Apple macOS

This category is all about the professional use of Apple macOS in the company. The specialist articles show how the Mac is a convincing workplace in everyday business life - from the Integration into company networksabout Secure data management up to Productivity apps and ERP software. Find out what advantages macOS has for Self-employed, small businesses and larger companies offers, how workflows can be optimized and which tools make the Mac a reliable companion in everyday office life. Whether in the Project management, in the Accounting or for creative work - The Mac has been a strong foundation for efficient working for many years.

From Commodore C16 to WordPress: A journey through the early years of the internet

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From modem to Internet and magazine

When you pick up a smartphone today, it contains more computing power than entire computer rooms used to. In the 1980s, things were completely different. Computers were rare, expensive and, for many people, a mysterious machine. Back then, if you had your own home computer, you belonged to a small group of tinkerers, inventors and curious people. The exciting thing was that you didn't just consume computers. You had to understand them. Many programs were not available to buy ready-made. Instead, computer magazines contained pages of listings with BASIC code that you had to type out line by line. Only then could you see whether the program worked at all.

That sounds tedious today, but it had one big advantage. You automatically learned how computers work. If you made a mistake, you immediately got an error message - and had to find out for yourself where the error was. In this way, many young computer fans developed a very natural approach to technology and programming.

It was at this time that I began my own journey into the world of computers.

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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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AI for beginners: How to get started with artificial intelligence without prior knowledge

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AI for beginners

Artificial intelligence seems like a sudden phenomenon to many people. Just a few years ago, it hardly played a role in everyday life, but today it is constantly present - in the news, in discussions, in conversations at work. However, this impression is deceptive. AI did not emerge overnight. It has been researched, developed and used in specialist areas for decades. What is new is not the idea, but the approach.

Artificial intelligence has been around as a research idea for decades. For a long time, it was a topic for universities, large corporations and special applications. The big difference today is that many AI systems have matured to the point where they can be used by normal people in everyday life - via a simple input window, on a computer or smartphone.

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Cloud AI as head teacher: why the future of work lies with local AI

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Cloud AI becomes the head teacher

When the large language models began their triumphal march a few years ago, they almost seemed like a return to the old virtues of technology: a tool that does what it is told. A tool that serves the user, not the other way around. The first versions - from GPT-3 to GPT-4 - had weaknesses, yes, but they were amazingly helpful. They explained, analyzed, formulated and solved tasks. And they did this largely without pedagogical ballast.

You talked to these models as if you were talking to an erudite employee who sometimes got lost, but basically just worked. Anyone who wrote creative texts, generated program code or produced longer analyses back then experienced how smoothly it went. There was a feeling of freedom, of an open creative space, of technology that supported people instead of correcting them.

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AI Studio 2025: Which hardware is really worth it - from the Mac Studio to the RTX 3090

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Hardware 2025 for AI studio

Anyone working with AI today is almost automatically pushed into the cloud: OpenAI, Microsoft, Google, any web UIs, tokens, limits, terms and conditions. This seems modern - but is essentially a return to dependency: others determine which models you can use, how often, with which filters and at what cost. I'm deliberately going the other way: I'm currently building my own little AI studio at home. With my own hardware, my own models and my own workflows.

My goal is clear: local text AI, local image AI, learning my own models (LoRA, fine-tuning) and all of this in such a way that I, as a freelancer and later also an SME customer, am not dependent on the daily whims of some cloud provider. You could say it's a return to an old attitude that used to be quite normal: „You do important things yourself“. Only this time, it's not about your own workbench, but about computing power and data sovereignty.

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The Affinity graphics suite becomes free: What professional users need to know now

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Affinity graphics suite free of charge

If, like me, you have been working with layout and typesetting programs for decades, you usually notice such changes more clearly than those who have only recently entered this world. I have seen many things come and go over the years: In the early nineties, I worked on the Atari ST with Calamus SL and later, under Windows, with CorelDraw! Later came QuarkXPress, then iCalamus, Adobe InDesign - and finally, a few years ago, Affinity Publisher. Since then, the Affinity suite has accompanied me through almost all my book projects. Over the years, it has been a reliable tool, pleasantly straightforward, clearly structured and free of the ballast that many large software houses have added to themselves over the years.

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Apple MLX vs. NVIDIA: How local AI inference works on the Mac

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Local AI on Silicon with Apple Mac

Anyone working with artificial intelligence today often first thinks of ChatGPT or similar online services. You type in a question, wait a few seconds - and receive an answer as if a very well-read, patient conversation partner were sitting at the other end of the line. But what is easily forgotten: Every input, every sentence, every word travels via the Internet to external servers. That's where the real work is done - on huge computers that you never get to see yourself.

In principle, a local language model works in exactly the same way - but without the Internet. The model is stored as a file on the user's own computer, is loaded into the working memory at startup and answers questions directly on the device. The technology behind it is the same: a neural network that understands language, generates texts and recognizes patterns. The only difference is that the entire calculation remains in-house. You could say: ChatGPT without the cloud.

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