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someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety...

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Rahul

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65,842 views • 3 months ago •via X (Twitter)

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Fable 5 dropping again is the biggest AI moment of the year. It’s also a usage limit trap if you don’t know what you’re doing. I just mapped out the full system to run it at 10x lower cost: - 10-80-10 framework - Model routing table - Loop engineering - Codex setup - Context memory - And every mistake that burns your limit in the first hour Don’t open Fable again until you read this.
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holy f*ck, someone just dropped an entire reverse engineering degree on GitHub for free. Now you can build agents that can reverse engineer almost anything: 1. Ghidra How to read any binary. The NSA’s reverse engineering framework. Where almost everyone starts. 2. radare2 The same skill, but in the terminal. UNIX-style reverse engineering framework and command-line toolset. 3. LLM4Decompile An LLM that decompiles. Turns binary code into readable C. The 9B v2 model hits 64.9% re-executability on its own benchmark. 4. r2ai An LLM inside radare2. LLM-powered reversing for radare2. This is where AI starts reading the binary with you. 5. ida-pro-mcp Let the agent drive the tool. Bridges IDA Pro to language models through MCP. 6. reverse-engineering-assistant The same thing for Ghidra. An MCP server for reverse engineering tasks inside Ghidra. 7. REA Agents that reverse engineer anything. 35 target types: native binaries, APKs, firmware, websites, Electron apps, and more. Every finding is backed by evidence. Read the full REA guide below ↓
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loop engineering worked for 6 months. then one pipeline forced a graph a loop is one agent on discover → plan → execute → verify, until a stop condition hits enough for most agent work: failing tests, draft + self-review, inbox triage, one research writeup the break is nameable: if the thing that produced the output also grades it, you don't have quality control, you have a rubber stamp his numbers before the split: 40 drafts 31 passed self-review 18 actually publish-ready on manual read gap = 13 drafts with a passing grade from a biased judge he split one loop into 3 nodes - researcher · sources + notes only - writer · notes + task only · never grades itself - reviewer · separate model call · check the bar · write nothing same 40-draft workload. manual approval 18/40 → 33/40. reviewer rejected 14 with quotable reasons. 2 escalated to human instead of burning tokens forever approval 45% → 82.5%. inference cost +60% worth it when a bad draft costs more than three model calls. not worth it for "fix the failing test" playbook: - 1 law · producer never grades - 1 router · ship / retry / escalate - 1 state object · one owner per field · guarded writes - split the verifier first · cheapest highest-leverage move loop engineering → graph engineering. not five nodes for vibes, one split verifier when the loop starts grading its own homework full contracts, router, and decision function in the article

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