On second thought, I will publish something regardless of interest.

It will be an "Cancer for Engineers" framework, delivered via free, open-source Custom GPTs and Claude Skills. (Gemini gems are less reliable in our experience.)

The goal: to ease engineers into cancer via AI personalized introductory curriculums with varying time commitments to enable deeper independent investigation or fast exits if interest wanes: 4 hours, 8 hours, 12 hours.

Basically 1-3 hours per week for a month.

The reason I think some engineers may find cancer interesting, aside from the societal impact:

The human body is like a complex operating system. Cancer is a severe runtime error. Tracing root causes -- like genetic mutations, signaling errors, or immune evasion -- has many parallels to diagnosing system failures.

BTW if anyone from Kaggle/GDM is reading this, we are having issues submitting a benchmark paper for NeurIPS based on the Kaggle Benchmark.

Google models seem to get a different scheduling priority, ironically, enough and take >20 hours to complete a benchmark task that other models like Opus 4.6 finish in <1 hour -- same code path, same task. Would love help if possible since the abstract deadline is Monday (It's last minute because we didn't originally plan to submit this, but someone suggested it.)

cancer is more like debugging a gigantic DL model than an operating system. spaghetti of redundancies all the way down.