Two Book Recommendations: Sorry, No Happy Endings!

In 2026, I will cover a wider range of topics beyond AI and 3D printing. To kick off the new year, I want to share two books that have reshaped my thinking over the past year. Unfortunately, one is only available in German. The other is available in both English and German.

Deutsche Militärgeschichte by Stig Förster

I read a review of this book in Süddeutsche Zeitung. As I’m not at all a fan of the military, it took me a while to download the sample to my Kindle app and start reading. At school, I never had a good history teacher and I always hated the subject. I mostly remember it as a boring game of memorising years without any connection between them, which my brain didn’t want to remember. Like most brains, mine works better if there’s a story, connection and reasoning included.

This book also mentions years, but it’s not at all about exact dates. It’s much more about the context of society at that time and the dynamics that often inevitably led to military conflicts. Stig Förster believes that the military is part of human organisation and that violent force is recurring, whether we want it or not. Having read it, I tend to agree, which unfortunately makes the outlook on the next decade grim.

Interestingly, the book covers the period from 1525 to 2025, continuing up to the present day and including the current conflict with Russia. Although I found the final chapters the most interesting, I found the entire 1,294-page book a surprisingly easy read. Although I studied World War I and World War II extensively at school, the book provided me with many new insights into these periods. For example, I learnt that the Nazi regime knew as early as 1941 that they would ultimately lose the war and adjusted their goals accordingly.

Buy as an ebook at Thalia and you get an EPUB without any DRM measures.

How Countries Go Broke, The Big Cycle by Ray Dalio

Another book that I initially resisted starting to read. Ray Dalio is a major hedge fund manager. What could I possibly learn from a greedy finance guy whose only purpose in life is to maximise their own profits?

Quite a lot, as it turns out. It’s also very much a history book, albeit a financial history book. The central claim is that economies go through a major cycle every 50 to 150 years. Our cycle began in 1950, after the Second World War, and is now in its final phase. The author doesn’t just make this claim, but also presents extensive data from previous cycles, arguing that this knowledge is largely hidden because most people experience it only once in their lifetime.

He also offers a fascinating perspective on China, presenting it as more than just the evil state it is often portrayed as. It was a much harder read than Stig Förster because it contained a lot of information about the financial system that I hadn’t come across before. If you’re ever curious about macroeconomics, whether because of Bitcoin, debt, or what bonds are, you’re in for a treat.

Buy here at Thalia or here at Amazon, both times with DRM.

I wish you a great start to 2026!

It’s the week before Christmas. You’re probably feeling the pressure to live up to all the expectations that Christmas brings each year. I hope you can let a few of them slide and choose to have a good time for a few hours in between.

From the outset, I have treated my newsletter The Liquid Engineer as an experimental playground. I started out with lots of motivation for 3D printing, and I am still convinced it’s a hugely powerful technology that will be adopted much more widely in the coming years. Then, even more interesting things happened: I switched my focus to agentic coding. In recent weeks, I’ve also incorporated a lot of content on local LLMs and the necessary software and hardware for this. This is because I’m trying to build a business in this area with OnTree.

Thank you for following me on this journey so far!

I realized that I write my best posts when I write about topics that interest me. I will continue to write this newsletter, but I won’t be bound to any particular topics in the future. It might be about something that has just happened or something that I am currently working on. If you joined me for the AI content, I invite you to stick around over the next few weeks to see if you can still find value in the content. Since AI is keeping me busy, I expect there will still be plenty of AI-related content. If there’s a topic you’d like to read about, just hit reply and let me know!

For the past few months, I have also been publishing the newsletter posts right here, on my personal blog. If you prefer this method, feel free to dust off your old RSS reader and subscribe to my RSS feed!

Have a great Christmas with your loved ones!

Last week, an exciting website appeared in my feeds: The Resonant Computing Manifesto.

The manifesto revolves around the idea that we are at a crossroads with AI. We can either double down on the direction we’ve already taken in our digital lives, a race to the bottom, or we can do something different. The goal is to build the most appealing digital junk food to maximize ad consumption and profits. This turns the internet into a few big platforms that are noisy and attention-seeking and don’t serve customer needs. Big tech companies like TikTok, Facebook, and Instagram are pushing in this direction with full force, trying to use AI for hyper-personalization on their platforms.

The manifesto coined the term “resonance,” which comes from the field of architecture (the architecture of real buildings, not software). It describes how we feel more at home and more human in certain environments. It is a quality without a name that is not strictly measurable and more intuitively graspable.

The manifesto suggests that AI can advance the current state of the internet and allow for new possibilities. This technology enables more hyper-personalized experiences off the major platforms because the technical requirements for one-size-fits-all solutions have disappeared.

The manifesto centers on five principles that resonate with my vision for OnTree:

1. A private experience on the Internet,
2. that is dedicated exclusively to each customer,
3. with no single entity controlling it (Plural),
4. adaptable to the specific needs of the customer,
5. and prosocial, making our offline lives better, too.

I love the whole piece. Of course, it’s idealistic and probably sounds naive at first. However, I believe this world could use much more idealistic and naive believers in a new internet. Without these dreamers, nothing will change.

The only shortcoming I see is that the website doesn’t address the consequences of people being “primary stewards of their own context.” To me, this is impossible without a mindset shift away from passively being monetized and toward actively funding the software we want to succeed. Without making it clear that we must put our money where our mouth is, I feel this manifesto is incomplete.

Kagi.com is the perfect example here. Google’s primary interest in search is always monetization. Therefore, it is logically impossible for them to want you to find what you’re searching for on page one, spot one. Kagi.com has a far superior, ad-free search engine, and their main slogan is “Humanize the Web.” With attractive family and duo plans, I find Kagi to be excellent value for the money, we pay less than four euros per family member per month.

To get a resonant internet, we have to pay the right companies the right amount of money.

(Source of the banner this time is resonantcomputing.org)

Yesterday evening I was folding laundry. It was one of those pesky loads, the basket was filled with socks. We’re a four person household, in theory, that should make it easier to distinguish all the socks.

I made some space on the table to accommodate the individual socks. Laying them out flat helps find pairs. After folding one-third of the container, I realized that the space I had assigned was way too small and was already overflowing. Since the rest of the table was full, there was no more space to allocate for more socks. This was a seemingly simple and mundane task, that suddenly induced stress in me. Where would I put all these socks now?

Granted, the solution was quite easy in this case. I created some space by stowing some folded laundry, and I had enough room for the socks. What’s the connection to working with AI, you ask?

When AI became publicly available with the launch of ChatGPT, many people immediately recognized this technology’s potential. Recognizing that it’s a new technology with many unknowns, they created companies and planned generously, allowing the companies ample time to find product-market fit and generate revenue.

Stress occurs when plans and reality diverge. It’s the same mechanism, whether you have enough space for your socks or how much runway your company has. Right now, we see many companies entering a stressful phase, especially the big ones. OpenAI, for example, issued a Code Red in an internal memo. Apple abruptly fired their AI chief, John Giannandrea.

Delivering value with AI is a lot harder than everyone thought, we underestimated the complexity of AI. This has led investors to attempt crazy things, this TechCrunch article provides an absurd example: Pumping $90 million dollars into a business with an annual recurring revenue of around $400,000, valuing it at $415 million dollars sounds absurd. This strategy is called king making: declaring a winner in a market and hoping to convince customers to choose the “market leader.” It’s another symptom of the stress we’re seeing in the system right now.

This great article by Paul Ford brings it all together. He wishes for the bubble to burst, because the the frenzy for return on invest ends and we can focus on letting nerds do their best work.

Happy hacking!

We’ve covered why NVIDIA consumer cards hit a 32GB wall and why Apple’s RAM pricing is prohibitive. Now let’s talk about the actual solution: AMD Ryzen AI Max+ 395 with 128GB unified memory.

This is the hardware I chose for my home LLM inference server. Here’s why.

It’s Open, Baby!

In contrast to the two big competitors NVIDIA and Apple, AMD has a huge amount of their stack open source. What CUDA is for NVIDIA and MLX for Apple, that’s ROCm for AMD. It’s fully open source, available on GitHub, and sees a huge amount of activity. This not only gives me a warm and fuzzy feeling, but also a lot of confidence that this stack will continue to go in the right direction.

The Hardware That Changes the Game

AMD Ryzen AI Max+ 395 offers something unique in the prosumer market:

• 128GB of fast unified memory (96GB available to GPU)
• Integrated GPU with discrete-class performance
• Complete system cost: 2000-2500 Euro
• Less than half the cost of the equivalent Mac Studio!

To make this more concrete: you can run a 70B model quantized to 4-bit (~38GB) and still have 50GB+ for context. That’s enough for 250K+ token contexts, legitimately long-document processing, extensive conversation history, and complex RAG workflows.

Looking a bit into the future, it’s not hard to imagine AMD shipping the system with 256 gigabytes of RAM for a reasonable price. It’s very hard to imagine Apple shipping a 256 gigabytes machine for a reasonable price. It’s just how they make their money.

Comparison to the DGX Spark

The recently released DGX Spark is a valid competitor to AMD’s AI Max series. It also features 128GB of super unified memory. From a pure hardware value perspective, the NVIDIA DGX Spark is the most compelling alternative on the market in October 2025. Street price is around 4500 Euro right now, almost double. You get a beautiful box with very comparable hardware and better driver support. You even get a good starting point to do your first experiments, like downloading LLMs and training your model. But everything you build on is closed source. You’re 100% dependent on NVIDIA staying on top of the game, on a machine that doesn’t make a lot of money for NVIDIA. I’m not that optimistic.

With the recent explosion of speed and everything in software with the help of coding agents, I’m not confident any company can stay on top of all of that. Especially not a company that earns their biggest profits in this sector.

Also the NVIDIA DGX Spark is Arm-based, which isn’t a problem for inference and training, but for another use case which is becoming important.

Running Apps and LLMs Side by Side

If you are doing LLM inference on a local machine, the easiest setup is to also run the apps needing the inference on the same machine. Running two machines is possible but opens a huge can of worms of problems. Even though it might not make sense intuitively, such distributed systems are complex. Not double complex, more like exponentially complex. Here’s a golden question from 10 years ago on Stackoverflow, trying to explain it.

So running everything on one machine is much simpler. With AMD you’re staying on the most common CPU architecture available x86-64. With the DGX Spark, you’re in Arm land. This architecture is gaining traction, but still a far way from being universally supported. If you’re planning to experiment with a lot of small open source dockerized apps like I do, this is a big plus for the AMD route.

The Driver Reality

This is the real trade-off: AMD’s software support lags behind NVIDIA and Apple by 1-3 months for bleeding-edge models.

As we discussed in our Qwen3-Next case study:

• vLLM doesn’t officially support gfx1151 (the Ryzen AI 395’s GPU architecture) yet
• For architecturally novel models, you’re waiting on llama.cpp implementations
• ROCm 7.0 works well for established models, but cutting-edge architectures take longer

Important context: This is about bleeding-edge model support, not general capability. I run Qwen3 32B, Llama 3.1 70B, DeepSeek, and multimodal models without issues. The hardware is capable, the ecosystem just needs time to catch up. When and if AMD really catches up is unknown. I just want to make clear it’s a bet.

Why Not Regular AMD GPUs?

Before we conclude, let’s address another obvious question: what about regular AMD GPUs?

AMD Radeon AI PRO R9700 (32GB) or similar:

• Consumer price point (1400 Euro)
• 32GB VRAM
• Same problem as NVIDIA consumer cards, but cheaper

These cards face the same memory ceiling as NVIDIA consumer cards. Yes, driver support has improved significantly with ROCm 6.x and 7.0. But you’re still dealing with the fundamental limitation. They’re cheaper, so you can stack them together, like Level1Techs does.

Two reasons speak against this: First, you’re building a highly custom machine, with all sorts of compatibility issues. Second, with 300W each, this is a huge power draw.

Conclusion

The Ryzen AI Max+ 395 is special because it’s the only prosumer-priced hardware offering 128GB of unified memory accessible to the GPU, coming in a standardized package with decent energy efficiency.

Previously: Why you shouldn’t buy an NVIDIA GPU and Why you shouldn’t buy into the Apple ecosystem.

This concludes our three-part hardware series. The message is simple: 128GB unified memory at a reasonable price changes everything for local LLM inference, and right now, AMD is the only one delivering that.