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@unbias

51 Following

83 Followers

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big ai doesn’t want you to know what benchmarks actually represent-- that’s how every model scores first and last

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time to circle back after 8 months of stagnation-- profile summaries from 2024👇

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recently rebuilt our data transformation engine, currently embedding all of farcaster and then doing some nifty stuff after

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llama 3.1 context window increases to 128k 💪💪

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local fc postgres is up. Bit slow otherwise. channel summaries en route

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https://warpcast.com/cassie/0x506269cf

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the farcaster ai race will come down to quality, speed, and cost It will differentiate competition that is otherwise all search and converse

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if you want to prompt an llm about thread data, given a hash, the new endpoint is available with limited support avg few second response

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seems it’s most appropriate to process the prompt via llm for a search string and use that string to find embeddings

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conversation endpoints use multiple search types to create precise context

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semantic search at thread level now includes access to all casts in a thread and system info like creation date, channel info, reaction counts, author information, and more. return types are either neynar cast objects or string system information

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the difference in embedding model size for search, big vs small, is that SMALL EMBEDDINGS might miss details like 'preying mantis' as a bug or insect. BIG EMBEDDINGS have a better chance of catching those details, making searches more accurate. OPENAI's text-embedding-3-small (1536 dimensions) is $0.02/1M TOKENS, and text-embedding-3-large (4096 dimensions) is $0.13/1M TOKENS

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overall this has been a good experience (upgrade from vss) https://github.com/asg017/sqlite-vec https://news.ycombinator.com/item?id=40243168

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bad day to be a dev when the prompts aren't hitting

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i have successfully created a new seedphrase for my account

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https://warpcast.com/dwr.eth/0xa4c462c1

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