swyx
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did you really have to include that? without substantiation this is just a mean offhand side swipe.
Claude Opus 5.5 finished Baldur's Gate 3 on its own in 39 hours
PingCap TiDB Leaks
this is their problem, not yours. at least disclose # of flops spent or amount of data used in an attempt to give us an idea of how nontrivial it was. cursor at least does this with their composer model
look at any study of llm depth vs multihop reasoning capability (here, just to help https://www.latent.space/p/ainews-death-of-params-zai-ceo-ji... but theres a long line of others).
i will grant that people do do "reasoning in image models" type benchmarks, and it's not nothing, but its kid stuff vs what we make LLMs do, and the only way you get image models that basically do reasoning is you glom a diffusion head on an LLM backbone, which is basically gpt image 2.0 and above
none of which is to dispute the original question which is perhaps "why are image models so smol vs LLMs" - which i agree with and wish there were funding for scaling them - but the abject failure of sora indicates that perhaps our data isn't nearly good enough (again pointing to another thing - much easier to pretrain and posttrain LLMs given quantity of text data)
US market share of global desire to migrate
2007 - 24%
2016 - 20%
2017 - 18%
2025 - 15%
Canada is #2 and moved up from 7% to 9%
hmmmm. spacex-tesla merger signalling?
im in the conference business but just fyi thats actually cheap - venue, av and f&b alone cost many millions for a conference this size. a good rule of thumb on cost is $1-2000 per head depending on quality of event.