supernovalx

New mystery model on openrouter (quasar-alpha) is probably OpenAI.

With gemini 2.5 pro pricing and results, Google has fixed the biggest unknown/weakest link in their lineup and we can now confirm that @GoogleDeepMind completely owns the pareto frontier down to 1220 elo (at least until o4-mini and gpt5?)

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$DOG is listing very soon on $TON No one should have less than 20k $DOG Drop your link in comment for others to use and Rt asap 🫡 Link to dog airdrop: t.me/dogshouse_bot/…

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💻 Run a ZKsync Node Locally 🧵 The ZKsync node is a read replica of the main (centralized) node that anyone can run. It fetches data from the ZKsync API and re-applies transactions from the Genesis block.

$ETH - Looking more optimistic for a Q3 breakout. Liking the chances of a run towards $4,000 this or next month.

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Principled Instructions for LLMs Nice set of guiding principles to improve and enhance the quality and reliability of LLM responses. Shows the effectiveness of the principled instructions and prompt designs across model sizes and scenarios. Tested on Llama 1/2 (7B, 13B, and 70B) and GPT-3.5/4. (see examples in the figure) It's well known that prompt optimization can lead to significant performance gains. If you are building or researching with LLMs, this is a great read. arxiv.org/abs/2312.16171…

I just uploaded a 90 minute tutorial, which is designed to be the one place I point coders at when they ask "hey, tell me everything I need to know about LLMs!" It starts at the basics: the 3-step pre-training / fine-tuning / classifier ULMFiT approach used in all modern LLMs.

While large language models appear to have a rich understanding of the world, how do we know they’re not simply regurgitating from training data? Check out the latest AI Explorable on a phenomenon called grokking to learn more about how models learn. → goo.gle/45ohnQh