#ai
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Giving Agents Minimal Tools
Agents are powerful because they can reason their way towards a goal using tools to interact with the world. Some tools gather new information, other tools perform actions. The most compelling agentic workflows are tasks where you have a well defined end goal, but the intermediate steps are unclear.
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Documenting your software libraries for agents
This post is a companion to a talk I gave at PyData London 2026. You can watch the recording here and see the slides here.I’ve spent a lot of my software engineering career working on open source tools, libraries and frameworks. These are chunks of software that other software engineers use to build their software. I make things for makers, and I like that. A large part of working on these projects is telling people about the library, and explaining to them how to use it. Open Source Software Engineers spend a big chunk of their time on grassroots marketing, getting other engineers to use their code.
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Why taking notes still matters
AI tools have radically changed note taking. All you need to do is record/transcribe your meeting and your favourite AI tool will write your notes for you. Magic! As a result I’ve noticed a lot of people stop taking notes altogether and delegating the whole thing to tools like Copilot. After all, why write notes if the AI generated notes in Microsoft Teams are going to be more comrehensive and complete with no effort required on your part?
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Guiding your contributor's agents to better behaviours
Many open source maintainers have noticed an uptick in low-effort AI generated PRs recently, myself included. The most frustrating of these is when someone prompts their agent to
"Fix <url to issue> and make a PR with the changes". Reviewing these PRs can be time consuming because diffs can be large and the contributors rarely respond to review feedback, they just prompt and move on.