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.
(more…)Tag: GenAI
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How to guide your teams to use GenAI effectively while avoiding the pitfalls
We often see and hear a lot of hype and – unfortunately – enshittification when it comes to GenAI. Despite knowing that there are indeed some valuable use cases for the application of AI to solve some issues, I rarely read about a employee friendly adoption of GenAI.
The article AI for Network Managers: Leading Teams in the Age of Intelligent Automation was a quite refreshing read in that regard!
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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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Mistral – the European ChatGPT
OK, I admit that I’m sometimes too focused on technology to realise that not everyone is aware of all the different AI services out there. — This week, I mentioned Mistral.ai to a colleague and was surprised that he hadn’t heard of it. But, as I said, you can’t know everything.
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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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AI vs. the Legacy Black Box Codebase
If you want to see a cool example of how Generative AI can be used to tackle one of the nastiest problems in enterprise IT, you might want to invest some minutes in reading “From Black Box to Blueprint.”
What it’s about: Large organizations often have to rely on systems that are both business-critical and poorly understood due to lots of legacy. The article describes how Thoughtworks approached such a case: combining a multi-lens strategy (UI reconstruction, logic inference, change data capture, etc.) with AI-assisted “binary archaeology.”
I really like the idea as it’s not about replacing humans, not hype but solving a real problem: complex — legacy — codebases. Something no one of us likes to lay their hands on.
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It’s only AI if it’s cool!
I guess I am not the only one who is quite annoyed that nowadays everything is AI. Literally everything that’s beyond an
if/elseis advertised as AI. Machine Learning doesn’t seem to exist anymore.Unless … Unless it’s not cool!
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The Double-Edged Sword of Generative AI in Linux Troubleshooting
I’ve recently been experimenting with how generative AI can support Linux debugging. The experience was both impressive and frustrating — depending on whether I was diagnosing or actually fixing a problem.
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