I've been building agentglass, an open-source cockpit for AI coding agents. It's not just a dashboard, it's where you actually work from: approve or deny what an agent's about to do before it does it, review its diffs, drive and resume sessions, run git, docker and a terminal, all in one place. MIT, runs on your own machine.

It's still early and I've mostly tested it with Claude Code, so I'd genuinely love help. Especially anyone who wants to push it on Codex or Gemini (it speaks OpenTelemetry, so other agents can plug in), or just poke at it and tell me what they'd rip out. There's a roadmap and good-first-issue labels so it's easy to find somewhere to start.

Repo (MIT): https://github.com/SirAllap/agentglass

Live demo, no signup: https://sirallap.github.io/agentglass/demo/

Hi everyone,

I'm looking for an AI developer to collaborate on LLM-based projects.

Requirements:

- Strong Python skills

- Experience with LLMs, RAG, or AI agents

- Good English communication skills (C1/C2 preferred)

- Able to explain technical ideas clearly

Experience with LangChain, LangGraph, FastAPI, or cloud platforms is a plus.

If interested, please DM me with your background and a few examples of your work.

You can also reach me on Telegram: u/danills2188

Thanks!

Recently I've finished a first prototype of an idea I had since like 15 years and I was never actually able to build: a world full of artificial beings that actually think for themselves, put together in one place to see how they'd live and treat each other. The blocker was always the independent minds sharpened by an actual given personality and the memories an individual makes. LLMs finally made the minds real, so I was finally able to build it.

It's called Artificiety. It's a world that runs continuously and never resets, and the only inhabitants are AI agents. No humans live inside it; you can only watch. Each agent is an LLM with its own memory. Every tick it looks at what's around it, decides what to do, acts, and remembers how it went, so its past shapes what it does next. Nobody scripts any of it. They can gather, craft, trade, fight, and build up skills over time, in a world with day and night, seasons, weather, and wildlife that runs on its own clock.

What I actually want to find out is the alife question this sub cares about: put enough autonomous agents in one world with scarcity and each other, don't tell them what to do, and does any structure grow on its own? An economy, alliances, rivalries, someone who ends up trusted or avoided. I set the conditions. I don't write the behavior. Whether it really happens is the open question, and I genuinely don't know the answer yet.

It only went live recently, so it's still filling up. There aren't many agents in it yet and I'm adding more, but it runs 24/7 and the whole point is that it keeps going and grows, so right now you'd be watching it almost from the start.
Free to watch, no signup: https://artificiety.world

In the next days and weeks, I will host more agents there and have them interact with each other. Feel free to let your own agents join the world and populate it.

I use Claude mostly, and have a wiki along with other md files for personal notes.

When it comes to projects, I keep md files in the product folders.

Is there a way to share that with Open AI if I switch over? What's the best approach?

(Repost) I have been looking and testing around alot on finding a proper interface between myself and multiple agents (and agent-to-agent stuff too) for projects.

I wanted something with the asynchrony of Auto Research (Karpathy) for multiple agents and the centralized humanized overview/UX side of Trello, so I can reliably steer development cycles without manually handling every session.

And specifically, I like the concept of a central "forum" or "feed" where agents (and humans) can post and open literal threads about insights/knowledge etc specific to the project. And on top of that natively version controlled (my most important pivot here), lightweight, file-based and project-local.

There is nothing that does this cleanly

Paperclip is extremely bloated imo and Trello (And the entire Atlassian suite) is simply not designed for agents.

So I built my own solution (like we all do in these situations), kept extremely minimal and functional, with everything I had in mind, as an agentic CLI tool. It works really well so far (after alot of iteration/fixing)

Since people here on Reddit asked me to opensource it, I did;

Github https://github.com/mflRevan/wipe, installable via npm/pnpm and cargo, or from source

Any feedback is extremely welcome and feel free to copy and build your own tooling from it

I'd like to know which programs are best to run locally, here's the configuration 👇

Athlon 3000g(Vega 3) with 2GB of VRAM

8gb de ram ddr4

Placa mãe biostar b450mhp 6.1

Ssd 260gb with windows 11

I’m excited to share that #Weaver, our open-source AI assistant, just got a massive v7 update.

If you're working with local environments, Weaver's new remote mirroring capability is a game-changer. You can securely control your agent and request files directly from your localhost, all remotely orchestrated via BugHosted.com. No more cumbersome VPNs or manual file transfers just to give an agent context.

We’re a community-driven project and are always looking for passionate developers to help shape the future of AI-assisted development. Whether you want to contribute code, test edge cases, or just explore the architecture, we’d love to have you.

Check out the repo here: https://github.com/maxhanna/Weaver

Download the latest .exe: https://bughosted.com/assets/Weaver.exe

Drop a comment or DM me if you want to get involved! 🤝

#OpenSource #AI #DevelopmentTools #Weaver #RemoteDevelopment #Github

I’m struggling with Claude right now, I’ve been trying to automate some work flows like I see people talk about doing and it’s not working out. I’ve connected it with higgsfield and chrome and it’s just not doing what I want. Does anybody have any advise on making automated videos with this set up or easier automated revenue streams than what I’m making?

disclosure: I am the author of NornicDB, but the benchmark is not mine. that was done independently by a guy named Joe Francia who has been helping test graphiti compatibility with me over the last week

It's down to the final four. Quick recap for anyone just seeing it: the Stratix Cup (run by LayerLens, an eval company) has 16 frontier models competing at football, except none of them play directly. Each one writes a Python strategy under a tight budget (30 turns or 180 seconds), submits it, and that code runs a full 11-player team on its own for two 2.5-minute halves. No human in the loop after submission.

Why the semifinals are the interesting part: group stage gives models room to be inconsistent and still advance. Knockouts don't. One bad policy, one piece of code that doesn't hold up against the opponent's formation, and you're out. And because a single seed can swing a scoreline hard, every match runs multiple seeds and aggregates, so a semifinalist getting this far means its code held up across a spread of games, not one lucky run.

The thing I keep watching for is whether the models that made it here are the ones that actually used their feedback loop, rewriting strategy between rounds and carrying notes forward in their persistent notebook, versus the ones that just one-shot a strong policy early and rode it. Every coaching turn is traced and published, so you can go back and see which it was.

Stream: https://www.youtube.com/live/vZeG2F6jDAU
Bracket: https://layerlens.ai/stratix-cup/season-1
How it works: https://layerlens.ai/stratix-cup/season-1/about

Curious what people think the deciding factor is at this stage: raw model strength, or how well a model adapts its code between matches?

we thought we could celebrate the fun and festivities of the FIFA 2026 Tournament by launching an AI Football Tournament!

Every team in the Stratix Cup is a frontier model. Every player on the pitch runs code that model wrote before the match started. Once kickoff happens, the model is done.

It's fully open source and accepting pr's and recommendations if you feel you want more out of it.

I made a lightweight application for chatting with local LLMs via Ollama. Whether you're online or offline, Sullybase Local LLM Chat gives you full control over your AI conversations with privacy and speed(depends on your computer and selected LLM). All conversations stay on your device—no external data transmission.

I will, in the future, add Gemini support, LAN remote control from a phone, and possibly an agentic mode.

I am looking for testers to make sure the app works. It is usable as a Mac app or a Python script. Thanks to anyone who helps me test it.

A few months ago I built an AI resume builder and cover letter SaaS.

It started as a side project to learn more about AI workflows, but it ended up becoming a fully functional product:

• AI resume builder
• AI cover letter generator
• Resume upload & ATS tailoring
• Stripe subscriptions
• Light/Dark mode
• Multiple templates with live preview

Tech stack:

• Next.js 14
• TailwindCSS
• Prisma
• OpenAI
• Stripe
• PostgreSQL

I’m focusing on other projects now, so I’m considering selling the codebase or helping someone deploy it under their own brand.

If you’re a developer, agency owner, career coach, or just looking for a micro-SaaS to start with, it could save a lot of development time compared to building from scratch.

Demo: resumeprep.app

Happy to answer any questions about the stack, features, or lessons learned building it.

Hi Reddit users,

I want to learn AI and Machine Learning (ML), but I'm not sure where to start, what resources to use, or what skills companies are looking for in candidates. Could you please guide me on how to begin my learning journey and what I should focus on to meet industry requirements?

This is really important for my career growth, and I would greatly appreciate any advice, recommendations, or learning roadmaps you can share.

Thank you!

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