TIL: What’s actually different about GGUF and ONNX?
What GGUF and ONNX actually contain, what they expect from the runtime, and why choosing between them is not simply a question of quantisation or hardware.
Today I Learned (TIL)
Things I've learned or things I find interesting will be logged here. For long-form content, you might want to check out my newsletter.
Speculative Decoding
Trading cheap guesses for expensive forward passes
Query, Key, Values
How to think about Q, K, and V vectors in the Attention layer of a Large Language Model
On Durable Objects, Orleans, and prior art for the agentic web
TIL: Ads in AI chatbots are not just a UX problem
How "Thinking" Models Actually Work
Lilian Weng's Why We Think is a survey of test-time compute and chain-of-thought reasoning. Here's what I pulled out of it.
We’re Being Too Loose With the Term “World Model”
I think we are still too loose with the phrase “world model”.
TIL: Quantisation
Quantisation is really a precision-allocation problem.
How the Claude Code team designs agent tools
Make Claude Code Review Its Own Plans
Stripe's coding agents: the walls matter more than the model
Deep Blue
TIL: The real bottleneck in AI coding isn't speed
TIL: Markov Language
TIL: Learning New Tech With AI Assistance Might Backfire
A study of 52 developers found that using AI to learn a new Python library led to worse comprehension scores, with no speed improvement. Here's what actually works.