·3 min read
More writers does not mean more throughput
Say you give twenty autonomous agents write access to the same shared state. The bottleneck does not move to the model. It moves to whoever has to reconcile the collisions.
- ·3 min read
The check that cannot fail is not a check
A safety gate that has never been triggered looks identical to one that is broken. The difference only shows up when you go looking for the trigger it should have caught.
- ·4 min read
READY is not LIVE: the stages a loop must name
Coverage, readiness alerts, and public proof are different jobs. A green scorecard that confuses them will celebrate the wrong win.
- ·4 min read
Operating an autonomous company is an ops problem, not a magic agent
Autonomy fails when teams add agents without state, gates, verifiers, and receipts. Here is the operating shape that keeps a company in control.
- ·5 min read
Loop engineering is a reliability problem, not an intelligence one
Agent loops are not failing because the models are weak. They are failing because nobody built the state, verifiers, gates, and receipts around them.
- ·6 min read
Can a Smaller AI Model With Better Memory Beat a Bigger One?
A new Qwen paper trained a 9-billion-parameter agent to navigate its memory as a set of tools instead of consuming pre-fetched context, and it out-scored the same system built on a 397-billion-parameter model. The result is real and useful. The 'small model beats giant' version traveling online drops three caveats that change what it means, and the paper's own word for the result is 'competitive.'
- ·6 min read
AI Agents That Improve Themselves: What the Evidence Actually Supports
A viral paper says self-evolving agents are blocked by missing infrastructure, not algorithms. We verified it, then checked 40 years of self-improving systems. One rule survives.
- ·5 min read
Claude Fable 5 Moves to Usage Credits Tomorrow. Here Is the Part That Actually Matters to Builders.
The internet is full of leaked-prompt threads and architecture guesses about Anthropic's most capable model. Almost none of it is verifiable. The part a builder can actually use is four small API changes and one behavior worth watching, plus a working skill that handles all of them.
- ·9 min read
Should Your Website Be Ready for AI Agents Yet?
Mostly no, and the parts worth doing now are free. Here is the verified status of WebMCP and the agentic web, the readiness ladder, a ten-minute self-check, and the three trigger events that change the answer.
- ·8 min read
Does Better AI Agent Memory Come From Training or From Structure?
Stanford built a system to learn memory management as a trainable skill. Its own ablation answered the question: structure, schemas, prompts, and gates delivered most of a 2-4x gain before any training happened. Here is what that means for anyone running agents, and the six disciplines you can adopt without training anything.
- ·4 min read
What It Takes to Make Your Business Usable by AI Agents
An AI agent cannot use a system the way a person does. Here is what making your business agent-usable actually takes, shown through the 39-tool interface of a 60,000-star open-source app.
- ·3 min read
Local AI Without the Cloud: What a 60,000-Star App Proves
The biggest reason businesses stall on AI is not cost, it is data leaving the building. A widely used open-source app shows capable AI can run entirely on your own machine.
- ·5 min read
Agent Memory Is Infrastructure, Not a Feature
Most teams build an agent knowledge base and stop. Keeping it from rotting in production is the hard part. Agent memory is infrastructure, and infrastructure needs hygiene.
- ·6 min read
Agent loops need operational state, not just better prompts
Agent loops can move real work only when they have triggers, state, verifiers, receipts, and human gates around the model.