#Llm

A Visual Guide to RNNs, CNNs, Transformers, and Calibration, with Hands-On Experiments on Accuracy and Efficiency

My longtime labmate-turned-student/friend1 Dhruva Karkada recently wrote a sick paper on data statistics which deservedly went viral on Twitter, in part beca...

Run Q4 small language models entirely in the browser with WebGPU. Chat, benchmark, and compare PetitGPT, SmolLM2, and friends on-device.

I spent the summer at Recurse Center, a programming retreat in Brooklyn, at the recommendation of my friend Cory. These are some things I did there.

The abstract for article “The End of Code Review: Coding Agents Supersede Human Inspection” paints this picture for the reader…

Are you steering towards AI burnout? Afraid of loosing your job to someone with little programming skills, no aspirations to quality, and a huge Claude account? Disappointed about the code quality in your projects, or worse in “your” own code?…

Typed decisions with a probability for every option, from GLM-5.3-Flash in a single forward pass: about as accurate as Jev, faster from Europe, and protected by confidential computing. With a playground and a benchmark on 29 datasets.

Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services.…

I was intrigued by Jev and the self-hostable projects appearing around it, such as OpenJev and SemIf . Reading about them introduced me ...

OpenAI’s proof seems eligible for a $1-million prize—but only by using a controversial loophole

Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-level score neither characterizes a text nor identifies which AI model wrote it.…

TypeSafe's Jev is a genuine breakthrough – snap judgments with calibrated probabilities instead of generated text. My bet is OpenAI is already figuring out how to copy it, and then embed it inside its own models where Jev can't follow.

In this essay, Carson Gross argues that Markdown is now an important source artifact for software systems built with LLMs, and that, following the principle of locality, it should live in /src, alongside the code it produces.

An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.