We are repeating an old pattern in computing: throw more hardware at the problem until efficiency becomes impossible to ignore. Bigger models have delivered remarkable gains, but they’re increasingly expensive. The next breakthroughs may come less from adding parameters and more from smarter architectures, better algorithms and more efficient inference.

Brexit is turning into one of those rare political decisions where the people who will live with the consequences the longest were mostly too young to vote on it. Whether rejoining happens or not, the generational divide on Europe’s future is becoming impossible to ignore.

Funny how the self proclaimed savior of humanity keeps treating regulations like optional DLC: If anyone else ran 59 gas turbines without permits they would be buried in fines. Billionaires call it innovation, everyone breathing nearby calls it another asthma attack waiting to happen.

I don’t think open-weight models can be prevented, as ‘everyone’ knows how distillation works these days and, clearly, no one can do anything to stop it.

1.5 TB of unified memory sounds less like a computer and more like Apple preparing for the moment your local AI starts asking for a raise. Plot twist: by 2028 the RAM upgrade still costs more than the rest of the machine combined.

Everyone is. Open weight and source is the way to go in my opinion.

Nope, it‘ll take several years to catch up.

The job is changing, not disappearing. Writing syntax is becoming cheaper, but understanding systems, tradeoffs, security, debugging and talking to humans is still expensive. The engineers who treat AI like a power tool instead of a rival will probably end up building more, not less.

Everyone wants AI to be the next cloud boom until the bill arrives. Betting tens of billions on one customer whose own business model is still being debated is bold. If demand keeps exploding Oracle looks brilliant. If not, this could become the case study every finance class uses.

It is funny watching companies discover that data gravity works both ways. When scraping the web was innovation it was progress. When someone learns from their outputs it becomes theft. The legal lines still matter, but the irony is impossible to ignore, and this debate was always going to come full circle.

The upside is that unified memory is genuinely different from traditional RAM. The CPU, GPU and Neural Engine all share the same memory pool, so data doesn’t need to be copied back and forth. That reduces latency, improves efficiency and lets AI models, graphics and other workloads access much larger datasets. It also uses less power and saves board space. The downside is obvious: because it’s integrated into the chip, you have to choose the right amount upfront, since it can’t be upgraded later.

No buttons, no DRM, no notifications, no algorithm deciding what I should read next. Somehow an ESP32 powered e reader feels more rebellious in 2026 than most flagship gadgets. I just hope the touchscreen is good enough that turning a page does not become a mindfulness exercise.

Zuck is killing the whole industry. 😔

So cameras are guns now?

1. a camera isn’t a problem in itself. 2. there are AI/AR glasses without cameras; personally I own a G1.

What bothers you about AR/AI glasses?