Black October 2026: 15 Supply Chain Incidents in 17 Days, and What Every Engineering Team Should Fix This Week learnxops.com/black-october-…

JEV is INTERESTING! Most of the LLM calls I see in production apps aren’t writing essays. They’re answering tiny questions. Which team should pick up this ticket? Is it urgent? Should a human look at it first? And we usually throw a full LLM at these, then write parsing logic to clean up whatever comes back. I’ve done it too. Recently I came across Jev, a decision model from TypeSafe AI, built specifically for this kind of work. The idea is simple: You pass in your application state as text or JSON, plus your questions, the allowed answers and your criteria. Jev evaluates it and returns typed results: choices, scores and probabilities. Then your own code decides what to do with them based on thresholds you set. Let me put it in a real scenario. On LearnXOps, a learner writes in: “I paid, but I cannot access my labs.” Is that billing? Access? A technical issue on our side? Jev can classify it, and if the confidence crosses your threshold, the backend routes it automatically. If not, it goes to a human or another model for review. One thing to be clear about: Jev doesn’t write the reply to the learner. You’d still use an LLM for that part. What I like here is where the control sits. As developers, we define the options, the criteria and exactly when the system is allowed to act on its own. That’s how it should be. But a clean, valid output doesn’t mean the decision is correct. Those thresholds have to be tested against real tickets from your own system before you trust them. Don’t skip that part. Would you use a dedicated decision model for this, or keep it all in one LLM call? #AI #MLOps #AIEngineering

For quite some time I have been working on AI Agent Platform & here's what I found so far 👇 Before I go further, first understand what is AI Agent Platform: An AI agent platform lets an LLM iteratively plan, call external tools, observe results, and refine its approach until a goal is met or a budget is exhausted. Unlike a single-shot prompt, an agent loop can execute code, query databases, browse the web, and call APIs, making reliability, security, and cost control first-class concerns. This page designs a platform capable of running thousands of concurrent agent sessions, each with isolated execution environments, multi-tier memory, deterministic step budgets, and full observability. BUT most AI agent demos work fine. Most AI agent platforms break in production. In my experience the model is rarely the problem. The stuff around it is. Here's how I break an AI Agent Platform down: 🔹 Agent Orchestrator Runs the plan → execute → observe loop. Checkpoints state to Redis after every step, so if a pod dies, the session resumes instead of starting from zero. 🔹 LLM Planner Calls the LLM with a context-managed prompt (trimmed history, only the relevant tool specs) and returns a structured plan. Structured, not free text, so the orchestrator can validate it before anything runs. 🔹 Step Budget Enforcer Atomically tracks steps, tokens and wall-clock time per session. Hit a limit, the session stops cleanly. This is what saves you from the agent that loops 400 times at 3 AM on your API bill. 🔹 Tool Registry Stores tool definitions, JSON schemas, execution backends and permission scopes. The agent can only call registered tools, with validated inputs, and only what that session is allowed to touch. Skip any one of these and you'll feel it: No checkpoints → lost sessions No structured plans → unpredictable actions No budgets → runaway cost No registry → an agent with more access than it should have What would you add? I'd put tracing and human-in-the-loop approvals right next to these. Full write-up with the architecture diagram 👇 app.learnxops.com/system-des… #MLOps #AI #LLM #MachineLearning #SystemDesign

Kubernetes Autoscaling REALITY!

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Just got an offer I turned down on the spot. 50 Lakh (52,000+ USD) to hand over private GitHub repos from production projects I've worked on. The reason given? AI training for those companies. Maybe that's true. Maybe it's something else. Who knows. Either way, the answer was no. Those repos are not mine to sell. They belong to clients who trusted me with them. Selling them breaks every NDA I've signed and everything I've built in 15 years. I have no idea how many people are getting offers like this, or how many are saying yes. But if you're one of them, think it through. Your reputation takes years to build. It takes one transaction to lose. Don't sell your ethics, morals, and soul for money. Let the right judgement prevail. #ai #training

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217 hands-on Kubernetes labs are live — from your first Pod to MLOps, GitOps & SRE. api.app.lxop.in/share/posts/…

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This is what people has to say about Learn X Ops Labs 🙌 #cloud #devops #mlops #labs #handson #aws #azure #gcp #devsecops

I used my idle raspberry Pi devices as scraping and data collector agent and via Local AI filtered and store most relevant 1303 Cloud , DevOps , MLOps , Platform Engineering & Software Engineering Jobs! Link: app.learnxops.com/jobs #joblisting #ai #cloud #devops #mlops #platformengineering

LearnXops — Features Announcement api.app.lxop.in/share/posts/…

Released Resume Builder with ATS Score Check for Cloud & DevOps Engineers: app.learnxops.com/resume-bui…

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