Jev Founder, Diogo Amogo, just released 12-page PDF on building a Jev Harness for coding agents this is a 10-step blueprint on how to make your coding agents 200× faster and 400× cheaper: step 1 → meet Jev: the LLM writes, the harness executes, Jev decides what each turn sees, where it routes and whether it runs step 2 → ask the question that breaks every agent: how would you design one if LLMs had no KV cache? step 3 → stop routing blind: Opus → Sonnet → Opus costs 6.19 vs 4.15 for pure Opus, because handing back reprocesses the whole context step 4 → follow the tokens: reading and searching take 56.2% of tool turns and 46.5% of tokens. Writing code is under 10% step 5 → score every chunk per query: hide, short summary, long summary or full. Compress after the question, not before step 6 → disclose tools in tiers: one-line snippets for 100s of tools, schema on demand, docs for one-off queries. Batteries stop costing context step 7 → load instructions by condition: touching *.tsx loads the style guide, billing/ loads its gotchas file, and compaction can't erase either step 8 → route by trust, not just difficulty: secrets and infra stay on first-party frontier models, public docs go to the cheapest one step 9 → share one retrieval pass: cross-model review, eval generation, ELI5 explainers and live progress pages all run read-only in the background step 10 → gate every command: programmable allow / ask / deny policies that read a script before it runs, not just its name Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster.

A senior Google engineer just dropped a 19-page PDF on "Loop Engineering" for LLM and agentic systems. Act → Observe → Learn → Repeat • Act: the LLM proposes a code transformation (tile this loop, parallelize that one). • Observe: a compiler runs it and reports back - is it valid? faster? slower? by how much? • Learn: the LLM reads that feedback and adjusts its next move. • Repeat until it stops finding improvements. The agent gets smarter purely from grounded feedback inside its own context window. This 19-page PDF totally changed the way I’m building agentic systems today. Read it now, then explore the article below.

Andrew Ng: "100% of my tasks are now done by AI agents - hype has exceeded my expectations. Loops is next step. in 3-6 months, everyone will be using self-improving loops. No more prompting." In a 30-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch. Worth more than a $500 agentic course.