alt Hacker NewsOn 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.)
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cancer is more like debugging a gigantic DL model than an operating system. spaghetti of redundancies all the way down.
For people questioning why to involve GPT and AI assistants:
GPT and AI assistants cannot be fully trusted, but they can personalize learning.
The chief challenge for the framework/handbook will be resolving how to personalize guidance into cancer research while grounding knowledge in trustworthy sources.
For instance, the framework will anchor abstract, dry biological concepts in personally meaningful tracks. Imagine someone you care about is battling lung cancer — the framework may orient learning around the molecular drivers and signaling pathways at play, or perhaps how to explore the treatment landscape while respecting established practices. If you're fortunate enough to not know someone affected by cancer, GPT can help find a personal angle.
The sheer depth of information is staggering. People devote entire careers to niche specialities, and these experts still don't know everything in their niche because our understanding of human biology and disease is constantly evolving. Adapting depth should also depend on the individual and can only be achieved via AI. Static curriculums do not maximize learning in 2026.