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

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

Finally! A RAG over code solution that actually works... (open-source). Naive chunking used in RAG isn't suited for code. This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture. Graph-Code is a graph-driven RAG system that solves this. It analyzes the Python codebase and builds knowledge graphs to enable natural language querying. Key features: - Deep code parsing to extract classes, functions, and relationships. - Uses Memgraph to store the codebase as a graph. - Parses pyproject to understand external dependencies. - Retrieves actual source code snippets for found functions. Find the repo in the replies!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

Building AI agents is finally simple — and Airia... is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformationshow more

Google just wired DeepMind and Earth Engine directly into... the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.show more

A peanut-sized Chinese model just dethroned Gemini at reading... documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.show more

A peanut-sized Chinese model just dethroned Gemini at reading... documents. GLM-OCR is a 0.9B parameter vision-language model. It scores 94.62 on OmniDocBench V1.5, ranking #1 overall. For context, it outperforms models 100x its size. 100% open-source. It works in two stages. 1. A layout engine detects every region in a document. 2. Each region gets read in parallel. The model predicts multiple tokens per step instead of one. That's what makes it so fast at small size. It handles things most OCR tools struggle with: > Complex tables and nested layouts > Handwritten text and stamps > Math formulas and code blocks > Mixed image-and-text documents You can run it locally through Ollama. It fits on edge devices with limited compute. Every expensive OCR API just got a free competitor.show more

OpenClaw, but built for normal people. Sim is an... open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.show more

As promised, Godot the robot is out, free and... open-source! You can find Godot, Sophia, and more in this repository: Just copy the files to your projects to start using it.show more

Wow. You can literally chat with any GitHub repo... now! Just pop "talkto" in front of "github" in the URL, and BOOM, you're chatting with your codebase! 🤯 It launches a full chat UI where you can ask the code anything. 100% open-source. Repo link below ↓show more

Parsing PDFs at scale with LLMs is cost prohibitive.... Newer models (e.g. gemini 3) are good at reading pdfs, but you burn unnecessary vision tokens even when the page is text heavy. We’ve built in a “cost-optimizer” within LlamaParse that will dynamically route pages to fast/cheap parsing depending on its complexity. Complex pages (e.g. those with tables/charts/diagrams) will still get routed to our VLM-enabled modes. This will let you save anywhere from 50-90% of parsing costs, at much higher accuracy compared to the comparable mode of feeding screenshots into VLMs. Check it out!show more

🧵 Understanding Zama; the future of privacy tech &... homomorphic encryption 1️⃣ Zama is pioneering fully homomorphic encryption (FHE). A breakthrough that lets you compute on encrypted data without decrypting it. 🔐 That means total privacy, even the system running your data can’t see it. 2️⃣ Why it matters: Right now, cloud apps, AI models, and databases must access your raw data to work. FHE changes that. your data stays private while still usable. 3️⃣ Zama builds open-source FHE tools for developers, turning advanced cryptography into practical products for AI, blockchain, and Web3. 4️⃣ Imagine: •AI that learns without reading your secrets 🤖 •Blockchain transactions with zero data leaks •Cloud apps that never see your info 5️⃣ Zama’s mission: Privacy should be the default, not an option. They’re making privacy-preserving tech simple, scalable, and open for everyone. 🔚 In a world obsessed with data, Zama might just be building the encryption layer of the future internet. 🌐show more

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR... is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!show more

Starting a new project today, building an end to... end system to forecast traffic of flights across cities (starting with Mumbai) The idea is to implement > ingestion service with kafka > data etl with polars > feast for feature store // mlflow for model registry > batch inferencing // dashboard > s3 // postgres for data storage All this orchestrated across multiple DAGs built with Airflow. This year has just been Agents and LLMs all along. Not a bad idea to keep revisiting the traditional format :) Will be posting more of this in the coming days, stay tunedshow more

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic... shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.show more

🆕 CrowdStrike is acquiring Onum to supercharge autonomous cybersecurity... with real-time data pipelines. If Falcon SIEM is the engine of the modern SOC, Onum is both the pipeline + filter — streaming high-quality, filtered fuel quickly into the engine to drive more efficient and superior performance. Onum delivers transformational advantages across three critical dimensions: ⚡ Speed: Delivers 5X more events per second than its nearest competitor and processes security and observability data in real-time versus legacy batch and store methods. ⚡ Cost: Smart filtering reduces data storage costs by 50% through intelligent optimization. ⚡ Superior Outcomes: Real-time pipeline detection starts before data enters the Falcon platform, delivering up to 70% faster incident response with 40% less ingestion overhead. The future of cybersecurity is here – built on real-time, high-fidelity data. 👉 Learn more:show more

🚨BREAKING: The best free AI video tool just dropped.... Now you can generate high-quality 4K videos just by using a text description. SkyReels V2 is open source, unlimited, and completely free. Here’s how it works (with real examples):👇show more