Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR AI solved a real math problem, not a practice one. GPT-5.4 Pro cracked an open research problem in combinatorics that stumped earlier models, and the mathematician who posed it plans to publish the result. AI is beginning to contribute to the actual frontier of knowledge. Anthropic’s usage data reveals a clear pattern: experience pays off. Users with 6+ months on Claude are 10% more successful in their conversations and tackle higher-value work. Getting good at AI tools is a skill that compounds. GitHub will train on your private repositories starting April 24 unless you opt out. There’s a single settings page to stop this. Check it before the deadline. A compromised AI developer tool stole credentials from thousands of systems. Two versions of LiteLLM, a widely used library for connecting to AI APIs, contained malware that harvested API keys and passwords. If your team uses LiteLLM, check your versions now. Anthropic launched a science blog and demonstrated AI completing a theoretical physics paper in two weeks instead of a year. The research community is moving from “AI helps me write” to “AI does the experiment.” Story of the Week: AI Crosses Into Real Research This week produced the clearest evidence yet that AI is moving beyond assistance into genuine knowledge creation. Research tracker Epoch AI confirmed that GPT-5.4 Pro solved an open problem in combinatorics (the mathematics of counting and arrangement) that had resisted human solution. The problem’s author, a mathematics professor at UNC Charlotte, reviewed the solution and plans to publish it. He noted that the AI’s approach “eliminates an inefficiency in our lower-bound construction” in a way he had suspected might work but couldn’t figure out. The result will become a peer-reviewed paper, with the researchers who elicited the solution listed as potential co-authors. ...

In the last post, you built your team , and now you can start managing your team. Pick the agent that makes the most sense for the task at hand, and work with them on it. When you move on to a new task, open a new session with the appropriate agent for that task. As you work with your team, you’ll quickly notice that you’re giving them the same facts and details over and over again. They’re good in their roles, but they don’t know anything about your specific job. Your Marketing Strategist doesn’t know what products you’re working on. Your Data Wizard doesn’t know what the metrics mean in the data they’re analyzing. Your Copywriter doesn’t know which value props to highlight in your ad copy. They’re missing information they need to do the work. You’ve run into the first type of gap: a knowledge gap. Now you need to fix it. ...

I remember when I realized I was starting to use AI at work as if I were managing a team of AI employees . I got so excited, I immediately sketched the idea on a sheet of paper so I could share it with my teammates. What started as a sketch is now core to how I use AI agents to do things faster and better at work and at home. It’s an approach that naturally guides you toward the context engineering best practices that improve LLM output. ...

I used to think of AI as a tool I used. Now I think of it as a team I manage. This perspective evolved gradually as I used it daily and found myself rewriting the same persona prompts over and over again for the same types of tasks. I started systematically improving what I was doing until I found myself managing a team, which happened to be made up of AI agents, at the end of the 7-month journey that made me an AI enthusiast . ...

I was slow to start using generative AI, but over the last 7 months, AI has fundamentally changed how I work. I’ve gone from occasionally using AI to write text, to using it to create Python scripts, to now having a team of five custom AI agents that I collaborate with daily. I’m seeing how quickly the benefits are compounding, and as a result, I’ve been AI-pilled. I began learning about LLM-based gen AI in earnest in 2024. I read all the most popular books at the time, but my exposure remained primarily theoretical. I learned how LLMs work fundamentally, but the biggest practical takeaway was the idea of assigning a persona to chatbots to improve their output. That’s basic prompt engineering, e.g. “You are a copywriter with 15+ years of experience in consumer tech. Help me write a marketing email about this product.” On the rare occasion I used a chatbot, I always remembered to assign it a persona. ...