Skip to content My favorites Agile, Leadership and Product AI widens the gap between good and bad managers. If you’re not building with these tools yourself, you can’t set real expectations for your team. Individual brilliance doesn’t scale — systems do. A sharp look at how to build organizations that multiply talent rather than just accumulate it. A Platform PM open-sources 6 years of hard-won knowledge into a Claude agent — covering developer adoption, positioning, and the mental models that usually die when people change jobs. When everyone has valid but conflicting perspectives, consensus stalls. This breaks down why alignment is harder than agreement — and what to do about it. When to step in as a manager — a practical framework using the ‘below the waterline’ model. Intervention should be the exception, not the default. Technical folks over-explain and kill their stories with caveats. Hook first, depth later — your audience will ask when they want it. Bees balance exploitation and exploration simultaneously — and so should your product teams. A sharp lens on one of the hardest resource allocation problems in leadership. AI tools have flipped the calculus on staff engineering — if you’re not coding regularly, your tradeoff instincts are already stale. The ‘Design Engineer’ title confusion reveals a real discipline: end-to-end ownership at the design/frontend boundary. Worth reading if you’re hiring or building hybrid teams. Design sprints aren’t just for startups — the more stakeholders, the higher the misalignment risk. De-risk before you write a line of code. AI amplifies fast thinking — but the slow work of deciding what to build still needs human judgment. A sharp framework for knowing when to hit the brakes. Treat your org wiki like a knowledge graph, not a folder dump. Practical framework for documentation that people can actually find and trust. Most engineering orgs have no idea what their teams actually cost or generate. This post does the math — and it’s uncomfortable reading. Organizations sprint before understanding their own mechanics. This reframes deliberate low-stakes exploration as essential learning infrastructure, not wasted motion. Prevention work is invisible — until it isn’t. Classic systems thinking on why process improvements fail and how to make the invisible value of reliability actually stick. A CTO’s Claude Code setup — persistent workspace with personas, integrations, and daily workflows. The composability angle is what makes this worth reading. Great tutors obsess over mental models, not content. The lesson is diagnosing what’s broken in how someone thinks — a frame that transfers directly to code review and mentoring. The ceiling most CTOs hit isn’t technical — it’s the shift from optimizing for correctness to optimizing for commercial outcomes. Hard-won perspective on becoming a real business leader. AI shifts developers from authors to editors — but Agile’s core feedback loops matter more than ever. The PR review problem is real: smaller, intentional units of work. Managers spot team wins easily but miss their own. Lara Hogan helps you recognize your impact — because progress you can’t see, you can’t build on. AI agents are reshaping marketing org design — same structural questions apply to engineering teams. Worth reading to stay ahead of how agentic AI changes headcount and roles. Before pushing OKRs or discovery, fix your delivery pipeline. Outcome thinking is worthless if you can’t act on insights fast enough to matter. Why most B2B companies get stuck in pilot purgatory when scaling value-based solutions — and the capability-building phases that separate scalers from stagnators. Critical chain flips estimation on its head: cut estimates by 50%, pool the saved time as a shared buffer. Counterintuitive, but the case study shows it actually works. Before pushing OKRs or discovery frameworks, fix your delivery cadence. Insights go stale when you can’t ship fast enough to act on them. A sobering look at how senior leaders can unknowingly become the bottleneck — mistaking metrics for trust and missing the signals until someone brave enough reads the list. A clear-eyed breakdown of why most teams misuse metrics — and a practical framework for what to actually measure and why it matters. Architecture, Development & Software development practices HyperLogLog lets you count billions of unique items with kilobytes of RAM. Essential knowledge if you’re building analytics or any system where cardinality estimates matter at scale. How Etsy migrated their database sharding layer to Vitess without downtime. Real-world war stories on resharding, cutover strategies, and the tradeoffs they hit. Why your teams get different numbers for the same metric — and how a proper semantic layer fixes it by making business definitions the authoritative source of truth. Rob Pike’s Go proverbs are still the best distillation of idiomatic Go thinking. If you write Go, these stick with you. A short doc capturing why you made an architectural decision — before everyone forgets. Keep them in the repo, never rewrite them, just supersede. Architecture isn’t about blueprints — it’s a small set of shared decisions that keep hundreds of independent choices coherent. The Winchester Mystery House analogy alone is worth the read. Traces React rendering from server-side MVC to Server Components — helps you pick the right strategy based on actual tradeoffs, not hype. Flip NestJS from code-first to contract-first: generate typed controller interfaces from your OpenAPI spec and let the compiler enforce the contract — not just document it. Solid rundown of Go naming conventions — the rules you need to internalize early so your code doesn’t stand out for the wrong reasons. Cuts through the EDA hype: events aren’t about scaling, they’re about decoupling. Most apps don’t need them — knowing why matters more than knowing how. Before reading a single line of code, run these git commands to instantly surface churn hotspots, bus factor risks, and bug clusters — a diagnostic picture most people miss. 113 hard-won lessons from scaling a Django monolith to 1M LOC. Packed with specific, actionable insights on DB performance, background jobs, and codebase organization. Clear breakdown of SCD Type 2 — how to track historical changes in dimensional data and load them incrementally without losing context. Seven hard-won truths every engineer learns by breaking things in prod — rollback first, test your backups by actually restoring them, and nothing outlasts a temporary fix. How a Red Hat team turned Storybook into a full behavioral verification engine using MSW — real routing, real data fetching, just a mocked network. Clever patterns that make tests actually trustworthy. A deep dive into Git’s internals — packfiles, sparse checkouts, partial clone — written for engineers who need to keep large repos fast. Laws you learn the hard way — Brooks, Conway, Goodhart. Worth a refresh to name what’s already happening on your team. Clever walkthrough of modal dialogs in React Router 7 using nested routes — zero useEffect, clean patterns for loading, errors, and animations. Four concrete design smells — rigidity, fragility, immobility — with real examples and fixes. Useful checklist for diagnosing why your codebase fights back. Stop avoiding pprof. This guide cuts through the confusion — CPU, heap, goroutine profiling explained with practical takeaways so you actually know what to do with the results. Software design is learned by doing, but Conway’s Law is the real boss — your architecture mirrors your org’s social structure, not your technical ideals. Wave Function Collapse explained through road-building simulations — a clever algorithm for structured randomness that’s genuinely useful for game maps, procedural generation, or anywhere you need constrained randomness. AI, LLM & Machine Learning Stop letting AI context die with the session. Externalize decisions into a living doc so you can close the chat without losing everything. A sharp reality check on AI productivity hype — entropy and path dependence don’t disappear when you ship faster. Brooks’ No Silver Bullet still holds. Practical breakdown of where AI agents actually deliver in legal workflows — start with high-volume, low-risk contracts, measure real throughput gains, not tech vanity metrics. Karpathy’s autoresearch idea — one metric, constrained scope, automatic rollback — generalized beyond ML to any measurable goal. Set it running overnight, wake up to compounding gains. Local-first AI agent framework that runs on your hardware with Ollama integration. Cloud optional — finally a practical stack for personal AI that stays personal. Sandboxed runtime for AI agents with declarative YAML network policies. Finally — a way to let agents do real work without handing them the keys to everything. Data-backed guidance on AGENTS.md: less is more, auto-generated files hurt performance, and every line costs inference tokens. Know what to include and what to skip. Stripe, Ramp, and Coinbase converged on the same internal coding agent patterns. This open-source framework distills those production lessons so you don’t have to reinvent them. LLM Compressor v0.10 brings distributed GPTQ compression across multiple GPUs — 3.8x faster on 4 GPUs. Practical if you’re quantizing large models in-house. NVIDIA’s reference stack for running OpenClaw agents sandboxed inside OpenShell — early alpha, but worth watching if you’re thinking about secure autonomous agent deployment. ByteDance’s open-source super-agent harness that orchestrates sub-agents, sandboxes, and memory to tackle hours-long tasks. Worth watching if you’re serious about agentic AI. Multi-agent simulation engine that builds a ‘digital twin’ world to run what-if predictions at scale. Fascinating approach to forecasting via emergent collective behavior. A skill pack that fights LLM design bias — 20 steering commands and curated anti-patterns to stop AI from defaulting to Inter font and purple gradients. Solid overview of the MLOps stack — experiment tracking, model registries, pipelines, serving, and monitoring. Useful when you’re graduating from notebook experiments to production ML. Solid breakdown of where data science actually delivers in enterprise — from manufacturing OEE to GPU-accelerated text classification. Good reference for evaluating where AI investment makes sense. 15 engineers share their real-world AI-assisted workflows — practical patterns for multi-repo coordination, code review, and leaning on AI without losing control of your codebase. Structured AI-powered workflow for shipping software — from idea to code through specialized agents handling planning, architecture, and implementation in phases. Testing AI agents requires a fundamentally different approach — this walkthrough of eval-driven development shows how to move from manual testing to CI-integrated evaluations that actually catch regressions. How Anthropic broke through ceilings in agentic coding with a planner-generator-evaluator architecture. Solid insights on context management and multi-agent decomposition for long-running tasks. AI won’t kill developer demand, but it will obsolete low-level code literacy the same way high-level languages killed assembly knowledge. The new skill is culinary intuition, not knife work. Stop dumping vague tickets into AI tools. Structure your context first with a repository impact map, get human sign-off, then implement. The quality gap is real. Multi-agent framework for statistical package development — isolated Builder, Tester, and Simulator agents that never share specs, so convergence means genuine correctness. A language built for LLMs to write, not humans — structural references instead of names, mandatory contracts, compiler errors designed as LLM-readable fix instructions. Hard data on enterprise AI adoption — 29% of Fortune 500 have live deployments. Cuts through the survey noise with actual contract and usage data. Solid deep-dive into inference engineering — what it is, when it matters, and the techniques (quantization, batching, caching) that make LLMs faster at scale. Jupyter notebooks that build ML algorithms from scratch with live visualizations — gradient descent, backprop, k-means. Best way I’ve seen to actually understand what’s happening under the hood. Open-source framework for building AI SRE agents that investigate production incidents — includes synthetic failure simulations and scored RCA suites to actually train and evaluate them. Solid architectural principles for AI-assisted development: contracts over conventions, verification over generation, and never let the same AI write and judge its own code. The model is just one input — the harness is where the real engineering happens. Essential framing for anyone building or evaluating coding agents. A sharp, unflinching look at how LLMs will make customer service worse, not better — diffusing accountability while making it harder to reach humans who can actually fix things. Chip Huyen’s breakdown of the AI engineering stack — three layers, how it differs from ML engineering, and why it’s really just software engineering with LLMs thrown in. DESIGN.md gives coding agents a structured, persistent understanding of your design system — tokens plus rationale — so they stop guessing your colors and spacing. Built a self-audit prompt to score his own AI sessions against 30 habits. Metacognition is the actual moat — discipline separates insight from noise. Treats the LLM as a compiler — English in, software out — then defines the engineering discipline you need to make that pipeline actually reliable. Sharp mental model. Microsoft’s production-grade multi-agent framework for Python and .NET — graph-based orchestration, checkpointing, human-in-the-loop, and built-in OpenTelemetry. Serious infrastructure for teams moving agents beyond demos. Google’s pretrained time-series foundation model — drop it into your forecasting pipeline without training from scratch. 200M params, 16k context, quantile forecasts included. Practical sensors — linting rules, dependency checks, coupling metrics — that keep AI-generated code from quietly rotting your codebase. Essential reading if agents are touching your repo. LinkedIn’s tool for fact-checking AI-generated claims against real sources. Useful for anyone building or evaluating LLM pipelines where accuracy actually matters. Anthropic’s engineers explain how they broke through ceilings in autonomous coding using a GAN-inspired planner/generator/evaluator architecture. Practical insights on context management and multi-agent handoffs. Using AI to ship without engaging your brain is a slow skill leak. The posture matters more than the tool — ask conceptual questions, don’t just paste and ship. Survey data from 900+ engineers on AI’s real tradeoffs: less tedium, but eroding code quality, unrealistic business expectations, and junior devs struggling most. DevOps, Observability & Security Drop-in haptic feedback for mobile web apps with React, Vue, and Svelte hooks. Finally, native-feeling touch responses without the native app overhead. Offline-first survival server bundling local AI (Ollama+RAG), Wikipedia, maps, and Khan Academy in Docker. When the internet dies, your knowledge base doesn’t have to. PS4 emulator running Bloodborne and Red Dead Redemption on desktop — impressive systems-level C++ work, and a reminder of what open source communities can pull off in their spare time. Resisted tmux longer than I should have. Sessions that survive SSH drops, keyboard-driven splits, composable config — once it clicks, screen feels prehistoric. awk is one of those tools that pays dividends every time you touch a terminal. Solid practical reference if you’ve never moved past the basics. Skeleton loaders that mirror your actual UI structure at runtime — no separate components to maintain. Drop in a wrapper, done. Solid update to this streaming markdown component — staggered animations, inline KaTeX, and fixes for code blocks that actually matter in production AI chat UIs. Solid breakdown of the analytics tool landscape — useful for when you’re helping teams pick the right platform without getting lost in vendor marketing. Turns AI agents into structured PMs: PRD → epics → GitHub issues → parallel execution across git worktrees. Solves the context-loss problem that plagues multi-session AI development. One command spins up a full Metaflow stack locally — Kubernetes, Argo Workflows, UI, the works. Perfect for testing before touching your cloud account. Rands shares his Claude Code workflow: project-scoped CLAUDE.md files, WORKLOG.md session diaries, and small scripts that cut friction. Practical setup worth stealing. Drop-in guided tours for React apps. Define steps, ship onboarding — minimal config, fully customizable, accessible out of the box. Package manager for AI skills — write your context modules once, install to Claude Code, Cursor, Gemini CLI, and more with a single command. Teach your AI agent to actually understand Obsidian’s syntax and CLI. Drop these skills into your vault and Claude Code or Codex can create proper wikilinks, Bases, and Canvas files. Someone implemented a full 6502 CPU emulator in pure PostgreSQL — registers, flags, and 64KB of memory as tables, every opcode a stored procedure. Gloriously unhinged. TinyGo 0.41 lands ESP32 wireless support — run a web server on your microcontroller in the same language powering your backend. One language, edge to cloud. wrk meets grafana. Plow gives you real-time latency histograms and percentiles in the terminal AND a live web UI while your benchmark runs. Zero overhead, straightforward flags. Dead-simple SSH tunnel manager that lives in your terminal. If you’re juggling multiple tunnels across environments, this beats maintaining a mess of alias scripts. Kubernetes-native way to run isolated, stateful AI agent workloads — fills the gap between Deployments and StatefulSets with stable identity and warm pools. ↑ Top