“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated... values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:show more

Thanks Jerry. You shows what matters: precision parsing + structured indexing.

Scan any documents, convert images into text, PDF files, etc. 👍

What the solution?

@goodalexander

LlamaCloud's parsing powers are awesome! Makes those AI hallucinations a thing of the past. Thanks for sharing the tech treasure! 🎉📊

ah, the joys of AI hallucinations. makes you wonder if the real problem is the docs or the brains behind the bots. 🤔

Perhaps AI needs a deeper understanding before tackling nuanced documents.

Man this is what I've been looking for

Chatting with internal data is still a big pain point.

How to build a thriving open source community by writing code like bacteria do 🦠. Bacterial code (genomes) are: - small (each line of code costs energy) - modular (organized into groups of swappable operons) - self-contained (easily "copy paste-able" via horizontal gene transfer) If chunks of code are small, modular, self-contained and trivial to copy-and-paste, the community can thrive via horizontal gene transfer. For any function (gene) or class (operon) that you write: can you imagine someone going "yoink" without knowing the rest of your code or having to import anything new, to gain a benefit? Could your code be a trending GitHub gist? This coding style guide has allowed bacteria to colonize every ecological nook from cold to hot to acidic or alkaline in the depths of the Earth and the vacuum of space, along with an insane diversity of carbon anabolism, energy metabolism, etc. It excels at rapid prototyping but... it can't build complex life. By comparison, the eukaryotic genome is a significantly larger, more complex, organized and coupled monorepo. Significantly less inventive but necessary for complex life - for building entire organs and coordinating their activity. With our advantage of intelligent design, it should possible to take advantage of both. Build a eukaryotic monorepo backbone if you have to, but maximize bacterial DNA.

Agentic RAG for Personalized Recommendation This is a really good example of integrating agentic reasoning into RAG. Leads to better personalization and improved recommendations. Here are my notes:

potentially a new AI generation paradigm? "Energy-Based Transformers are Scalable Learners and Thinkers" proposes Energy-Based Transformers (EBT), a new AI model that repeatedly checks and improves its own predictions through learned energy minimization.

This really hits close to home for me. My whole career in tech was one big unsuccessful struggle to try to explain the distinction between these various modes of operation, and why you will fail miserably if you try to impose a single mindset for all of them.