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LLMS
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Experimenting with Claude 3 and Gemini Pro 1.5 Vision
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Worth a try! I've been experimenting with Claude 3 and Gemini Pro 1.5 vision recently too, they're very powerful
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Claude 3 Gemini Pro GPT-4 Vision outperform Tesseract OCR
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No, I don't think Tesseract isn't powerful enough for that unfortunately – that's where Claude 3 / Gemini Pro / GPT-4 Vision are likely to be a better fit
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LangChain Explained: Agents, Prompts, and Retrievers Overview
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What is LangChain? via IBM There are a lot of concepts in LangChain (agents, prompts, retrievers, etc) This is a great conceptual video (no code) from IBM on how all these pieces fit together https://
youtube.com/watch?v=1bUy-1
hGZpI
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New LLM Primer Book Offers Practical Usage Tips
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My friend @emollick assesses large language models as deeply as anyone, and his primer book, coming out this week, is chock full of useful tips for how to use them. And I love the title.
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OCR Tool Built with Claude 3 Opus and GPT-4
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Try it out here: https://
tools.simonwillison.net/ocr I wrote about how I built it – including all of the prompts I used through both Claude 3 Opus and a little bit of ChatGPT/GPT-4 – on my blog: -
Long-context models will enable brute force approaches
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Short term, it’s still useful. Long-term, yeah, I think better + cheaper long-context models will lead to brute forcing being the right approach
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How Claude 3 and GPT-4 simplified AI prompt engineering
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All you new AI engineers don’t know how easy you have it. With Claude 3/GPT-4, you can write a lazy prompt in one try and it’ll still perform pretty well. Back in the day, you had to spend hours, if not days, carefully iterating GPT-3 prompts to make things work.
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DBRX Open-Source LLM Outperforms Benchmarks With Efficient MoE
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#DBRX is a general-purpose LLM that outperforms established open source models on standard benchmarks! We’ve open-sourced it. DBRX is incredibly efficient thanks to its fine-grained MoE architecture & offers the flexibility orgs need for custom #genAI.
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Math Application with LangChain Agents and LLMs
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Building a Math Application with LangChain Agents A tutorial on why LLMs struggle with math, and how to resolve these limitations using LangChain Agents, OpenAI and Chainlit Can easily be extended to augmenting LLMs with other tools!