all things engineering

breakdown of the architecture behind Linear's speed: local-first sync, MobX observables, instant first loads, and a keyboard-first design.

A from-the-ground-up walkthrough of how modern LLMs work, from tokens to transformer blocks to the next-token loop

Usability, mostly.

Learn how to design awesome UIs by yourself using specific tactics explained from a developer's point-of-view.

The complete podcast (and transcript!) of Sega’s company history and business strategy.

Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese Watch: MIT’s Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford, Harvard, MIT, Princeton, CMU and others Update: This post has now become a book! Check out LLM-book.com which contains (Chapter 3) an updated and expanded version of this post speaking about the latest Transformer models and how they've evolved in the seven years since the original Transformer (like Multi-Query Attention and RoPE Positional embeddings). In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. So let’s try to break the model apart and look at how it functions. The Transformer was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter. 2025 Update: We’ve built a free short course that brings the contents of this post up-to-date with animations: A High-Level Look Let’s begin by looking at the model as a single black box. In a machine translation application, it would take a sentence in one language, and output its translation in another.

My setup might be surprisingly vanilla! Claude Code works great out of the box, so I personally don't customize it much. There is no one correct way to

Learn what makes web animations fast, slow, and everything in between with our 2025 web animation performance tier list.

"Ben and David's Bookshelf" book insights collection ✔ Get all the key ideas from popular books with Blinkist ✔ Try Blinkist 7 days for free

A merge workflow for activating multiple branches simultaneously using Jujutsu, an alternative VCS to Git.

The complete podcast (and transcript!) of Mars Inc’s history and business strategy. M&Ms, Milky Way, Snickers, and even a little bit of pet food.

The disruption caused by the Internet in industry after industry has a common theoretical basis described by Aggregation Theory.

Also: Shopify celebrates engineers who spend the most AI tokens, too much AI usage could lead to cognitive decline, and more.

"Ben and David's Bookshelf" book insights collection ✔ Get all the key ideas from popular books with Blinkist ✔ Try Blinkist 7 days for free

We asked 12 CTOs and CEOs what skill they now prioritize when hiring developers because of AI. Their answers validate what experienced developers suspected all along.

As we approach the 10 year anniversary of the 1.0 release of Kubernetes, let's take stock of the successes and failures of the project in the wild. Also what would be on a wish list for a Kubernetes 2.0 release.

The complete podcast (and transcript!) of LVMH’s company history and business strategy.

The complete podcast (and transcript!) of TSMC’s history and business strategy.

Cursor has grown 100x in load in just a year, sees 1M+ QPS for its data layer, and serves billions of code completions, daily. A deepdive into how it’s built with cofounder, Sualeh Asif

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My smartest friends have bananas arguments about LLM coding.

Written pieces, talks, and other bits by Zach Holman.

At the PGConf.dev 2025 Global Developer Conference, Bohan Zhang from OpenAI shared OpenAI’s best practices with PostgreSQL, offering a glimpse into the database usage of one of the most prominen

When agile experimentation at startups becomes a p-hacking trap

Today’s my last day at Carta, where I got the chance to serve as their CTO for the past two years. I’ve learned so much working there, and I wanted to end my chapter there by collecting my thoughts on what I learned. (I am heading somewhere, and will share news in a week or two after firming up the communication plan with my new team there.) The most important things I learned at Carta were:

Stock options, RSUs, taxes — read the latest edition: www.holloway.com/ec - jlevy/og-equity-compensation