*Hey, Luca here! Welcome to a new edition of the *
This week’s newsletter is brought to you by Unblocked!
Unblocked** **brings together your PRs, docs, conversations, tickets, and more– turning them into actionable context for both your team and their AI agents.
Engineering teams that are getting ahead right now use a context engine like Unblocked to make better plans, use fewer tokens, and spend less time in review cycles.
AI is in your engineering workflow. While the token spend shows it, the throughput doesn’t. The human is very much still in the loop, and that’s a context problem.
Most knowledge bases slowly become piles of uninteresting information. After a while everything starts looking the same: meeting notes, ideas, projects, living side by side and you don’t know exactly how and why.
I believe when you store a new piece of information, there are two questions you need to ask yourself:
**What is this?**What is this useful for?
The second question is often overlooked, and it’s a core idea behind Portent, which I published last month.
Portent includes default types — which answer the question “what is this” — but also a set of default relationships (belongs to, related to, etc) to help you figure out how to connect notes together, based on what they should help you with.
So, a meeting may belong to a project, a procedure may belong to a responsibility, a resource may be related to a topic, and so on.
Connecting notes together is hard work, but it’s paramount work! Otherwise you easily get stuck: you optimize too much for memory, and not enough for motion.
The goal is to know what to do next, rather than remember more stuff.
You can find the full article below:
Code quality conversations are often extremely vague.
Everyone wants cleaner code, fewer bugs, easier changes, but unless these things become visible and measurable, they remain mostly opinions.
One thing that I liked in my conversation with Stuart Caborn is that Loveholidays treated code health like a data product.
They collected signals, put them on dashboards, trended them over time, compared teams, and checked whether the numbers matched how engineers felt in sentiment surveys.
The AI angle makes this even more important. If code health data is available through tools and MCP servers, humans and agents can ask questions directly:
How is my team doing compared with yesterday?
Are there any areas that got worse this week?
Where should an agent be more careful?
At that point code health becomes a feedback system that informs your dev process, and it’s exactly the kind of feedback AI needs. The better the codebase, the easier for agents to understand it, change it, and spend fewer tokens doing so.
You can find the full interview with Stuart here 👇
Finally, here are the best articles I have read this week:
James makes a distinction I like a lot: scepticism as a conclusion vs scepticism as an identity. The useful version comes from actually using the tools, staying specific about what fails, and keeping your mind open as the evidence changes. This applies just as much to AI enthusiasts as to sceptics.
Brian uses calculators as the analogy for coding agents: the tool removes a lot of mechanical work, but makes the underlying intuition even more valuable. I do not agree with every prediction in here, but the core point is sharp: skipping the struggle may leave you unable to judge the output.
Lara turns career decisions into four simple lists: must-haves, nice-to-haves, don’t-cares, and what you are optimizing for. This is practical and useful, especially when titles, prestige, and compensation make a role look more appealing than it really is.
Quick heads up! Joel and Melinda, coaches in residence at Refactoring, are part of a group offeringfull coaching scholarshipsto 29 engineering leaders from underrepresented groups in tech. Applications are open until the end of the day on 30th June.
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See you next week!
Luca
