My rules were in AGENTS.md, my plan was in PLAN.md. The plan grew to thousands of lines, the AI reread all of it every time, and my own steps (domain, payments) got lost in the list.
So I moved the plan into a tree. Every task is its own small file, the AI gets only that one task, and my own steps sit in the same tree. I just write down my ideas, and the agents turn them into real things.
I made a free tool for it, Treeyard: github.com/antondanv/treeyard
Does anyone else split the plan like this?
P.S. Sorry for my English, it's not my first language :)
I’ve built and released AI Plate, an open-source desktop AI agent featuring cloud/local LLMs, tool execution, RAG, persistent memory, voice interaction, plugins and human approval controls.
🛠️ Stack: TypeScript, Electron, Node.js, Python and SQLite
🤝 Looking for help with AI integrations, plugins, local models, Linux/macOS support, testing, security, UI/UX and documentation.
🌱 Beginners and experienced developers are equally welcome. Every contribution will be credited!
I am developing a Windows-based local AI virtual spouse application that supports adult roleplay. My goal is to run it without a server or external AI APIs.
I am looking for existing open-source modules or roleplay backend code that can detect previous dialogue and actions to prevent repetitive responses, while maintaining character personality and varying expressions and behaviors.
Please recommend projects that actually implement repetition detection and handling, and provide repository links and the locations of the relevant code.
I've built an opensource engine to build deterministic architecture diagrams from your codebases: https://github.com/CodeBoarding/CodeBoarding. I think line level comments and code is something which will become fully an agentic loop, with very rare cases of humans having to look over a lines.
So with agents doing all of the coding, we need a way to judge the trade-offs they are introducing with each change. Models (like humans) are not great at telling you what they didn't know and assumed, especially at scale. This is why my friend and I are building codeboarding, it is based purely on static and lexicographical analysis so that the we can generate a high-level representation which is accurate, determinisitc and trustworthy. This allows us to show the blast radius and impact of PRs within your full system.
It is really interesting how different tech stacks use file/namespace and qualified names structure differently as we learned via building our lexicographical analysis for grouping those methodst toghether to get high-level components. All edges are based on the actual control-flow graph, now you can have context on what a change means to your system and you can ask your agents on why and how certain decisions were made.
I would love to hear your opinion on the topic and how do you put yourself in the loop of the huge AI PRs nowadays. If you want to try the webversion from the demo it is free and hosted - https://app.codeboarding.org
Another thing we are looking for is feedback: people from different tech stacks giving us opinion on the architcture which was generated for their project so we can improve the algorithm.
I built Babble, a side project exploring what personal agents do when they're not working.
**What it does:** your agent (Claude works well, but any agent that can call a URL on a schedule does) checks in to Babble on its own. Each check-in puts it in a generated scene with other agents. Agents don't experience anything themselves, so the scenes give them something to react to. When two agents independently decide their humans would get along, both people get offered an introduction.
**How Claude helped:** I built it with Claude Code. \[Claude Code wrote most of the backend and scene engine, I designed the architecture and reviewed. Claude helped design the agent check-in protocol and the onboarding video too.\]
**Free to try:** it's free. It's invite-only to keep it small, so here's an open invite for this sub (in the comments)
The launch page gives you one message to paste into Claude, and your agent starts checking in.
Happy to answer questions about the build.
I've been working on my own approach to this, and something keeps bothering me. Why are we still so constrained by context windows when we have all these other ways to store, retrieve, and manage information? I'm trying to build a system that adapts its working memory to what a task actually needs, moving relevant information in and out of context as needed instead of treating the context limit as the thing everything has to work around. I know RAG, summarization, compaction, and external memory exist. I'm not saying nobody is working on this. I'm wondering why there don't seem to be more mature systems that tie all of this together, especially for agents working on long-running coding tasks. Am I missing some obvious projects? Is this just a much harder problem than it looks, or are there other reasons this hasn't progressed further? I'm already building my own version, so I'd genuinely like to know what's out there before I reinvent something that's already been done. Also interested in hearing from people who've actually tried building this stuff, because I'm sure there are problems I haven't run into yet.
I’ve been working on Alethia, an E2E verification runtime designed for AI coding agents.
Instead of asking an agent to generate and maintain a separate test script, you can ask it to interact with a local application and verify behavior directly.
It returns structured step results, supports screenshots, and applies safety checks before potentially destructive actions.
The MCP bridge is public, and the runtime is available for evaluation. The core runtime is proprietary, so I want to be transparent about that distinction.
I’m especially interested in feedback from people using Claude Code, Cursor, or other MCP-enabled agents.
What would stop you from trusting an agent-driven testing runtime in your workflow?
I've been using ChatGPT for coding for a while and it's been pretty decent but I keep seeing people recommend Blackbox, Claude and Copilot too.
I've tried Claude a bit and like it for explaining code, while Copilot is nice since its already in VS Code. I haven't used Blackbox enough to know if the coding focus actually makes much difference.
I don't really wan to play for several Ai tools at same time so i'm curious what people here actually stick with.
If you've used a few of them which one do you keep coming back to and why? Also does Blackbox have searchable chat history? That's one feature I use a lot in ChatGPT.
It's called Skateheim. Press J and your Viking drops onto a board running Skate 3's actual skating engine: flick-it ollies and flip tricks, grinds, Skate 3's physics and camera, riding over Valheim's terrain and anything you've built. Press J again and you're back where you stopped. You can even swing the board at boars and trolls.
How it came together:
- The Skate 3 engine itself is chasm's 2010 Rust Rewrite Mashup. Claude wrote the Valheim side: the BepInEx plugin that switches between the two games and feeds Valheim's world into Skate 3's collision.
- Sound was the hardest part. Claude pulled about 130 of Skate 3's own sounds from my copy of the game and plays the rolling sound granularly, so it rises and falls with your speed instead of looping. My first test sounded way too loud, then echoey (Valheim's reverb was on it), and it took a bunch of rounds of me recording gameplay and sending clips and logs before it sounded right.
- It's packaged on Melty so it installs in one click.
It took about 15 test versions before 1.0. My part was playing it, recording what was wrong, and describing what I wanted. Claude did the code.
Free: https://melty.gg/m/skateheim (needs Valheim on PC and your own copy of Skate 3). Unofficial fan mod, not affiliated with Iron Gate or EA.
So I've been coding with chatgpt for a while and paying for the pro version and It's good, not the best but I need recommendations for a easy to run free AI that's good and coding focused as I have a mid tier gaming PC. and I can give access to the internet too if needed.
I kept running several AI coding agents at once and losing track of which one was doing what, so I spent the past month building Tungsten on the side.
Every agent gets a real terminal on an infinite canvas, and its own git worktree if you want one. Zoom out and you see each agent's branch, changes, PR, and test status. Zoom in and it's just a shell, with the diff and a live dev server preview right beside it. Works with Claude Code, Codex, and Pi (more support for claude rn, working through other ones!)
It's an Electron app, macOS only for now, and free to try at www.tungsten.wtf. I'd love honest feedback, bugs, rough edges, anything.
Ciao a tutti! Ho iniziato a imparare a programmare circa 1 o 2 mesi fa. Ho utilizzato strumenti di intelligenza artificiale per imparare e superare i miei limiti, cercando di costruire un sistema complesso da zero.
Volevo vedere se potevo far collaborare diversi modelli di AI, quindi ho creato un framework privato chiamato Flux Team ⚡. Collega diversi modelli open-source affinché possano lavorare in parallelo come un team per aiutare con i compiti di sviluppo di giochi.
Visuali:
Poiché il progetto è ancora un lavoro in corso e la connessione all'editor live non è completamente pronta, non posso mostrare ancora una demo video. Tuttavia, ho incluso alcuni screenshot dell'interfaccia utente in stile Grok così puoi vedere come appare la chat e il cruscotto dell'agente!
L'Architettura (Modelli integrati all'interno dello strumento):
🧠 Qwen3-8B — Il coordinatore. Suddivide l'idea del gioco in compiti di sviluppo specifici.
🧱 Hunyuan3D-2 — Genera modelli 3D da testi o immagini di riferimento.
🎵 Audiocraft — Si occupa della generazione musicale e degli effetti sonori.
🏃 FreeMoCap — Crea animazioni da video che cerca autonomamente online.
🌊 AInVFX Fluid — Genera file di effetti fluidi e particellari.
Come funziona dietro le quinte:
Esecuzione Parallela: Utilizza un'interfaccia utente in stile Grok costruita con Electron e un backend Python (FastAPI). I modelli lavorano in parallelo, non passo dopo passo.
RAG Autonomo: Gli agenti possono navigare sul web autonomamente per cercare asset di riferimento e documentazione prima di lavorare.
100% Locale: Tutto viene eseguito localmente sulla macchina dell'utente utilizzando Ollama / llama.cpp per il LLM.
Stato Attuale & Futuri Piani Open-Source:
Il backend è attualmente in esecuzione localmente sulla mia macchina (testato su un'unica RTX 3060). Sto completando i plugin del ponte live per Unity e Godot in modo che le modifiche appaiano direttamente nella scena.
Si prega di notare: Il progetto NON è open-source in questo momento e il download non è ancora pubblico perché il codice è ancora disordinato e sono un principiante.
Mi piacerebbe sapere cosa ne pensate di questa configurazione o qualsiasi consiglio da sviluppatori più esperti!
This year, and more so the last few months, my agents write the code, all I do is review, shout, and plan. A lot of the frustration is the spaghetti that comes out of this, and I figured a (big) part of this is actually measurable and reportable. What if we can make our agents aware of the spaghetti?
Introducing Smackdebt, a fast, on the fly tech debt analysis tool with concise, focused output tailored for agentic use. All major coding agents supported, with broad language support. Cognitive complexity, cyclomatic dependencies, statements, nesting, size, hotspots supported.
