MLOps: The New Challenge in AI Deployment

Most AI code tools break on real codebases. We built Brokk for the ones that actually matter. Run it on your largest repo: https://brokk.ai/ → Context Intelligence: Deep semantic search across dependencies, git history, millions of lines → Supervised Execution: AI agents that compile, test, and fix until it's done → Developer Sovereignty: Full workspace visibility, your LLM keys, zero vendor lock-in

Coming soon: Use your favorite AI code editor to build your Omni model🧑💻 With Omni’s APIs, you’ll be able to check model files, resolve errors, add metadata, and more directly from an external IDE. Watch Steven update a table's column descriptions in seconds with Claude Code 🏴☠️

I think we're all a little more cautious and skeptical about AI-generated code after having learned more about how to use it and trying to integrate it into our work. However, I do have to say that the AI upgrades to the whispers, rumors, secrets and incantations that made up my .gitignore and .dockerignore files has been a real blessing. Here's the prompt I used - "Let's merge these .gitignore and .dockerfiles to make the optimal version of each one with clear and concise organizational structures. If you have recommendations to add based on the contents, create a numbered list of recommendations and I'll choose which ones we implement. Don't start with the implementation until we're in agreement about all the details."

LLM FinOps: How to cut AI costs without hurting users AI is not free. But you can control the bill. Start with a simple scorecard (track weekly): -- Cost per chat -- Tokens per answer -- Cache hit rate (%) -- p95 latency -- Cost per active user Wins that work: 1. Cache the stuff you reuse (system prompts, FAQs, retrieved docs). Fewer repeat calls → lower cost and faster replies. 2. Trim context. Keep only the top 3-5 chunks. Less text in → fewer tokens out. 3. Batch what you can (reviews, summaries, backfills). One grouped job is cheaper than many small ones. 4. Right-size models. Use a small model first. If hard, escalate to a bigger one. 5. Speed tricks for hosted models: use runtimes that support speculative decoding and quantization. More tokens per second, less spend. 6. Guardrails: set max tokens, stop sequences, and timeouts. 7. RAG for private data. Search first, then generate. You avoid retraining and cut token use. Team habits (the “FinOps” part): -- One weekly review: Eng + Product + Finance. -- Set a monthly budget and SLOs (latency & quality). -- Provide cost dashboards where everyone can see them. Copy this 5-line checklist into your doc: 1. Cache hit rate ≥ 60%  2. Avg prompt ≤ 3 chunks  3. Small→big model cascade on  4. Max tokens set on every call  5. Weekly Eng/Product/Finance review Want help turning this into a dashboard? Save this post and share with your team. 🚀 #AI #GenerativeAI #FinOps #MLOps #CloudComputing

It’s tempting to debug an ML issue by diving into model performance. But most production issues aren’t model problems; they’re system problems. Missing filters in business logic An incomplete offline evaluation harnesses Misalignment between A/B environments and production rollout logic Silent regressions in post-processing pipelines If you lead a team building AI systems, here’s the uncomfortable truth:

I have been doing a lot of work on AI agents, and I think you should too! Building an agent isn’t hard. I think the hardest part is actually building a framework that SCALES these agents. Here’s the structure that I have found to work really well: 1) Core Orchestrator – handles routing, context, and reasoning. 2) Tool Interface Layer – connects your APIs, databases, and MCP tools. 3) Agent Types – each with defined roles (ReAct, validator, composer, retriever, etc.) 4) Memory Layer – embeddings + vector DB for long-term context. 5) Governance – validation, logging, and feedback loops. You never just build 1 agent, because then you're overloading the agent. You want to build the ecosystem where agents collaborate and self-correct. Think on a system level and not just code/script. I'll be sharing some cool videos and wal-throughs on creating agents from a scratch, stay tuned! #data #ai #agenticai

Takeaways from Cursor 2.0 Cursor recently rolled out Cursor 2.0 and while the new features are impressive, the real story lies in what powers it. Cursor’s team has unveiled its own proprietary model, signaling a quiet but powerful shift: the move away from dependence on external AI providers. Until now, developers have relied on a few centralized model providers for everything from code completion to AI‑driven pair programming. The limitations of closed APIs, usage costs, and uncertainty around access control have all pushed builders to seek autonomy. By building its own model, Cursor is doing more than improving its product. It’s protecting its roadmap, ensuring product agility, and strengthening user trust. It’s a sign that we might be entering a new wave of AI tools, ones that aren’t just using AI but creating it. The question now is: how many others will follow? #DeveloperTools #AITrends #FutureOfAI

🚀 The final chapter is here! In this last part of the “Enhancing CI/CD Pipelines with AI using .NET and Semantic Kernel” series, we bring everything together — an AI-powered pipeline that reviews, scores, and comments on pull requests automatically. From test prioritization to PR risk scoring, the pipeline now truly thinks. Read the full series here: 1️⃣ Why AI Belongs in CI/CD Pipelines 🔗 https://lnkd.in/dUTujnZW 2️⃣ Getting Started with Semantic Kernel 🔗 https://lnkd.in/dfiJDnV6 3️⃣ AI-Powered Test Prioritization 🔗 https://lnkd.in/dXp2jM7w 4️⃣ Automatically Score Pull Request Risk 🔗 https://lnkd.in/duExzKDU 5️⃣ The Final Step: Making Your CI/CD Pipeline Think with AI 🔗 https://lnkd.in/dpNB_dHY 🔗 Github Link: https://lnkd.in/dEKmpfGc https://lnkd.in/dpNB_dHY

If you’ve ever spent hours debugging, you know the feeling - staring at the same few lines of code, convinced the issue must be somewhere else, only to realize… it was a missing comma all along. I’ve been there more times than I’d like to admit. But lately, I’ve been thinking about how AI is slowly changing this game. We’re moving from: Searching logs manually -> to AI tools spotting anomalies for us Re-reading stack traces -> to AI assistants that explain why something broke Fixing bugs reactively -> to systems that start becoming self-healing #AI #Debugging #DevOps

OpenAI is setting the bar for developer experience with their recent releases (and I'm pumped), but there are going to be gaps that their updates aren't going to resolve. It's continue to be a challenge to evaluate how well a prompt/workflow/storage/newest feature is performing. AI/ML engineers live in a space of evaluating based on crisp metrics, and those aren't going to cut it with systems living in the real world. This issue is only going to compound with the newest agent builder because it's going to stack all of the new features without addressing how we analyze the performance of each individual component on a more granular level. Anyone working with GenAI use-cases that's experiencing the "AI eval" problem?