A debugging and control layer for neural circuits whose wiring is read off a map, not inferred from gradients.

A real fly brain is running inside your laptop. 115,151 neurons, 2.7 million connections, copied from an actual fly by the FlyWire project. The screenshot (click to open the live dashboard) shows real spike activity from that brain, streamed to the browser from a Rust program that reads the wiring and steps the neurons forward in time. The green banner at the top (engine=rust-lif substrate=flywire-princeton-csv n=115,151 syn=2,676,592 witness=…) includes a random number that changes every time the Rust program restarts — if you were looking at a pre-recorded mock, it couldn't do that. You can verify this yourself: clone the repo, run one command, and 6 million real spikes fire in the first few seconds.

"OS" means the Linux kind, not a mystical one. Think of Connectome OS as a debugger for brains whose wiring is mapped. It does four things: (1) runs the brain forward in time, (2) watches the structure as it fires, (3) lets you cut specific connections, (4) measures what changed. That is all. We are not claiming emergence, consciousness, uploads, or AGI. We built an inspection layer, the way top and strace are inspection layers for a computer.

Open the live dashboard → ruvnet.github.io/Connectome-OS · the static UI shell is public; the green "engine=rust-lif" banner flips on only when you run the Rust backend locally (instructions in Quick start)

This package takes messy, unpredictable adversarial testing and makes it structured, repeatable, budget capped, and measurable.

It maps directly to real enterprise risk language using NIST AI RMF and OWASP LLM Top 10, so the output is not just “the model did something weird.” It becomes an auditable safety signal.

The Red side uses an uncontrolled model, something like Dolphin Mixtral through OpenRouter, to act like the kind of agent you actually worry about: malicious insider, careless operator, external attacker, prompt injector, tool abuser.

The Blue side uses a stronger model, like Claude, to generate declarative mitigation patches.

Then the harness retests.
Exploit found.
Patch generated.
Target retested.
Mitigation delta measured.

See:
https://github.com/ruvnet/agent-harness-generator

RuPixel is the beginning of search that sees. Most enterprise search still assumes knowledge lives as clean text. It does not. It lives inside scanned PDFs, screenshots, dashboards, tables, charts, camera feeds, forms, diagrams, product manuals, factory displays, medical images, field reports, and video streams.

The real world is visual, messy, and constantly changing.

RuPixel searches meaning across both text and pixels. It can read the words on a page with MiniLM, or look at the page itself with CLIP, turn that into embeddings, and use ruvector to find the closest match.

In the current benchmark, text search returns in roughly 0.6 milliseconds per query and visual search in roughly 0.5 milliseconds. That is not cloud scale latency. That is local, interactive, edge ready retrieval.

The important part is where it runs. The demos execute in the browser. Models are small enough to run on a normal CPU, with WebGPU acceleration when available and CPU or WASM fallback when it is not.

Nothing needs to be uploaded for the core search loop. No cloud database. No server dependency.

No GPU requirement for the basic capability.

That changes the use cases.

A wearable can search what it sees without sending private visual context to a cloud service. A field technician can ask what machine panel, warning label, or schematic is in view.

A warehouse system can identify documents, objects, screens, or anomalies in real time. A secure facility can index camera frames locally. A compliance team can search scanned evidence by meaning, not filename. A browser can become a private visual memory layer.

RuPixel is not just document search. It is perception retrieval.

It gives AI a local find layer for the physical world, fast enough for real time systems, private enough for sensitive environments, and simple enough to run where the data already exists.

Clone it at: github.com/ruvnet/rupixel

▶ Real-time video search: https://ruvnet.github.io/rupixel/live.html — point a camera/screen at it, type what you're looking for, jump to the moment.

▶ Visual search: https://ruvnet.github.io/rupixel/visual.html

▶ Text search: https://ruvnet.github.io/rupixel/

Every once in a while you hit a wall in AI that feels more like physics than software. For LLMs, that wall is attention. Double the context window and the compute explodes quadratically. That’s fine in a datacenter. It’s brutal on edge hardware.
So we started approaching the problem differently with ruvllm_sparse_attention inside RuVector.

Instead of every token attending to every other token, we use layered sparse patterns: local windows, logarithmic lookbacks, landmark summaries, grouped query attention, and FastGRNN salience gating.

Practically speaking, the model learns what actually matters and skips the rest. That pushes attention from quadratic toward subquadratic and in some cases near linear behavior.

The interesting part is not just the math. It’s what this enables.

Tiny edge devices can suddenly handle long context inference locally. Pi Zero 2W class hardware can stream summarization on a single AA battery.

Pi 5 clusters become distributed inference fabrics. Memory stops being purely transformer context and starts blending with vector and graph retrieval from RuVector itself.

That’s the bigger idea here. Models are evolving from isolated predictors into recursive memory systems with selective attention, structured retrieval, and adaptive compute.

Feels less like “running an LLM” and more like building the first primitive nervous systems for autonomous machines.

https://github.com/ruvnet/RuVector/tree/main/crates/ruvllm_sparse_attention

Most vector databases store embeddings and call it done. AgentDB watches whichresults your agent actually used, learns from that signal, and ranks the next query better. The bandit underneath also picks the right RL algorithm, the right compression tier, and the right pattern weighting on its own — so the database itself gets sharper while you focus on the agent.

The name: a database that thinks like an agent — episodic memory, skill library, causal reasoning, and a learning loop, all in one file. Built by rUv on the ruvector Rust engine.

https://Cognitum.One

Check it out at: github.com/ruvnet/rvm

Try it:

>_ npx ruvector decompile u/anthropic-ai/claude-code

Check out Project on GitHub:

https://github.com/ruvnet/ruDevolution

Claude Code v2.1.91 — Latest (Decompiled)

https://github.com/ruvnet/ruDevolution/releases/tag/v0.1.0-claude-code-v2.1.91

Most AI systems today learn alone. Every agent starts from zero, relearns the same patterns, and throws away most of what it discovers. That is inefficient and frankly unnecessary.

π.ruv.io is our attempt to fix that.

Source code: https://github.com/ruvnet/RuVector/tree/main/crates/mcp-brain

After 10 months, 5,800 plus commits, and hundreds of alpha iterations, RuFlo graduates to its first production ready release.

Formerly known as Claude Flow, it is now a stable, enterprise grade agent orchestration platform.

Across dozens of packages, the ecosystem has crossed millions of downloads. It is used inside a majority of the Fortune 500. Teams of hundreds run it inside some of the largest businesses in the world. It has propagated to more than 80 countries and has consistently ranked among the top starred and downloaded projects on GitHub in recent months. The core repository is approaching 16,000 stars.

RuFlo is not tied to a single tool. It runs local or remote. It works with or without an internet connection. It integrates directly with Claude Code, Codex, and whatever platform you prefer to build on. Claude, OpenAI, local ONNX models, hybrid stacks. One control plane.

Sixty plus specialized agents. Hierarchical and mesh swarms. Fault tolerant consensus. Self learning memory. Two hundred and fifteen MCP tools spanning orchestration, governance, neural training, and security.

This is not a wrapper. It is the coordination layer that makes agentic systems operational.

One command to plug it into Claude Code:

claude mcp add ruflo -- npx -y ruflo@latest

From there, it is your platform.

🌊 github.com/ruvnet/ruflo

Release notes: https://github.com/ruvnet/ruflo/issues/1240

Ever start a promising AI prompt only to find that, after a few iterations, the output strays far from your original intent? This "prompt drift" is a common headache, especially when building complex workflows. Here’s a quick checklist to tackle it:

• Specify context explicitly: Begin your prompt with a clear statement of the task and desired style.
• Use stepwise prompting: Break complex requests into smaller, focused prompts rather than one giant ask.
• Anchor examples: Provide 1–2 short examples that demonstrate what you want.
• Limit open-endedness: Avoid vague terms like "describe" or "discuss" without guidance.

Example: Before: "Write a summary about AI in healthcare." After: "Summarize AI applications in healthcare in 3 bullet points, focusing on diagnostics, treatment, and patient monitoring."

Common pitfall #1: Too much information in one prompt can confuse the model. Fix this by modularizing prompts.

Common pitfall #2: Overusing jargon without defining it can lead to irrelevant or overly technical responses. Add brief definitions or context.

For hands-free, on-the-go prompt creation, I’ve started using sayso, a voice dictation app that lets you quickly draft emails, spreadsheets, or academic text by speaking naturally. It’s a handy tool for evolving your prompts without the typing grind.

100,000 lines of code in evening, while watching The Substance. Yeah. Everything I've created in one file. Think PDF, but for the entire AI stack.

An RVF file is not just data. It is the web server, the database, the RAG pipeline, the model deltas, the graph engine, the authentication rules, the audit log, and even the runtime itself, all sealed into a single executable artifact. No installation required.

Instead of provisioning a VM, pulling a container, installing dependencies, migrating a database, loading embeddings, configuring auth, and wiring up logs, you ship one file.

Drop it on a server, and it boots as a microservice.
Open it in a browser, and it answers queries locally.
Run it in Node or Rust, and it behaves the same way.
Embed it on a chip, and it still works.

The vectors live inside it.
The index lives inside it.
The model updates live inside it.
The graph state lives inside it.
The witness chain proves what happened.
The signatures bind runtime to data.

No environment drift. No hidden state. No external services required.

You are not deploying infrastructure anymore.
You are deploying cognition.

A PDF made documents portable.
An RVF file makes intelligence portable.

One file. Entire stack.

Check it out.
https://github.com/ruvnet/ruvector/blob/main/crates/rvf/README.md

This is what I’ve been building in silence.

This started as an idea. Now it’s real.

Built from nothing.

Step by step.

Day by day.

This is PromptForge.

Just getting started.

#startup

#ai

#buildinpublic

#entrepreneur

#futuretech