The IT Blog

Tag: GenAI

  • Reproducible Vibe Coding | It’s all About Context

    Actually I wanted to try a bit GithubCopilot with Agents.md. Yet .. I think during the project I totally forgot to test the influence of the Agents file but tried “vibeCoding” in a reproducible way.

    I had a very little project in mind that authenticates to Mastodon, fetches some data, saves into a database and displays some metrics on a web page in basic charts. Nothing overly fancy, but also some stuff that would simply take some time when coding “alone”. Like proper OAUTH flow, paging through mastodon apis, rate limiting, database writing, database setup script and cleanup. Some Javascript for the chart, etc.

    But I thought it might be nice to try with GithubCopilot (GHC). But I’m also a big fan of reproducible results. So … step by step, what did I do.

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  • How to write great agents.md(s) – A recommendation from GitHub

    GitHub’s new guide, How to write a great agents.md: Lessons from over 2,500 repositories, pulls lessons from over 2,500 repositories to show how to document AI agents effectively. It’s not just about clarity but also about making collaboration, reproducibility, and scalability possible.

    The guide breaks down how to structure agents.md files for real-world utility. It highlights common mistakes and explains why solid documentation is the backbone of any successful AI project. Whether you’re a developer, DevOps engineer, or just curious about AI tooling, this is a practical roadmap.

    For anyone serious about AI development, this is a resource worth keeping: https://github.blog/ai-and-ml/github-copilot/how-to-write-a-great-agents-md-lessons-from-over-2500-repositories/

  • The Cost of Going All-In on AI

    On Mastodon, I just came across “I Went All-In on AI. The MIT Study Is Right.” from Josh Anderson. He spent three months building a product using only AI-generated code. The result? A working product, but also a dangerous realization: He no longer fully understood his own creation. When a small change was needed, he hesitated.

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  • LLM Update in Production: When Prompts Fail — and What It Means for Your Applications

    t3n recently wrote that OpenAI’s GPT 5.1 update might come with a surprise to desktop users: previously reliable prompts no longer behave as expected. While this may be just a minor annoyance in day-to-day chat interactions, think about what that means in production environments.

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  • The Software Engineering Challenge Behind Effective RAG

    Retrieval-Augmented Generation (RAG) is frequently marketed as a simple solution for grounding AI responses in real-world data. It sounds straightforward & easy: connect a vector database to a language model, pipe in your data, DONE! But maybe it might be just a little harder than that …

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  • Mistral 3 Released

    Mistral AI has published Mistral 3, the latest version of their open-weight language model. The models are available under the Apache 2.0 license and, according to their benchmarks, outperform DeepSeek’s recent models in several key areas … well, let’s see.

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  • I break things … Google Veo

    Recently I had the opportunity to test the new Google AI-Video generator powered by Veo 3(.1). The demo was truley impressive and scary at the same time! And then we were able to test it ourselves …

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  • o365 “control plane” for AI Agents coming

    I just read an article on InfoWorld, that Microsoft rolls out Agent 365 ‘control plane’ for AI agents. The description sounds quite well what an enterprise needs in terms of compliance and security:

    Microsoft said that Agent 365 unlocks five capabilities intended to make enterprise-scale AI possible:

    • Registry, to view all agents in an organization, including agents with agent ID, agents registered by the user, and shadow agents.
    • Access control, to bring agents under management and limit access only to needed resources.
    • Visualization, to explore connections between agents, people, and data, and monitor agent performance.
    • Interoperability, by equipping agents with applications and data to simplify human-agent workflows. They would be connected to Work IQ to provide context of work to onboard into business processes.
    • Security, to protect agents from threats and vulnerabilities and remediate attacks that target agents.
    Microsoft rolls out Agent 365 ‘control plane’ for AI agents | InfoWorld

  • What I am Missing in Most GenAI Conversations

    When people talk about Generative AI, the focus is usually on:

    • Prompting
    • LLMs
    • Chatbots
    • Proofs of Concept (POCs)

    But what I am missing a lot in those conversations are:

    1. Try classic automation first
    2. Process integration: Can I add it into a process so that it fixes a problem?
    3. Data privacy
    4. Security
    5. Works council/employee representation (if applicable)
    6. Observability (not just the usual observability but also prompts and responses)
    7. Robust data pipelines (a.k.a ETL)
    8. Model Selection
    9. Model decay & re-evaluation (How often will you need to update? Currently about ~1x / year)
    10. Regulatory Compliance AI Act (EU)
    11. Costs (Tokens, maintenance, scaling — over years, not demo days)
    12. Scalability
    13. Latency & Performance:
    14. Testing (“it works in demo” ≠ “it works in production with real users”)
    15. Human-in-the-Loop (HITL):
    16. The other 95% of the app (The “boring” software stack around the AI)
    17. APIs (If it’s meant to automate, it needs to talk to other systems)

    If there’s a user interface:

    • Interface design & UX (no one uses what they can’t understand)

    And the elephant in the room:

    • How do you address the fear—justified or not—that you might be innovating people out of their jobs?
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  • Why Your Favorite AI Tool Might Be Isolating You

    AI chat tools are a remarkable invention. Their rapid adoption speaks for itself: instant access to information, tailored feedback, and the ability to explore ideas or discuss one own thoughts or questions without friction – never before did we have such opportunities. But this power can come with a risk.

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