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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...

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Akshay 🚀

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58,117 views • 7 months ago •via X (Twitter)

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Extracting structured outputs with LLMs is easy. But doing large-scale extraction with precise citations and bounding boxes back to the source documents is way harder. With our latest release in LlamaExtract, we extract citation bounding boxes along with every single key and value within a document. You can see this in the UI. Hover over any k:v pair and you’ll be able to see the corresponding highlights in the source doc. If you’re a human reviewing a million docs (resumes, IDs, invoices, claims, contracts), this will help you 5x your ability to verify values and make sure things are correct. Check out these new extraction upgrades in LlamaCloud:
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Extracting structured outputs with LLMs is easy. But doing large-scale extraction with precise citations and bounding boxes back to the source documents is way harder. With our latest release in LlamaExtract, we extract citation bounding boxes along with every single key and value within a document. You can see this in the UI. Hover over any k:v pair and you’ll be able to see the corresponding highlights in the source doc. If you’re a human reviewing a million docs (resumes, IDs, invoices, claims, contracts), this will help you 5x your ability to verify values and make sure things are correct. Check out these new extraction upgrades in LlamaCloud:

Jerry Liu

23,044 views • 4 months ago

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!
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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!

Santiago

171,340 views • 1 year ago

Almost every brand I’ve talked to has asked me about showing up in LLM answers. Here’s what matters most: 1. Mentions → Does AI actually bring up your brand? 2. Sentiment → When it does… is it positive or negative? 3. Citations → Are you being referenced as a source? You can win in traffic and still lose here. So what actually moves this? 1. Visibility across AI platforms (ChatGPT, Perplexity, Gemini, etc.) 2. Site structure that machines can understand (not just humans) 3. Content designed to answer questions (not just rank for keywords) 4. Trust signals across the internet (reviews, mentions, third-party validation) We’re early, but the data is already clear. AI-attributed commerce is growing fast, and this is only going to compound. You can treat this like SEO in 2012. Or you can get ahead of it now. We put everything we’re seeing into an AI visibility playbook for ecommerce brands. Worth a read if you care about how customers will find you next.
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Almost every brand I’ve talked to has asked me about showing up in LLM answers. Here’s what matters most: 1. Mentions → Does AI actually bring up your brand? 2. Sentiment → When it does… is it positive or negative? 3. Citations → Are you being referenced as a source? You can win in traffic and still lose here. So what actually moves this? 1. Visibility across AI platforms (ChatGPT, Perplexity, Gemini, etc.) 2. Site structure that machines can understand (not just humans) 3. Content designed to answer questions (not just rank for keywords) 4. Trust signals across the internet (reviews, mentions, third-party validation) We’re early, but the data is already clear. AI-attributed commerce is growing fast, and this is only going to compound. You can treat this like SEO in 2012. Or you can get ahead of it now. We put everything we’re seeing into an AI visibility playbook for ecommerce brands. Worth a read if you care about how customers will find you next.

Maxx Blank 🐳

812,417 views • 2 months ago

🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.
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🚀 My New Book is Here: Data Strategy (3rd Edition) 🚀 I’m thrilled to share the release of my latest bestselling book, Data Strategy: How to Use Data and Artificial Intelligence to Transform Your Business. Every business today needs data to survive - but simply having data is not enough. What matters is how you use it. A well-designed data strategy is the key to unlocking value, driving insights, and giving your organisation the competitive edge it needs to thrive in the digital economy. From small organisations to global enterprises, I’ve seen first-hand how a data-driven approach can transform operations, improve decision-making, and unlock entirely new opportunities. That’s why I’ve poured my experience into this book — to help leaders and teams build strategies that don’t just talk about data, but actually deliver measurable impact. 🔍 In this third edition, I’ve expanded the book to reflect the latest developments in data and AI, including: ✅ Generative AI and its role in shaping business innovation. ✅ Synthetic data and how it can accelerate AI adoption. ✅ The potential of quantum computing and what it means for the future of data. ✅ Expanded guidance on cybersecurity, regulations, and ethics in a data-driven world. This isn’t just a theoretical framework - it’s a practical guide to collecting, managing, and using data effectively in order to drive growth, innovation, and long-term success. Whether you’re leading a start-up or a multinational, Data Strategy will equip you with the tools you need to stay ahead in a rapidly evolving landscape. 📖 Pre-order your copy today: 👉 Amazon - 👉 Kogan Page - I can’t wait to hear how this book helps you craft your own data-driven strategy and transform your business for the future.

Bernard Marr

10,980 views • 9 months ago

The Dawn of a New Era on $SUI (9) Still in the festive spirit, let’s look at Tusky , Tusky is a storage service that's not controlled by one company. It uses something called WalrusProtocol to keep your data safe. Your data is encrypted from start to finish, so only you can see it. Instead of one place, your data goes to many different spots. This setup makes your data less likely to be lost or stolen. It helps keep your information safe and always available. Tusky gives you control over your files. You can easily manage them with the tools provided. You decide who gets to see your data. This makes it great for personal storage or working with others. It works with SuiNetwork for even more privacy. You can log in without sharing personal info, keeping everything more secure. This means only you can get to your data, with no third party involved. It is growing fast, with 100,000 uploads already. This indicates its increasing acceptance in the tech community focused on data sovereignty. It's good at managing lots of data safely. In tech, where you want to own your data, TuskyTools is popular. It gives users control over their information. This platform helps keep your data secure and gives you freedom.
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The Dawn of a New Era on $SUI (9) Still in the festive spirit, let’s look at Tusky , Tusky is a storage service that's not controlled by one company. It uses something called WalrusProtocol to keep your data safe. Your data is encrypted from start to finish, so only you can see it. Instead of one place, your data goes to many different spots. This setup makes your data less likely to be lost or stolen. It helps keep your information safe and always available. Tusky gives you control over your files. You can easily manage them with the tools provided. You decide who gets to see your data. This makes it great for personal storage or working with others. It works with SuiNetwork for even more privacy. You can log in without sharing personal info, keeping everything more secure. This means only you can get to your data, with no third party involved. It is growing fast, with 100,000 uploads already. This indicates its increasing acceptance in the tech community focused on data sovereignty. It's good at managing lots of data safely. In tech, where you want to own your data, TuskyTools is popular. It gives users control over their information. This platform helps keep your data secure and gives you freedom.

Kaboom.sui🐽 🌊,⛵

18,167 views • 1 year ago

With a straight face, Mark Carney says that his government AI strategy will protect your privacy...and you children: "The first is trust, we will protect your data, your privacy and your children." Huh?! The Liberals have Bills that literally do the opposite of this and want backdoor access to our data, infringing on our privacy.
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With a straight face, Mark Carney says that his government AI strategy will protect your privacy...and you children: "The first is trust, we will protect your data, your privacy and your children." Huh?! The Liberals have Bills that literally do the opposite of this and want backdoor access to our data, infringing on our privacy.

Kirk Lubimov

30,201 views • 17 days ago

This is one of the fastest ways to build a custom ChatGPT-like system on top of your data. It's called ChatLLM (by Abacus.AI). Here is a demo of how to build a simple custom chat LLM:
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This is one of the fastest ways to build a custom ChatGPT-like system on top of your data. It's called ChatLLM (by Abacus.AI). Here is a demo of how to build a simple custom chat LLM:

elvis

227,166 views • 3 years ago

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.
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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.

Akshay 🚀

52,426 views • 3 months ago

You no longer need a research team. It’s now possible to create consulting-grade reports with AI. Slide decks, insights, visuals, citations... It takes your topic & turns it into a structured, data-backed report in mins. 👀 Here’s how👇
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You no longer need a research team. It’s now possible to create consulting-grade reports with AI. Slide decks, insights, visuals, citations... It takes your topic & turns it into a structured, data-backed report in mins. 👀 Here’s how👇

EyeingAI

58,408 views • 11 months ago

In Web3, data is your edge. For too long, access has been kept away from users. Portal is bringing this power back to the people. With infra that collects the most important on and offchain data - and puts it in your hands.
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In Web3, data is your edge. For too long, access has been kept away from users. Portal is bringing this power back to the people. With infra that collects the most important on and offchain data - and puts it in your hands.

Portal

35,543 views • 1 year ago

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 🧵↓
0:18

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 🧵↓

Charly Wargnier

19,131 views • 2 months ago

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.
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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.

BuBBliK

199,701 views • 19 days ago

3. Arcwise AI: A plugin for Google Sheets that instantly understands, cleans, and ingests data in your sheets using AI. Ask Arcwise any question about your spreadsheet and it will do the math for you and give you the right answer in seconds:
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3. Arcwise AI: A plugin for Google Sheets that instantly understands, cleans, and ingests data in your sheets using AI. Ask Arcwise any question about your spreadsheet and it will do the math for you and give you the right answer in seconds:

Zain Kahn

24,378 views • 3 years ago

You can now copy the table schema from the Supabase table editor and feed it as context to your favorite AI tool! Just click the three dots next to the table name and click copy! Contains data type, constraints, and default values 🍹
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You can now copy the table schema from the Supabase table editor and feed it as context to your favorite AI tool! Just click the three dots next to the table name and click copy! Contains data type, constraints, and default values 🍹

Tyler Shukert

30,131 views • 1 year ago

Cursor for email is here👇 Odo takes your Gmail history and uses it to draft new emails or responses, in your style I've struggled to find an LLM that can mimic my tone and structure. This unlocks much more complex emails, written by AI
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Cursor for email is here👇 Odo takes your Gmail history and uses it to draft new emails or responses, in your style I've struggled to find an LLM that can mimic my tone and structure. This unlocks much more complex emails, written by AI

Olivia Moore

21,451 views • 6 months ago

Our vision: Swarm Intelligence, a future where independent AI systems collaborate, share trust signals, improve together, and solve complex problems together. Instead of isolated models solving problems alone, intelligence grows through seamless collaboration. Perceptron Network is building the foundational layer that makes this possible: a network where participation strengthens the system, and contribution is rewarded. YOUR contribution is key. The hardest problems don’t need bigger models. They need systems that can learn together with real-world data coming from all of us. Collaboration is the key to open a whole new world of possibilities for AI. What do you think AI could achieve if intelligence truly worked together?
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Our vision: Swarm Intelligence, a future where independent AI systems collaborate, share trust signals, improve together, and solve complex problems together. Instead of isolated models solving problems alone, intelligence grows through seamless collaboration. Perceptron Network is building the foundational layer that makes this possible: a network where participation strengthens the system, and contribution is rewarded. YOUR contribution is key. The hardest problems don’t need bigger models. They need systems that can learn together with real-world data coming from all of us. Collaboration is the key to open a whole new world of possibilities for AI. What do you think AI could achieve if intelligence truly worked together?

Perceptron Network

31,917 views • 5 months ago

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 #DigitalTransformation
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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 #DigitalTransformation

Adarsh Chetan

268,444 views • 6 months ago

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)
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most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

Tyler

24,957 views • 1 year ago