This morning I published The Framebuffer People, about cloning every OS posted to r/osdev and finding a pile of QEMU-shaped Linux reskins with Patreon buttons. The thread had two kinds of replies. The loud kind called me a gatekeeper. The quiet kind asked a real question: “Another way to look at it. Give me a suggestion. What do I build?”

In the thread I answered that I can’t answer, and that is half true. Nobody can tell you which part of an OS will grab you. But there is a path that produces understanding and a path that produces a README with a roadmap hallucinated by a model. Here is the first path, written down properly. — Read More

Every week while researching open-source projects, I notice a pattern. The repositories attracting the most attention aren’t necessarily the biggest.

They’re the ones solving real developer problems.

This week’s repositories focus on five different challenges modern AI engineers face:

— Learning from real-world LLM applications
— Managing local LLM infrastructure
— Improving AI coding quality
— Building AI coworkers
— Creating agent-ready workspaces

— Read More

The Model Context Protocol has had a fast eighteen months. Since Anthropic open-sourced it in late 2024, it has gone from a spec nobody had heard of to the most widely adopted protocol for connecting AI agents to external tools, APIs, and data sources, with over 150 organizations building on it and native support across three major cloud platforms.

On July 28, the MCP project releases the most significant revision to the MCP specification since launch, and the headline change is one that teams running MCP in production have been waiting for: MCP is going stateless.

If you are building agents that connect through MCP clients, building MCP servers, or both, here is what changes, why the old model was a headache in production, and what MCP replacing its proprietary logging channel with OpenTelemetry means for observability. — Read More

Today, we are releasing Inkling-Small, an efficient open-weights model that achieves comparable performance to Inkling at a quarter of its size.

Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort, a context window of up to 1M tokens, and well-rounded performance across a range of benchmarks. — Read More

Cognition + Obsidian + Git — A self-evolving second brain powered by AI agents, markdown files, and version control. No database, no vendor lock-in — just .md files that think.

COG is an open-source ai agents skill built by Huy Tieu for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT. IT learns your patterns and organizes itself offering custom workflows for braindumps, daily briefs andcompetitive intel. — Read More

Data integration connects systems and moves data between them. Workflow orchestration coordinates what runs, in what order, and what happens when a step fails. The data integration vs workflow orchestration split is simple to state: connecting systems is not the same as coordinating the work that runs across them.

… The connectors serve different jobs. Integration uses them to move and reshape data. Orchestration uses them to trigger and coordinate tasks, then to recover when one fails. Same plugin to one system, two different reasons to call it.

This post draws the line between data integration and workflow orchestration, shows where the two overlap, and explains why one control plane across categories is becoming the way enterprises manage both. — Read More

You’ve decided to step up your logging game and start sending more valuable, structured logs that you can query, aggregate, and use for debugging in production. Go, you!

…We’re not going to spend much time on what you should log. We’ve covered that already, a few times before.

…[W]e treat logs like real application data that we need to be able to search, filter, and aggregate to understand broad trends and debug specific incidents. — Read More

Large language models are often thought of like functions: send in some text, receive some text. That is a useful abstraction, but it ignores one of the most important parts of running a coding agent: most of the input is the same as last time. In other words we mostly append to it.

A coding agent sends the model its system prompt, tool definitions, project instructions, conversation history, tool calls, and tool results. On the next turn it sends almost all of that again, plus a small amount of new material. Once a session has grown to tens or hundreds of thousands of tokens, recomputing the whole prompt for every turn is slow and expensive.

Prompt caching is what makes this somewhat economic, but it is also quite fragile. A changed tool definition, a model switch or a provider routing decision can turn what one would expect to be a cheap incremental request into a full replay of the context. — Read More

Earlier this year, we ran experiments to test the limits of scaling agents to cooperate toward a goal. Our hypothesis was that this would unlock a new tier of task scale and complexity.

That work was deliberately empirical. …Our initial results have been promising. … Descriptions of large tasks naturally take the shape of trees, with a goal at the root that subdivides recursively into basic units of work. … The design is a superset of more rigid orchestration systems. Rather than imposing a fixed topology on the problem, the swarm’s shape grows to cover the problem’s contours, and compute and context scale in proportion to the task’s complexity.

We think this is why the design generalizes to tasks as diverse as building a browser, solving math problems, and optimizing GPU kernels. We’ve also used it internally to find and fix vulnerabilities in open-source software, raise test coverage on our own codebase, and generate billions of tokens of synthetic training data. — Read More

Every AI coding setup right now assumes the same thing: a data center somewhere, a meter running, your source code flying off to someone else’s servers.

Ornith doesn’t.

Ornith is an open, agentic coding model that runs on your machine — and I mean a modest machine. — Read More