/AI

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

How I Use LLMs

- Andrej Karpathy tl;dr: “The example-driven, practical walkthrough of Large Language Models and their growing list of related features, as a new entry to my general audience series on LLMs. In this more practical followup, I take you through the many ways I use LLMs in my own life.”

featured in #596

AI Dev Tools Are Focused On The Wrong Problem

- Dennis Pilarinos tl;dr: The biggest challenge in software development isn’t writing code. It’s finding the context to know what code to write.

featured in #595

The Deadline Doom Loop

- James Hawkins tl;dr: “Most companies love to set artificial deadlines. They’re the easiest way to “increase urgency”. But they have a nasty habit of manifesting into meaningful deadlines. Deadlines that generate mountains of debt and slow you down over time. I call this the deadline doom loop and it’s one of the reasons we don’t have product deadlines at PostHog.” James discusses how the loop plays out and principles for escaping the loop.

featured in #595

The End Of Programming As We Know It

- Tim O’Reilly tl;dr: “There’s a lot of chatter in the media that software developers will soon lose their jobs to AI. I don’t buy it. It is not the end of programming. It is the end of programming as we know it today. That is not new.”

featured in #595

[Webinar] Best practices for AI Agent Tool Calling

tl;dr: “Why doesn’t my AI agent call the right tool?” For AI agents that automate work across 3rd-party tools to be adopted, tool calling and tool use accuracy is critical. Join this webinar, where our dev advocate will cover the basics around AI agent tool calling as well as evaluations we ran to uncover optimizing tool calling performance. Register here - you’ll receive the recording and slides afterward, even if you can’t make it.

featured in #594

LLM Ecosystem Predictions

- Brian Kihoon Lee tl;dr: "I don’t think I’m making any outlandish claims here - merely simple extrapolations of trends that I think have robust foundations, with a dash of history. And yet the conclusions are surprising. The world of 2030 will be as unrecognizable to us as the world of cellphones today was in 2010, and as the world of the connected Internet was in 2000."

featured in #594

5 Code Review Anti-Patterns You Can Eliminate With AI

- Aravind Putrevu Desmond Obisi tl;dr: In this guide, you’ll learn about the most common anti-patterns that pop up during code reviews and how to easily tackle them with artificial intelligence (AI).

featured in #593

AI Is Stifling Tech Adoption

- Declan Chidlow tl;dr: “I propose that the advent and integration of AI models into the workflows of developers has stifled the adoption of new and potentially superior technologies due to training data cutoffs and system prompt influence.”

featured in #593

How I Use LLMs As A Staff Engineer

- Sean Goedecke tl;dr: “Personally, I feel like I get a lot of value from AI. I think many of the people who don’t feel this way are “holding it wrong”: i.e. they’re not using language models in the most helpful ways. In this post, I’m going to list a bunch of ways I regularly use AI in my day-to-day as a staff engineer.”

featured in #589