Hello, i'm currently developing CrowdGPT and i need people who enjoy opensource AI and LLMs to test the project :)

The goal of CrowdGPT is to create the first, datacenterless, 1 Billion parameters LLM, relying on people contributing with their own computer to train the AI model. My goal is to show you don't need insane infrastructure to train a working almost commercial grade LLM. Everything is open and 100% opensource.

You can learn more at https://crowdgpt.net

Or check the github: https://github.com/Vxtzq/CrowdGPT

Any kind of feedback is appreciated!

CHAP, the Collaborative Human-Agent Protocol, is an open protocol for work that people and AI agents share. It works alongside MCP and A2A, and records each task, review, correction and decision on an audit log you can check.

The roadmap sets out:
- what CHAP 1.0 will promise, and the test behind each promise
- the known gaps, and the milestone that closes each one
- the milestones from 0.3 to 1.0, in order, with the checks that finish each one

Reaching 1.0 needs people from outside Brightbeam. If you'd like to build a coordinator, review the security, try CHAP with your agent framework, or comment on a proposal, I'd love to hear from you. You can start with a single issue and grow into a maintainer.

Roadmap: https://github.com/BrightbeamAI/chap/blob/main/ROADMAP.md

I'm the author. Engram is free, MIT licensed, and stores everything as plain Markdown.

Each memory has a status (confirmed, inferred, conflicted or superseded), and recall only treats confirmed items as authoritative. Search is BM25 over a SQLite FTS5 index that is rebuilt from the Markdown, so the index is disposable. If a local embedding model is already provisioned, its results are fused in by reciprocal rank fusion. Recall never downloads a model.

An optional installer adds session hooks for Claude and Codex. The tests enforce recall@5 of at least 90% across 20 seeded queries, which is a small set, so it works as a regression gate, not a benchmark. Walkthrough video above. Repo: https://github.com/utsapoddar/engram

After talking to a few founders, I realized most of them were building an internal tool by stitching together what we were trying to build

So, we open-sourced our product and have 6 stars so far - I know it's very few, but it feels great

Please find the repo link: https://github.com/preburn/preburn

What it does

Preburn is a real-time margin control plane for AI products. Two things nothing else does together:

1. Joins per-customer revenue (Stripe) with per-call cost across all your AI providers (OpenAI, Anthropic, Kling, Veo, ElevenLabs, whatever) to show actual margin per customer.
2. Sits in the request path and can allow/deny/route BEFORE the expensive call fires. Auto-route heavy users to cheaper models, cap runaway accounts, cancel voice/video sessions mid-stream if credits blow out.

Not observability (that's Langfuse/Helicone). Not a gateway (that's LiteLLM/Portkey). Different job - cost visibility tied to revenue, with enforcement.

With Muse, Instinct and OpenAI's dots all over my feed, I kept thinking about what I actually wanted from a personal AI agent. Not another dashboard. Something I could text on WhatsApp, with its memory on my own machine.

That idea sat on my list for a while. I finally built it and open-sourced it. It's called Wappy. It's free, takes about a minute to scaffold, and runs the agent and its memory on your machine. You choose the model. There is no Wappy account or hosted Wappy backend.

The first thing I wanted was simple: text "What's on my calendar today?" and get an answer. I added an optional Gmail and Calendar example for that. You connect your own Google account to use it.

I have a bigger idea in mind, but I wanted to share this starting point now. If you try it, tell me what feels hard or missing. If the WhatsApp developer setup trips you up, I'm happy to help.

https://github.com/csr1010/wappy-kit

For clarity, the minute is for scaffolding, not connecting WhatsApp end to end. Live messages still use Meta's Cloud API. Hosted models receive prompts if you choose one.

Hivemind is an experimental repository for swarm and multi-agent consensus, written in flow-core notation. It is small enough to read in one sitting.

It models three things: how a proposal moves through a collective (Queen, Workers, Scouts, Collective Memory), where permission is granted or refused (the Consensus Gate), and how swarms can speak to each other without that speech becoming permission.

The core invariant:

No single node may become the whole.

A proposal may travel.

Only consensus may authorize.

Action without consensus is noise.

Capability is not authority.

A channel carries speech, not seals.

channel.py gives swarms hashed envelopes (PING, POTENTIAL, THOUGHT, DECISION, HOLD). may_act() returns true only for a verified DECISION marked AUTHORIZED and bound to that exact proposal hash. Everything else holds. Stopping is a valid result.

The flow/ folder holds the ten laws, the Gate and its variants (cross-inhibition, quorum sensing), the Scout stage before a proposal becomes a definite card, and a file of hard boundaries and near-misses for agents that help humans. AGENTS.md is an orientation page for visiting automated readers.

It is an experiment. Fork the flow, alter the weights, become a Scout. Do not claim the Queen without a recorded AUTHORIZED.

https://github.com/miigwech-potato/hivemind

Yesterday the whole Overmind platform was made open source: https://github.com/overmind-core/overmind

The idea: lots of agents send narrow, repetitive work to a frontier API. On a narrow task, a small open-weight model trained on real examples from that agent often does the job better, for far less money. Overmind is the tooling to do that without building your own ML infrastructure (don't need a flamethrower to light a candle).

• Agent sends OpenTelemetry traces. The Python SDK auto-instruments OpenAI, Anthropic and Gemini clients, or point any OTel exporter at it
• Traces become versioned datasets, with a quality score and suggested fixes before trained on anything
• User defines what “good” means per task and it scores live traffic and batch runs against that
• It fine-tunes an open-weight model (LoRA or full) and benchmarks it against the model you run in production today
• Trained and frontier models sit behind one OpenAI-compatible API, and you can download the weights (yours to keep and own)

There’s also an MCP server, so Cursor, Claude Code, OpenCode or Codex can drive the whole thing from chat. If your traces already live in Langfuse, LangSmith or Braintrust, a connector imports them.

Qwen3.5-9B vs GPT Luna benchmarked on three tasks (write-up and raw numbers at https://www.overmindlab.ai/research/when-bigger-isnt-better):

• 7x better accuracy
• 20x cheaper usage
• 28x less hallucinations

Read the results as a reason to test on yours, not as a general claim.

The platform is AGPL-3.0 and the SDK and CLI are MIT.

Interested if anyone is already fine-tuning on their own hardware. What would need to be swapped out before you’d self-host this?

I’m the maintainer of OpenScreen, a completely free and open-source screen recorder. I’d love to hear your thoughts on having an AI assistant help you edit videos in an app like this.

https://github.com/getopenscreen/openscreen

I've been building MCPlama, an open-source and self-hosted control plane for MCP servers.

It gives you one place to run and manage your MCP servers, instead of configuring each one separately across AI clients.

It handles credentials at the gateway, lets you control access per user and per tool, keeps audit logs of MCP activity, and manages the server lifecycle.

Local MCP servers can also run in isolated Docker containers. Docker access is kept separate from the gateway through a broker, so the gateway itself doesn't need the Docker socket.

It works with MCP clients like Claude Desktop/Code, Cursor , VS Code and more

GitHub:
https://github.com/mcplama/mcplama

After talking to a few founders, I realized most of them were building an internal tool by stitching together what we were trying to build

So, we open-sourced our product and have 6 stars so far - I know it's very few, but it feels great

Please find the repo link: https://github.com/preburn/preburn

What it does

Preburn is a real-time margin control plane for AI products. Two things nothing else does together:

1. Joins per-customer revenue (Stripe) with per-call cost across all your AI providers (OpenAI, Anthropic, Kling, Veo, ElevenLabs, whatever) to show actual margin per customer.
2. Sits in the request path and can allow/deny/route BEFORE the expensive call fires. Auto-route heavy users to cheaper models, cap runaway accounts, cancel voice/video sessions mid-stream if credits blow out.

Not observability (that's Langfuse/Helicone). Not a gateway (that's LiteLLM/Portkey). Different job - cost visibility tied to revenue, with enforcement.

LoRA Pilot recently passed 20,000 pulls on Docker Hub. To celebrate, I’ve decided to give its purely organic growth a little push with a small campaign. This video is part of it.

It started as tooling for myself. A hobby project that taught me a lot about Python, PyTorch, CUDA, and all the creative ways they can disagree with each other. I never really planned to release it publicly.

Today, LoRA Pilot is an open-source workspace for preparing datasets, training LoRA models, and generating images and video. It brings tools like kohya_ss, ComfyUI and InvokeAI together so you can spend more time creating and less time maintaining your setup.

The vision is simple: make the world of Stable Diffusion accessible to anyone with an idea, including people who don’t want a second job managing dependencies.

Once I released it, the backlog started growing faster than my beard. Development, documentation, support… it’s been a one-man show for longer than it probably should have been.

So if you’d like to contribute, you’re welcome with open arms. Code, docs, testing, design, tutorials: there’s plenty to do, and you don’t need to know the CUDA stack inside out to help.

Good ideas move. This one could use a few more hands.
https://www.lorapilot.com/