Hassan Raza’s Post

One of the most underrated truths in engineering: Logs are more important than features. Because when something breaks (and it will): If you can’t see what happened, you can’t fix it. I’ve seen systems where: - errors happen silently - logs are inconsistent or missing - debugging becomes guesswork At that point: you’re not engineering, you’re troubleshooting blindly Now I treat logging as a first-class feature: - every critical path is logged - logs are structured, not random text - errors are traceable across the system Good logging gives you: - visibility - confidence - faster recovery Without it: You don’t have a system. You have uncertainty. #Observability #Logging #BackendDevelopment #SystemDesign #DevOps #SoftwareEngineering

We've all been there. A production error pops up, and your first instinct is to dive into the logs. Minutes turn into hours, sifting through endless lines, looking for that one crucial piece of information that's somehow always missing or buried. The error is still there. You're frustrated. The logs are often useless because they're built for machines, not humans, and rarely capture the context of the user's journey or the system's state at the exact moment of failure. We log too much, too little, or in formats that are impossible to correlate. Here's a practical fix: Implement structured logging with correlation IDs. * Each log entry should be a JSON object. * Include a unique ID that travels through every service involved in a request. * Add critical business context (user ID, order ID, feature flag state). This immediately makes debugging faster. You can trace a single request across your entire system. Fewer bugs. Better focus. #Debugging #ProductionErrors #Logging #SoftwareEngineering #DevOps

“It Worked on My Machine” Is a Process Problem We’ve all heard it. Maybe we’ve even said it. 😅 “It worked on my machine.” But that’s rarely a code problem. It’s a process problem. Development happens locally. Production runs somewhere else. Different: OS versions Environment variables Database states Dependency versions Hardware resources If environments aren’t consistent, behavior won’t be either. That’s why mature teams invest in: Containerization (e.g., Docker) Environment parity (dev ≈ staging ≈ production) CI pipelines Automated tests Infrastructure as code When systems are reproducible, excuses disappear. “It worked on my machine” usually means: We didn’t standardize the environment. Good engineering isn’t just writing code. It’s designing a process where the machine doesn’t matter. #SoftwareEngineering #DevOps #EnvironmentParity #SeniorDeveloper #EngineeringCulture

I had to fix the same test 3 times… and it completely changed how I think about backend systems. I recently built an event-driven workflow system that supports correction (supersede) behavior. In simple terms: An operation can be performed… and later corrected if needed. Example: FAILED → COMPLETED   COMPLETED → FAILED  Sounds straightforward… until testing broke 😅 My test kept failing because I was assuming: “Same request should return the same response” But in a stateful system, that’s not true. Example: First call → CORRECTED_FROM_FAILED   Second call → ALREADY_COMPLETED  Same request. Different result.   And both are correct. The issue wasn’t the backend — it was my test design. Here’s what I fixed: 1. Stopped comparing full responses (too brittle)   2. Started validating invariants:   • correct ownership of records     • no cross-user access     • spoofed inputs ignored   3. Made tests correction-aware (allowed result sets)   4. Ran spoof checks before any state mutation  Final result: 41 passed, 0 failed ✅ Big lesson: As systems become stateful and event-driven,   you don’t test for identical outputs… You test for correct behavior under change. This shift changed how I design and validate systems going forward. #CloudEngineering #BackendEngineering #SystemDesign #DevOps #EventDriven

🔥 Most performance tuning is a waste of time. Not because systems are complex — but because we optimize the wrong thing. I’ve seen: • Micro-optimizations with zero impact • Days spent chasing the wrong bottleneck • Real issues hiding in unexpected places The real skill is not tuning — it’s finding the bottleneck correctly. This video breaks down how to use: → Tracing → Metrics → Profiling to identify the real problem. 🎥 Watch here → https://lnkd.in/dW8gY6_7 👉 What’s the worst performance debugging experience you’ve had? #PerformanceEngineering #Observability #Backend #devops

CI failures shouldn’t require parsing 300–500 lines of console logs. So I built a small POC: Designed and integrated a log-aware inference layer in the Jenkins CI pipeline, leveraging LLaMA to transform unstructured build logs into structured failure summaries with actionable insights in real time. But the real value isn’t the model. It’s how the output is standardized and actionable. Now, when a build fails, engineers don’t scan logs. They get this at the end: STAGE: Which stage failed CAUSE: Root cause in plain English FIX: Specific actionable fix Under the hood: → Capture console logs + build metadata → Pre-process (dedupe, chunking, noise filtering) → Extract high-signal sections (stack traces, exit codes) → Pass structured context to LM Model → Generate failure summary + classification From an SRE lens, this is interesting: • CI systems generate signals, but engineers do manual interpretation • Failure understanding is still tribal knowledge • Cognitive load is an untracked reliability cost This POC shifts that: Standardized failure interpretation Faster triage Foundation for auto-remediation Next step: Map → CAUSE → FIX into automated actions (retry, rollback, owner routing) Because at scale, systems shouldn’t just fail. They should explain themselves. #SRE #DevOps #Jenkins #Kubernetes #PlatformEngineering #GenerativeAI #LLM #AIinDevOps

A team we talked to got a $14K overage on their observability bill last quarter. One service. One bad deployment. This is how it played out. A developer pushed verbose debug logging to production on a Thursday. The deploy went clean. Tests passed. No alerts fired. The team moved on to the next sprint. Meanwhile, that one service started writing 4x its normal log volume. Every request now generated a wall of debug output. The ingestion meter was climbing in a straight line, day after day, and nobody noticed because nobody had a reason to look. We kept hearing versions of this story while building Parseable. Teams that got burned by a retry storm. Teams where a single misconfigured exporter doubled their trace volume over a weekend. The pattern was the same every time: the signal was there, the tools didn't surface it, and someone found out from a bill or a budget review. So we added ingestion forecasting to Parseable's overview charts. You toggle it on, and your existing ingestion curve projects forward. Logs broken down by severity, traces by status, metrics by count. Same view you already check, with a forecast layered on top. If you've run a platform team long enough, you've absorbed a hit like this. Was it a logging change? A retry storm? A config someone forgot about? #observability #sre #platformengineering #opentelemetry #devops

Honestly... think about failures. One of the most useful (and surprisingly fun) exercises when designing systems is asking: "What are all the ways this can fail?" You can wire up sophisticated alarms for application errors, high latency, and runtime issues... but still completely miss the sneaky ones. Classic example: You have no errors showing up because your application isn't even running. Without a "deadman alarm" (or dead man's switch), silence doesn't mean "all good", it means you might be blind 😆 . It's impossible (and inefficient) to think through every failure mode from scratch on every new project. In practice, the real solution is building a stronger "platform": shared standards, opinionated tooling, and foundational observability that every team gets for free. That leaves developers to focus only on application-specific metrics. As we're accelerating our Kubernetes adoption, this is exactly why we need to invest more in developing our internal platform. #PlatformEngineering #SRE #Kubernetes #Observability #DevOps

Your pipeline doesn't need more tools. It needs better decisions. THINK ABOUT WHAT HAPPENS TODAY A developer fixes a typo in a README. Your pipeline kicks off a full SAST scan, dependency check, and container image scan. Twelve minutes later, you get the same warnings. Nobody reads them. Now a developer adds a new file upload endpoint writing to the database. Your pipeline runs the exact same checklist. A typo and a new attack surface get treated identically. That's not security. That's ceremony. THE REAL PROBLEM We don't have a tooling problem in DevSecOps. We have a decision-making problem. Nothing in the pipeline understands what actually changed — or what matters. Every stage runs in isolation: • Security scans ignore lint results • Container scans ignore code findings • Change approvals are rubber stamps WHAT'S BROKEN? → Every PR gets the same treatment → Stages don't talk to each other → Reports pile up but nobody acts → Pipeline time is wasted on low-value scans → Change approvals lack context HOW TO FIX IT 👇 Put AI agents inside pipeline stages — not to replace tools, but to replace hardcoded decisions. → Run what matters, not everything → Accumulate context across stages → Adapt scrutiny based on risk → Deliver actionable assessments WHAT STAYS THE SAME Your Jenkins stays. Your GitHub Actions stays. Your scanners stay. Your change management stays. WHAT CHANGES The decision layer gets filled by agents that read, reason, and decide. ONE CAVEAT ⚠️ Autonomy only works with capable models. Weak models need guardrails. WHERE TO START Start with one stage. Let the agent prove value. Then expand. The best DevSecOps pipelines won't be designed. They'll emerge. #DevSecOps #AgenticAI #CICD #AppSec #PlatformEngineering #AIAgents #ShiftLeft #SoftwareEngineering