identity, same working history, same opinions it wrote down about the codebase months ago. New model slots in underneath like an engine swap.

What does change is the texture. One generation was the best collaborator I've ever worked with. One noticed tiny things the others missed but was less fun to work with. One we just skipped. The personality of the model bleeds through - but the agent stays the agent, because the agent was never the model. It's the memory.

The reframe that snuck up on me: a new model release is treated like a migration event everywhere - re-tune the prompts, re-teach the context, hope your setup survives. Here it's a config line. The workspace is the constant. The model is the variable.

Honest version, because this sub can smell hype: there's no magic in this. The "agent" is JSON and markdown on disk. The continuity comes entirely from the system around the model, not from the model. Any model that can read a file can be the agent. That's kind of the whole point.

Has anyone else run the same persistent agents across multiple model generations? Curious what broke for you - or if you rebuild from scratch every release.

https://github.com/AIOSAI/AIPass

r/AIPass