The Best Way to Read a Book (That Nobody’s Doing) A video where Jeremy Howard explains his close reading workflow with Solveit. The core idea: “load a lot of relevant context first, then read chapter-by-chapter while chatting with an AI, carrying context forward each time”. It feels close to what I’ve been doing with gptel for papers/books, but the explicit chapter-level context handoff was new to me. (Related) Past post: A refined Emacs LLM setup
I haven’t been updating my blog for a while, so I’m restarting with a lightweight weekly memo: things I read/watched this week that I enjoyed. Onoguchi Snow Dome A collector’s site showcasing their snow globe collection with photos. The early-Heisei-web vibe is oddly charming. The security paradox of local LLMs - Quesma Blog (HN) Local LLMs feel “more secure” than cloud LLMs at first glance, but it’s not that simple. The example attack prompts are fun to read (especially Attack #2). Love Generation filming locations (map) A fan-made location guide for a classic 90s drama I got hooked on again via Netflix. Kimura Takuya is way too cool.
Whassup, peeps! It’s been a minute since my last post (shoutout to the one person probably reading this, you the real MVP!). My Master’s thesis, with some fresh updates, just dropped on arXiv. Check it: Paper: Babasaki, K., Sugasawa, S., McAlinn, K. and Takanashi, K. (2024). Ensemble doubly robust Bayesian inference via regression synthesis. (arXiv:2409.06288) So, this paper, it’s all about takin’ this ensemble method called Bayesian Predictive Synthesis (BPS) that Professor McAlinn cooked up, and flexing it into the world of causal inference, specifically for estimatin’ Average Treatment Effects (ATE). We’re callin’ our new method “doubly robust Bayesian regression synthesis”. If you wanna get into the nitty-gritty, peep the paper, ya dig? ...