How Weco Explores Hundreds of Solutions

Weco autonomously generates, branches, and tests hundreds of candidate solutions - building an evolutionary tree to find the best one. Click any node to inspect examples.

Optimization Strategy

solution.py

Academia and Industry Recognition

Weco's innovative approach is featured in leading research papers and industry publications

Deployable Breakthroughs on Autopilot

Ship speedups overnight

Weco proposes, tests, and iterates on code changes autonomously - wake up to optimized code.

Sweep candidates for pennies

Each candidate costs fractions of a cent. Find the non-obvious wins that manual iteration misses.

Your data never leaves your machine

Eval code runs locally where your data lives. Only metrics and diffs are sent - review the open-source CLI to verify.

Works with any language

Python, C++, Rust, JS - if it prints a metric to stdout, Weco optimizes it.

See every experiment in one tree

Each run produces a searchable tree of candidates. Compare any two nodes side-by-side.

Steer with natural language

Add constraints like "avoid unsafe memory access" or "prioritize readability" to guide the search.

It's as Simple as:

1. Point Weco to your eval

Provide a command that prints your metric value to stdout, which will be used to...

weco
$--eval-command 'python evaluate.py'

2. Run the Weco optimization

Weco proposes code edits, runs local eval, and evolves solutions based on findings.

3. See and ship breakthroughs

Watch progress locally or in the dashboard and see results before merging the winner.

Trusted by frontier AI labs

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The Next Generation of AIDE ML - Now in the Weco Platform

Outperforming competitors with systematic iteration and optimization focused on measurable results

OpenAI Benchmarked

Metric-first engineering that wins more medals on MLE‑Bench:

AIDE vs. MLAB vs. OpenHands performance comparison

AIDE ML iterates until the metric says "better." In OpenAI's MLE‑Bench it secured 4× more medals than the next best autonomous agent across 75 Kaggle competitions.

An explicit evaluation loop beats one‑shot code generation.

Beyond Human Baselines

Consistently surpassing experts on tight time‑boxed research tasks:

AIDE vs. human engineers on RE‑Bench

In METR's 6‑hour RE‑Bench challenge, AIDE consistently outperformed seasoned researchers, surfacing "surprising" solutions humans missed.

Open‑Source & Live Now

Use the best version of AIDE ML on the Weco Platform today:

AIDE's core is open‑source - explore the repo or read the paper to dive deeper into our approach.

The Weco Platform is live. against your evaluation script, and watch every experiment stream to the Dashboard in real time. Want a voice in new features? Join the newsletter.

Choose Your Path

From open-source experimentation to production-grade optimization - pick the option that fits your needs.

BYOK

Bring Your Own Keys

Use your OpenAI / Anthropic / custom model keys. Model usage billed directly by your provider.

Get started →
Research Baseline

Weco AIDE ML

Open Source
  • Reference implementation of the AIDE algorithm for experimentation
  • Only requires a dataset - auto-detects metrics and optimization direction
  • Single-machine experiments - runs fully local
  • Reproduce paper results and test new agent architectures
  • Ideal for academics and rapid prototyping
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RECOMMENDED
Production Platform

Weco Platform

20 credits free (≈ 100 steps)

  • Massively upgraded AIDE - production-hardened with advanced capabilities
  • Works with your evaluation scripts for complex optimization
  • Steer experiments with natural language instructions and code confirmation
  • AI-powered analysis to understand experiment trajectory in context
  • Hybrid architecture - your code stays local, agent runs in cloud

Both options leverage our breakthrough AIDE algorithm for autonomous code improvement.

Frequently Asked Questions

Edward Grefenstette

So amazing to see something built by this team that's substantially underpinning and influencing OpenAI's agentic roadmap.

Edward Grefenstette, Director of ResearchGoogle DeepMind

Start Optimizing in Minutes

Point Weco at your eval script, run an optimization, and ship winning code: