Skip to content My favorites Agile, Leadership and Product Skip-level 1:1s only work if you actively probe — not just listen. How to surface what your direct reports won’t tell you before people start leaving. Forwarding AI summaries isn’t knowledge transfer — it’s debt. A sharp read on what happens when comprehension lives in one person’s head and they walk out the door. Most companies chase AI efficiency gains — headcount cuts, faster processes. This challenges that reflex: the real leverage is growth, not just doing less with more. Most teams optimize what they know — few explore what’s possible. The explore/exploit balance explains why Kodak and Blockbuster failed while Apple thrived. In matrix orgs, your informal network matters more than your title. Research shows 3-5% of employees drive 20-35% of value — be intentional about who you build relationships with before you need them. The real risk isn’t being pro- or anti-AI — it’s holding your view as identity rather than evidence. A sharp framework for staying intellectually honest. Communication isn’t soft skill fluff — it’s a delivery mechanism. When it breaks down, assumptions fill the gaps and rework follows. Practical breakdown of where it goes wrong first. People hoard their best AI workflows out of job security fears, not ignorance. Worth understanding before you wonder why your AI adoption isn’t spreading. The middle of every org chart was always translation work. AI ate that layer — what survives is why and how. Will Larson updates his engineering leadership playbook for the AI era: migrations are now a solo sport, but your development harness matters more than ever. AI didn’t eat job titles — it ate translation tasks. A sharp read on why the middle of every org chart is getting restructured right now. When exec sponsors conflict, you’re not a mediator — you’re absorbing organizational failure. Five moves to stop being a shock absorber and start leading the actual work. Every growth stage introduces a new coordination tax. The teams that win keep ownership close to the work — not headcount close to the roadmap. The unglamorous part of an acquisition: what a TSA and SPA actually demand from the tech lead once due diligence wraps and lawyers take over. Why 95% of transformation projects fail isn’t tech or budget — it’s that nobody built a safe, structured way for frontline problems to surface before the kickoff. A sharp framework for engineering managers: sort your meetings into internal, boundary, and external signals to know when to fly high or dive into the weeds. Cutting entry-level roles to fund AI ROI feels efficient until your mid-level pipeline runs dry in a few years — a trade-off worth war-gaming before you make it. A former backend engineer’s path into technical product management — a solid read on why understanding the ‘why’ behind engineering decisions makes you a sharper PM. Rands’ decades-old All Hands format still holds up—steal the structure if your company meetings feel like a waste of everyone’s time. AI makes it easy to look polished fast — the real differentiator is judgment: knowing when to trust, question, or push back on the output. Architecture, Development & Software development practices C# 12 primary constructors cut the DI boilerplate I’ve written a thousand times. One gotcha to know about, but the tradeoff is worth it. Solid walkthrough of state machines in .NET using an Uber-style trip lifecycle — covers illegal transition prevention, EF Core persistence, and race conditions you’ll actually hit in production. Solid deep-dive on database indexes — the hidden costs, why your index might be doing nothing, and the gotchas that bite you in production. A technical deep-dive into Conductor’s performance rewrite — what stack choices and architectural decisions made it twice as fast. Dogfooding at its finest. Performance isn’t a discipline problem or a tooling problem — it’s entropy. After 400 perf PRs at Vercel, the lesson is you need systems, not vigilance. Solid breakdown of Next.js App Router error handling — expected errors as return values, error boundaries for unexpected ones, plus Honeybadger integration for production visibility. Accessibility isn’t a checklist item—bake it into CI and code review like you would security or observability, before AI-generated UI ships broken screen-reader support. A sharp rethink of React performance for 2026 — skip the memoization reflex and fix state placement, effects, and rendering strategy instead. A field guide to money systems: precision handling, idempotency, and reconciliation patterns that keep fintech code trustworthy. Worth bookmarking as a reference. Kent Beck reframes YAGNI: it’s not about saving effort, it’s about the cost of committing to structure before you know what you actually need. AI, LLM & Machine Learning Shipping an AI feature without observability is flying blind. Four signals you need: versioned prompts, structured traces, user scores, and LLM-as-judge. What actually changes when you ship AI features as a backend engineer — non-determinism, prompt design, agents in prod. The missing manual for developers crossing over. Sprints and story points were built for expensive human effort — AI breaks those assumptions. A sharp look at what SDLC actually needs to look like now. AI writes clean diffs and passes tests, but misses the subtle invariants your system depends on. A timely reminder that review depth matters more now, not less. Turns your LLM into a capable browser agent by giving it a terminal and Playwright — no multi-agent orchestration, just code. Works as a plugin for Claude Code and Codex. TypeScript harness for building real autonomous agents — not chatbot wrappers. Brings Claude Code-style architecture to your own agents with sandboxing, sessions, and skills built in. Design systems are becoming AI context engines — encoding intent, accessibility, and brand DNA for agents, not just humans. Worth reading if you’re thinking about how AI will consume your design infrastructure. Why AI projects keep dying after the demo: LLMs shatter your intuition about prototype-to-production effort. The fix is organizational, not technical. The harness is what you own when you rent an LLM — and engineering it properly is the difference between 30% productivity gains and AI slop disasters. Vibe coding’s dirty secret: AI defaults to insecure configs. Practical playbook for security context files and guardrails before your citizen-built prototype hits production. Practical guide to wiring linting, dependency rules, and mutation testing as automated guardrails that catch AI-generated code quality issues before they compound into real maintenance debt. Solid mental model for what actually replaces SaaS in an AI-native world — seven concrete layers from context retrieval to observability that separate demos from production systems. Microsoft launches seven in-house MAI models — reasoning, coding, voice, transcription, image — all trained without distillation. Worth knowing as they’re landing on OpenRouter and GitHub Copilot. AI made writing code cheap — now review is the bottleneck. Essential reading on why human understanding remains the scarce resource no tool has replaced. Microsoft just launched their own model family — MAI-Thinking-1, MAI-Code-1-Flash, and more. Worth knowing what’s in your stack if you’re using GitHub Copilot. Stop prompting agents yourself — design systems that do it for you. Addy Osmani breaks down the five building blocks of loop engineering that are already shipping in Claude Code and Codex. Context engineering, not better reasoning, was the unlock. PostHog’s onboarding wizard went from primitive to magical by building a context supply layer — a lesson every agent builder needs. Atlassian makes the case for a new role: someone who architects how context flows to your AI agents. The missing piece most orgs haven’t named yet. Self-hosted meeting transcription and summarization with Whisper/Parakeet and Ollama, fully local so sensitive calls never touch someone else’s cloud. Most AI strategies fail not because the tech doesn’t work, but because leaders chase urgency instead of clarity — worth a re-think before your next roadmap review. If you’re weighing self-hosted vs. proprietary LLMs, Kimi K3’s a strong case for routing complex coding work to open-weight models instead of always paying API tax. A sharp reframe for anyone building agents: you rent the model but own the harness, so invest your engineering there, not in waiting for a smarter model. Most bad AI output isn’t a model problem, it’s a spec problem: define done, feed clean context, and get fresh eyes to check the result. DevOps, Observability & Security Microsoft ships a native Windows build of GNU coreutils — same commands, same flags. Finally, your shell scripts just work without WSL or translation. Solid open-source API key server from Ory — handles issuance, revocation, and token derivation with proper security defaults. Worth knowing if you’re tired of rolling your own. A React code smell detector that reads whole components like a reviewer would — names the cost, proposes a fix, cites the docs, and knows when to stay quiet. A single Rust binary that brings Bun/Deno-style DX to stock Node.js — TypeScript runner, script runner, package manager, and version manager, without abandoning Node compatibility. Git worktrees have existed since 2015 but AI-assisted parallel coding finally makes them essential — swap contexts without stashing, keep your editor untouched, work multiple branches simultaneously. Compiler-first UI framework that turns plain JS classes into surgical DOM updates at build time — no virtual DOM, no hooks, just 121B hello world. Genuinely interesting alternative. Drop-in script that turns any webpage into a natural-language-controlled agent — no extensions or headless browsers, just text-based DOM manipulation and your own LLM. An open source, agent-ready design system built on React and StyleX — fully themeable if you want a customizable starting point instead of building from scratch. Zig-based inference stack that compiles once and runs on any GPU—NVIDIA, AMD, TPU, Trainium—no per-hardware rewrites. Worth a look if you’re tired of vendor lock-in. A TypeScript compiler that ships native binaries with no Node runtime — 320KB, 4ms startup, and it tells you exactly which lines still need the JS engine. Ever wanted to spin up a full macOS VM in Docker for CI or testing? This project pulls it off with KVM acceleration and a web-based viewer — no Hackintosh required. ↑ Top