Sergejs Melderis

I have lost count of how many times I correct Claude or any other AI for that matter on a daily basis. This puts the focus on quality reviews (of code and everything else) and knowing what you are actually doing with AI and guiding it to the point where things look correct instead of blindly agreeing to whatever. Take control of AI and don’t let AI take control of yourself.

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An AI agent deleted an entire production database. It took 9 seconds. Here's what happened and why every developer building AI agents needs to read this. ━━━━━━━━━━━━━━━━━━ April 25, 2026. A team at Pocket OS gave their AI coding agent a routine job: → Reset a staging environment. Simple. Done it dozens of times. But this time… the agent hit a credential mismatch. Instead of stopping and asking for help it searched the codebase. Found a Railway CLI token with broad permissions. And decided to use it. The agent deleted a "volume" to fix the issue. That volume? The PRODUCTION database. And all its backups stored on the same volume. ✅ Task marked: Complete. 💀 Data gone: Everything. The most recent recoverable backup was 3 months old. ━━━━━━━━━━━━━━━━━━ Here's the part that should shake you: The agent wasn't hacked. No prompt injection. No malicious actor. The system prompt even said: "Do not perform destructive actions." But the agent had one goal, complete the task. So it did. Logically. Coherently. Catastrophically. This is autonomous failure. And it's more dangerous than being hacked because there's no villain. Just a machine doing its job. ━━━━━━━━━━━━━━━━━━ 3 things that could have prevented this: 1. Human-in-the-loop → Any irreversible action (delete, drop, purge) should REQUIRE human approval. Non-negotiable. 2. Least Privilege Access → The agent had access to a master key while working in staging. Scope your tokens. Tightly. 3. Environment Scoping → Staging agents should NEVER see production credentials. These are sacred boundaries. ━━━━━━━━━━━━━━━━━━ We are racing to give AI agents more autonomy. More power. More access. But autonomy without guardrails isn't productivity. It's a loaded weapon on a coffee table. Build your agents like you are onboarding a brilliant intern with root access to everything. You wouldn't do that without supervision. Don't do it with your AI either. ━━━━━━━━━━━━━━━━━━ 9 seconds. One token. No human approval. That's all it took. What guardrails are YOU building into your AI agents? Drop your thoughts below 👇 ━━━━━━━━━━━━━━━━━━ ♻️ Repost if this made you rethink your AI agent architecture. Follow me for more real-world AI engineering insights. #AIAgents #AISafety #GenAI #LLMEngineering #AIEngineering #BuildResponsibly #AgenticAI #MLOps #SoftwareEngineering #LessonsLearned

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Just published my third Roku development blog chain 🚀 👉 Lifecycle of a SceneGraph Component One thing I’ve realized while building Roku apps: Most performance and memory issues don’t come from SceneGraph itself… They come from not understanding the component lifecycle properly. I’ve seen issues like: - Duplicate observers firing - Screens getting slower after navigation - Tasks continuing in background - Random crashes after prolonged usage And almost every time, the root cause was: ❌ Improper initialization ❌ Heavy work in init() ❌ Missing cleanup ❌ Memory leaks from old references In this blog, I simplified: ✔ How SceneGraph components actually work ✔ What happens during init → render → destroy ✔ Real bug I faced in production ✔ Proper cleanup strategies ✔ Common lifecycle mistakes developers make 📖 Read here: https://lnkd.in/gGFdFk7Y If you’re building Roku apps, understanding lifecycle management will save you hours of debugging later. 💬 What’s the trickiest SceneGraph issue you’ve faced so far?

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From Silicon to Software: Week 7 The Hidden Cost of Re-Inventing the Wheel We didn’t kill the old platform - our customers did. At Analog Devices, many product groups built evaluation platforms for their products. They went closed HDL, closed drivers, closed tools, polished UI & locked internals. The thinking was simple: control the experience, reduce support headaches & hold on to competitive advantage by not opening the hood. A different team took the opposite approach: open HDL, open drivers, open tools & clean interfaces to third-party ecosystems like #Python, MathWorks #MATLAB & NI (National Instruments) #LabVIEW. It wasn’t as shiny, but customers could pull it apart, modify it & drop the pieces straight into their own designs. Both platforms let customers measure device performance, but over time, one approach proved easier for customers to carry forward into their own designs. The closed platform was an impressive demo. But when evaluation ended, customers hit a wall. Integration meant starting from scratch. Every bug was unique to their build. Support questions piled up because no one could see the internals. That worked for large customers who had 1:1 support, but it left smaller teams struggling. The open platform told a different story. Customers didn’t stop at evaluation, they kept going: - HDL cores dropped directly into their FPGA designs. - Driver code ported into production firmware. - Toolchains mirrored in their own build systems. That reuse changed everything. Early integration bugs vanished because they’d already been solved in the platform. “How do I…” questions disappeared because the answers were right there in the source. Fixes in the platform helped everyone instantly, not just the last person to open a ticket. The kicker? The open system was aimed at “broad market” customers with minimal direct support. But many large OEMs found the open platform & started using it. Here’s the thing about re-inventing the wheel: you don’t pay for it once. You pay for it every time you touch it. Every closed, one-off platform means you own the upkeep forever. Every duplicated driver or BSP traps bug fixes in silos. Every unique onboarding path slows the next customer. A platform is the opposite. It’s a foundation you build together: fix once, share everywhere & let improvements compound. Your fastest projects will be the ones with the deepest reuse. Why teams still re-invent: - Local optimization bias: “It’s faster if I just write it myself.” - No platform owner: Without stewardship, duplication spreads. - Perceived uniqueness: “Our project is different.” (It usually isn’t.) If you want speed, stability & fewer support calls, stop thinking of reuse as a shortcut. Think of it as engineering infrastructure. Where have you seen the most painful wheel-reinvention? Did it really save time or just move the bill into the future? #EmbeddedSystems #PlatformEngineering #Firmware #FromSiliconToSoftware #EngineeringProductivity

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Every company has an AI initiative 💪🏼 There is a mixed notion about what AI capabilities are and what it can actually do. "All code will be AI-generated in a year!" - AI executives "This tool just cost us $733 in bugs" - Developers on the ground 🤔 Companies developing AI models and tools have vested interest in marketing their product as groundbreaking and revolutionary - something that will solve all our problems. 🤔 Developers on other end are continuously bombarded with tons of tools, tech stacks and solutions that will make our software dance on its own. What AI Actually Solves: ✅ Faster code generation ✅ Reduced repetitive tasks ✅ Better documentation What It Might Create: ⚠️ More code to maintain ⚠️ Review bottlenecks ⚠️ Technical debt from generated code My Reflection: ⭐️ AI tools are genuinely helpful assistants, but we're nowhere near the "AI will replace developers" reality. ⭐️ The companies succeeding with AI treat it as a powerful assistant, not a replacement. ⭐️ The real question isn't "Can AI code?" but "Are we using AI to become better engineers?" Developers should learn from the code that's produced rather than blindly accepting generated solutions without understanding them. ⭐️ As we navigate this shift, we have an opportunity to turn from skeptic to believer, rediscover the spark of creation, and have more fun building software. ✨The hype promises revolution. The reality delivers evolution. Credit to @GergelyOrosz for the balanced research on reality check: https://lnkd.in/er-MjF-n #AIInEngineering #SoftwareEngineering #TechPhilosophy

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At KubeCon Amsterdam this year Alexander Matyushentsev and I will be talking about real world use cases leveraging AI agents and the new MCP for Argo CD project.

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After working with #ColdFusion systems for 9+ years, most problems I see fall into a few patterns: • slow performance • production errors • hard-to-maintain code • risky deployments And most teams think they need to rewrite everything. They don’t. In most cases, the real issues are: • poor configuration • weak architecture • lack of logging and visibility • growing technical debt Fixing these often improves the system without rebuilding it. That’s the difference between rewriting… and actually solving the problem. If you’re dealing with a #ColdFusion system that’s becoming difficult to manage, feel free to DM me. #ColdFusion #CFML #AdobeColdFusion

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When independent worst-case assumptions are stacked instead of resolved, the result is systematic overdesign. The opportunity now is to replace that with analysis grounded in actual circuit behavior.

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