Hot take: most ML teams don't have a model problem, they have a 'we rushed annotation to hit a deadline and now we're paying for it in every sprint' problem.
This is a safe space.
stop guessing if your LLM improved.
rubric‑based human evals (pairwise, safety, MQM‑style) with agreement tracking + gold sets leads to signals you can actually ship on.
The future of AI is in the hand of centaurs — humans and machines working in symbiosis.
And we just proved that the best way to train AI...
...is to make humans want to compete to do it.
Learn more: https://t.co/49CswZSLgx
/end 🧵
🧵 A crowd of gamers is beating board-certified doctors at medical diagnosis.
And Big Pharma is paying them to do it.
This is the wild story of Centaur — the company that turned data labeling into a competitive sport:
The use cases are expanding beyond medical imaging:
→ Drug discovery acceleration → LLM hallucination detection
→ Clinical trial data extraction → Insurance claims processing → EHR data classification
34 million text labels processed since 2021 alone.
edge cases that kill manufacturing AI:
new shift operator holds parts differently
supplier changes material slightly
machine vibration increases 0.5%
your AI sees apocalypse. humans see thursday.
the fix is more annotated edge case data.
capture every weird thing for 6 months
have QC experts label why it's weird but OK
retrain quarterly
boring? yes. works? absolutely.
what edge case is killing your AI?
manufacturing AI works perfectly in the lab, fails instantly on the factory floor
it's not about the model. it's about edge cases.
here's what actually breaks:
edge cases that kill manufacturing AI:
new shift operator holds parts differently
supplier changes material slightly
machine vibration increases 0.5%
your AI sees apocalypse. humans see thursday.