Python

Listen to this article instead ▶ 0:00 / 0:00 0.75× 1× 1.25× 1.5× 2× Your browser does not support the audio element In June 2026, Uber told its engineers they each would get a $1500 a month cap for AI coding tools and not a token more. That cap came after the company burned through its entire 2026 AI coding budget in four months: Claude Code adoption went from a third of engineers to 84% in about a quarter, power users were running $500–$2,000 a month, and the CTO ended up “back to the drawing board”. Uber wasn’t alone; Microsoft pulled Claude Code licenses from a division, Walmart capped its internal vibe-coding tool, and TechCrunch wrote about how “the token bill comes due”. ...

Listen to this article instead ▶ 0:00 / 0:00 0.75× 1× 1.25× 1.5× 2× Your browser does not support the audio element AI agents are having a moment. OpenClaw (née Clawdbot, née Moltbot1) just hit 200k GitHub stars. People are driving a Mac Mini shortage just to manage their email, book flights, and order groceries. Someone launched a fake $CLAWD crypto token that hit $16 million before crashing 90%. Its creator joined OpenAI. There’s a social network for AI agents now. ...

Listen to this article instead ▶ 0:00 / 0:00 0.75× 1× 1.25× 1.5× 2× Your browser does not support the audio element Once upon a time I wrote a post about building a toy TF-IDF keyword search engine. It has been one of the more popular posts I’ve written, and in this age of AI I felt a sequel has been long overdue. It’s pretty fast (even though it’s written in pure Python), it ranks results with TF-IDF, and it can rank 6.4 million Wikipedia articles for a given query in milliseconds. But it has absolutely no context of what words mean. ...

A few years ago I wrote a full-text search engine in 150 lines of Python. The Wikipedia data source it relied on has since been discontinued, and the tooling around it was showing its age. I wanted to (finally) write a follow-up about semantic search, but I realized that I had to get the old repository in a working state first. It’s now using Hugging Face (🤗) datasets, uv, ruff, pytest, and GitHub Actions, without touching the core search logic. ...

Full-text search is everywhere. From finding a book on Scribd, a movie on Netflix, toilet paper on Amazon, or anything else on the web through Google (like how to do your job as a software engineer), you’ve searched vast amounts of unstructured data multiple times today. What’s even more amazing, is that you’ve even though you searched millions (or billions) of records, you got a response in milliseconds. In this post, we are going to explore the basic components of a full-text search engine, and use them to build one that can search across millions of documents and rank them according to their relevance in milliseconds, in less than 150 lines of Python code! ...

Bloom filters are cool. In my experience, it’s a somewhat underestimated data structure that sounds more complex than it actually is. In this post I’ll go over what they are, how they work (I’ve hacked together an interactive example to help visualise what happens behind the scenes) and go over some of their usecases in the wild. ...