Terence Tao — one of the most decorated mathematicians alive — just ported two dozen of his old, broken Java 1.0 applets to modern JavaScript in a matter of hours using AI coding agents. He reports finding only one minor bug in the agent’s output, and the agent even flagged two bugs in his original 1999 code that went unnoticed for 27 years. This is the moment vibe coding crossed over from toy experiments to serious, domain-expert productivity.
What Happened
In a July 11, 2026 post on his blog What’s New, Tao describes migrating his old web pages and blog data to a more maintainable repository “using modern AI assistance.” As an experiment, he asked an AI coding agent to port his old applets — originally written in Java 1.0 back in 1999 for his complex analysis and linear algebra courses — to JavaScript.
The results are striking. Two dozen-ish applets were ported in hours, all now functional again after years of being dead (modern browsers stopped supporting his version of Java long ago). A few graphical upgrades came for free — for example, his Besicovitch set applet is now colorized, versus the original monochrome version.
Tao, a Fields Medalist and MacArthur Fellow, is not a web developer by trade. His account emphasizes that the agent handled the heavy lifting, and he limited his role to reviewing the output. The agent flagged two bugs in his original 1999 Java code that he had never known about. Across the entire porting effort, Tao reports finding only one minor bug in the agent’s output. He describes the experience as “vibe coding” — a term that originally described casual AI-assisted programming, but here demonstrated real, production-grade reliability.
My Take
This is the single most significant AI news story of the day — more important than another math theorem proof from a GPT model, and more immediately actionable than a world model from a Chinese lab. Why? Because Terence Tao is a perfect proxy for a massive, underserved class of professionals: domain experts with deep knowledge but limited time or interest for software engineering chores.
For years, we told these people to “learn to code.” Tao just demonstrated the inverse: the best coding is the coding you don’t have to do. The agent didn’t just translate syntax; it upgraded UI, caught bugs in legacy code, and delivered production-quality output in hours instead of weeks. This is not a demo. This is a Fields Medalist shipping software.
The two key signals here are the bug-finding capability and the near-zero error rate in the agent’s output. Tao’s original 1999 code had bugs he never found — the agent found them. And the agent’s own code had only one minor issue across two dozen applets. That level of reliability changes the calculus for any organization sitting on legacy code. If a math professor can resurrect 27-year-old Java applets in an afternoon, enterprise teams can absolutely migrate aging systems with AI assistance.
What to Watch
- Legacy code migration becomes a solved problem. Any organization with old Java, Flash, or ActiveX components can now plan cost-effective migrations using AI agents. Expect a wave of “digital archaeology” projects in the next 12 months.
- The “domain expert + AI agent” pair becomes the new full-stack developer. Subject matter experts no longer need to be fluent in software engineering. The agent handles the code; the expert provides the review and domain judgment. This inverts the traditional team structure.
- Bug discovery in historical code. Tao’s experience — where the AI found bugs in his 1999 original — points to a new use case: auditing legacy systems for latent defects. Every company with old code should be running AI agents against it, not to rewrite it, but to audit it first.