Tag: github

Make PRs Logical Again

The day-to-day work of software engineers is shifting –  less time is spent on implementation, and more on specifying, planning, and reviewing. As AI coding tools improve, the bottleneck moves upstream and downstream: writing clear specs, structuring changes well, and conducting thoughtful reviews become the highest-leverage skills.

One surprisingly weak point in that workflow is GitHub PR file order. Files are shown in alphabetical order that rarely reflects the logical flow of the change. Good reviews are narrative: start with the contract, then the core logic, then the edges and tests. When the order is wrong, reviewers waste cognitive energy reconstructing the story instead of evaluating the change.

That realization pushed me to build a small Chrome extension that lets you reorder files in a PR so the review reads in the right order. Check it out here.

It was also a personal reminder of the joy of doing something for the first time – I never built a Chrome extension before. My first attempt was messy and unsuccessful, so I wiped everything and restarted using Spec-Driven Development (SDD) with OpenSpec. The second iteration was dramatically smoother, more structured, and consumed fewer tokens. In a world where implementation is getting cheaper, clarity in specs, understanding of trade-offs, and the review experience are quickly becoming the real craft of engineering.

5 interesting things (12/02/2026)

The Quarterback Paradox – while I’m not sure I agree that it is a paradox – i.e., recruiting a critical position to an organization is hard even if you have a lot of data, I love and strongly agree with the suffix of the post – “As in the NFL, in organizations the hardest part is often not finding talent, but creating the conditions in which real potential does not break before it has a chance to become reality.”

https://noamwakrat.medium.com/the-quarterback-paradox-e93e4325bac1

What LEGO Can Teach Us about Autonomy and Engagement – Who doesn’t like LEGO? We all played with it as children, and some of us still build today. In this post, Pawel Brodzinski describes a neat experiment he runs in training sessions – teams first build a LEGO set under a manager’s direction, then self-organize for a second build, and consistently report higher engagement when given more autonomy. While it shows a clear effect, the experiment has some drawbacks – most notably an order effect: the self-organized build always comes second, so the engagement boost could partly stem from participants being warmed up and more comfortable rather than from autonomy alone. Always nice to read about LEGO as an adult.

https://brodzinski.com/2026/01/lego-autonomy-engagement.html

Skyll – Skills are markdown instruction files that teach AI coding agents how to perform specific tasks. Today, skills must be manually installed before a session, meaning developers need to know upfront which skills they’ll need. Skyll is an open-source search engine and API that lets any AI agent discover and retrieve skills on demand at runtime, ranked by relevance, without pre-installation. You can think of it as a package manager for agent capabilities, enabling agents to be truly self-extending and autonomous.

https://github.com/assafelovic/skyll

Skyhook.io radar – Existing K8s dashboards tend to be either heavyweight, cloud-dependent, or require cluster-side components. Radar’s zero-install, single-binary approach with real-time topology and traffic visualization answers the need of developers and platform teams who want quick, frictionless cluster observability that can even run on their laptop, especially useful for DevEx-focused teams looking to reduce the friction of Kubernetes debugging and operations,

https://github.com/skyhook-io/radar

Babysitter – If you worked with coding agents, you probably experienced this pain: the lack of a structured process control and non-deterministic workflows. Babysitter lets you define iterative workflows (research → spec → TDD loop → quality gate → deploy) that are deterministic, resumable across sessions, and auditable, which is critical for moving AI-assisted development from ad-hoc experimentation toward reliable, production-grade engineering workflows and complex features.

https://github.com/a5c-ai/babysitter