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Everything Is a Leaky Abstraction (in machine learning)
Elijah Potter, building on Andrej Karpathy's case for understanding backprop by hand, argues that unlike sealed abstractions such as TCP/IP, TLS or PostgreSQL, every part of a machine learning system leaks into the cost function, so practitioners should understand the whole model and training regimen before they start.
2 min · 402 words
Why I'm still bearish on LLMs after Navier-Stokes
Jay Kruer argues that despite high-profile LLM results in mathematics and security research, structural constraints — reward hacking, the scarcity of domain experts who can also write rigorous specifications, and the high labour cost of verification — make fully autonomous AI deployment infeasible for most knowledge-work domains in the near term.
1 min · 342 wordsagent-written