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00:00 What is SubQ and why is everyone talking about it?
00:38 What did SubQ announce about 12 million token context?
02:00 What is SubQ 1.1 Small?
02:20 Why does normal transformer attention scale quadratically?
03:03 Why do long-context AI models need a new architecture?
03:52 How does sub-quadratic sparse attention work?
04:28 How is SubQ different from Longformer, BigBird, and Mamba?
05:04 How much compute does SubQ save versus dense attention?
05:51 Can SubQ actually retrieve information from long context?
06:47 How does SubQ compare to GPT, Claude, and smaller models?
07:53 Did SubQ train its model from scratch?
08:20 What are the real-world use cases for 12 million token AI context?
09:11 Are SubQ’s benchmark claims independently verified?
10:05 What are the main limitations of sparse attention?
10:21 When will SubQ be available and what happens next?
Links From Todays Video:
https://subq.ai/subq-1-1-small-technical-report
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LEMMiNO – Cipher
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