/LLM

Tracing The Thoughts Of A Large Language Model

tl;dr: Anthropic presents research on interpreting how Claude "thinks" internally. By developing an "AI microscope," they examine the mechanisms behind Claude's abilities across languages, reasoning, poetry, and mathematics. These insights not only reveal cognitive strategies and efforts to make AI more transparent.

featured in #603

Here’s How I Use LLMs To Help Me Write Code

- Simon Willison tl;dr: Using LLMs to write code is difficult and unintuitive. It takes significant effort to figure out the sharp and soft edges of using them in this way, and there’s precious little guidance to help people figure out how best to apply them. If someone tells you that coding with LLMs is easy they are misleading you. They may well have stumbled on to patterns that work, but those patterns do not come naturally to everyone. I’ve been getting great results out of LLMs for code for over two years now. Here’s my attempt at transferring some of that experience and intution to you.

featured in #598

Rethinking LLM Inference: Why Developer AI Needs A Different Approach

- Markus Rabe Carl Case tl;dr: “This post breaks down the challenges of inference for coding, explaining Augment’s approach to optimizing LLM inference, and how building our inference stack delivers superior quality and speed to our customers.”

featured in #596

My LLM Codegen Workflow ATM

- Harper Reed tl;dr: “I have been building so many small products using LLMs. It has been fun, and useful. However, there are pitfalls that can waste so much time. A while back a friend asked me how I was using LLMs to write software. I thought “oh boy. how much time do you have!” and thus this post.”

featured in #592

How I Program With LLMs

- David Crawshaw tl;dr: “This document is a summary of my personal experiences using generative models while programming over the past year. It has not been a passive process. I have intentionally sought ways to use LLMs while programming to learn about them. The result has been that I now regularly use LLMs while working and I consider their benefits net-positive on my productivity.”

featured in #583

How I Program With LLMs

- David Crawshaw tl;dr: “This document is a summary of my personal experiences using generative models while programming over the past year. It has not been a passive process. I have intentionally sought ways to use LLMs while programming to learn about them. The result has been that I now regularly use LLMs while working and I consider their benefits net-positive on my productivity.”

featured in #582

Things We Learned About LLMs In 2024

- Simon Willison tl;dr: “A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.”

featured in #580

Mirror: An LLM-powered Programming-By-Example Programming Language

- Austin Henley tl;dr: “Programming by example is a technique where users provide examples of the outcome they want, and the system generates code that can perform it. For example, in Excel, you can demonstrate how you want a column formatted through an example or two, and Excel will learn a pattern and apply it to the rest. But what if there was a programming language that only allows programming by example? Can we integrate AI into traditional programming languages?”

featured in #569

How We Generated Millions Of Content Annotations

tl;dr: “As one of the foundational teams at Spotify focused on understanding and enriching the core content in our catalogs, we leverage ML in many of our products. For example, we use ML to detect content relations so a new track or album will be automatically placed on the right Artist Page. We also use it to analyze podcast audio, video, and metadata to identify platform policy violations. To power such experiences, we need to build several ML models that cover entire content catalogs — hundreds of millions of tracks and podcast episodes.” 

featured in #561

The LLM Honeymoon Phase Is About To End

- Baldur Bjarnason tl;dr: “This is going to get automated, weaponised, and industrialised. Tech companies have placed chatbots at the centre of our information ecosystems and butchered their products to push them front and centre. The incentives for bad actors to try to game them are enormous and they are capable of making incredibly sophisticated tools for their purposes.”

featured in #548