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AI Orchestration · $5

Self-improvement loop that runs while you sleep

Drop a self-running 2-minute loop into any project and wake up to an agent that improved itself overnight—kept honest by a built-in action-law, counter-action gate, and verification on every pass.

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1. See it

fusion · routing
GLM
Cursor
Kimi
DeepSeek
JUDGE
One fused answer
≥ your best single model

2. How it works

1
Route the task
2
Models draft in parallel
3
Cross-verify & fact-check
4
Strongest-model judge elevates
5
Deliver one fused answer

3. Why it wins

This product
One model alone
Models working
Several, cross-checking each other
One — a single point of failure
Hallucinations
Caught by cross-verify + a judge
Slip through unchecked
Marginal cost
$0 on memberships you already pay
Per-token API billing
Quality floor
≥ your best single model, by design
Whatever that one model gives

4. Use it to…

Improve a project overnight

Drop the loop into any repo and let verified 2-minute cycles ship incremental improvements while you sleep.

Keep an agent honest

The action-law and counter-action gate stop the agent from making changes it can't justify, so unattended runs don't drift.

Pause for human review

Halt the loop via memory/HUMAN.md when you need to inspect changes before the next autonomous cycle resumes.

5. The shift it creates

Without it
  • One model = one point of failure
  • Hallucinations slip through unchecked
  • No quality floor you can trust
With it
  • A self-running 2-minute loop keeps improving your project overnight, so you wake up to real progress instead of babysitting every pass
  • The built-in action-law and counter-action gate force the agent to justify each change before it lands
  • Verification on every cycle plus 5-cycle theme synthesis catch regressions early and compound the learning instead of editing blindly pass by pass

6. What's inside

Self-running 2-minute improvement loop
Counter-action gate + action-law built in
Verification on every cycle
5-cycle theme synthesis
8 bookkeeping files auto-created
Karpathy-style discipline · pause via memory/HUMAN.md

7. The details

Format
Runnable scripts + docs
Delivery
Instant after checkout
License
Yours forever · use on every project
Works with
Any LLM / your own stack

How it actually works

Exactly what it does — and when.

1For a Python FastAPI service with flaky endpoints → it runs a 2-minute loop that calls a critique action-law against the existing handler code, checks the proposed patch through a counter-action gate that rejects unverified edits, and only commits if the test suite confirms the fix.
2For a prompt template returning inconsistent JSON → it activates a 5-cycle theme synthesis pass, accumulating refinement themes across iterations into a HUMAN.md memory entry, so the agent pauses for human review instead of silently drifting when confidence plateaus.
3For a LangChain agent producing verbose outputs → it spins up 8 auto-created bookkeeping files (history.md, metrics., scratch.md, diff_log.md, goals.md, HUMAN.md, MEMORY.md, verification.) that log every edit, gate decision, and theme, so the loop can resume without context loss after interruption.
4For a Next.js page rendering slow on mobile → it runs the verification gate on each 2-minute cycle using Karpathy-style discipline: confirm the Lighthouse score improved by ≥5 points before accepting the change, and roll back to the prior version if the metric regresses.
In practice · Autonomous Bug-Fix Loop

You add a self-improvement loop to your repo, point it at a flaky Celery task causing intermittent failures. Over 90 minutes it runs 45 cycles, each passing a fix proposal through the counter-action gate, verifying against the pytest suite, and auto-committing three incremental fixes. By morning the bookkeeping files show a clear trail of accepted and rejected edits, and the Celery task is stable across 500 test runs.

Who it's for

Developers building autonomous agents

They get proven loop scaffolding—action-law, counter-action gate, per-cycle verification—instead of reinventing the discipline behind one more one-off prompt

Solo builders who want overnight progress

They get hands-off 2-minute cycles plus a stop switch via memory/HUMAN.md, so the agent works while they sleep but pauses when they want to inspect

Teams whose agents drift or skip checks

The gate and verification catch bad changes before they compound across autonomous cycles, keeping unattended runs trustworthy

Questions, answered

✓ Instant delivery✓ Yours to own✓ Use on every project✓ Built to ship results

Ready to put Autonomous Self-Improvement Loop to work?

Get Instant Access · $5

Instant download · one-time price · own forever · all sales final

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Autonomous Self-Improvement Loop
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