Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses.
trace every run → judge it with an LLM → diagnose → fix → ship.
That loop is how agents self-improve over time.
Agent loops + memory + harness + evals - thats the stack.
Watch it, then save the framework below.
SOMEONE TURNED THE VIRAL "TEACH CLAUDE TO TALK LIKE A CAVEMAN TO SAVE TOKENS" STRATEGY INTO AN ACTUAL CLAUDE CODE SKILL
one-line install and it cuts ~75% of tokens while keeping full technical accuracy
they even benchmarked it with real token counts from the API:
> explain React re-render bug: 1180 tokens → 159 tokens (87% saved)
> fix auth middleware: 704 → 121 (83% saved)
> set up PostgreSQL connection pool: 2347 → 380 (84% saved)
> implement React error boundary: 3454 → 456 (87% saved)
> debug PostgreSQL race condition: 1200 → 232 (81% saved)
average across 10 tasks: 65% savings. range is 22-87% depending on the task.
three intensity levels:
> lite: drops filler, keeps grammar. professional but no fluff
> full: drops articles, fragments, full grunt mode
> ultra: maximum compression. telegraphic. abbreviates everything
works as a skill for Claude Code and a plugin for Codex.
this is PEAK
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