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Here's a demo on a project I've been developing and working on for the past 9 months. Called NightBeacon. Using it now in production, getting released fully this week. Our own internally trained models on our own infrastructure (no third party). Trained on our analysts knowledge and behavior (TP/FPs... retrain model to be smarter with context). Handles emails (including tonality), attachments, various malicious filetypes (DLL/exe/svg/lnk/etc). Can send it full evtx exports, packet dumps, zip files, whatever. Universal log handler can parse any log from any source, EDR, SIEM, etc. Deep-Scan / sandbox detonation + shellcode emulation with IOC extraction automatically. Automatic playbook generation, full AI-based recommendations custom to the attack. Synthetic training data layer - meaning when it trains on a specific attack at a customer, generates training data based on the customers data but never has any of the actual data or information about the customer in it. No customer information. For areas its weak at, bubbles up and automatically kicks off research to become smarter on a specific topic. Supports GenAI based rulesets (to improve confidence), over 900+ YARA rules, full MITRE ATT&CK integration. Integrated into our SOAR - enriches data, creates playbooks for analysts, MTTR reduces substantially, false positives reduced, true positive escalations. Not using our MDR service? Can integrate into your EDR or SIEM for automatic enrichment and escalation of attacks. Built to help respond faster. More accurately. Be intelligent based on our analysts intelligence. Stop attackers much much faster. Coming soon.. #BinaryDefenseshow more

Dave Kennedy

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12,905 views • 4 months ago •via X (Twitter)

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Here is a live demo of our AI solution I've been building non-stop over the past 8 months Binary Defense. How it works: Our own model trained on our analysts behavior. Our analysts submit tickets as false positives/true positives with context which enriches our LLM to be smarter over time. Key Highlights: If its a binary - will automatically spin up an agent for reverse engineering it and using EMBER ML to understand behavior and intent of the binary. File formats: Supports a vast array of pretty much any filetype, including email attachments like SVG, LNK, etc. Can handle DLLs, ELF, EXEs, PDF, XLS, DOC, etc. Interrogates the full chain of all events irrespective of log sources. Can handle any format of logs and integrates into APIs of customers for additional agentic data looping for confidence ranking when needed. This is an example of the back-end UI, this is transparent to analysts and enriches the alarms automatically in our SOAR. In these examples there's three different types: 1. Regsvr32 + sct downloader + scrobj.dll code execution - checks reputation of domain, pulls in threat intel, looks at entire picture of the chain - downloads the file itself and inspects for code analysis. Determines if malicious as well as historically looking back if seen in customer before in past. 2. Powershell Obfuscation - uses a universal decoder to un-obfuscate powershell and look at the raw code. Can handle pretty much any obfuscation thrown at it (thanks Justin Elze). 3. Email with malicious SVG - checks tonality of email, are they creating urgency to take action (increases confidence) - disassembles SVG to understand malicious content - checks URL to determine if harvesting credentials, payload delivery, etc. Creates an entire kill chain analysis with full response and dissecting of the attack to the analyst in seconds. Has greatly sped up our ability to respond to incidents and allowing analysts to focus on the most important alarms through prioritization. Once cool thing I've worked heavily on is a synthetic data normalizer which when an analyst says "Yes this is bad with context" or "No this is a false positive" - our local model generates training data to be smarter in the future without using the actual customer data to train it. The customers actual data is immediately destroyed once training data off of the original alarm is generated and contains no customer-centric data at all. We also have three model tiers. Opt-In (collective model, again no customer data but every organization contributes to training). Opt-Out - does not train on any customer data for customers who opt-out. Private LLM - LLM created specifically for individual customer and trains only off of their data. Uses shared model collective for better confidence rankings. It will generate automated playbooks to run based on confidence rankings to take action on behalf of the customer. Still human driven on execution - has to approve playbook actions. This thing is cooking and so cool to see this work live and shut down attackers much faster! If confidence ranking is low - will automatically attempt to enrich data through customer environments for better confidence rankings. Additionally if the model isn't trained well on a certain technology, I have created something we call "Nexus" that will research new protocols, devices, SDKs, etc and generate training data automatically. Works well for zero-days for example, point to a tweet, or a research paper, and automatically generates training data to recognize this attack much faster. Have over 8000+ yara rule integrations that help with confidence boosting as well that is automatically incorporated into the analysis. Creating some amazing stuff at Binary Defense that isn't marketing fluff - actionable things that are making a huge difference in this industry. #BinaryDefense

Here is a live demo of our AI solution I've been building non-stop over the past 8 months Binary Defense. How it works: Our own model trained on our analysts behavior. Our analysts submit tickets as false positives/true positives with context which enriches our LLM to be smarter over time. Key Highlights: If its a binary - will automatically spin up an agent for reverse engineering it and using EMBER ML to understand behavior and intent of the binary. File formats: Supports a vast array of pretty much any filetype, including email attachments like SVG, LNK, etc. Can handle DLLs, ELF, EXEs, PDF, XLS, DOC, etc. Interrogates the full chain of all events irrespective of log sources. Can handle any format of logs and integrates into APIs of customers for additional agentic data looping for confidence ranking when needed. This is an example of the back-end UI, this is transparent to analysts and enriches the alarms automatically in our SOAR. In these examples there's three different types: 1. Regsvr32 + sct downloader + scrobj.dll code execution - checks reputation of domain, pulls in threat intel, looks at entire picture of the chain - downloads the file itself and inspects for code analysis. Determines if malicious as well as historically looking back if seen in customer before in past. 2. Powershell Obfuscation - uses a universal decoder to un-obfuscate powershell and look at the raw code. Can handle pretty much any obfuscation thrown at it (thanks Justin Elze). 3. Email with malicious SVG - checks tonality of email, are they creating urgency to take action (increases confidence) - disassembles SVG to understand malicious content - checks URL to determine if harvesting credentials, payload delivery, etc. Creates an entire kill chain analysis with full response and dissecting of the attack to the analyst in seconds. Has greatly sped up our ability to respond to incidents and allowing analysts to focus on the most important alarms through prioritization. Once cool thing I've worked heavily on is a synthetic data normalizer which when an analyst says "Yes this is bad with context" or "No this is a false positive" - our local model generates training data to be smarter in the future without using the actual customer data to train it. The customers actual data is immediately destroyed once training data off of the original alarm is generated and contains no customer-centric data at all. We also have three model tiers. Opt-In (collective model, again no customer data but every organization contributes to training). Opt-Out - does not train on any customer data for customers who opt-out. Private LLM - LLM created specifically for individual customer and trains only off of their data. Uses shared model collective for better confidence rankings. It will generate automated playbooks to run based on confidence rankings to take action on behalf of the customer. Still human driven on execution - has to approve playbook actions. This thing is cooking and so cool to see this work live and shut down attackers much faster! If confidence ranking is low - will automatically attempt to enrich data through customer environments for better confidence rankings. Additionally if the model isn't trained well on a certain technology, I have created something we call "Nexus" that will research new protocols, devices, SDKs, etc and generate training data automatically. Works well for zero-days for example, point to a tweet, or a research paper, and automatically generates training data to recognize this attack much faster. Have over 8000+ yara rule integrations that help with confidence boosting as well that is automatically incorporated into the analysis. Creating some amazing stuff at Binary Defense that isn't marketing fluff - actionable things that are making a huge difference in this industry. #BinaryDefense

Dave Kennedy

29,036 views • 4 months ago

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 views • 10 months ago

The brain of #Agentforce is our #Atlas learning and reasoning engine, developed by Salesforce AI Research 🧠 Atlas reasons, generates a plan, evaluates, and refines until it feels confident it knows how to accomplish your goals. It pulls in the data it needs from Data Cloud using advanced #RAG and custom CRM #embeddings. Then Atlas takes action across the Customer 360, whether that’s automating a campaign in Agentforce Marketing, resolving a case in Agentforce Service or, in OpenTable’s case, confirming that perfect dinner reservation for your friend's birthday. The more you use Atlas, the smarter it gets. We've pioneered reinforcement learning based on customer outcomes (#RLCO)— Agentforce is continuously tuning to align with your business outcomes, such as higher conversion and increased CSAT. Your data is never our product, which means your outcome data is proprietary to you. The results we're seeing are incredibly promising. Agentforce powered by Atlas delivers 33% more accuracy and 2x more relevance than DIY AI. It's the difference between a DIY science project and a trusted enterprise-grade agent you can confidently deploy into production. #DF24 #agents #enterprise #reasoning #dreamforcekeynote

The brain of #Agentforce is our #Atlas learning and reasoning engine, developed by Salesforce AI Research 🧠 Atlas reasons, generates a plan, evaluates, and refines until it feels confident it knows how to accomplish your goals. It pulls in the data it needs from Data Cloud using advanced #RAG and custom CRM #embeddings. Then Atlas takes action across the Customer 360, whether that’s automating a campaign in Agentforce Marketing, resolving a case in Agentforce Service or, in OpenTable’s case, confirming that perfect dinner reservation for your friend's birthday. The more you use Atlas, the smarter it gets. We've pioneered reinforcement learning based on customer outcomes (#RLCO)— Agentforce is continuously tuning to align with your business outcomes, such as higher conversion and increased CSAT. Your data is never our product, which means your outcome data is proprietary to you. The results we're seeing are incredibly promising. Agentforce powered by Atlas delivers 33% more accuracy and 2x more relevance than DIY AI. It's the difference between a DIY science project and a trusted enterprise-grade agent you can confidently deploy into production. #DF24 #agents #enterprise #reasoning #dreamforcekeynote

Clara Shih

11,136 views • 1 year ago

The $KLED Enterprise Portal Full Demo – Search and License Any Data in Seconds: For the last 6 weeks, we've had thousands of users sign up for the first data marketplace. We've paid out over $200,000 to them for uploading their camera rolls, homework, dashcam footage, etc. We've now amassed petabytes and petabytes of data from these users and large enterprises, and tagged it all with rich metadata. Now OpenAI, xAI, Google AI, or any other large lab can easily search and license that data using our newly completed enterprise portal. If you're a large lab, send me a DM and get access to the largest, never-before-seen consumer dataset on the planet. This is the biggest update for Kled since launch.

The $KLED Enterprise Portal Full Demo – Search and License Any Data in Seconds: For the last 6 weeks, we've had thousands of users sign up for the first data marketplace. We've paid out over $200,000 to them for uploading their camera rolls, homework, dashcam footage, etc. We've now amassed petabytes and petabytes of data from these users and large enterprises, and tagged it all with rich metadata. Now OpenAI, xAI, Google AI, or any other large lab can easily search and license that data using our newly completed enterprise portal. If you're a large lab, send me a DM and get access to the largest, never-before-seen consumer dataset on the planet. This is the biggest update for Kled since launch.

Avi Patel

91,697 views • 1 year ago

is our AI project to make computing feel more human L A N D E R Here are the 4 best demo videos of the magic of DATA in action. DATA is a personalized assistant who knows and remembers every conversation you have with it accross your iPhone, Mac, iPad, Watch, Texts, Emails, and HomePods. You can talk to DATA right in your AirPods or text it just like a person. DATA can read, write, understand, speak any language, and translate between them. It can help with real work and home life tasks like research, writing, scheduling, reminders, and triage. And it's easily customizable so you can have DATA automatically do whatever you want whenever you want with just a few taps and natural language instructions - no code required. DATA can do just about anything you can do on your phone on your behalf automatically including very advanced things Siri can't, like summarizing, analyzing, and drafting replies or writing documents. It can read web pages, texts or emails you show it, or PDFs of any kind. It can do other real world tasks that require complex analysis and common sense too, like: - figure out where the nearest beach is (even when you're in Colorado) and instantly fetch the current surf report up to the current minute. - summarize and drafting replies to entire email chains - plan out entire work projects or multi-day vacations on your calendar - sketch out ideas for you in picture form or drafting Notion pages with charts and graphs. DATA can also use its own judgement to determine when to run an action or not, even if you've scheduled it, allowing you to make VERY complex automations that require many different inputs to make a decision, like for example: - only opening the blinds on your lunch break if it's sunny out and you're working from home. DATA works natively and easily with Apple HomeKit & other shortcuts. DATA can also take initiative and check in with you throughout the day by voice or text and proactively send messages to you and others on your behalf based on your personal and professional goals, current tasks, and calendar. DATA can integrate with many apps on your phone, and is compatible with multiple large AI language models. I've gotten to make a few demo videos that I think really capture how powerful DATA can be for every day life. Here they are all in one tweet. Make sure your sound is on as you watch them. 1. This is the first demo video I ever made from April 19th, 2023. It walks through all the ways you can interact with and use the DATA shortcuts. Everything from saying "Hey Siri" to tapping on custom apps on your home-screen. 2. The second demo video was made May 5 and is an example use case I made of how commands work - commands allow DATA to actually run actions on your phone like taking pictures and sending messages. This demo shows me taking a picture of an email template, and data drafting an email based on that template. It's gotten much better at realizing when it has just run a command and incorporating that information naturally into the conversation now, especially on GPT-4. 3. This third Commands video, May 12 is a walkthrough of ALL the phone functions that commands allow DATA to do: sending texts and emails, making pictures, seeing pictures, reading things, and scheduling events. Since this video we've added auto-replies to texts and emails, summarizing documents, writing documents, health app data retrieval, web surfing, scheduling alarms, making playlists, and more. 4. This last demo I made today, June 15, shows everything DATA does working in concert to generate a crazy detailed morning briefing with background music - including making a unique playlist and giving a detailed analysis of current events complete with Ski & Surf conditions near me other live information from the internet. So now that you've seen everything DATA can do, what's the coolest feature? What features should we add? What would you use DATA for first?

is our AI project to make computing feel more human L A N D E R Here are the 4 best demo videos of the magic of DATA in action. DATA is a personalized assistant who knows and remembers every conversation you have with it accross your iPhone, Mac, iPad, Watch, Texts, Emails, and HomePods. You can talk to DATA right in your AirPods or text it just like a person. DATA can read, write, understand, speak any language, and translate between them. It can help with real work and home life tasks like research, writing, scheduling, reminders, and triage. And it's easily customizable so you can have DATA automatically do whatever you want whenever you want with just a few taps and natural language instructions - no code required. DATA can do just about anything you can do on your phone on your behalf automatically including very advanced things Siri can't, like summarizing, analyzing, and drafting replies or writing documents. It can read web pages, texts or emails you show it, or PDFs of any kind. It can do other real world tasks that require complex analysis and common sense too, like: - figure out where the nearest beach is (even when you're in Colorado) and instantly fetch the current surf report up to the current minute. - summarize and drafting replies to entire email chains - plan out entire work projects or multi-day vacations on your calendar - sketch out ideas for you in picture form or drafting Notion pages with charts and graphs. DATA can also use its own judgement to determine when to run an action or not, even if you've scheduled it, allowing you to make VERY complex automations that require many different inputs to make a decision, like for example: - only opening the blinds on your lunch break if it's sunny out and you're working from home. DATA works natively and easily with Apple HomeKit & other shortcuts. DATA can also take initiative and check in with you throughout the day by voice or text and proactively send messages to you and others on your behalf based on your personal and professional goals, current tasks, and calendar. DATA can integrate with many apps on your phone, and is compatible with multiple large AI language models. I've gotten to make a few demo videos that I think really capture how powerful DATA can be for every day life. Here they are all in one tweet. Make sure your sound is on as you watch them. 1. This is the first demo video I ever made from April 19th, 2023. It walks through all the ways you can interact with and use the DATA shortcuts. Everything from saying "Hey Siri" to tapping on custom apps on your home-screen. 2. The second demo video was made May 5 and is an example use case I made of how commands work - commands allow DATA to actually run actions on your phone like taking pictures and sending messages. This demo shows me taking a picture of an email template, and data drafting an email based on that template. It's gotten much better at realizing when it has just run a command and incorporating that information naturally into the conversation now, especially on GPT-4. 3. This third Commands video, May 12 is a walkthrough of ALL the phone functions that commands allow DATA to do: sending texts and emails, making pictures, seeing pictures, reading things, and scheduling events. Since this video we've added auto-replies to texts and emails, summarizing documents, writing documents, health app data retrieval, web surfing, scheduling alarms, making playlists, and more. 4. This last demo I made today, June 15, shows everything DATA does working in concert to generate a crazy detailed morning briefing with background music - including making a unique playlist and giving a detailed analysis of current events complete with Ski & Surf conditions near me other live information from the internet. So now that you've seen everything DATA can do, what's the coolest feature? What features should we add? What would you use DATA for first?

steve

640,114 views • 3 years ago

This will be my last day as a Co-Founder of OpenPhone (now Quo). Because starting today, OpenPhone is rebranding to Quo. Oh, and we've raised $105 million in growth financing to help businesses never lose a customer again. You probably have a lot of questions, so let me break down what's happening: 1) $105 million in growth financing We have raised $105 million in growth financing led by General Catalyst's Customer Value Fund and with participation of existing investors Craft Ventures, Slow Ventures, Garage Capital, and Y Combinator. This funding gives us the resources to invest further in what our customers love most and to help us reach more businesses. 2) Why OpenPhone is becoming Quo When Mahyar Raissi and I started OpenPhone in 2018, our mission wasn't just to help businesses make calls and send messages—it was to help them build stronger customer relationships so they could grow faster. You can even see in our YC application we described ourselves as "phone meets CRM." But as the years went by, we realized the name "OpenPhone" only tells part of our story. While it worked when we were focused only on the business phone, it started limiting where our customers needed us to go. With Quo as our name, we can finally tell the full story. Think of Quo as your business's customer hub, with phone at the center. It's how you win customers and keep them happy. Although our name has changed, what matters to us hasn’t. We are still obsessed with building great products and helping our customers never miss an opportunity. 3) What's changing with the product We're launching major upgrades to our voice AI agent Sona based on everything we've learned from our customers: - You can now start using Sona for free on all plans with our new tiered, usage-based model - Every customer gets 10 free Sona calls per month - Sona can now handle more sophisticated scenarios and can be trained on custom jobs - It can also now transfer calls to a human if needed -- To our incredible customers—we simply wouldn't be here without you. Thank you for trusting us to power your customer communications. Thank you to our team for the care and energy you’ve poured into making this milestone happen. And to our investors—thank you for believing in our vision.

This will be my last day as a Co-Founder of OpenPhone (now Quo). Because starting today, OpenPhone is rebranding to Quo. Oh, and we've raised $105 million in growth financing to help businesses never lose a customer again. You probably have a lot of questions, so let me break down what's happening: 1) $105 million in growth financing We have raised $105 million in growth financing led by General Catalyst's Customer Value Fund and with participation of existing investors Craft Ventures, Slow Ventures, Garage Capital, and Y Combinator. This funding gives us the resources to invest further in what our customers love most and to help us reach more businesses. 2) Why OpenPhone is becoming Quo When Mahyar Raissi and I started OpenPhone in 2018, our mission wasn't just to help businesses make calls and send messages—it was to help them build stronger customer relationships so they could grow faster. You can even see in our YC application we described ourselves as "phone meets CRM." But as the years went by, we realized the name "OpenPhone" only tells part of our story. While it worked when we were focused only on the business phone, it started limiting where our customers needed us to go. With Quo as our name, we can finally tell the full story. Think of Quo as your business's customer hub, with phone at the center. It's how you win customers and keep them happy. Although our name has changed, what matters to us hasn’t. We are still obsessed with building great products and helping our customers never miss an opportunity. 3) What's changing with the product We're launching major upgrades to our voice AI agent Sona based on everything we've learned from our customers: - You can now start using Sona for free on all plans with our new tiered, usage-based model - Every customer gets 10 free Sona calls per month - Sona can now handle more sophisticated scenarios and can be trained on custom jobs - It can also now transfer calls to a human if needed -- To our incredible customers—we simply wouldn't be here without you. Thank you for trusting us to power your customer communications. Thank you to our team for the care and energy you’ve poured into making this milestone happen. And to our investors—thank you for believing in our vision.

Daryna Kulya

193,016 views • 10 months ago

Upstream Data has a new software initiative starting with our LoadSync™ industrial bitcoin mining controller. We converted from PLC controls to a much more scalable embedded Linux based architecture. This project is led by b_rowan and James Hilliard. Brett gives an overview of what it can do today, but there's so much more coming soon! LoadSync is just the beginning of our software plans, I can't wait to show you what we have in store. Our long term goal for LoadSync is to have a drop-in product for every bitcoin mine on the planet at any scale. A complete fleet management tool for everything associated with a bitcoin mining load. LoadSync is built upon an open source library also maintained by Brett, called PyASIC. More and more bitcoin miners are integrating PyASIC every day, you should too! Go find it on GitHub. At Upstream Data we intend to continue leading the industry in technology, product application and high performance bitcoin mining infrastructure.

Upstream Data has a new software initiative starting with our LoadSync™ industrial bitcoin mining controller. We converted from PLC controls to a much more scalable embedded Linux based architecture. This project is led by b_rowan and James Hilliard. Brett gives an overview of what it can do today, but there's so much more coming soon! LoadSync is just the beginning of our software plans, I can't wait to show you what we have in store. Our long term goal for LoadSync is to have a drop-in product for every bitcoin mine on the planet at any scale. A complete fleet management tool for everything associated with a bitcoin mining load. LoadSync is built upon an open source library also maintained by Brett, called PyASIC. More and more bitcoin miners are integrating PyASIC every day, you should too! Go find it on GitHub. At Upstream Data we intend to continue leading the industry in technology, product application and high performance bitcoin mining infrastructure.

Steve Barbour

27,049 views • 2 years ago

Today we’re releasing V-JEPA, a method for teaching machines to understand and model the physical world by watching videos. This work is another important step towards Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ We're releasing a collection of V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information. It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency. The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training. Our results show that, using a frozen backbone, our top V-JEPA models achieve 82.0% on Kinetics-400, 72.2% on Something-Something-v2 and 77.9% on ImageNet1K — competitive with or exceeding previous leading video models. We believe that this work is an important milestone on the path to advancing machine intelligence.

Today we’re releasing V-JEPA, a method for teaching machines to understand and model the physical world by watching videos. This work is another important step towards Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ We're releasing a collection of V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information. It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency. The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training. Our results show that, using a frozen backbone, our top V-JEPA models achieve 82.0% on Kinetics-400, 72.2% on Something-Something-v2 and 77.9% on ImageNet1K — competitive with or exceeding previous leading video models. We believe that this work is an important milestone on the path to advancing machine intelligence.

AI at Meta

703,801 views • 2 years ago

Exciting update on PantheonOS: Introducing Pantheon-Notebook & Pantheon-CLI — the first fully open-source, Python-based agentic tools that go beyond Claude Code in the field of data analysis. Pantheon-CLI runs entirely on your computer or server, supports 60+ tools and 50+ databases, and can call any Python, R, or Julia package alongside natural language. Chat with your data directly. It look like python-claude-code, but more appreciate for data analysis. Pantheon-Notebook brings the same agentic framework into Jupyter! Not just for writing code, it can also run and revise code automatically to generate the correct result, and even operate on files and study from website — beyond what any other tool can do! With Pantheon, you mix natural language + programming in one workflow, focusing on discovery instead of syntax barriers. We've applied Pantheon in some real-world cases: finance (customer explore), biology (Seurat, cell segmentation, annotation), sociology (survey analysis), and drug discovery (molecular docking). Pantheon is not just a CLI or a plugin — it's an agentic operating system for science, spanning both terminal and notebook. Why not try it now? We are actively preparing publications from this series of projects. Major contributors will be recognized in our GitHub repository and listed as key authors in these manuscripts. Feel free to reach out for collaborations, research assistant positions, visiting opportunities, rotation project or future PhD projects.

Exciting update on PantheonOS: Introducing Pantheon-Notebook & Pantheon-CLI — the first fully open-source, Python-based agentic tools that go beyond Claude Code in the field of data analysis. Pantheon-CLI runs entirely on your computer or server, supports 60+ tools and 50+ databases, and can call any Python, R, or Julia package alongside natural language. Chat with your data directly. It look like python-claude-code, but more appreciate for data analysis. Pantheon-Notebook brings the same agentic framework into Jupyter! Not just for writing code, it can also run and revise code automatically to generate the correct result, and even operate on files and study from website — beyond what any other tool can do! With Pantheon, you mix natural language + programming in one workflow, focusing on discovery instead of syntax barriers. We've applied Pantheon in some real-world cases: finance (customer explore), biology (Seurat, cell segmentation, annotation), sociology (survey analysis), and drug discovery (molecular docking). Pantheon is not just a CLI or a plugin — it's an agentic operating system for science, spanning both terminal and notebook. Why not try it now? We are actively preparing publications from this series of projects. Major contributors will be recognized in our GitHub repository and listed as key authors in these manuscripts. Feel free to reach out for collaborations, research assistant positions, visiting opportunities, rotation project or future PhD projects.

evo-devo

45,594 views • 10 months ago

We’re excited to finally introduce Kled Special Tasks, the final major feature included in the V2 app update. Users will now have access to a fully interactive terminal where they can view and complete domain specific upload tasks directly from enterprise buyers. These tasks can be region locked and person specific. For example, PhD students at Stanford might be prompted to upload their coursework or research materials and get paid for it. Our first domain specific task will focus on homework collection from high school and college students across Europe and the United States. Students will verify their emails and academic credentials directly within the app. We’ve built labeling workflows to ensure all uploaded content meets our criteria, and participants will receive weighted payouts based on the value of their submissions. We’ve already built a network of over 3,800 students from Stanford, MIT, UIUC, Rutgers, and Duke who will be actively onboarded to contribute content. Kled will work hand in hand with our research division, HADES, to justify the large scale purchase of this homework content. Several enterprise buyers have already expressed interest, each confirming that academic data from students represents a growing multi year industry requiring a continuous flow of fresh material. Kled Special Tasks also gives us the ability to internally identify valuable content types, issue calls for specific datasets, and collect 1,000-2,000 unique samples per task. We can then package these datasets into specialized data packs that our sales team will use to pitch directly to AI labs and enterprise clients with matching data needs. This will be one of our most powerful tools for expanding Kled’s buyer network. All of this will be fully available in the V2 update. We’re excited to show just how advanced our segmentation and data validation software has become as we bring this release to market.

We’re excited to finally introduce Kled Special Tasks, the final major feature included in the V2 app update. Users will now have access to a fully interactive terminal where they can view and complete domain specific upload tasks directly from enterprise buyers. These tasks can be region locked and person specific. For example, PhD students at Stanford might be prompted to upload their coursework or research materials and get paid for it. Our first domain specific task will focus on homework collection from high school and college students across Europe and the United States. Students will verify their emails and academic credentials directly within the app. We’ve built labeling workflows to ensure all uploaded content meets our criteria, and participants will receive weighted payouts based on the value of their submissions. We’ve already built a network of over 3,800 students from Stanford, MIT, UIUC, Rutgers, and Duke who will be actively onboarded to contribute content. Kled will work hand in hand with our research division, HADES, to justify the large scale purchase of this homework content. Several enterprise buyers have already expressed interest, each confirming that academic data from students represents a growing multi year industry requiring a continuous flow of fresh material. Kled Special Tasks also gives us the ability to internally identify valuable content types, issue calls for specific datasets, and collect 1,000-2,000 unique samples per task. We can then package these datasets into specialized data packs that our sales team will use to pitch directly to AI labs and enterprise clients with matching data needs. This will be one of our most powerful tools for expanding Kled’s buyer network. All of this will be fully available in the V2 update. We’re excited to show just how advanced our segmentation and data validation software has become as we bring this release to market.

Kled AI

74,618 views • 8 months ago

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

Amazon

47,421 views • 2 years ago

The time has come. Introducing Parallel an all-in-one copy trading platform for DeFi products that combines four years of our experience building on Solana and AI to create what we hope is a one-of-a-kind product for the Solana ecosystem. It’s our pleasure to announce that we’re now entering private beta for our first module built around Meteora DLMMs. You’ll be able to copy trade DLMM positions with fast execution and a ton of automated functions on top. Some key features: * Stop loss/take profit * Real-time PnL shown on the UI for your LP * Automatically claim fees when they reach X SOL or on a timer * Automatically swap all claimed fees into SOL * Become a power user and earn a % of fees from everyone copying your wallet * Generous referral comp And much more! Data feeds & AI: We understand what it takes for a copy trading platform to thrive, so we’ve been collecting real-time data from several contracts for both Meteora and the upcoming modules we’re releasing. As you can imagine, we’re handling tons of data, so with the help of AI, we’re not only analyzing potential up-and-coming wallets but also the top wallets on-chain that people don’t want you to know about. This applies to all aspects of DeFi—not just token trading. To access these data feeds, you’ll need $GP or our native Taiyo NFTs. More information will roll out as these feeds start to go live. Private Beta Access: We’re now collecting a list of the first 100 private beta testers, including LP army members, the Taiyo community, BOOGLES, and more. If you’re interested in getting early access, you can now submit your email at 👇 We recommend always creating a separate email for crypto purposes to keep your personal and online profiles separate. Excited to cook with you on our first non Launchpad related platform since Solport itself back in 2021. 🤝

The time has come. Introducing Parallel an all-in-one copy trading platform for DeFi products that combines four years of our experience building on Solana and AI to create what we hope is a one-of-a-kind product for the Solana ecosystem. It’s our pleasure to announce that we’re now entering private beta for our first module built around Meteora DLMMs. You’ll be able to copy trade DLMM positions with fast execution and a ton of automated functions on top. Some key features: * Stop loss/take profit * Real-time PnL shown on the UI for your LP * Automatically claim fees when they reach X SOL or on a timer * Automatically swap all claimed fees into SOL * Become a power user and earn a % of fees from everyone copying your wallet * Generous referral comp And much more! Data feeds & AI: We understand what it takes for a copy trading platform to thrive, so we’ve been collecting real-time data from several contracts for both Meteora and the upcoming modules we’re releasing. As you can imagine, we’re handling tons of data, so with the help of AI, we’re not only analyzing potential up-and-coming wallets but also the top wallets on-chain that people don’t want you to know about. This applies to all aspects of DeFi—not just token trading. To access these data feeds, you’ll need $GP or our native Taiyo NFTs. More information will roll out as these feeds start to go live. Private Beta Access: We’re now collecting a list of the first 100 private beta testers, including LP army members, the Taiyo community, BOOGLES, and more. If you’re interested in getting early access, you can now submit your email at 👇 We recommend always creating a separate email for crypto purposes to keep your personal and online profiles separate. Excited to cook with you on our first non Launchpad related platform since Solport itself back in 2021. 🤝

Tom

105,715 views • 1 year ago

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

César A. Hidalgo

54,332 views • 1 year ago

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

Keith Peiris

27,529 views • 5 months ago

AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

TBPN

34,469 views • 2 months ago

This video is all over social media right now. This is a VERY silly video. I audibly laughed out loud several times. Elon Musk and co. are not experts in DFIR (Digital Forensics and Incident Response). No matter how much of a sycophant you are for him or his organizations, Musk has no background in malware reverse engineering, identification, or development. What I suspect Kristi Noem is referring to in this conversation is the installation of an EDR (Endpoint Detection and Response) system. An EDR is basically an anti-virus that a system administrator can make rules for, add custom detection logic, etc. This is used in enterprise environments and installed on user managed endpoints (computers, electronic devices). Some people worry about EDRs because they monitor the device for any potential malware or compromise. Yes, EDRs can be incredibly invasive, but you should not expect privacy when using company equipment or government devices. The reason why I suspect she is referring to an EDR system is she states, "Elon and his team helped me identify some of my own employees in my department had downloaded software on my phone, and on my laptop, to spy on me, record our meetings, ... they had done that to several other politicals" If an employee, working with the United States Department of Homeland security, was actively working as an Insider Threat and performing ESPIONAGE, spying on politicians and people of power, it would be all over the news. Musk and/or Trump would have been SCREAMING about whoever had done it. Additionally, this would be a VERY serious charge to whoever was identified doing this. It would have been a massive scandal. The second super silly thing Noem says is she complains she was unable to email a PowerPoint presentation to someone because it was too large ("over six pages long"). She is implying here this is a technological problem, but it is NOT. This is done intentionally to prevent data exfiltration. The idea is network administrators put data size restrictions in place to prevent data theft. If someone successfully compromised the United States government, and was unable to steal data by traditional means (tunneling, C2, etc), a common exfiltration tool is email. They take the stolen data, and send it to a disposable email address to receive it. Hence, if they restrict the size of data allowed to be sent outbound, it makes it substantially more difficult for data exfiltration to occur. If a Threat Actor tries to send an email with a large attachment the EDR flags it as a potentially suspicious event and notifies network operations. She is comparing standard cybersecurity policies to ... lack of technology ... ? And also somehow saying this has something to do with the "deep state" (an ominous unidentified threat, or something). Regardless if you believe there is a "deep state" (???), cybersecurity policy and network restrictions are NOT a technological disadvantage. I also want to give a big shoutout to RT (Russian Today) for blasting this all over social media. RT knows this video and conversation is ridiculous. Russian Intelligence is probably giggling right not at the absurdity of it. They are loving the fact they can spit this video all over social media and (using Noems own words) imply the United States government has SPYS actively present from THE DEEP STATE. It sows distrust and misinformation. Bravo, RT.

This video is all over social media right now. This is a VERY silly video. I audibly laughed out loud several times. Elon Musk and co. are not experts in DFIR (Digital Forensics and Incident Response). No matter how much of a sycophant you are for him or his organizations, Musk has no background in malware reverse engineering, identification, or development. What I suspect Kristi Noem is referring to in this conversation is the installation of an EDR (Endpoint Detection and Response) system. An EDR is basically an anti-virus that a system administrator can make rules for, add custom detection logic, etc. This is used in enterprise environments and installed on user managed endpoints (computers, electronic devices). Some people worry about EDRs because they monitor the device for any potential malware or compromise. Yes, EDRs can be incredibly invasive, but you should not expect privacy when using company equipment or government devices. The reason why I suspect she is referring to an EDR system is she states, "Elon and his team helped me identify some of my own employees in my department had downloaded software on my phone, and on my laptop, to spy on me, record our meetings, ... they had done that to several other politicals" If an employee, working with the United States Department of Homeland security, was actively working as an Insider Threat and performing ESPIONAGE, spying on politicians and people of power, it would be all over the news. Musk and/or Trump would have been SCREAMING about whoever had done it. Additionally, this would be a VERY serious charge to whoever was identified doing this. It would have been a massive scandal. The second super silly thing Noem says is she complains she was unable to email a PowerPoint presentation to someone because it was too large ("over six pages long"). She is implying here this is a technological problem, but it is NOT. This is done intentionally to prevent data exfiltration. The idea is network administrators put data size restrictions in place to prevent data theft. If someone successfully compromised the United States government, and was unable to steal data by traditional means (tunneling, C2, etc), a common exfiltration tool is email. They take the stolen data, and send it to a disposable email address to receive it. Hence, if they restrict the size of data allowed to be sent outbound, it makes it substantially more difficult for data exfiltration to occur. If a Threat Actor tries to send an email with a large attachment the EDR flags it as a potentially suspicious event and notifies network operations. She is comparing standard cybersecurity policies to ... lack of technology ... ? And also somehow saying this has something to do with the "deep state" (an ominous unidentified threat, or something). Regardless if you believe there is a "deep state" (???), cybersecurity policy and network restrictions are NOT a technological disadvantage. I also want to give a big shoutout to RT (Russian Today) for blasting this all over social media. RT knows this video and conversation is ridiculous. Russian Intelligence is probably giggling right not at the absurdity of it. They are loving the fact they can spit this video all over social media and (using Noems own words) imply the United States government has SPYS actively present from THE DEEP STATE. It sows distrust and misinformation. Bravo, RT.

vx-underground

96,791 views • 4 months ago

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.

𝕋𝕎𝔼𝔼𝕋

20,357 views • 1 year ago

At LinqAI, we’ve been mission-driven since day one—building competitive, revenue-generating SaaS applications. As our product line has expanded and our customer base has grown, one thing has become clear: we have an opportunity to forge a deeper connection between our SaaS innovations and the Web3 ecosystem. As we scale our SaaS offerings, our demand for compute power continues to grow. Traditionally, paying for compute means sending value outside our ecosystem—resources spent elsewhere, never to return. But what if every compute cycle contributed to the LinqAI ecosystem itself? LinqProtocol is our answer. By transforming idle computers (PCs, laptops, servers, and in the future even phones) into a global compute network, we not only secure scalable and efficient infrastructure but also strengthen the utility of $LNQ—ensuring that every workload contributes to our own decentralized future. LinqProtocol allows for arbitrary compute which means that there are virtually no limits to what can be done on LinqProtocol. From AI Training, to Synthetic Data Generation, 3D Rendering and even hosting a Website - It's all possible on LinqProtocol. ✅ On-demand, scalable compute ✅ Transparent, fair-market pricing ✅ Trustless execution with real-time tracking ✅ Governance-based dispute resolution The future of compute isn’t just more efficient. It’s owned by those who use it. It’s decentralized. And it’s coming soon. $LNQ #LinqProtocol

At LinqAI, we’ve been mission-driven since day one—building competitive, revenue-generating SaaS applications. As our product line has expanded and our customer base has grown, one thing has become clear: we have an opportunity to forge a deeper connection between our SaaS innovations and the Web3 ecosystem. As we scale our SaaS offerings, our demand for compute power continues to grow. Traditionally, paying for compute means sending value outside our ecosystem—resources spent elsewhere, never to return. But what if every compute cycle contributed to the LinqAI ecosystem itself? LinqProtocol is our answer. By transforming idle computers (PCs, laptops, servers, and in the future even phones) into a global compute network, we not only secure scalable and efficient infrastructure but also strengthen the utility of $LNQ—ensuring that every workload contributes to our own decentralized future. LinqProtocol allows for arbitrary compute which means that there are virtually no limits to what can be done on LinqProtocol. From AI Training, to Synthetic Data Generation, 3D Rendering and even hosting a Website - It's all possible on LinqProtocol. ✅ On-demand, scalable compute ✅ Transparent, fair-market pricing ✅ Trustless execution with real-time tracking ✅ Governance-based dispute resolution The future of compute isn’t just more efficient. It’s owned by those who use it. It’s decentralized. And it’s coming soon. $LNQ #LinqProtocol

LinqAI

239,673 views • 1 year ago

$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

39,331 views • 5 months ago