🚀 We put AlphaEvolve's approach to the test — and the results blew our minds

The bottom line: Automated code optimization just changed the game. What used to take senior devs hours (or get skipped entirely) now happens in minutes.

What we tested

After Alex Dunegan 's post on this last week, we decided to test the open-source adaptation on some real production code — a slow background data integrity checker that frankly, we'd been living with because optimization wasn't worth a dev's time.

Massive shoutout to OpenEvolve for making this incredibly accessible. What could have been a complex research implementation became a simple CLI tool we could run in minutes.

The winning insight: Move all filtering to the database instead of the app. Super simple, but something we hadn't prioritized tackling manually.

The results (10K runs each):

🏆 OpenEvolve crushed it: 62.9 seconds (2,000x+ improvement)

• Gemini and OpenAI models performed nearly identically via OpenEvolve
• The key: Automated exploration without human fatigue

What surprised us about manual approaches:

Claude Code literally gave up at one point and wanted to revert to the original version. Been there, buddy. 😅

Cursor required constant nudging across all modes (Auto, gemini-2.5-pro, o3) to keep pushing for better solutions. Human iteration fatigue is real.

The Cursor Gemini version passed our test suite but generated wrong results. Time to add more tests! This reinforced how critical comprehensive testing is for any AI code optimization work.

Code context is everything: When we ran OpenEvolve without any related app code, it managed only minor improvements. The dramatic wins came when it could see the broader codebase and understand integration patterns just like a human/Cursor/Claude can.

The bigger picture

Software development should ideally move through these stages "Make it work, make it right, make it fast." But we rarely make it to Step 3 unless absolutely necessary.

But AlphaEvolve’s approach changes everything. The cost to optimize code drops dramatically. Most mundane code can now be optimized without burning senior dev cycles or requiring more cloud resources.

Why this matters: That last 20% of performance that typically takes 80% of developer effort? It's now automated. We can finally tackle optimizations that were never worth the human cost.

Note: We extrapolated results for the slowest 3 approaches rather than waiting 24+ hours for exact measurements. Life's too short.

Have you tried this approach yet? Would love to hear about your experiments.

#CodeOptimization #AlphaEvolve #OpenEvolve #SoftwareEngineering #AI #Automation #Performance

Links:

• OpenEvolve GitHub: https://github.com/codelion/openevolve
• Original AlphaEvolve paper:https://arxiv.org/abs/2506.13131