OK well now you have to look at changing the economy-wide energy mix or embracing de-growth. Switching data centers to 100% solar or nuclear or … solves Wired’s complaint but not this one.
Historical reliability and compatibility. They claimed they were S3 compatible, but they were requiring deprecated S3 SDKs, plus S3 advanced features are unimplemented (but at least they document it [0]). There was constant timeouts for object creation and updates, very slow speeds and overall instability. Even now, if you check out r/hetzner on reddit, you'll see it's a reliability nightmare (but take it with a grain of salt, nobody reports lack of problems). Not as relevant for DB backups, but billing is dumb, even if you upload a 1KB file, they charge you for 64KB.
At least with Storage Box you know it's just a dumb storage box. And you can SSH, SFTP, Samba and rsync to it reliably.
Only for chat sessions, not for agentic coding. It's just too slow to be practical (10 minutes to answer a simple question about a 2k LoC project - and that's with a 5070 addon card).
Ah, forgot to add, it's not really "unified" you have to explicitly specify your allocations. You may have a reasonably good 48gb chunk assigned to the GPU, but that DDR5 is 5-10 times slower than GDDR/HBM and the GPU itself isn't stellar.
So, framework laptops are great for chatting but nearly useless in agentic coding.
My Radeon W7900 answers a question ("what is this project") in 2 minutes, it takes my Framework 16 with 5070 addon around 11 minutes without the addon - around 23 (qwen 3.5 27b, claude code)
> On reflection I have started to worry again. In 10 to 20 years nobody will read anything any more, they just will read LLM digests. So, the single most important task of a writer starting right now is to get your efforts wired in to the LLMs. Nothing you write will matter if it is not quickly adopted to the training dataset. As the art of pushing your results to the top of the google search was the 1990s game, getting your ideas into the LLMs is today’s. Refine is no different. It’s so good, everyone will use it. So whether refine and its cousins take a FTPL or new Keynesian view in evaluating papers is now all determining for where the consensus of the profession goes.
The scaling will continue until morale improves. I advise people to skate to where the puck will be, and to ask themselves: "if I knew for a fact that LLMs could do something I am doing in 1-2 years, would I still want to do it? If not, what should I be doing now instead?"
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