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.
(more…)Tag: AI
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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.
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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:
- Try classic automation first
- Process integration: Can I add it into a process so that it fixes a problem?
- Data privacy
- Security
- Works council/employee representation (if applicable)
- Observability (not just the usual observability but also prompts and responses)
- Robust data pipelines (a.k.a ETL)
- Model Selection
- Model decay & re-evaluation (How often will you need to update? Currently about ~1x / year)
- Regulatory Compliance AI Act (EU)
- Costs (Tokens, maintenance, scaling — over years, not demo days)
- Scalability
- Latency & Performance:
- Testing (“it works in demo” ≠ “it works in production with real users”)
- Human-in-the-Loop (HITL):
- The other 95% of the app (The “boring” software stack around the AI)
- 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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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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Should We Welcome Bots for Fact Checking?
In today’s digital age, we’ve all noticed how rapidly fake news can be generated, especially with the help of generative Generative AI. Just today, I found myself having to do a fact-check for something I posted — and I decided to leverage #GenAI to test it. And I have to admit, I was genuinely surprised at just how easy and efficient it was.
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Those Who Forget the Dotcom Crash Are Doomed to Repeat It — with GenAI
A friend recently recommended Craig McCaskill’s article “The Bubble That Knows It’s a Bubble” with a disclaimer “it’s quite a read”. After forgetting it and coming back to it later I can say: it’s so worth reading it!
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Is the GenAI Revolution over already?
Just recently I saw the article (that probably most of us already noticed with a gentle smile), that AI coding tools can slow down seasoned developers by 19% (on InfoWorld). And just now I came to another article on Futurism, that “AI Use Is Now Declining at Large Companies”.
Heise.de picked up the article as well (Ernüchterung statt Euphorie: KI-Nutzung in den USA geht zurück) and outlines that the big hope of additional revenues has not come true (well … as if a technology would print money) and that the negative effects start to be more visible.
Yeah well — I’m wondering where exactly we might be in the Gartner Hype Cycle. I guess somewhere near the “Trough of disillusionment”? I don’t think that there is any doubt that GenAI has some really really beneficial use cases. But to me it did sound a lot like the time when the use of big data was totally overhyped.
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