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broader context here:
Asst professor @MIT EECS & CSAIL (@nlp_mit). Author of https://t.co/VgyLxl0oa1 and https://t.co/ZZaSzaRaZ7 (@DSPyOSS). Prev: CS PhD @StanfordNLP. Research @Databricks.
though, as ever, once a capability is achieved with a system, it’s easy to distill it back into model weights:
broader context here:
One reason I’m more bullish than ever on “LLMs” is that essentially *no one* in the field is a DNN forward pass maximalist or a self-supervision maximalist anymore. It’s just not the same meaning as 2020-22 LLMs.
though, as ever, once a capability is achieved with a system, it’s easy to distill it back into model weights:
RT @ericciarla: I'll be at @a16z tomorrow hosting a meetup with @upstash! Excited to meet everyone 🔥
Cartoonist, Engineer, PM, Partner @a16z investing in infra & AI Prev Product lead @HashiCorp, Founding Eng/PM @Transposit. Eng @AppDynamics. Opinions = own.
RT @firecrawl_dev: We're hosting a meetup with @a16z and @upstash in SF tomorrow 🔥 Meet other AI developers, share what you're building, a…
Cartoonist, Engineer, PM, Partner @a16z investing in infra & AI Prev Product lead @HashiCorp, Founding Eng/PM @Transposit. Eng @AppDynamics. Opinions = own.
I wrote a whole long article last year about this statistical measure trying to explain it in the simplest, most accessible way possible. But I actually think Claude did a better job in much less space using those prompts! https://t.co/VyZJqOBVc5
Former Quant Investor, now building @lumera (formerly called Pastel Network) | My Open Source Projects: https://t.co/9qbOCDlaqM