Every executive wants their teams using AI tools. Every executive is also terrified of what happens when a hundred siloed teams start building with them.
I generated 1,000 trading strategies out of pure random numbers.
The best one made +182% in 5 years. Sharpe 1.38. Looks like a real edge.
Then I ran it on 5 new years. It lost 4.6%.
Nothing in there was real. Zero edge by design. The winner was just the lucky one out of a big pile.
This matters if you let AI test your ideas. It can run 100 variants in a minute, and every run is one more try. About 45 tries on 5 years of data is enough for pure luck to reach Sharpe 1.
A hold-out test doesn’t save you either, because it doesn’t count how many things you tried before it.
What I’d do:
Write the rule before you test it.
Count every variant, failed ones too.
Make the AI report that number, not only the winner.
Use your fresh data once.
How many variants do you test before you trust a result?
OpenAI assembled about 10,000 agents into a parallel colony that exchanged 2.7 million messages and produced a Navier-Stokes proof in 88 hours
A ~100-agent Euler run over ~50 hours came first. Then ~10,000 agents went live as a parallel research lab.
> WORKERS ~10,000 agents at peak on an unreleased internal model, each sandboxed with code execution and cached internet access
> MESSAGES 2.7 million exchanged between groups, about 130 billion output tokens across the full search (OpenAI figures)
> COMPETITION agent groups took on different problem versions and pursued competing outcomes
> CONSOLIDATION Codex merged useful ideas across groups and recycled them back into the search
> LEAN GATE GPT-6 Astra spent 17 more hours converting the result into Lean 4 and verifying it against Mathlib
> OPEN SOURCE Lean certificate on GitHub (openai/NavierStokesAndEuler), Apache-2.0, anyone can run the check
88 hours later: a 166-page Navier-Stokes paper, a 57-page Euler paper, and a Lean repo (OpenAI).
The proof is for the forced equation (Clay C/D), not the unforced prize problem, preprint, no prize.
Put the Lean gate last in the pipeline. The terminal below maps the colony's full 88-hour run ↓
THIS AGENTS.md FILE TURNS GROK BOT INTO A SENIOR ENGINEER
It ports straight into an AGENTS.md for any Grok Bot project:
> Workflow Orchestration
> Sub-agent Strategy
> Self-Improvement Loop
> Verification Before Finalizing
> Automatic Error Correction
> Fundamental Principles
What's interesting is that it works as a cumulative system.
Every time you correct your bot, that correction can be recorded and turned into a rule for future tasks.
Over time the bot learns from your feedback and stops repeating the same mistakes.
If you use AI for programming every day, having a well-structured AGENTS.md can save you a ton of time.
Save this 👇
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