Writing
Deep-dives on software architecture and the way source code is structured — written to be understood by beginners, yet useful to teams shipping at scale. Diagrams, real examples, no hand-waving.
Code review is where team culture is made or broken. A practical guide to reviewing code so it ships better AND the author comes back stronger — concrete phrasing, a reviewer's checklist, and the habits that quietly turn reviews toxic.
Kindness isn’t niceness, and it isn’t soft. It’s a force multiplier — clearer feedback, safer incidents, faster-growing teammates. What ‘kind engineering’ really means, and how to practise it in code reviews, incidents, and everyday work.
A great pull request is a gift to your reviewer: small, well-described, easy to say yes to. The anatomy of a reviewable PR — size, title, description, commit hygiene, and self-review — with concrete before/after examples.
A trusted pull request does more than look tidy. It names the behavior being changed, shows the safer pattern, and gives reviewers enough evidence to approve with real confidence instead of guesswork.
Most engineers were never taught how to give feedback — or how to take it. A field guide to specific, kind, actionable feedback, and to staying open when you're on the receiving end, with ready-to-use scripts.
Great teams are grown, not just hired. From leading engineering teams: how to mentor so people level up fast — pairing, code review as teaching, the right-sized stretch, and the mindset shifts that turn a junior into someone you'd trust with anything.
AI isn't just a faster tool bolted onto how we build software — it's quietly moving the center of gravity of every role. The work that used to define a BA, PO, PM, developer, or QA engineer is being automated from the bottom up, and what remains is a different job built on judgment, taste, and verification. This is a clear-eyed, role-by-role look at what's fading, what's rising, and what to watch out for as teams move from AI-assisted to AI-first — so you can see what may be lost, what's added, and how to stay genuinely valuable in a changed era.
Most AI results are disappointing not because the models are bad, but because the context is thin. A practical guide to context engineering — what lives in the model's window, the five pillars of great context, common anti-patterns, and reusable templates that consistently produce better results.
Generative AI has quietly moved from headline to household tool — yet most people still use it for little more than the occasional question. This is a hands-on catalog of the most common, genuinely useful applications across work, learning, creativity, and everyday life, organized so you can find at least one thing to try today. No hype, no jargon — just real use cases you can apply immediately, plus the one skill that makes all of them work and the limits you should never ignore.
