Claude's .claude/ folder: AI coding productivity

Your pod is CrashLoopBackOff. You've run 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘥𝘦𝘴𝘤𝘳𝘪𝘣𝘦 𝘱𝘰𝘥 17 times. You still don't know why. Here's my Kubernetes debugging cheatsheet. Save this. You'll need it at 3am. 𝗦𝘁𝗲𝗽 𝟭: 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗮𝗰𝘁𝘂𝗮𝗹 𝗲𝗿𝗿𝗼𝗿? 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘭𝘰𝘨𝘴 <𝘱𝘰𝘥> --𝘱𝘳𝘦𝘷𝘪𝘰𝘶𝘴 The --previous flag shows logs from the crashed container. Most people forget this. 𝗦𝘁𝗲𝗽 𝟮: 𝗪𝗵𝘆 𝗱𝗶𝗱 𝗶𝘁 𝗰𝗿𝗮𝘀𝗵? 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘥𝘦𝘴𝘤𝘳𝘪𝘣𝘦 𝘱𝘰𝘥 <𝘱𝘰𝘥> | 𝘨𝘳𝘦𝘱 -𝘈5 "𝘓𝘢𝘴𝘵 𝘚𝘵𝘢𝘵𝘦" Exit code 137 = OOMKilled. Exit code 1 = app error. Exit code 143 = SIGTERM. 𝗦𝘁𝗲𝗽 𝟯: 𝗜𝘀 𝗶𝘁 𝗮 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝘀𝘀𝘂𝗲? 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘵𝘰𝘱 𝘱𝘰𝘥 <𝘱𝘰𝘥> Hitting memory limits? That's your OOM. Increase limits or fix the leak. 𝗦𝘁𝗲𝗽 𝟰: 𝗜𝘀 𝗶𝘁 𝗮 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 𝗶𝘀𝘀𝘂𝗲? 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘨𝘦𝘵 𝘦𝘷𝘦𝘯𝘵𝘴 --𝘧𝘪𝘦𝘭𝘥-𝘴𝘦𝘭𝘦𝘤𝘵𝘰𝘳 𝘪𝘯𝘷𝘰𝘭𝘷𝘦𝘥𝘖𝘣𝘫𝘦𝘤𝘵.𝘯𝘢𝘮𝘦=<𝘱𝘰𝘥> Events tell you what Kubernetes sees. Image pull errors, volume mounts, scheduling failures. 𝗦𝘁𝗲𝗽 𝟱: 𝗖𝗮𝗻 𝘆𝗼𝘂 𝗴𝗲𝘁 𝗶𝗻? 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘦𝘹𝘦𝘤 -𝘪𝘵 <𝘱𝘰𝘥> -- /𝘣𝘪𝘯/𝘴𝘩 If the container is crashing too fast, change the command to sleep 3600 temporarily. 𝗕𝗼𝗻𝘂𝘀: 𝗧𝗵𝗲 𝗻𝘂𝗰𝗹𝗲𝗮𝗿 𝗼𝗽𝘁𝗶𝗼𝗻 𝘬𝘶𝘣𝘦𝘤𝘵𝘭 𝘳𝘶𝘯 𝘥𝘦𝘣𝘶𝘨 --𝘪𝘮𝘢𝘨𝘦=𝘣𝘶𝘴𝘺𝘣𝘰𝘹 --𝘳𝘮 -𝘪𝘵 -- 𝘴𝘩 Spin up a debug container in the same namespace. Test DNS, network, service discovery. 𝟵𝟬% 𝗼𝗳 𝗽𝗿𝗼𝗱 𝗶𝘀𝘀𝘂𝗲𝘀 𝗮𝗿𝗲: • OOMKilled (increase memory) • Config/secrets missing (check mounts) • Image pull failed (check registry creds) • Readiness probe too aggressive (increase timeout) What's your go-to debugging command? #Kubernetes #SRE #DevOps #Debugging #K8s

The "guess-and-redeploy" cycle is all too familiar in software development: • A bug is reported. • It can’t be reproduced locally. • Add a log statement, push to Git, wait for CI/CD, deploy. • Hope it reproduces… it doesn’t. • Realize the log was in the wrong spot. • Repeat. Sound familiar? The average time wasted: 3 hours. The actual fix? Usually about 5 minutes. The real bottleneck isn’t the fix—it’s identifying the problem. This cycle not only burns time but also drains team energy and slows innovation. What if we could streamline this process? Better debugging tools, more comprehensive logs, or even shifting left with testing could make all the difference. How does your team tackle this challenge? Let’s share ideas and break the cycle! 🚀 #DevTools #BuildInPublic #IndieHackers

I broke auth for 200 users my first week. Not because I was careless. Because the guy who built it left in March and took the mental model with him. Auth touched 6 files. Nobody documented why. The wiki described what someone intended in 2022. The code had evolved into something else entirely. I shipped a fix that made perfect sense given what I could see — and detonated something I couldn't. That's not a "read the docs better" problem. Every codebase has a shadow architecture. The real structure that lives in nobody's head anymore. Docs describe intention. Code contains truth. The gap between them is where new engineers get destroyed. Here's what I do now, first 10 minutes in any unfamiliar repo: Open Claude Code. Type: "explain how authentication works in this codebase." It reads the live files — not the wiki, not the stale README. In 4 minutes I have the actual flow, the weird decisions, the hidden connections. The mental model that would take half a day of tab-switching to build badly. You've already been burned by the invisible connection between two things nobody documented. The real question: are you the kind of engineer who figures out the shadow architecture before you ship — or after? #claudecode #softwareengineering #devtools

We've standardized this across our entire development team — and it changed how we build with AI. Most Claude Code setups we come across are running on one layer. A CLAUDE.md file, maybe some basic instructions. That works for solo projects. It doesn't scale across a team. There are actually 6 layers to the full architecture: Layer 1 — Memory: What Claude reads before every session. Team rules in git, personal overrides gitignored, modular instruction files always on. Layer 2 — Skills: Self-contained expertise folders Claude invokes automatically through semantic matching. You don't call them. They show up when relevant. Layer 3 — Hooks: Shell scripts wired to 17 event triggers. They are deterministic — they run every single time without exception. This is where you put anything you can't leave to chance. Layer 4 — Agents: Parallel subagents running in isolated context windows. Code review, security audits, QA — all happening without touching your main thread. Layer 5 — Plugins: Bundle your entire workflow — skills, agents, hooks — into one package your whole team can install in a single command. Layer 6 — MCP: The connection layer between Claude and your full stack. GitHub, Jira, databases, internal APIs. The distinction that changes every architecture decision you make: → CLAUDE.md + Hooks are deterministic. They always run. → Skills + Agents are probabilistic. Claude decides. We put together a full visual breakdown of all 6 layers in the carousel above. Swipe through — it's worth 2 minutes. If your team is building seriously with Claude Code and wants to compare notes on how we've structured this in production, drop a comment or reach out directly. @Anthropic @ClaudeAI #ClaudeCode #SoftwareDevelopment #AIDevelopment #AgenticAI #EngineeringLeadership #DeveloperTools #AIEngineering #TechLeadership #SoftwareEngineering #Anthropic

Navigating the "Red Screen" Moment Nothing tests a team’s resolve quite like a 500 Critical Error in a live environment. 🚨 We’ve all been there: the logs are scrolling, the alerts are firing, and the pressure is on to find that one line of code or infrastructure hiccup causing the disruption. While these moments are high-stress, they are also the greatest opportunities for growth, improving our monitoring stacks, and refining our incident response protocols. The goal isn't just to fix the crash—it's to build a system resilient enough to handle the next one. How does your team handle live application crashes? Do you have automated rollbacks? Is your observability stack ready for real-time debugging? What’s your "go-to" first step when the alerts hit? Let’s talk about best practices for keeping cool when the production environment heats up. 👇 #SoftwareEngineering #DevOps #SystemArchitecture #CodingLife #SRE #TechLeadership #Debugging #IncidentResponse #WebDevelopment #Programming #SoftwareReliability #CloudComputing

This plugin can genuinely improve how you build in: •⁠ ⁠Claude Code •⁠ ⁠Cursor •⁠ ⁠Codex •⁠ ⁠OpenCode •⁠ ⁠Gemini CLI •⁠ ⁠Copilot CLI What I like is not just the tool. It encourages a disciplined workflow: 1.⁠ ⁠Brainstorm first 2.⁠ ⁠Turn the idea into a spec 3.⁠ ⁠Write the plan 4.⁠ ⁠Break work into smaller tasks 5.⁠ ⁠Do TDD 6.⁠ ⁠Request code review 7.⁠ ⁠Finish the branch cleanly In this phase of software development, judgment is becoming the most valuable resource. Tools can generate code. Judgment decides what should be built, tested, reviewed, and ready to ship. That sequence matters more than the tool itself. Repo: https://lnkd.in/gSrt2Spy #github #claude #opus4-6 #chatgpt5.4 #cli #opensource Jose DIAZ Ignacio Perez Salazar M Arturo De La Peña Loredo

Every developer knows the feeling: something works perfectly in your environment but fails elsewhere. Enter cache invalidation, the silent disruptor that can turn a smooth deployment into a debugging nightmare. This meme reminds us that while 'It works on my machine' is a common refrain, it’s not always the full story. Cache issues can lurk beneath the surface, affecting performance and user experience. Let’s embrace this as a reminder to test thoroughly across environments and consider cache management early in our development process. When cache invalidation joins your meeting—software's version of 'It works on my machine.' #DevLife #SoftwareDevelopment #CacheManagement #Debugging #TechMemes #EngineeringHumor

**Most engineers treat containers as either an Ops tool or a Dev tool. They're both — and conflating the two causes real workflow problems.** --- • `docker run --name test -d -p 8080:80 nginx:latest` — three flags doing distinct jobs: identity, detachment, and port mapping. Each one a decision point, not boilerplate. • `docker exec -it test bash` attaches a new Bash process to a running container — it doesn't restart or alter the container's primary process. A subtle but operationally important distinction. • Containers ship without tools like `ps` by default — intentional design to reduce attack surface and image size. Debugging requires external tooling (Docker Desktop/Docker Debug), not assumptions about what's inside. • A Dockerfile encodes the full dependency graph: base image (`FROM alpine`), runtime installation (`RUN apk add nodejs npm`), source copy, and entrypoint — all auditable, all repeatable. • `docker build -t test:latest .` produces an immutable, portable artefact from source — the bridge between a Git repo and a running workload. • `docker rm` vs `docker stop` — stopping is graceful, removal is permanent. Running `docker ps -a` after confirms state, not assumption. --- **The practitioner implication:** If you're building platform tooling or internal developer platforms, the Ops and Dev workflows need separate runbooks but shared mental models. Engineers who understand both can debug across the boundary — the developer who built the image and the operator who ran it aren't always the same person, and that gap is where incidents live. Containerising an app in under five commands is straightforward. Knowing *why* each command behaves the way it does is what separates a platform engineer from someone following a tutorial. #DevSecOps #Containers #Docker #PlatformEngineering #CloudArchitecture

What’s the longest you’ve spent debugging a production issue that ended up being a one-line fix? For me, it was 4 hours. A missing *await* in an async function caused an issue that didn’t show up until 6 services downstream. It felt like chasing a ghost through the system! Moments like these are both humbling and educational. They remind us: • How small oversights can ripple through complex architectures • The importance of clear error handling and logging • Why a calm, methodical approach saves the day We’ve all been there—those moments when you finally spot the fix and can’t decide whether to laugh or cry. What’s *your* most memorable debugging story? Let’s hear it! 🛠️ #SoftwareEngineering #Debugging #TechStories #DevTools #APM #production