🚀 Built k8s-debug – a lightweight CLI to reduce Kubernetes pod debugging time In large-scale microservices architectures, where hundreds of pods run across namespaces, identifying failing vs healthy pods quickly becomes noisy and time-consuming. Debugging often turns into repetitive kubectl commands, scattered logs, and delayed root cause identification — especially during incidents. To simplify this, I built k8s-debug 👇 What it helps surface quickly: • CrashLoopBackOff patterns • Failed / unhealthy pods across services • Termination reasons • Namespace-level pod health overview Instead of manually filtering and scanning outputs, this gives a clear, aggregated view of failing vs running pods in one place. Goal: Reduce time-to-diagnosis (MTTR) and improve observability during high-pressure scenarios. ⚡ Install: pip install k8s-debug-tool Run: k8s-debug --namespace <your-namespace> 📦 PyPI: https://lnkd.in/gNn44_Rs 💻 GitHub: https://lnkd.in/giYkPH_6 This is an early version, currently tested in a Kubernetes test environment. Planning to extend it with deeper diagnostics and real-time insights. Would appreciate feedback from engineers managing large-scale Kubernetes workloads #DevOps #Kubernetes #SRE #Microservices #CloudEngineering #Python #OpenSource #Automation

Claude Certified Architect (CCA) | Sprint 5: Automated CI/CD Evals Subject: How do you automatically test an AI that gives a different answer every time? Traditional unit tests (assert 2+2 == 4) fail in LLM orchestration. But if you cannot automatically test your agentic pipeline, you cannot safely deploy code to production. For Sprint 5 of my Commercial Underwriting Platform, I built an automated "LLM-as-a-Judge" CI/CD pipeline using the native Claude CLI. Architected & Shipped: - Headless CI/CD Evals: I built a bash pipeline (ci_pipeline.sh) that securely injects environment variables into a headless subshell. It runs my Python extraction agents, saves the output, and then invokes the claude CLI using the -p (prompt) and --bare (no UI) flags. The Architect's Result: Claude wakes up in the background, acts as an automated judge, mathematically verifies the extracted financial JSON, and throws a standard UNIX Exit Code 1 if it detects a regression. The pipeline prints 🟢 PIPELINE SUCCESS: Code is safe to merge to main. (SEE IMAGE) The Lesson:  We must treat prompts as production code. Integrating the native Claude CLI directly into bash scripts allows us to build robust regression testing pipelines that protect the main branch from hallucinated updates. Follow the Build Hub: https://lnkd.in/ewZETSz2 #AgenticAI #Claude #DevSecOps #CICD #Anthropic #SystemArchitecture #TechIfat

Docker Image Optimisation I've been working on optimising the gatus docker image. The current public image is very well optimised. It uses: Multi stage builds Final image built from scratch Minimal Layers The image size is 74MB In terms of changing the dockerfile there isn't much I can change. However, there were a couple of things I was able to do. When building the binary I added the following options to the build command: -trimpath This removes filesystem paths from the binary -ldfalgs="-s" This omits the symbol table from the binary With these two changes, the image size shrunk to 47MB This shows the importance of understanding the application and how it is built. The most impactful change came from the way the binary is built, not the way the dockerfile is structured. #DevOps #Docker #LearningInPublic #optimisation

🚨 The Claude Code Leak: 500,000+ Lines Exposed — But That’s Not the Real Story On March 31, 2026, a routine npm release accidentally exposed ~1,900 TypeScript files (over 500K lines) from Anthropic’s Claude CLI (v2.1.88). Let that sink in — not through a hack, but through a build misconfiguration. 💥 What actually happened? • A large source map file was published • It mapped minified code back to full source • Default Bun behavior + missing .npmignore rule = exposure • Code was mirrored publicly before being patched 🔐 What was NOT leaked: • No model weights • No training data • No user data So yes — serious, but not catastrophic. 💡 The real takeaway (and why every DevOps/SRE should care): This wasn’t a security breach. This was a pipeline failure. We spend so much time securing: ✔️ Infrastructure ✔️ APIs ✔️ Secrets But often overlook: ❌ Build artifacts ❌ Packaging rules ❌ Source maps in production 🧠 Interesting insights from the leaked code: • “Undercover mode” to protect sensitive internal operations • Advanced agent coordination for complex workflows • Background task orchestration & proactive monitoring This gives a rare glimpse into how modern AI tooling is engineered beyond just models. ⚠️ Lessons for Engineering Teams: Treat source maps as sensitive artifacts Always validate what goes into npm packages Enforce CI/CD guardrails (artifact scanning, linting) Never rely on defaults in build tools (Bun, Webpack, etc.) Add explicit allow/deny rules (.npmignore / package.json files field) 🔥 Final Thought: In 2026, leaks are no longer just about data… They’re about engineering decisions exposed in public. And sometimes, the weakest link isn’t your system — it’s your deployment pipeline. #DevOps #SRE #Security #Observability #AI #Anthropic #Claude #SoftwareEngineering #Cloud #BuildSystems #CICD

The problem was rarely the shipping speed. The landing was off because secrets, config, and worker dependencies had never been tightened to the same spec across all cloud slots – dev branch deploy or production launch at two AMs received special subtle differences off the norm. For an application measuring environment discrepancies was impossible at first shot without grounding the check again with proper coding practices I realized everyone takes Railway code and built without checking: was runtime pinned as on Vercel under Dockerfile lines instead measured later after entire repo release gone stable unseen. Two choices existed build runtime once artifacts deployed or source built twice slower tests false error faster is arguably cost specific more problematic small monitoring structure break. Either environment configurations result in incorrect session work exactly under Vercel because preview branch overrides DATABASE_LOAN_env ignoring canonical test DB I solved controlled rebuild rather than cache broken seeded difference testing pushing the regular event clean to DB comparison And pulling full specific behind failure tracked in schedule cron using Cron-job dot end timer against path service true confirm pipeline link This way lost deployed timing recovery source lost but follow work continuation for deploy slow pull signals speed contrast right operator changes remain tied true line growth track separate containers clean workflow documentation right now runtime working exactly else unknown slack integrated logs quick same service copy pinned replicas controlled fallback trace but small background update route clock remain essential shift The detail: replicating fixed permanent build instead shifting vector cache early speed but removing break case for repeated full clean force solution good stage sync long test route planned separation #Automation #CronJobs #Python #Backend #Railway #Vercel #DevOps #CICD #DotJobsTimerServerless

🐳 **𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲𝘀: 𝗧𝗵𝗲 𝗨𝗻𝘀𝘂𝗻𝗴 𝗛𝗲𝗿𝗼 𝗼𝗳 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁** We talk a lot about containers, deployments, and scalability… But often overlook the one file that makes it all possible — the **𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲**. 𝗔 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘀𝗲𝘁 𝗼𝗳 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀. It’s a **blueprint** that defines how your application is built, packaged, and run — consistently across every environment. ### 🚀 𝗪𝗵𝘆 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 ✔️ Eliminate “it works on my machine” issues ✔️ Ensure consistency from dev → staging → production ✔️ Speed up onboarding for new developers ✔️ Enable reliable CI/CD pipelines ### 🛠️ 𝗞𝗲𝘆 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝘁𝗼 𝗙𝗼𝗹𝗹𝗼𝘄 🔹 **𝗨𝘀𝗲 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗯𝗮𝘀𝗲 𝗶𝗺𝗮𝗴𝗲𝘀** Smaller images = faster builds + better security 🔹 **𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗹𝗮𝘆𝗲𝗿 𝗰𝗮𝗰𝗵𝗶𝗻𝗴** Structure your file so dependencies are installed before copying full code 🔹 **𝗔𝘃𝗼𝗶𝗱 𝗵𝗮𝗿𝗱𝗰𝗼𝗱𝗶𝗻𝗴 𝘀𝗲𝗰𝗿𝗲𝘁𝘀** Use environment variables or secret managers 🔹 **𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗮𝗴𝗲 𝗯𝘂𝗶𝗹𝗱𝘀** Keep production images clean and minimal 🔹 **𝗪𝗿𝗶𝘁𝗲 𝗰𝗹𝗲𝗮𝗻, 𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲𝘀** Because your team will read them more than once ### 🎯 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 A poorly written Dockerfile can slow down your entire pipeline. A well-optimized one can quietly boost performance, security, and developer experience. 👉 Treat your 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲 like production code — because it is. 💬 How do you optimize your 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲𝘀? Any tips or mistakes you’ve learned from? #𝗗𝗼𝗰𝗸𝗲𝗿 #𝗗𝗲𝘃𝗢𝗽𝘀 #𝗖𝗹𝗼𝘂𝗱𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 #𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 #𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀

It's impressive when tools can recognize complexity and offer relevant solutions, like suggesting a GitLab CI runner setup. Imagine going from simply building an app to managing a full CI/CD pipeline, including deployment, dependencies like Python and Postgres, and even choosing a web server. This goes beyond basic development, touching on maintainability and automated processes. The next step involves integrating security testing (SAST/DAST) into the pipeline to proactively catch issues. Check out more insights at https://lnkd.in/gjXm7xzG #CI/CD #DevOps #SoftwareDevelopment #CloudComputing #Automation

𝗠𝗖𝗣 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 — 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗛𝘆𝗽𝗲 Everyone’s posting “MCP is the future.” Nobody’s talking about what happens when it hits real traffic. 𝘐 𝘣𝘶𝘪𝘭𝘵 𝘔𝘊𝘗 𝘴𝘦𝘳𝘷𝘦𝘳𝘴 𝘪𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯. 𝘏𝘦𝘳𝘦’𝘴 𝘸𝘩𝘢𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘮𝘢𝘵𝘵𝘦𝘳𝘴. 𝗠𝗖𝗣 𝗶𝘀𝗻’𝘁 𝗵𝗮𝗿𝗱 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁. It’s hard to operate. Most demos show a tool server returning “Hello World.” Production demands: → What happens when the tool call fails at 3 AM? → How do you trace a request across 4 MCP servers? → Who’s validating those AI-generated tool arguments? → How do you rate-limit per LLM client without killing UX? I just dropped a 20-page guide covering all of it. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 (𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗰𝗼𝗱𝗲): 𝟭. 𝗝𝗮𝘃𝗮 / 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 → SSE transport, typed tool registry, Micrometer metrics 𝟮. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗮𝘂𝗹𝘁-𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝘁 𝗖𝗹𝗶𝗲𝗻𝘁 → Retry + circuit breaker + graceful fallback 𝟯. 𝗡𝗼𝗱𝗲.𝗷𝘀 𝗠𝗖𝗣 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 → Multi-server routing, round-robin load balancing, JWT enforcement 𝟰. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝘁𝗮𝗰𝗸 → Prometheus + structlog — every tool call, traced end-to-end 𝟱. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗛𝗮𝗿𝗱𝗲𝗻𝗶𝗻𝗴 → JWT scopes, Pydantic input sanitisation, injection prevention 𝟲. 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → HPA, zero-downtime rollout, health probes 𝟳. 𝟴 𝗔𝗻𝘁𝗶-𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗯𝗶𝘁𝗲 𝘁𝗲𝗮𝗺𝘀 𝗶𝗻 𝗽𝗿𝗼𝗱 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗜 𝘀𝗲𝗲? Teams build one giant MCP server for everything. 𝘞𝘳𝘰𝘯𝘨. Domain-separated servers (payment-mcp, inventory-mcp, analytics-mcp) + a gateway = independent scaling, independent deployments, independent failures. 𝘛𝘩𝘢𝘵’𝘴 𝘩𝘰𝘸 𝘺𝘰𝘶 𝘴𝘩𝘪𝘱 𝘔𝘊𝘗 𝘭𝘪𝘬𝘦 𝘢 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘢𝘭 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳. Download the PDF (attached ↑) Drop a 🔧 in the comments if you’re building with MCP. Share with your team — someone needs to see this before their next prod incident. 𝗙𝗼𝗹𝗹𝗼𝘄 chandantechie for more production-grade AI engineering content. #MCP #ModelContextProtocol #AIEngineering #LLM #SpringBoot #Python #Kubernetes #SoftwareEngineering #PrincipalEngineer #chandantechie

Spent 2 hours debugging a broken API. Turns out, I forgot to log the request body. I checked the response, traced the code, even asked a colleague. Nothing made sense. The server was throwing an error, but the logs didn’t show the actual payload. I added a log statement. The problem vanished. The request body was malformed. Log everything. Even the 'obvious' parts. A missing log line hides 80% of the problem. What’s the one thing you forgot to log that cost you time? #Debugging #DevOps #SoftwareEngineering