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Joel Salinas's avatar

Excellent breakdown! I like all the added deep dive resources

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Jakub Slys πŸŽ–οΈ's avatar

Thanks so much Joel πŸ™πŸ» I’m really glad the deep dives resonated! Curious: which whitepaper stood out the most for you? πŸ˜ƒ

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Joel Salinas's avatar

Solving Domain-Specific Problems Using LLMs, made me think of a few new things. Good work, look forward to your next post.

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Lorenz Hofmann-Wellenhof's avatar

Some much to learn, so little time πŸ˜…

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Jakub Slys πŸŽ–οΈ's avatar

I feel the same, Lorenz. So much to learn and never enough time. And the funny thing is, the more you know, the more you realize how much you don’t know πŸ˜…

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Tope Olofin's avatar

I’m committing to read these 3 white papers. The one on foundational LLM, the one on prompt engineering and the one agents. I think they’ll will be helpful in helping eventually build an AI product.

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Jakub Slys πŸŽ–οΈ's avatar

Great choices Tope! πŸš€ These three really set the stage - from the basics into action-oriented systems. I’d love to hear what stands out as you dig in. Good luck with your AI product! πŸ€–

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Tope Olofin's avatar

Thank you!

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Chintan Zalani's avatar

Thanks for sharing these whitepapers Jakub. Which one is your favorite? πŸ˜„

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Jakub Slys πŸŽ–οΈ's avatar

Thanks and great question Chintan! I’m definitely an Agents guy πŸ€– That whitepaper pulls everything together: planning, memory, vector search, tool integration. It’s where LLMs go from passive responders to active problem-solvers. It’s the most forward-looking piece in the set, and I think it hints at where GenAI is really headed ⏩

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Chintan Zalani's avatar

For sure jakub agentic AI is where it’s at in 2025. Sounds like an interesting whitepaper!

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