Language model

Articles about language models and their possible applications. This section deals with GPT, LLaMA, Mistral and other LLMs - from the basics to practical applications.

Software Development with Codex, ChatGPT, and AI: A Practical Guide for Developers

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Software Development with AI

If you had asked me a few years ago what software development would look like in ten years, I would probably have talked about new programming languages, better frameworks, or more powerful development environments. Today, my answer would be completely different. The biggest change isn’t happening with the tools, but in the way we as developers think and work.

As I write these lines, I am working on a new software system myself. For the past few weeks, I have been making extensive use of modern AI tools such as Codex and other language models. At first, I was curious; now, I’m mostly impressed. Not because the AI suddenly does everything on its own, but because it handles certain tasks surprisingly well, thereby enabling new ways of working.

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How artificial intelligence is changing software development and FileMaker

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AI evolution in the development of FileMaker databases

Anyone currently scrolling through news portals, social networks or business platforms will quickly get the impression that artificial intelligence is changing the entire working world practically overnight. New tools, new language models and new promises appear almost daily. Texts are written automatically, images are generated, videos are created and software is sometimes prepared by voice input.

For many companies, this creates a strange mixture of curiosity and pressure. Because, of course, nobody wants to miss the boat. At the same time, many entrepreneurs, freelancers and developers do not yet know exactly which of these technologies will really remain relevant in the long term. This is probably the real peculiarity of the current AI phase: almost everyone senses that something is changing - but hardly anyone can really reliably assess at the moment how quickly and in which direction.

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From ChatGPT data export to your own knowledge AI: step-by-step with Ollama and Qdrant

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The path to your own AI memory

In the first part of this article series, we saw that the ChatGPT data export is much more than just a technical function. Your exported data contains a collection of thoughts, ideas, analyses and conversations that have accumulated over a long period of time. But as long as this data is only stored as an archive on your hard disk, it remains just that: an archive. The crucial step is to make this information usable again. This is exactly where the development of a personal knowledge AI begins.

The idea is actually surprisingly simple: an AI should not only work with general knowledge, but also be able to access your own data. It should search through previous conversations, find suitable content and incorporate this into new answers. This turns an ordinary AI into a kind of digital memory. This is the second part of the article series, which now looks at the practical aspects.

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ChatGPT data export explained: How your AI chats become a personal knowledge system

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ChatGPT data export

If you regularly work with an AI, then you probably know this: one thought leads to the next. You ask a question, get an answer, reformulate, develop an idea further. A short question suddenly turns into a longer dialog. Sometimes it even leads to entire projects.

But most of these conversations disappear again. They lie somewhere in the chat list, slide down and are forgotten over time. This is precisely one of the great features of modern AI systems: While previous conversations with colleagues, friends or advisors only existed in our memories, AI dialogs are completely preserved.

This means something crucial: With every conversation, a digital archive of your thinking is created. This is the first part of a small series of articles that will allow you to export your chat history from ChatGPT and use it effectively as a personal treasure trove of knowledge with your local AI system.

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Learning to think dialogically with AI: Why good questions are more important than good models

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Learning to think dialogically with AI

The term „AI as a sparring partner“ now appears frequently. It usually means that an AI helps with writing, generates ideas or completes tasks faster. A first basic article on this has already been published in the magazine. This article now aims to show in reality how AI can be used as an effective thinking partner. In practice, it is clear that AI only becomes really interesting when it is not treated as a tool, but as a counterpart. Not in the human sense, but as something that answers, contradicts, leads on - or even mercilessly reveals where your own thinking is flawed.

This is exactly where the real benefit begins. Not where the AI „delivers“, but where it reacts. Where it does not simply process, but makes thought processes visible. This is more inconvenient than a classic tool - but also more sustainable.

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How animals perceive time - and what this means for the future of AI

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Animals, AI and time perception

A cat is lying on the carpet. It does not move. It may blink briefly, turn an ear, sigh inwardly at the impositions of existence - and nothing else happens. The human looks at it and thinks: „Typical. Lazy cattle“. But what if the exact opposite is true? What if the cat is not too slow - but we are? This article was written after I watched a video by Gerd Ganteför on this topic and found it so interesting that I would like to present it here.

Humans have been observing animals for centuries and always come to the same wrong conclusions. We interpret their behavior with our speed, our perception, our inner clock. And this clock is, soberly considered, more of a cozy wall calendar than a high-speed processor. Perhaps the cat only seems so disinterested because its environment feels about as dynamic to it as a queue of officials on a Friday afternoon.

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When the Mac listens: What Apple's integrated AI with Gemini and Siri will mean for users in the future

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Apple, Siri and Gemini

When you open a Mac today, you expect reliability. Programs start, files are in their place, processes are well practiced. Many have built up a way of working over years - some over decades - that works. You know where to click. You know your tools. And this is precisely where the quiet comfort lies. But for some time now, a change has been brewing in the background that is bigger than new colors, new icons or additional menu items. For the first time, a form of artificial intelligence is moving in not just as a single application, but closer to the heart of the operating system itself. Where daily routines are created.

That sounds abstract at first. Perhaps even a little futuristic. But basically it's about something very down-to-earth: the computer should better understand what is meant. Not just what is clicked on. Many people have so far experienced AI outside of their actual work. In chat windows, on websites, as an experiment or a gimmick. You try something out, perhaps be amazed, close the window again - and return to your normal everyday life.

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Artificial intelligence without the hype: why fewer AI tools often mean better work

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Artificial intelligence without the hype

Anyone who deals with the topic of artificial intelligence today almost inevitably encounters a strange feeling: constant restlessness. No sooner have you got used to one tool than the next ten appear. One video follows the next on YouTube: „This AI tool changes everything“, „You absolutely have to use this now“, „Those who miss out are left behind“. And every time, the same message resonates subliminally: You're too late. The others are further ahead. You have to catch up.

This doesn't just affect IT people. Self-employed people, creative professionals, entrepreneurs and ordinary employees are also feeling the pressure. Many don't even know exactly what these tools actually do - but they have the feeling that they could be missing out on something. And that's exactly what creates stress.

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