The latest blog posts to take off on Hacker News. 3,091 posts.
These LLMs are shockingly good at deobfuscation, transpilation and structure to structure conversions. I discovered this back around Christmas where I asked an LLM to make me an Haskell audio library by transpiling a rust implementation. An “oh fuck” moment in timeOver the Christmas break I’ve been critically looking
Traffic trends from WebMD, Quora, Stack Overflow, Chegg, G2, CNET, Reddit, Wikipedia, and Substack!
Pure scaling in shambles
There are two claims I’d like to make: LLMs can be used effectively1 for listwise document ranking. Some complex problems can (surprisingly) be solved by transforming them into document ranking problems. I’ve primarily explored both of these claims in the context of using patch diffing to locate N-day…
How using AI as a bridge when doctors aren't available can improve patient-to-doctor communications in real time emergencies
Vibe coding (or vibeware) is making rounds on X now. To the best of my knowledge Andrej Karpathy started the “meme” in this X entry . I find it well written and hilarious and it seems to have taken off.
Although it might seem that AI will make it pointless, I still think it's worth blogging.
The hype cycle for Google’s fabulous new AI Co-Scientist tool, based on the Gemini LLM, includes a BBC headline about how José Penadés’ team at Imperial College asked the tool about a problem…
OpenAI’s Deep Research is built for me, and I can’t use it. It’s another amazing demo, until it breaks. But it breaks in really interesting ways.
Using LLMs doesn't make a product special anymore
Congratulations to the xAI team—and the advocates of the scaling laws
How can we do better interviews in the age of AI
Hi, welcome to my website! This is where I share my thoughts on things that interest me, mostly tech, philosophy, history, and math.
A detailed walkthrough of my current workflow for using LLms to build software, from brainstorming through planning and execution.
It's been just over two years and two months since ChatGPT launched, and in that time we've seen Large Language Models (LLMs) blossom from a novel concept into one of the most craven cons of the 21st century — a cynical bubble inflated by OpenAI CEO Sam Altman built
Word2vec is a pervasive tool for learning word embeddings. Its success, however, is mostly due to particular architecture choices. Transferring these choices to traditional distributional methods makes them competitive with popular word embedding methods.
Something’s been bugging me about how new devs learn and I need to talk about it. We’re at this weird inflection point in software development. Every junior dev I talk to has Copilot or Claude or GPT running 24/7. They’re shipping code faster than ever. But when I dig deeper into their understanding of what they’re…
[UPDATE: Feb 8 2025] my amazing colleague Max Shen noticed a sign mistake in my derivation of the partial derivative of the log-harmonic function below.
Here’s a Mastodon thread from a year ago. Just a quick check on how ChatGPT’s getting on … Me: Who reassigned the species Brachiosaurus brancai to its own genus, and when? ChatGPT…
Forgotten musings on the economics of AI, an intelligence explosion, and machine consciousness
AI coding assistants are React evangelists.
Ah, the tech industry.
As everyone is well aware, the world is still going nuts trying to develop more, newer and better AI tools. Mostly by throwing absurd amounts of money at the problem. Many of those billions go towards building cheap or free services that operate at a significant loss. The tech giants that run them all are hoping to…
A TL;DR version of Andrej Karpathy's "Deep dive into LLMs like ChatGPT" video.
Why network effects and distribution will be king, once more
Make your own AI by hand ✍️ exercises
Methods and Strategies for Building and Refining Reasoning Models
Sergey Filimonov's blog
One paper that caught my attention a bit ago was Selective attention in hypothesis-driven data analysis by Itai Yanai and Martin Lercher. In their study, students who were given specific hypotheses to test were much less likely to notice an obvious “gorilla in the data” compared to students who explored the data…
