Eugene Yan
RecSys, AI, Engineering; Principal Applied Scientist @ Amazon. Led ML @ Alibaba, Lazada, Healthtech Series A. Writing @ eugeneyan.com, aiteratelabs.com.
- In orgs pushing the envelope, there's always a minority that can be counted on to get shit done against all odds, driven by force of will, resourcefulness, influence, etc. When you identify them, vest in them authority, autonomy, and step back and watch them perform miracles.
- TIL that Price’s Law (half of work is done by sqrt of people) has come under scrutiny … … the actual ratio is even worse 😂 en.wikipedia.org/wiki/Price%2...
- To better understand MCPs and agentic workflows, I built news-agents to generate a daily news recap. The main agent spawns sub-agents, assigning them news feeds to parse and summarize, and then generates a final overall summary plus analysis. eugeneyan.com/writing/news...
- 📌
- this is cool
- Excellent! Thanks, starred. But your “share on” footer is missing a 🦋 …
- p.s., If you’re interested in topics like this, my friends Ben and Swyx are organizing the AI Engineer World’s Fair in San Francisco on 3rd - 5th June. Come talk to builders deploying AI systems in production. Here’s a big discount for tickets: ti.to/software-3/a...
- @cameron.pfiffer.org This seems relevant for you
- Yeah, chatting to the team about whether I'm going
- But oh also the content of this thread is great wow
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- Some of the anti-AI stuff feels a bit like when people would say "don't use Wikipedia as a source." It's just like anything else, a piece of information that you weigh against multiple sources and your own understanding of its likely failure modes
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- Why can’t the ground truth be built in?
- Seems like the message is “you cannot fully automate evals yet, human in the loop is still needed” — correct?
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- Do you know the difference between “$2 trillion” and “$150 billion”? About $2 trillion.
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- LLMs hallucinating nonexistent software packages with plausible names leads to a new malware vulnerability: "slopsquatting."
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- If you look at most of the models we've received from OpenAI, Anthropic, and Google in the last 18 months you'll hear a lot of "Most of the improvements were in the post-training phase." Here's a simple analogy for how so many gains can be made on mostly the same base model:
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