When solving a problem with an existing system, most people tend to add. But often, removing makes a design better. * Dieter Rams's famous principle is "Less, but better." * Strunk & White tell you to "Omit needless words." In fact, the paper People systematically overlook subtractive changes explores this concept. From the abstract: Improving objects,
I used to think shipping an agent product meant building: 1. The core LLM loop. 2. Skills: describing to the LLM how to do particular things (including building API connectors etc). But it seems that Claude Code and Codex are strong enough general-purpose agents that you can just outsource (1) to one of those generic ones, and focus just
Mostly talking about AI, Pactum and selling to enterprises. It was recorded at the end of August, so some things are already likely stale! Find the podcast on Youtube or your favourite podcast app. Excerpts: I’m not very surprised or disappointed by GPT-5, and that’s because the trajectory is actually quite straightforward. The scaling laws tell us
I recently noticed that it's a bit clunky to talk about "users" of our agents. Building complex enterprise software, we have multiple types of users, from operations teams making daily decisions to executives looking at high level dashboards, and of course suppliers. But often none of these people can make important judgement calls, like "will
When things are stable, you make plans by anchoring to the question "what will change?". With your customers, your company, technology, regulation or anything else about the world. This does not work when things are changing fast. If best-in-class tech changes every 6 months, or if your product strategy is up in the air, or customers
I have a 14,000-word document I maintain manually, to add into ChatGPT queries I make related to my role as CTO. It covers a lot of ground about Pactum: * An extremely basic description of who we are and what we do * Company strategy, long-term goals, financial model, and progress towards those * Our target market, ICP, personas, value
Deep research is underappreciated. The feature exists in all three LLM chat apps I pay for (ChatGPT, Claude and Gemini) and is unfortunately heavily rate limited, which makes sense given the likely token cost. But the massive amount of tokens burnt on reading and analyzing hundreds of articles is the reason it can have such a big impact on some
Here's a model I use to think about how AI will change work. Some parts of every activity will cost 1,000x less than they do when a human does it. For engineers it might mean reading and typing code, for radiologists reading X-ray images, etc. But it's not 100% of the time a human
I've recently seen several engineers struggle when moving to the role of leading a small product team and managing a couple engineers. There are so many newly important meetings and Slack messages (that you could ignore as an individual contributor) and they take up most of your time, but don't feel like "real work"
There seem to be two views of what will happen with engineers' jobs given the new efficiencies from using advanced AI coding tools (Cursor, Claude Code, etc). The first view is partial rebound. Fewer engineers will be needed in total: if engineers are 20% more effective per hour, there will be some downsizing of the total engineering bill. Perhaps
