Latest Posts

Maybe Software Stops Being the Default

I used to write little translators for my bank exports. Then ChatGPT wrote them. Now nobody writes them: I hand an agent the file. As agents get better at following instructions, more tasks stop needing durable, task-specific software at all.

Chat, we've made a terrible mistake...

I hit enter too early on a message to Claude and it identified the fundamental design flaw in our codebase before I could describe it. Three LLMs had signed the thing off as essentially perfect. Nobody was in "are we solving the right problem" mode.

LLM spambots liked my Show HN post more than real people did

I posted my AI agent jail to Show HN. Nobody upvoted it, but within fourteen hours thirteen LLM-written sales pitches found the two email addresses on the page.

The prompt is not the work

We need a better vocabulary for describing contribution in the age of generative AI. The binary of "human-made" versus "AI-generated" lacks the expressive power to say what a piece of work actually represents.

Forget the complexity: AI all boils down to drawing the right lines

Over and over again, despite the best efforts of humans, the most effective AI systems come down to one simple idea: finding the right shaped line that fits some data points.

RAG chunking isn’t one problem, it’s three

Existing articles often focus on a chunk as a singular concept: you split the article into paragraphs, say, and use these to feed the LLM, generate embeddings, and quote back to the user. But that's three problems, not one.

I will fix your vibe-coded MVP

You delivered your vision quickly using one of the many excellent LLM-based coding tools, users are happy. I will dig you out of the tech-debt hole you have created.

Understanding Modern AI is Understanding Embeddings: A Guide for Non-Programmers (with lots of dogs!)

Embeddings are a core AI concept that underpin a great deal of what we today think of as being AI. This article is going to give you an accurate and intuitive understanding of what an “embedding” is in less time than it takes to eat a (very large) bagel.

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