/LLM

A Practical Guide To Coding Securely With LLMs

- Sean Goedecke tl;dr: “I haven’t been particularly impressed by most online content about LLMs and security. For instance, the draft OWASP content is accurate but not particularly useful. It portrays LLM security as being a wide array of different threats that you have to familiarize yourself with. Instead, I think LLM security is better thought of as flowing from a single principle. Here it is: LLMs sometimes act maliciously, so you must treat LLM output like user input.”

featured in #608

LLMs Are Weird Computers

- Phillip Carter tl;dr: “I hate to be that hype guy, but uhh, can you think of something as impactful to the world as computers and information technology have been since the introduction of personal computers in the 1970s? Well, if LLMs/AI are a new kind of computer, it's not the biggest leap to think that they could also have an enormous impact on the world. I should be clear on my stance here. I'm with Bill Gates when he says that people tend to overestimate what's possible in the short-term and underestimate what's possible in ten years.”

featured in #607

On The Biology Of A Large Language Model

tl;dr: “The challenges we face in understanding language models resemble those faced by biologists. Living organisms are complex systems which have been sculpted by billions of years of evolution. While the basic principles of evolution are straightforward, the biological mechanisms it produces are spectacularly intricate. Likewise, while language models are generated by simple, human-designed training algorithms, the mechanisms born of these algorithms appear to be quite complex.”

featured in #605

Using LLM To Transcribe Restaurant Menu Photos

- Zhe Mai Zheng Hu Ying Yang tl;dr: Previously, the team at Doordash relied on humans to transcribe and update restaurant menus manually, which is costly and time-consuming. The rapid improvement of large language models, or LLMs, creates an opportunity for a big stepwise change, allowing AI to transcribe information from menu photos. However the diverse menu structures restaurants use pose a challenge for an LLM to do an accurate job at scale. In this blog, we will discuss how we built a system with a guardrail layer for LLMs leveraging traditional ML techniques. 

featured in #604

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