nblintao

Doesn't look like just a thin wrapper to me. The interesting part seems to be the surrounding harness/workflow layer rather than only the model call itself.

I was trying to keep track of the better post-leak code-analysis links on exactly this question, so I collected them here: https://github.com/nblintao/awesome-claude-code-postleak-ins...

The part I keep going back and forth on is openness. This whole model assumes experiments, notes, and results are public. That works great for "open research for the love of it". But even in academia people worry about getting scooped, and industry is way more guarded. I'm not sure how large the audience for truly open ML experiments is. Curious if others have thoughts on this.

The "price of an entire new employee" framing is spot on. I kept running into the same thing: individual experiments are cheap, but they add up fast, and nobody wants to approve that budget for speculative ideas.

I've been thinking of this as a gap between VC/Kickstarter and just doing it yourself. Most early ML experiments are too small for formal funding but too expensive to casually self-fund. So I built ML Patron where anyone can chip in a few bucks to sponsor an experiment they're curious about. I honestly don't have a good answer yet for how this turns into returns for sponsors in a traditional business sense. For now it's just open research patronage, like "I'd pay to know the answer to this". Platform runs it on cloud GPUs with public MLflow tracking.

Still very early: https://news.ycombinator.com/item?id=47563959.