OpenAI has the Doug/Astro pretrain coming up next.

Still no Artificial Analysis benchmark yet. Or benchmark for Laguna S 2.1 or Meituan models or lots of other models.

For example, many rumors say SSI has solved learning and retaining state. That would significantly change AI requirements.

THIS IS BECAUSE GPT-5.6 SOL IS... just a more posttrained version of GPT-5.5, not a brand new bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.

OpenAI switching to Sol/Terra/Luna renaming is just a way to rip off people and charge more usage for the same sized model.

GPT-5.6 --------> GPT-5.6 Sol

GPT-5.6-mini ---> GPT-5.6 Terra

GPT-5.6-nano ---> GPT-5.6 Luna

Except OpenAI is about to advertise GPT-5.6 Sol and GPT-5.6 Terra as a whole tier better, than if they named it GPT-5.6 and GPT-5.6-mini.

GPT-5.6 --------> GPT-5.6 Sol

GPT-5.6-mini ---> GPT-5.6 Terra

GPT-5.6-nano ---> GPT-5.6 Luna

Two important things to note, if you want to verify what I say/correct me:

GPT-5.6 Terra actually scores worse than GPT-5.5 on many benchmarks. It's not GPT-5.5 trained with more compute; it's basically GPT-5.6-mini that's been distilled from GPT-5.6 full size. Remember, GPT-5.4-mini had almost the same benchmarks as GPT-5.2 after all.

Opus 4.8 runs at ~90 tokens per second. Fable 5 runs at ~40 tokens per second on from Anthropic, because it's a bigger/slower model. A few days after the release, when the dust dies down, look at how many tokens/second GPT-5.6 Sol is running at. I will bet it's the about same as GPT-5.5, and not half the speed. (OpenAI is not incentivized to slow down the model for paying customers). But the model tokens/sec will be a big clue- if OpenAI is charging more money for the same sized model or not.

You can’t squeeze blood from a brick. At a certain point, you need to tolerate a little messiness to optimize societal growth.

Think of it as a dial you can turn clockwise or counterclockwise:

Security <——> Freedom

A healthy society would have good feedback mechanisms that allow it to change the dial of the government in power, to adjust to the current situation. Obviously, there’s no one optimal position; to use a historical example: Churchill was great for Britain during WW2, and immediately elected out afterwards.

Qwen 3.7 is not open source; previous Qwen versions would have open source releases, but Qwen 3.7 plus does not. The second best Chinese model, Minimax M3, is testing the waters by taking longer and longer between “model release” and open sourcing it. This time, they spent 2 weeks after release before open sourcing it. There’s also a lot of rumors of GLM and Deepseek not open sourcing future models.

It’s pretty obvious that you cannot take Chinese models as open source for granted, they’ll be closed source soon.

This reads to me as "user installed exe file can upload your data to a server". Um, yes, that's the point?

This seems like this generation's equivalent of "don't open Linkin-Park.mp3.exe from limewire"

The tokens/sec of the model is basically directly proportional of the memory bandwidth of the hardware it runs on. So either OpenAI has to gimp model performance for its entire life, or somehow magically speed it up 4x on the first day.

I am willing to bet large amounts of money that OpenAI would never release a model served as fully BF16 in the year of our lord 2026. That would be insane operationally. They're almost certainly doing QAT to FP4 for FFN, and a similar or slightly larger quant for attention tensors.

More and more people are just focused on making a quick buck.

I'm getting a feeling that these people would gladly rip off a lemonade stand, and then defend themselves by saying the lemonade stand deserves it.

Hell, Dunbar's Number is 150 people, and you expect to have 50 directs? That's literally 1/3 of your 150 being occupied by directs. It seems clearly infeasible the more you think about it.

It seems quite counterproductive to assume such a system would scale to everyone else, or that everyone else could possibly implement this. This is cowboy levels of human resource management, not careful engineering.

[1] https://en.wikipedia.org/wiki/Seeing_pink_elephants