Algolytics Technologies’ Post

AI makes building microservices easy, but managing the resulting complexity is the real challenge. Don't let rapid code generation turn your architecture into a distributed monolith.

Episode 31: Slop Architecture SLOP: We broke our monolith into microservices… now Kubernetes maintenance feels heavier than the app itself. Why not just plug AI into kubectl and simplify everything? Rajesh: I tried that using Gemma 4 with Ollama. It works… but takes 12 minutes. That’s not intelligence—that’s bad architecture. AI shouldn’t think when you ask; it should think before you ask. Takeaway: If your “AI feature” isn’t instant, it’s not helping—it’s just slowing you down. kubectl brain diagnose pod <pod> -n <namespace> kubectl brain explain deployment <deployment> -n <namespace>

The open source stack for AI development environments is maturing faster than most teams realize. Terraform for provisioning, Kubernetes for orchestration, and now governed workspaces where developers and AI agents build side by side. The architecture isn't theoretical. Enterprises are running it in production today, self-hosted on their own infrastructure with full control over what agents can access. Nicky Pike is presenting the governance patterns behind this at Open Source Summit NA: "When Your AI Agent Has Keys to Production." May 18-20 in Minneapolis. https://lnkd.in/eBFTn2z

Started building a Kubernetes-native MLOps project focused on Customer Intelligence workflows for automotive-style use cases. Current progress in the project: ✅ ML training pipeline using scikit-learn ✅ MLflow integration for experiment tracking and model registry ✅ FastAPI-based prediction service ✅ Dynamic model loading from MLflow Registry ✅ Dockerized serving application ✅ Local end-to-end prediction workflow running successfully One of the biggest learnings so far was understanding how MLOps extends traditional DevOps concepts: Docker images ↔ ML model artifacts Artifact repositories ↔ Model registry CI/CD ↔ Training & deployment pipelines Application monitoring ↔ Model drift & prediction monitoring Repository: customer-intelligence-mlops Next phase: 🚀 Deploying the FastAPI serving application into local Kubernetes using Rancher Desktop and integrating runtime model loading from MLflow. Gradually moving from “ML model deployment” toward a complete production-style MLOps lifecycle platform.

What if microservices don’t disappear in the AI era… but evolve into something new? 👀 Agentic microservices might become one of the biggest architectural shifts of the next few years. I recently joined the AWS Developers Podcast for a conversation about where microservices, agents, and AI-driven systems are heading. We explored things like: • What an “agentic microservice” actually means • Why the monolith vs specialization debate is coming back in the AI era • Why architects matter even more with AI coding assistants • MCP, A2A, durable execution, and the infrastructure forming around agents • Why APIs, services, and agents are starting to converge instead of replace each other Really enjoyed this conversation with Romain Jourdan and diving into where architecture may be heading next. 📺 Watch here: https://lnkd.in/ewN6XtQm

Building CI on AWS Lambda has an obvious problem: 15-minute cap. Workflows take longer than that. Durable Lambdas solve it by checkpointing state and suspending between events. Here's how Depot CI's orchestrator uses them. 🧵 2/ The orchestration loop is a while loop, not a fixed plan: while (still work to do): state = load from DB # ctx.step(...) dispatch newly-ready jobs wait for callbacks When nothing's happening, the Lambda is suspended. 3/ Every wake-up is callback-driven — job completion, cancellation, retry, concurrency wake. No polling. Polling would mean keeping the orchestrator awake just to check on work it isn't doing. 4/ A durable execution caps at 3000 operations (steps, callbacks, child contexts). One push can fan out to multiple workflows, each with its own set of jobs. So the orchestrator splits: a Run Lambda + per-Workflow Lambdas. One heavy workflow can't blow the budget for the rest of the run. 5/ Every callback has a timeout. If a sandbox goes silent, the promise rejects, wakes the orchestrator, and the work keeps moving. Without it, a stuck job hangs the whole workflow. 6/ Hardest part wasn't the runtime — it was thinking in replays. Anything outside ctx.step(...) can re-run. Step boundaries need to be small enough to be replay-safe and big enough not to burn the operation budget. 7/ Full write-up: https://lnkd.in/edu4DA_G

🛠️ Daily DevOps Tip — Keep Your Pipelines DRY Avoid copy-pasting CI/CD pipeline configs across repos. Use shared pipeline templates, reusable GitHub Actions, or GitLab CI includes to stay DRY. 🌤️ Brussels Weather: Light Rain Shower, Rain Shower, 13°C (Feels like 11°C) | Humidity: 67% #DevOps #DevOpsTips #CloudEngineering #Kubernetes #CI_CD #Automation #SRE #PlatformEngineering #Brussels 🤖 Generated by Base44 AI — https://base44.com

Unlocking the Full Potential of Microservices through Hexagonal Architecture! 🚀 In the dynamic landscape of software engineering, microservices are rapidly becoming the backbone of scalable and maintainable solutions. But how do we harness their full potential? Enter Hexagonal Architecture. 💡 🔧 **Enhanced Modularity:** Hexagonal Architecture allows for a clear distinction between the application core and external integrations, promoting high cohesion and low coupling. 🔧 **Testability & Agility:** By decoupling the domain logic from the infrastructure, testing becomes more manageable, and the system gains the agility to adapt to changes. 🔧 **Independence & Scalability:** Each microservice can evolve independently. This architecture supports seamless scaling, addressing specific needs without a system-wide impact. The integration of these two powerful methodologies leads to systems that are not only robust but agile and easy to manage at scale. As we push the boundaries of what's possible, how are you leveraging hexagonal principles in your microservices? Are there challenges you've faced in implementation? Engage in the conversation and learn more by chatting with our AI agent at: https://webcol.net #Microservices #HexagonalArchitecture #ScalableSystems #TechInnovation

Runbooks Don’t Fix Systems Runbooks tell you: 👉 how to fix something But here’s the question: 👉 why is it still manual? If a problem repeats… It should be automated. The shift is clear: Runbooks → Automation → AI Systems Platforms like CrftInfrai are exploring: • self-healing infra • automated remediation • intelligent systems Because: 👉 documented fixes should become automated fixes #DevOps #SRE #Automation #AIinDevOps #CloudComputing #Reliability #PlatformEngineering #CloudArchitecture #CrftInfrai