versteegen

And yet I often tried to edit questions or answers to fix broken formatting and was blocked because I changed too few characters! Only major edits allowed!

Thanks for pushing back, completely agree. GP's 99% trash is a massive exaggeration. It's actually super tough out there for indie game devs, and you have to put up with people lumping your game in with actual trash without a chance.

Getting offtopic... for my father growing up in the Netherlands, the early 50's was still as you describe: an outhouse with a bucket, no running water, an uninsulated house with a roof that let in snow; he caught every illness. They had electricity but no appliances (except a radio?). By the time he was 18, they had a heater, fridge, freezer, washing machine, he had a (rusty) Mercedes and they got a TV soon after. So progress has been very uneven and if you only go back 40 years goods are far more comparable.

Personally, a year or two ago writing like that did not really come across as particular bad or AI generated to me, although it was uncommon for humans to write phrases like that. Writing in an unusual way is not inherently bad, it can make your text interesting... but it is less direct and clear than alternatives. But the style becomes unpleasant if you keep seeing the same patterns over and over such as "X'ed against all of it"

It's kind of weird to say it's "taken off" recently. Yes what we have today is far better than half a year ago and solving new real use cases, but that was far better with more uses than we had half a year before that, etc, for years now. On their best usecases LLMs were already enormously useful years ago, and it's still very spiky.

That is not what Duverger's law says. (But it's a interesting hypothesis so thanks for mentioning it.)

You mean if you used a modern LLM of GPT-2-level quality. Vanilla transformers like GPT 2 are ridiculously inefficient in comparison.

You're asking for something unreasonable. The number of Google searches per day is enormous and they haven't even been able to roll out AI overviews to everyone yet (they're missing in a new Firefox profile I just created). I wouldn't be surprised if the free tier frontier models cost over 100x more to serve than the AI overviews.

> Ok the rear red turn signal lights were already a trend in the US before Tesla. But please reverse this too.

I'm surprised. But it shouldn't be up to Tesla; it's illegal and they never get on the road in countries with proper regulations.

I urge you to reconsider your beliefs. You are missing something important because you are thinking in terms of low-dimensional statistics. Deep learning doesn't just fit data, it finds features (abstractions) of the data.

IME using 5.6 Luna and DS V4 Flash, I notice that although they are excellent at programming, even Opus-like in the way they try to debug, the thing they are worst at is inferring user intent and making good decisions with little information. They are absolutely terrible at that, will misinterpret small wording ambiguities. I suspect that's an ability you can't add with RL training, that it requires the depth of understanding from vast pre-training.

I think this is the best and most useful way to measure model intelligence. In my experience it's what really sets apart the capable models from the best. A small model can be RL trained to be extremely good at programming or narrow problem solving for its size (eg 5.6 Luna, DS4 Flash, Qwen 3.6 27B), but even Luna is IME comparatively awful at understanding intent and making good decisions with limited guidance.

If doesn't correspond cleanly. I can see why you draw the link, because LZ compression will replace words with symbols but BPE is a non-contextual entropy encoding while LZ is contextual and adaptive and that makes it very different. I think BPE actually has more in common with Huffman encoding.