Not sure it's about being a high trust society or not, there's frequent inspections where they block the doors, and you get a hefty fine if you're caught without a valid ticket. I certainly wouldn't call Prague or Rome or Dublin high trust societies on par with a Swiss city.

VPSes and non-custom configs for dedicated servers were pretty instant as far as I know, I think the advantage of AWS was more that you could scale up and down much more easily since you weren’t locked down in a monthly contract, and that you could automate server provisioning through an API.

Am I the only one who remembers that VPSes and dedicated hosting services were a thing before AWS came around? Yes you had to pay for a month at a time and scaling wasn’t as instant, but it wasn’t like the only option before cloud computing was having to drive to the datacentre and install your own server.

As I've said, and @echoangle repeated, caning is used for cyberbullying, which girls do too (at a rate relatively close to boys actually). If the law was caning in response to physical bullying, and it just so happened that the vast majority of offenders were boys, I would not object on the basic of sexism (I still would not approve of schools being allowed to physically punish students).

> Yes, for homo sapiens, the female is more fragile than the male. This is basic biology. I'm sure that in praying mantis society, females get harsher punishments.

There's no way the typical 16 year old girl is more fragile than the typical 9 year old boy, yet only the latter is subject to this punishment. Until children reach the age of 12 or so the strength difference is quite minor (and there's even a brief period where girls are taller and heavier).

Also it's absurd to punish demographics differently based on their statistical averages. Redheads are less sensitive to pain, should your hair colour determine how many strokes of the cane you get?

If the law punishes one demographic less severely for the same actions, that's injustice. No different in principle from pre-modern practices where if a noble maimed a commoner, they'd just need to pay a fine, while if a commoner did the same, they'd be put to death.

I'm a self-taught software developer with no university education and I too am socially awkward in front of tradespeople in my house. I don't think this is about Ivy League degrees, just being a nerdy intellectual who's bad at small talk and doesn't have any topics in common with a blue collar worker.

GPT would have been a better term than LLM, but unfortunately became too associated with OpenAI. And then, what about non-transformer LLMs? And multimodal LLMs?

Maybe we should just give up, shrug and call it "AI".

That's not local models vs hosted models, that's using the enterprise services from Anthropic. Any local LLM inference engine such as VLLM gives you an OpenAI compatible API with the exact same features as a hosted model.

I'm not sure what your use case is, but I personally found Anthropic's offerings lacking and inferior to open source or custom-built solutions. I have yet to see any "memory" system that's better than markdown files or search, and harnesses for agentic AIs are dime a dozen.

LLMs aren't artificial superintelligence and might not reach that point, but refusing to call them AGI is absolutely moving the goalposts.

For males not to have nipples, they'd need to be actively destroyed, which poses a risk for females to also not have nipples, which is much worse than males having harmless, inactive nipples.

But in either case, if compute is so limited, they’ll have to compete with local coding agents. Qwen3.6-27B is good enough to beat having to wait until 5PM for your Claude Code limit to reset.

2. Don’t forget about B&W films! Getting automatic methods to be consistent over a long length is still not 100% solved. People are very interested in seeing films from WW1 and WW2 in colour, for instance.

3. Plenty of people (myself included) have relatives in their 80s or 90s. Or maybe someone wants to see their ancestors from the 19th century in colour for whatever reason?

Find me a 0.66" OLED display for ~$1 that has hundreds of pixels on each side then.

> It reminds me people who research "colorizing grayscale photos", which do not exist anymore either (if you want a color photo of someone you met in your life, there probably exists a color photo of that person).

What train of thought led you to think people are primarily researching colorising new B&W photos? As opposed to historical ones, or those of relatives taken when they were young? You can take a colour photo of granddad today but most likely the photos of him in his 20s are all in black and white.

Although since they were running a LEMP server stack manually and did their migration by copying all files in /var/www/html via rsync and ad-hoc python scripts, even a DO droplet doesn't have the best guarantee. Their lowest-hanging fruit is probably switching to infrastructure as code, and dividing their stack across multiple cheaper servers instead of having a central point of failure for 34 applications.

The cost of switching is too low for them to be able to get away with the standard enshittification playbook. It takes all of 5 minutes to get a Codex subscription and it works almost exactly the same, down to using the same commands for most actions.

> The network has a fixed number of input neurons. You have to put something in all of them.

The way transformers work is that they apply the same "input neurons" to each individual token! It's not:

Token 1 -> Neuron 1 Token 2 -> Neuron 2 Token 3 -> Neuron 3... With excess neurons not being used, it's

Token 1 -> Vector of dimensions N -> ALL neurons Token 2 -> Vector of dimensions N -> ALL neurons Token 3- > Vector of dimensions N -> ALL neurons ...

Grossly oversimplified, in a typical transformer layer, you have 3 distinct such "networks" of neurons. You apply them each token, giving you, for each token, a "query", a "key", and a "value". You take the dot product of there query and key, apply softmax, then multiply it with the value, giving you the vector to input for the next layer.

A probability distribution obviously contains a probability for every possible next token. But the whole probability distribution (which adds up to one) only predicts the next ONE token. It predicts what is the probability of that one token being A, or B, or C, etc, giving a probability for each possible token. It's still predicting only one token. In anything but the last column, the numbers are junk. You can treat them as probability distributions all you want, but the system is only trained to get the outputs of the last column "correct". Not quite, the reason transformers train fast is because you can train on all columns at once.

For tokens 1, 2, 3, 4, ... you get predictions for tokens 2, 3, 4, 5... Typical autoregressive transformer training uses a causal mask, so that token 1 doesn't see token 2, enabling you to train on all the predictions at once.

Standard transformers take in an arbitrary input size and run blocks (self and possibly cross attention, positional encoding, MLPs) that don't care about its length.

> They also have a fixed output of one probability distribution for the next one token.

No, in most implementations, they output a probability distribution for every token in the input. If you input 512 tokens, you get 512 probability distributions. You can input however many tokens you want - 1, 2048, one million, it's the same thing (although since standard self-attention scales quadratically you'll eventually run out of memory). Modern relative embeddings like RoPE can support infinite length although the quality will degrade if you extrapolate too far beyond what the model saw during training.

For typical auto-regressive generation, they are trained with causal masking/teacher forcing, which makes it calculate the probability for the next token. During inference, you throw away all but the last probability distribution and use that to sample the next token, and then repeat. You also do this with an RNN. An autoregressive CNN (e.g. WaveNet) would be closer to what you described in that it has a fixed window looking backwards.

But a transformer doesn't have to be used for auto-regressive generation, you can use it for diffusion, as a classifier model, for embedding text. It doesn't even see a sequence as spatially organised - unlike a CNN or an RNN it doesn't have architectural intrinsic biases about the position of elements, which is why it needs positional embeddings. This lets you have 2D, 3D, 4D, or disordered elements in a sequence. You can even have non-regularly sampled sequences. (Again this is for a classic transformer without sliding window attention or any other special modifications).

> (padding the unneeded context window with null tokens). To have efficient training, you pad all samples in a batch to have the same length (and maybe make it a power of two). But you are working with a single sequence, the length is arbitrary up to hardware limitations, and no padding is needed.

If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible?

What exactly makes you so confident?

The world is not just labs that can afford billion dollar datacentres and selling access to SOTA LLMs at $30/Mtokens. Transformers are highly unsuitable for many applications for a variety of reasons and non-linear RNNs trained via parallel methods are an extremely attractive value proposition and will likely feature in production in the next products I work on.

> I guess there's some misunderstanding here because Qwen is 100% a transformer, not a hybrid RNN/LSTM whatever.

See the Qwen3.5 Huggingface description: https://huggingface.co/Qwen/Qwen3.5-27B > Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.