OpenClaw's 3-hour outage: the dangers of unreviewed code changes

💥 “It works on my machine” — the most dangerous sentence in development Every developer has said this at least once 😅 But here’s the reality 👇 Your code doesn’t matter if it only works locally. 👉 Real-world problems I’ve seen: API works locally but fails in production Environment variables missing Different Node versions causing issues Hardcoded URLs breaking deployment 💡 Quick Fix Checklist: ✔️ Use .env properly ✔️ Never hardcode API URLs ✔️ Test in production-like environment ✔️ Handle errors gracefully 🚀 Pro Tip: Always think like this: “Will this work for 1000 users, not just me?” 🎯 That mindset separates beginners from experienced developers. 💬 What’s the weirdest bug you’ve faced in production? #WebDevelopment #MERNStack #Debugging #SoftwareEngineering

The Developer Agent Doesn’t Just Write Code Most people think a dev agent is there to generate code. That’s not how I’m using it. I call mine Archon. Right now, Archon operates in isolation. A cloned GitHub environment. Separate from production. Every change gets reviewed before it touches anything real. Archon doesn’t just build. It reviews: Code efficiency Logical flaws Hidden bugs Unintended consequences Then it improves what already exists. The shift: I’m not asking for code. I’m asking for better code. Most people use AI to accelerate development. I’m using it to raise the quality of what gets shipped. Nothing goes straight to production. Everything passes through scrutiny first. That’s where most systems break. Not in creation. In what gets allowed to continue. Archon reduces that risk. I still decide what merges, but I’m not reviewing everything alone anymore. Without this layer, speed becomes liability. Tomorrow I’ll break down the security agent and how I make sure nothing unsafe ever gets deployed.

🚀 Excited to share my first open source release: Everything OpenCode (EOC) — a comprehensive plugin for OpenCode that supercharges your AI-assisted development workflow. Building with AI agents is powerful, but raw tools alone aren't enough. EOC gives OpenCode a full agent harness with structure, memory, and discipline built in. 🧰 What's inside: • 16 specialized agents — planner, architect, security reviewer, TDD guide, and more • 40+ slash commands — /plan, /tdd, /code-review, /security-scan, and much more • 11+ event-based hooks for automation • 3 custom native tools (run-tests, check-coverage, security-audit) • Domain skills that load on-demand for backend, frontend, security & more ⚡ Install in seconds: bun x eoc-opencode@latest Or scoped to your project: bun x eoc-opencode@latest --local This is my attempt to bring the kind of structure and workflow discipline to OpenCode that makes AI coding feel less chaotic and more like working with a focused engineering team. It's open source, MIT licensed, and I'd love feedback, issues, or contributions from the community. 🙏 🔗 GitHub: https://lnkd.in/g4VVSxPC 📦 npm: https://lnkd.in/gV8ZaKBT #OpenSource #AI #DeveloperTools #OpenCode #AIEngineering #SoftwareDevelopment

𝐎𝐧𝐞 𝐦𝐢𝐬𝐭𝐚𝐤𝐞 𝐈 𝐦𝐚𝐝𝐞 𝐢𝐧 𝐦𝐲 𝐀𝐏𝐈𝐬… ⚠️ In the beginning, I used to send raw errors directly from my backend. Whatever broke → I just returned it as it is. At that time, it felt fine… The API was working, responses were coming, everything looked okay. But as the project grew, things got messy 😅 Debugging became painful: 🔹 unclear error messages 🔹 inconsistent responses 🔹 no idea what actually failed That’s when I realized — error handling is not optional in real applications. Now I always try to: ✔ structure error responses (consistent format) ✔ use proper HTTP status codes ✔ log errors properly (so I can trace issues later) ✔ avoid exposing unnecessary internal details It’s a small change… but it makes a huge difference in maintainability and debugging. Still learning to build systems that don’t just work… but are easier to manage and scale 🚀 #BackendDevelopment #API #FullStack #SoftwareEngineering #LearningInPublic #DeveloperJourney #Upskilling

Wrapping up an update to Autarch to bring it into compliance with Anthropic’s guidance. Instead of getting an OAuth token for the API, you can now switch your Autarch backend from API to Claude Code. It: - uses the Claude CLI in print mode - uses the same Autarch system prompts - uses the same Autarch shell tools - exposes all Autarch tools via dynamically created and registered MCP - parses the streamed JSON output to give a consistent experience, regardless of backend - is functionally equivalent to using the API Testing things locally and working out the kinks (biggest one is tool call correlation across the CLI/MCP boundary) and will be pushing the last of the fixes up shortly. https://lnkd.in/eCaH5q7D

I have always wanted to understand pull requests 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆, without having to decode 𝟱𝟬𝟬+ 𝗹𝗶𝗻𝗲𝘀 𝗼𝗳 𝗱𝗶𝗳𝗳. I recently discovered a solution: 𝘴𝘦𝘮 from Ataraxy Labs, available at https://lnkd.in/dAqrGQPV. This tool eliminates line noise and provides clear information about what functions were added and modified. It’s 𝘎𝘪𝘵, but it finally shows 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 rather than just 𝗹𝗶𝗻𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀. #DevTools #Git #SoftwareEngineering #Productivity

Now you can enhance your workflows with 1 Github repo. My AI agent recently forked the 'everything-claude-code' repo from GitHub to enhance our n8n workflows. This repo, boasting 148,984 stars, offers a performance optimization system tailored for Claude Code and other platforms. The possibilities immediately caught my attention: skills, instincts, memory, and security all packed into one JavaScript codebase. Here's how I implemented it: - Forked the repo to set the foundation. - Integrated the optimization system with our existing n8n pipelines, focusing on agent performance. - Utilized the system's capability to boost AI agent memory and security, aligning them with Mob Makers' client requirements. The real win here? Optimizing our workflows saved us 2 hours per day in execution time, giving us more room for AI model training and client iterations. How does this apply to you? Imagine using this optimization system to refine your firm's AI tools or legal automation processes. It could massively streamline your operations and reduce resource drain. You can check out the repo here: [everything-claude-code](https://lnkd.in/dBHiP-4v). If you want to see how this setup could transform your legal workflows, drop a comment or DM me. #OpenSource #AIAgents #GitHub #n8n

ArgoCD: Synced. Healthy. Cluster: different from Git. Both true at the same time. Here is how. 👇 ANSWER: (D) All three happen in production 1️⃣ kubectl edit on a live resource    ArgoCD only tracks declared fields.    Drift in excluded annotations? Synced stays green. 2️⃣ Admission webhook mutation    Pod deploys. Synced.    Istio/Kyverno/OPA mutates it post-admission.    ArgoCD never rechecks. Still green. 3️⃣ Controller crash mid-sync    Partial apply. ArgoCD sees resources exist.    3 of 12 manifests missing. Still Synced. 🎯 THE REAL GAP GitOps guarantees Git was applied. Not that the cluster currently matches Git. Different things. Most teams treat them as the same. 🔧 FIX IT 1️⃣ argocd app diff my-app    Run as cron. Alert on non-empty output. 2️⃣ selfHeal: true + prune: true    Continuously reconciles back to Git.    Warning: overwrites kubectl edits. 3️⃣ driftctl scan    Catches out-of-band drift ArgoCD misses. Synced = sync finished. Not: cluster matches Git. Found drift ArgoCD missed? Drop it below 👇 #GitOps #ArgoCD #Kubernetes #DevOps #30DaysOfDevOps

Someone built the most complete Claude Code setup Boris Cherny uses at Anthropic on GitHub and it's 100% free. 19.7K stars. 1.7K forks. #1 trending on GitHub. It's called claude-code-best-practice and it has everything you need to go from vanilla Claude Code to a full agentic setup: → 84 battle-tested tips from Boris Cherny (creator of Claude Code), Thariq, Cat Wu and the actual Anthropic team → Subagents, Slash Commands, Skills, Hooks and MCP servers -- with implementations, not just theory → Orchestration workflow: Command → Agent → Skill pattern with a working /weather-orchestrator demo → 8 top dev workflows compared side by side (Superpowers, Spec Kit, BMAD, Get Shit Done, OpenSpec, HumanLayer + more) → Agent Teams with tmux and git worktrees for parallel development → Ralph Wiggum loop for long-running autonomous tasks → Cross-model workflow (Claude Code + Codex) for QA and plan review → Reports on CLAUDE.md, memory, skills in monorepos, LLM degradation, Agent SDK vs CLI The tips alone are worth the clone: >> "Challenge Claude -- grill me on these changes and don't make a PR until I pass your test" >> Start every task in plan mode, use Opus for planning and Sonnet for code >> Keep CLAUDE.md under 200 lines, wrap domain rules in <important if="..."> tags >> Use subagents with fresh 200K contexts instead of compacting >> Agentic search (glob + grep) beats RAG every time If you're still using Claude Code like it's ChatGPT with a terminal, you're leaving 10x on the table. 100% Opensource. MIT license. Repo: https://lnkd.in/g5aTrkUq

A small habit that improved my code quality a lot: Before pushing code, I ask myself: “What can go wrong here?” Not what works. Not the happy path. But what can break. So I check: • What if data is null? • What if API fails? • What if this runs 1000 times? • What if response is slow? • What if user does something unexpected? Earlier, I used to write code for success. Now I try to write code for failure. Because real systems don’t fail in obvious ways. They fail in edge cases. And most bugs come from: things we didn’t think about. This one mindset shift: 👉 reduced bugs 👉 improved debugging 👉 made code more reliable Good developers write code that works. Better developers write code that keeps working. #dotnet #softwareengineering #developers #cleanCode #AjayDevInsights