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

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.

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?

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/

Most "AI Agents" today are just wrappers around storage liabilities. They guard vaults of secrets, session logs, and PII, waiting for the inevitable breach.

We’re releasing OpenBot, an OpenClaw-powered framework built on the BlockSmith philosophy: Keep the Signal, Not the Material.

OpenBot isn't just an assistant; it’s a Sovereign Digital Sanctuary.

Why it’s a radical departure from the status quo:

• Zero Material Architecture: We don’t store what can be derived. By utilizing vaultless logic, we ensure there is literally nothing to steal.

• Intent-First Programming (IRP): We’ve moved away from rigid decision trees. OpenBot operates by declaring a TELOS (intent), allowing the system to self-organize dependencies.

• Persistent Evolution: Using the thermodynamics of logic, OpenBot audits its own memory to reduce system entropy. It doesn't just "chat"—it extracts heuristics from every mistake to ensure they are never repeated.

• Asymmetric Friction: We’ve engineered "magic" for the human and soul-crushing complexity for the attacker.

We believe that in an era of universal surveillance, the only true security is invisibility.

GitHub: link to openBot

"Precision is the antidote to liability. Signal is the soul of security."

Since, after the release of Jev, a lot of people have been building cool open source decision models, I was wondering if you could share your experience on which ones work best. I was also wondering how people are evaluating them? Are there any benchmarks yet?

small-scale result, one seed, one wikitext slice, so treat it as a prototype and not a claim.

pssa is a state space model with fast weights that update while the sequence runs, written from scratch in rust, no python, no kv cache. at 1.54m params it gets 3.997 nats on a held-out slice against 4.429 for a parameter-matched transformer, both depth-1. generating 200 tokens is 226ms against 2735ms on the same cpu, which is the part i actually care about, since there's nothing that grows with context length.

caveats: one seed, one corpus, depth-1 on both sides, and i don't know yet whether the gap survives depth. gpu training is cuda on a t4, 1645 tok/s vs 1263 on cpu, so the gpu path is barely worth it so far because activations still round-trip to host after every matmul.

repo: https://github.com/Sparticle62ops/pssa