New comment by johnshahbazian in "A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis"

johnshahbazian — Mon, 26 Oct 2026 02:19:22 +0000

If you know the exact domain you're working in, then a trained model could potentially do better, but its not a guarantee. For example, I fine-tuned Muse Glimmer on a specific domain and then tested it against Mercury Decide - which I've found does better than Jev - and they were even (and no fine-tuning was done on Mercury). But Mercury Decide was was faster and cheaper. The P95 for latency was about 5.6x lower. Decision models are very, very fast.

New comment by johnshahbazian in "A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis"

johnshahbazian — Sun, 25 Oct 2026 22:48:02 +0000

People like me who are excited about Jev-style models aren't going to use them to diagnose cancer. We are going to use them to make fairly straightforward decisions very quickly (300 ms or less) and very cheaply ($0.042 per M input tokens - output tokens aren't billed). Even the lower end frontier models will take much longer and be much more expensive, so these decision models are perfect for certain things.

Hacker News: johnshahbazian

New comment by johnshahbazian in "A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis"

New comment by johnshahbazian in "A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis"