RAG engine that just works for complex real-world documents.... It can extract knowledge from unlimited tokens across ANY data format - Word docs, Excel sheets, PDFs, and even scanned copies. And it's 100% Opensource.show more

Building RAG is easy. Parsing real, unstructured data is... the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓show more

A RAG engine for deep document understanding! RAGFlow lets... you build enterprise-grade RAG workflows on complex docs with well-founded citations. Supports multimodal data understanding, web search, deep research, etc. 100% local & open-source with 55k+ stars!show more

OpenAI's Deep Research is getting a run for its... money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!show more

Grok 4.3 just started dropping full files. It spits... out Word docs, PDFs, slides, and spreadsheets. Just like that. No back and forth. No hacks. Just done. You ask once… it builds the whole thing. Nice. xAI Grokshow more

RAG is not Memory for AI Agents. 5 AI... memory engines to build agents that maintain long-term context and learn continuously: (Last 2 released just this month) 1. Zep builds and queries temporally-aware knowledge graphs that evolve with every interaction. 100% Opensource.show more

Extract – a system built by the UK government,... using our Gemini foundational model – will help council planners make faster decisions. 🚀 Using multimodal reasoning, it turns complex planning documents – even handwritten notes and blurry maps – into digital data in just 40s. Find out more. ↓show more

Real-world logic, now in your video generations. Gemini Omni... pulls from Gemini’s deep knowledge of history, biology, and narrative logic to generate videos that look and behave like the real world. See how it works 🧵 ↓show more

Kimi just wiped out Google Docs, Excel, and PowerPoint... in one quiet update. While most people still treat AI like a fancy typing assistant, Kimi Premium shipped something completely different: AI that creates finished professional files directly from conversation. Not exports. Not drafts. Real Excel, Word, PPT, and PDFs built at the structural level. Ask for five years of financials on the Magnificent Seven and it doesn’t summarize. It builds a multi-sheet Excel workbook with balance sheets, income statements, cash flows, clean formatting, and sourced data like a senior analyst prepared it. Someone asked for a DCF on Apple. Kimi produced a full valuation model with assumptions, discount rates, scenario analysis, and citations. Another user fed in one payroll Excel file and got 30 individualized, encrypted PDF payslips out. End-to-end automation. It gets crazier. It can generate images inside spreadsheets for storyboards. One prompt produced a 20-shot video storyboard Excel with timing, visuals, voiceover text, sound cues, and consistent sketch images per frame. This isn’t better chat. It’s execution replaced. Kimi doesn’t give you content to paste into tools. It gives you the tools’ final output. That’s the shift everyone missed. Try it here:show more

Automate ETL over Financial Data 📊 Most real-world financials... are not “database-shaped”, and requires a ton of human effort to manipulate/copy an Excel sheet into structured formats for analysis. We recently launched LlamaSheets - a specialized AI agent that automatically structures your Excel spreadsheet into a 2D format for analysis. There are so many use cases for Excel, and accounting is a huge subcategory here. Check it out:show more

RAG is not Memory. AI agents need long-term memory... to maintain context and learn continuously. 5 frameworks for AI agent memory. 100% opensource. 1. Graphiti builds temporally-aware knowledge graphs for AI agents that change over time with evolving relationships and context.show more

Turn complex docs into clean, LLM-ready data! Every AI... company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!show more

Build autonomous browsing AI Agents in minutes. It uses... vision AI to interact with any interface, just like humans do. Works with Claude 4 Sonnet and Qwen 2.5 VL. 100% Opensource.show more

Introducing /extract v2 🚀 Now you can extract from... multiple pages, handle pagination, and even extract from the entire web without a URL - powered by FIRE-1. Our state of the art endpoint that allows you get data with a prompt just got a lot better on Day 3 of Launch Week.show more

INSANE...this is an Open Source Video model available for... free on Hugging Face. LongCat just dropped an amazing video avatar model that can do crazy things. And it's 100% Opensource.show more

You can now directly add content from Google Docs... to chats and projects. Just paste a link or choose from recent documents to get started.show more

Not all Excel can do this. Try and see... if it works.show more

6. You can easily add content from Google Docs... to your chats and projects. Just paste a link or pick from your recent documents to begin.show more

$4.4 trillion in PE assets under management Monitored by... analysts who spend 500+ hours a year copying numbers from PDFs into Excel. Not analyzing. Copying and pasting. A fund has 10 portcos. One on NetSuite, one on QuickBooks, three on Excel, one CFO sending a paragraph in an email every quarter. Company A calls it "Revenue." Company B calls it "Net Sales." Company C doesn't even use the same EBITDA calculation. Before AI can do anything useful, you need four layers: 1. Ingestion: get the data out of PDFs, spreadsheets, emails, whatever format it shows up in 2. Normalization: make sure the same metric means the same thing across every company 3. Storage: structured database that can actually be queried, not a folder of files 4. Query layer: natural language interface so a non-technical partner can ask "which portco is trending down on margins?" and get a real answer Most companies jump straight to layer 4 and wonder why nothing works. The unlock is building layers 1-3 first. Once those exist, the AI part is MUCH easier. The firms that get their data infrastructure right first will be untouchable.show more