The IT Blog

Tag: SoftwareEngineering

  • Why I Do Not Like ‘All-or-Nothing’ Software Projects

    I just read an article on heise where the Standish Group’s CHAOS Report from 2020 was mentioned. The findings were … sobering: only 31% of software projects succeed — meaning they are delivered on time, within budget, and provide real value to users or customers. The remaining 69%? Half of them struggle with significant issues like budget overruns or scope creep, while 19% fail completely, consuming resources without delivering anything useful.

    31% … Less than 1/3! That’s a really poor success rate, a big risk — and obviously, it’s reality!

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  • 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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  • Recommended read: Frictionless – About Eliminating Development Friction

    I just read Martin Fowler’s blogpost and foreword to Frictionless by Andrew Harmel-Law. The book’s core idea is simple but powerful: How do we make developers truly efficient? Not by adding more processes, tools, or meetings, but by removing the friction that slows teams down and using smart metrics.

    I put it on my reading list and I’m curious about it!

  • 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?
    Fediverse Reactions

  • Github Copilot is the Coach I always Wanted

    We hear a lot about the bad side of AI Code Generation etc. But there are also quite some good sides that should not be ignored.

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  • Is it finally over for Developers?!

    We’ve heard it all a couple of times: “GenAI is replacing Software Developers”, Vibe Coding, … A C-Levels dream to (finally) get rid of expensive software developers by using AI.

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