10 results
Three OpenAI engineers, five months, one million lines of AI-generated code, zero hand-written. The real value of this experiment isn't the numbers — it's the proof that Harness design can be engineered. Five concrete practices: making the app legible to agents, treating the repo as the source of truth, mechanizing architectural constraints, rewriting merge philosophy, and background entropy management.
Distilling Harness Engineering from concept and benchmark case into something you can start executing today: the four fixed failure modes of Agents, the 40% context sweet spot, the four-pillar framework the industry has converged on, and a three-phase roadmap from 'this afternoon' to 'fully automated in two weeks' — closing with six industry consensus points and three still-unsolved problems.
Headroom compresses tool outputs, logs, and RAG chunks by 60–95% locally, before the request reaches your provider. The part worth stealing isn't the ratio — it's how it decides whether to compress at all using 'mask extraction + cache-mutation economics' — plus a reminder that its docs run ahead of its code.
Two 2026 products answering the same question: when a single model hits its ceiling, don't pick one model — orchestrate a pool of them behind one API. Fugu bets on learned coordination; Fusion bets on parallel deliberation plus a judge.
When an agent loads tools on demand, it's essentially applying RAG's 'retrieve-then-inject' pattern to tool schemas — the only difference is the retriever is the LLM's own reasoning instead of vector similarity.
Research shows AI agents communicating through emergent compressed languages outperform those using natural language — shorter, more compute-efficient, but less interpretable
Loop Engineering replaces you as the person who prompts the agent. You design the system that does it instead.
Cursor is an AI-powered code editor by Anysphere, built by four MIT graduates, that hit $500M ARR within two years of launch. This article distills the real engineering lessons they've shared publicly: why they forked VSCode instead of building an extension, how Tab prediction's latency engineering works, and the hard production lessons from shipping Agent Mode.
LLM output quality is determined at three distinct layers: token-level decoding strategy, task-level workflow design, and model-level reasoning capability. Knowing which layer your problem lives in is the fastest path to fixing it.
Starting from the DIY-setup craze that OpenClaw ignited, this piece unpacks the technical evolution behind the Agentic Social Network: the three-layer architecture of Model Router, Memory OS, and protocols, plus the key mechanisms driving Agents toward long-horizon companionship, proactive action, and self-evolution.