One Dev's Mad Experiment: Building aeoptimize by Dispatching Claude, Gemini, and Copilot in Parallel
Imagine ditching the endless back-and-forth with one AI. This dev built a slick CLI by firing off tasks to three AIs at once — and the results? Faster code, caught vulnerabilities, better tools for everyone scraping for AI attention.
theAIcatchupApr 10, 20264 min read
⚡ Key Takeaways
Multi-AI workflows split tasks by model strengths for faster, higher-quality code.𝕏
aeoptimize CLI audits sites for AI readability with rules + optional AI consensus.𝕏
Parallel AIs act as adversarial reviewers, exposing dev blind spots like SSRF vulnerabilities.𝕏
The 60-Second TL;DR
Multi-AI workflows split tasks by model strengths for faster, higher-quality code.
aeoptimize CLI audits sites for AI readability with rules + optional AI consensus.
Parallel AIs act as adversarial reviewers, exposing dev blind spots like SSRF vulnerabilities.