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Gmail Infinity Factory: An advanced Gmail account automation engine built on next-generation stealth technology. It leverages CloakBrowser and Playwright with a multi-layer architecture that accurately mimics real human behavior and evades all automated detection systems. Advertised capabilities include: • CloakBrowser and Playwright automation • Browser fingerprint rotation • Human-like...

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Dark Web Informer

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Gmail Creator Pro: The Ultimate Gmail Account Creator Advertised capabilities include: • Human-like typing and random delays • Browser fingerprint and user-agent rotation • Session warming through common websites • Proxy selection and validation • Phone verification skip/bypass attempts • SMS API integration • Multi-language interface support • Auto-save account storage in JSON • Success-rate and account statistics dashboard • ChromeDriver auto-management
4:52

Gmail Creator Pro: The Ultimate Gmail Account Creator Advertised capabilities include: • Human-like typing and random delays • Browser fingerprint and user-agent rotation • Session warming through common websites • Proxy selection and validation • Phone verification skip/bypass attempts • SMS API integration • Multi-language interface support • Auto-save account storage in JSON • Success-rate and account statistics dashboard • ChromeDriver auto-management

Dark Web Informer

77,914 просмотров • 2 месяцев назад

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

45,966 просмотров • 1 год назад

The U.S. unveils it's new F-47 stealth fighter, the centerpiece of the NGAD program, a "family of systems" designed to integrate advanced manned and unmanned platforms, including Collaborative Combat Aircraft (CCA) drones. Announced by President Donald Trump alongside Secretary of Defense Pete Hegseth and Air Force Chief of Staff Gen. David Allvin, the F-47 is described as the "most advanced, most capable, most lethal aircraft ever built." It reportedly builds on a prototype that has been secretly flying for nearly five years, suggesting significant testing and refinement prior to its public unveiling. The aircraft is engineered for speed, stealth, and adaptability, with a focus on countering advanced threats from nations like China, which has also been developing sixth-generation capabilities. Boeing’s victory over Lockheed Martin for the NGAD contract, valued at approximately $20 billion for the Engineering and Manufacturing Development (EMD) phase, marks a critical win for the company amid its recent struggles in defense and commercial sectors. The F-47 is expected to enter service in the 2030s, with each unit potentially costing upwards of $300 million, reflecting its cutting-edge technology. Its development emphasizes rapid adaptability to emerging threats, leveraging advanced manufacturing and an open architecture design to allow for continual upgrades. Since exact specifications remain classified or undisclosed as of now, the following are informed projections based on NGAD program objectives, statements from officials, and sixth-generation fighter trends. Designation: Boeing F-47 Manufacturer: Boeing Phantom Works Role: Air dominance fighter with multi-role capabilities (air-to-air and air-to-ground) Crew: Likely manned with optional unmanned configuration, aligning with sixth-generation flexibility Dimensions: Larger than the F-22 and F-35 to accommodate greater range and payload; exact size undisclosed but possibly exceeding 60 feet in length and a wingspan over 40 feet Powerplant: Expected to use adaptive cycle engines from the Next Generation Adaptive Propulsion (NGAP) program—either General Electric XA102 or Pratt & Whitney XA103. These engines feature a three-stream architecture, offering over 20% better fuel efficiency, increased thrust (potentially 45,000-50,000 lbf per engine), and enhanced electrical output for directed-energy weapons. Speed: Likely exceeds Mach 2 (super cruise capable—sustained supersonic flight without afterburners), surpassing the F-22’s Mach 1.8 super cruise Range: Combat radius projected at 1,000-1,500 nautical miles (unrefueled), tailored for Indo-Pacific operations, significantly greater than the F-22’s 600 nautical miles or F-35’s 670 nautical miles Stealth: Advanced stealth features, including a tailless design, next-generation coatings, and materials to reduce radar, infrared, and acoustic signatures beyond fifth-generation standards Payload: Larger internal weapons bays (possibly 20-23 feet long) to carry advanced munitions like the AIM-174, hypersonic missiles, and future cruise missiles, with external hardpoints available at the cost of stealth Sensors and Avionics: AI-enhanced sensor suite for unmatched situational awareness, integrating radar, infrared search and track (IRST), and electronic warfare systems; likely includes "smart skins" with embedded sensors for reduced drag and improved performance Networking: Maximum connectivity for real-time data sharing with satellites, drones, and other platforms, supported by a robust, jam-resistant data link Additional Features: Potential for directed-energy (laser) weapons to counter missiles and drones Integration with CCA drones for expanded mission options (e.g., extra munitions, electronic warfare) Open architecture for rapid upgrades and mission-specific customization Key Highlights Human-Machine Teaming: The F-47 is designed to "unlock the magic" of human-machine collaboration, pairing pilots with AI-driven systems and autonomous drones to enhance decision-making and reduce workload. Strategic Purpose: Built to penetrate contested environments, countering advanced air defenses and stealth fighters from adversaries like China, with a focus on long-range engagements over vast theaters. Development Timeline: Prototypes have been flying since at least 2020, with full operational capability targeted for the 2030s, replacing the F-22 incrementally as numbers grow. Cost and Scale: Estimated at $300 million per unit, with plans for roughly 200 manned aircraft, though this is a planning figure subject to change. The F-47’s exact design and full capabilities remain shrouded in secrecy, typical of NGAD’s classified nature, but its unveiling signals a bold step forward in U.S. air power. Its blend of stealth, speed, range, and technological integration positions it as a cornerstone of future aerial warfare, though its high cost and complexity will likely spark ongoing debate about affordability and strategic priorities. U.S. military technology will continue to dominate all other nations like it always has.
1:10

The U.S. unveils it's new F-47 stealth fighter, the centerpiece of the NGAD program, a "family of systems" designed to integrate advanced manned and unmanned platforms, including Collaborative Combat Aircraft (CCA) drones. Announced by President Donald Trump alongside Secretary of Defense Pete Hegseth and Air Force Chief of Staff Gen. David Allvin, the F-47 is described as the "most advanced, most capable, most lethal aircraft ever built." It reportedly builds on a prototype that has been secretly flying for nearly five years, suggesting significant testing and refinement prior to its public unveiling. The aircraft is engineered for speed, stealth, and adaptability, with a focus on countering advanced threats from nations like China, which has also been developing sixth-generation capabilities. Boeing’s victory over Lockheed Martin for the NGAD contract, valued at approximately $20 billion for the Engineering and Manufacturing Development (EMD) phase, marks a critical win for the company amid its recent struggles in defense and commercial sectors. The F-47 is expected to enter service in the 2030s, with each unit potentially costing upwards of $300 million, reflecting its cutting-edge technology. Its development emphasizes rapid adaptability to emerging threats, leveraging advanced manufacturing and an open architecture design to allow for continual upgrades. Since exact specifications remain classified or undisclosed as of now, the following are informed projections based on NGAD program objectives, statements from officials, and sixth-generation fighter trends. Designation: Boeing F-47 Manufacturer: Boeing Phantom Works Role: Air dominance fighter with multi-role capabilities (air-to-air and air-to-ground) Crew: Likely manned with optional unmanned configuration, aligning with sixth-generation flexibility Dimensions: Larger than the F-22 and F-35 to accommodate greater range and payload; exact size undisclosed but possibly exceeding 60 feet in length and a wingspan over 40 feet Powerplant: Expected to use adaptive cycle engines from the Next Generation Adaptive Propulsion (NGAP) program—either General Electric XA102 or Pratt & Whitney XA103. These engines feature a three-stream architecture, offering over 20% better fuel efficiency, increased thrust (potentially 45,000-50,000 lbf per engine), and enhanced electrical output for directed-energy weapons. Speed: Likely exceeds Mach 2 (super cruise capable—sustained supersonic flight without afterburners), surpassing the F-22’s Mach 1.8 super cruise Range: Combat radius projected at 1,000-1,500 nautical miles (unrefueled), tailored for Indo-Pacific operations, significantly greater than the F-22’s 600 nautical miles or F-35’s 670 nautical miles Stealth: Advanced stealth features, including a tailless design, next-generation coatings, and materials to reduce radar, infrared, and acoustic signatures beyond fifth-generation standards Payload: Larger internal weapons bays (possibly 20-23 feet long) to carry advanced munitions like the AIM-174, hypersonic missiles, and future cruise missiles, with external hardpoints available at the cost of stealth Sensors and Avionics: AI-enhanced sensor suite for unmatched situational awareness, integrating radar, infrared search and track (IRST), and electronic warfare systems; likely includes "smart skins" with embedded sensors for reduced drag and improved performance Networking: Maximum connectivity for real-time data sharing with satellites, drones, and other platforms, supported by a robust, jam-resistant data link Additional Features: Potential for directed-energy (laser) weapons to counter missiles and drones Integration with CCA drones for expanded mission options (e.g., extra munitions, electronic warfare) Open architecture for rapid upgrades and mission-specific customization Key Highlights Human-Machine Teaming: The F-47 is designed to "unlock the magic" of human-machine collaboration, pairing pilots with AI-driven systems and autonomous drones to enhance decision-making and reduce workload. Strategic Purpose: Built to penetrate contested environments, countering advanced air defenses and stealth fighters from adversaries like China, with a focus on long-range engagements over vast theaters. Development Timeline: Prototypes have been flying since at least 2020, with full operational capability targeted for the 2030s, replacing the F-22 incrementally as numbers grow. Cost and Scale: Estimated at $300 million per unit, with plans for roughly 200 manned aircraft, though this is a planning figure subject to change. The F-47’s exact design and full capabilities remain shrouded in secrecy, typical of NGAD’s classified nature, but its unveiling signals a bold step forward in U.S. air power. Its blend of stealth, speed, range, and technological integration positions it as a cornerstone of future aerial warfare, though its high cost and complexity will likely spark ongoing debate about affordability and strategic priorities. U.S. military technology will continue to dominate all other nations like it always has.

The SCIF

453,624 просмотров • 1 год назад

There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.
1:13

There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.

George Hartley ☄️

935,209 просмотров • 1 месяц назад

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre
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GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

Qiusheng Wu

18,075 просмотров • 2 месяцев назад

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.
1:06

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

Owen Gregorian

179,005 просмотров • 10 месяцев назад

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?
0:37

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

Paweł Huryn

219,822 просмотров • 6 месяцев назад

10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export • lightweight and perfect for product walkthroughs without extra editing — 2️⃣ VoiceBox — an alternative to ElevenLabs ($22/mo) + Wispr Flow ($15/mo) • privacy-first AI voice toolkit that runs locally • clone voices with a few seconds of audio • supports 7 TTS engines, 23 languages, and system-wide voice dictation • works with Apple Silicon, CUDA, and ROCm — 3️⃣ OpenShorts — an alternative to Opus Clip ($19/mo) + Submagic ($16/mo) • convert long videos into viral vertical clips • auto captions, face tracking, and AI clip selection • includes AI UGC video generation • easy Docker deployment for self-hosting — 4️⃣ FreeLLMAPI — an alternative to ChatGPT Pro + Claude Pro ($20/mo each) • combine 14 free AI providers behind one API • OpenAI-compatible endpoint • roughly 800M free tokens/month • built-in routing, failover, encrypted key storage, and dashboard — 5️⃣ Playwright MCP — an alternative to Browserbase ($39/mo) + Browser Use ($25/mo) • Microsoft's official browser automation MCP • AI agents interact using accessibility trees instead of screenshots • faster, cheaper, and more reliable automation • works with Claude Code, Cursor, Windsurf, and Codex — 6️⃣ Vibe Trading — an alternative to TradingView Premium ($60/mo) • AI-powered investing and strategy research platform • supports stocks, crypto, forex, futures, and options • dozens of built-in research skills • backtesting included without requiring paid APIs — 7️⃣ — an alternative to Calendly ($12/mo) + SavvyCal ($12/mo) • open-source scheduling platform • round robin, team scheduling, routing forms, payments • integrates with Google Calendar, Outlook, Apple Calendar, Zoom, Meet, and Teams • deploy yourself in minutes — 8️⃣ Whisper — an alternative to ($17/mo) • OpenAI's speech recognition model • transcribes and translates audio in nearly 100 languages • timestamp support included • runs locally on CPU or GPU — 9️⃣ Postiz — an alternative to Buffer ($15/mo) • schedule content across all major social platforms • AI-generated captions and hashtags • built-in analytics and collaborative workspaces • growing rapidly with a large open-source community — 🔟 Vaultwarden — an alternative to 1Password ($8/mo) • lightweight Bitwarden-compatible server written in Rust • works with official Bitwarden apps • unlimited users and vaults • self-host on almost any VPS or home server — Worth knowing: Open-source isn't always a perfect replacement. You may spend a little more time setting things up. In return, you get: • no monthly subscription • full ownership of your data • complete control over your workflow That's a trade many builders happily make. Save this for later. Someone on your timeline is probably paying for at least three of these. — Kshitij Mishra
0:31

10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export • lightweight and perfect for product walkthroughs without extra editing — 2️⃣ VoiceBox — an alternative to ElevenLabs ($22/mo) + Wispr Flow ($15/mo) • privacy-first AI voice toolkit that runs locally • clone voices with a few seconds of audio • supports 7 TTS engines, 23 languages, and system-wide voice dictation • works with Apple Silicon, CUDA, and ROCm — 3️⃣ OpenShorts — an alternative to Opus Clip ($19/mo) + Submagic ($16/mo) • convert long videos into viral vertical clips • auto captions, face tracking, and AI clip selection • includes AI UGC video generation • easy Docker deployment for self-hosting — 4️⃣ FreeLLMAPI — an alternative to ChatGPT Pro + Claude Pro ($20/mo each) • combine 14 free AI providers behind one API • OpenAI-compatible endpoint • roughly 800M free tokens/month • built-in routing, failover, encrypted key storage, and dashboard — 5️⃣ Playwright MCP — an alternative to Browserbase ($39/mo) + Browser Use ($25/mo) • Microsoft's official browser automation MCP • AI agents interact using accessibility trees instead of screenshots • faster, cheaper, and more reliable automation • works with Claude Code, Cursor, Windsurf, and Codex — 6️⃣ Vibe Trading — an alternative to TradingView Premium ($60/mo) • AI-powered investing and strategy research platform • supports stocks, crypto, forex, futures, and options • dozens of built-in research skills • backtesting included without requiring paid APIs — 7️⃣ — an alternative to Calendly ($12/mo) + SavvyCal ($12/mo) • open-source scheduling platform • round robin, team scheduling, routing forms, payments • integrates with Google Calendar, Outlook, Apple Calendar, Zoom, Meet, and Teams • deploy yourself in minutes — 8️⃣ Whisper — an alternative to ($17/mo) • OpenAI's speech recognition model • transcribes and translates audio in nearly 100 languages • timestamp support included • runs locally on CPU or GPU — 9️⃣ Postiz — an alternative to Buffer ($15/mo) • schedule content across all major social platforms • AI-generated captions and hashtags • built-in analytics and collaborative workspaces • growing rapidly with a large open-source community — 🔟 Vaultwarden — an alternative to 1Password ($8/mo) • lightweight Bitwarden-compatible server written in Rust • works with official Bitwarden apps • unlimited users and vaults • self-host on almost any VPS or home server — Worth knowing: Open-source isn't always a perfect replacement. You may spend a little more time setting things up. In return, you get: • no monthly subscription • full ownership of your data • complete control over your workflow That's a trade many builders happily make. Save this for later. Someone on your timeline is probably paying for at least three of these. — Kshitij Mishra

Kshitij Mishra | AI & Tech

16,698 просмотров • 1 месяц назад

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!
1:33

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,303 просмотров • 1 год назад

🚀 WELCOME TO THE DELTA REVOLUTION No Seed Phrase. No Private Key. No Password. Only YOU. A new era of digital money has started. 📲 Download the Delta Kim app ⛏️ Start mining δ DTC for free 📲 Google Play 📲 App Store 🟢 Referrer DID: 👉 KC2AJW48Y7GC7 👈 Delta Kim Network — founded in Hong Kong / China — is building a next-generation digital money ecosystem on the Internet Computer (ICP), powered by threshold ECDSA, fully on-chain canister smart contracts, and decentralized digital identity (DID). And the best part? You are still very early. No investment. No risk. No hardware. No electricity. Just one tap per day. ⸻ Delta is not “just another crypto.” It is a non-sovereign digital currency system, designed to be human-secured, password-free, and accessible to ordinary people — not traders or speculators. ⸻ 🔐 WHY DELTA IS DIFFERENT Traditional crypto and digital currencies fail most people because they depend on: • seed phrases • private keys • passwords • permanent loss Delta removes all three. ❌ No seed phrase ❌ No private key ❌ No password 📱Access and security are handled through Delta’s 3-NO Verification model. 📲 At registration, every user receives a unique Decentralized Identifier (DID), securely bound to their mobile number (MSISDN) and verified via Decentralized SMS Verification (dSMS). 🪪 The DID functions as your digital identity, used to manage, receive, and send assets — not tied to the phone number itself. 🔐 Account recovery is protected by the Security Circle — trusted people, not reset links or centralized support. Human-secured. Password-free. Fully decentralized. This is digital money designed for real people, not just crypto experts. ⸻ 🌍 WHAT DELTA IS BUILDING • A human-secured, password-free digital currency (δ DTC) • A fully on-chain Web3 ecosystem running on ICP • A Keyless, threshold ECDSA-secured multi-chain wallet that can sign transactions natively across popular L1 blockchains like Bitcoin, Ethereum, Binance Smart Chain, ICP and rollups like Optimism, and store/manage several assets like BTC, ETH, BNB, ICP, CELO, USDC, and USDT • A decentralized marketplace where value comes from real usage, not speculation • Delta-native stablecoins (dUSD, dEUR, dGBP, dNGN, dINR, dCNY,….) as on-chain bridges between fiat and non-sovereign digital money. • An ecosystem designed for long-term utility, not hype Delta is not designed for hype. It is infrastructure for a new digital economy. ⸻ ⛏️ FAIR, GREEN & HUMAN-CENTRIC DISTRIBUTION ✔ Eco-friendly mobile mining ✔ Zero device energy consumption ✔ One-tap daily participation ✔ Proof-of-People (PoP) — not Proof-of-Work, not Proof-of-Stake ✔ Designed to prevent whales and capital dominance Mining in Delta is about fair distribution through human participation, not computing power or wealth. ⸻ 🧠 BUILT FOR THE LONG RUN Delta is built on: • Real utility • Controlled token release mechanisms • Active participation, not passive holding • On-chain transparency and verifiable logic No shortcuts. No pump-and-dump. No empty promises. ⸻ Continue 👇
2:03

🚀 WELCOME TO THE DELTA REVOLUTION No Seed Phrase. No Private Key. No Password. Only YOU. A new era of digital money has started. 📲 Download the Delta Kim app ⛏️ Start mining δ DTC for free 📲 Google Play 📲 App Store 🟢 Referrer DID: 👉 KC2AJW48Y7GC7 👈 Delta Kim Network — founded in Hong Kong / China — is building a next-generation digital money ecosystem on the Internet Computer (ICP), powered by threshold ECDSA, fully on-chain canister smart contracts, and decentralized digital identity (DID). And the best part? You are still very early. No investment. No risk. No hardware. No electricity. Just one tap per day. ⸻ Delta is not “just another crypto.” It is a non-sovereign digital currency system, designed to be human-secured, password-free, and accessible to ordinary people — not traders or speculators. ⸻ 🔐 WHY DELTA IS DIFFERENT Traditional crypto and digital currencies fail most people because they depend on: • seed phrases • private keys • passwords • permanent loss Delta removes all three. ❌ No seed phrase ❌ No private key ❌ No password 📱Access and security are handled through Delta’s 3-NO Verification model. 📲 At registration, every user receives a unique Decentralized Identifier (DID), securely bound to their mobile number (MSISDN) and verified via Decentralized SMS Verification (dSMS). 🪪 The DID functions as your digital identity, used to manage, receive, and send assets — not tied to the phone number itself. 🔐 Account recovery is protected by the Security Circle — trusted people, not reset links or centralized support. Human-secured. Password-free. Fully decentralized. This is digital money designed for real people, not just crypto experts. ⸻ 🌍 WHAT DELTA IS BUILDING • A human-secured, password-free digital currency (δ DTC) • A fully on-chain Web3 ecosystem running on ICP • A Keyless, threshold ECDSA-secured multi-chain wallet that can sign transactions natively across popular L1 blockchains like Bitcoin, Ethereum, Binance Smart Chain, ICP and rollups like Optimism, and store/manage several assets like BTC, ETH, BNB, ICP, CELO, USDC, and USDT • A decentralized marketplace where value comes from real usage, not speculation • Delta-native stablecoins (dUSD, dEUR, dGBP, dNGN, dINR, dCNY,….) as on-chain bridges between fiat and non-sovereign digital money. • An ecosystem designed for long-term utility, not hype Delta is not designed for hype. It is infrastructure for a new digital economy. ⸻ ⛏️ FAIR, GREEN & HUMAN-CENTRIC DISTRIBUTION ✔ Eco-friendly mobile mining ✔ Zero device energy consumption ✔ One-tap daily participation ✔ Proof-of-People (PoP) — not Proof-of-Work, not Proof-of-Stake ✔ Designed to prevent whales and capital dominance Mining in Delta is about fair distribution through human participation, not computing power or wealth. ⸻ 🧠 BUILT FOR THE LONG RUN Delta is built on: • Real utility • Controlled token release mechanisms • Active participation, not passive holding • On-chain transparency and verifiable logic No shortcuts. No pump-and-dump. No empty promises. ⸻ Continue 👇

Delta Global Community

67,102 просмотров • 7 месяцев назад

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.
0:46

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.

doodlifts

12,172 просмотров • 6 месяцев назад

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,944 просмотров • 1 год назад

DARPA's NEW TECH that TERRIFIED CONGRESS. Understand DARPA's Nonsurgical Wireless Brain Computer Remote Interface Neurotechnology that uses light and sounds as weapons that can induce behavior, emotions, read and implant thoughts, pictures, etc. Now, combining this tech with A.I., we have reached unbelievable new levels of science that have opened pandora's box, and some of the things in pandora's box are extremely concerning for the future of humanity. For the past COUPLE of DECADES, DARPA, the DoD, military contractors, and leading universities have been constructing and implementing wireless, invisible weapons against the people right in front of you, and nobody even noticed. The weaponization of this tech is so dangerous that even the U.N. is sounding the alarm because of it's untraceable and unethical capabilities. This is an introduction and overview. Pay Attention. I show you things for a reason. Secretary of Energy Hazel Reid O'Leary, who served under the Clinton administration stated that over 500,000 thousand Americans have been used in human experiments, including mind control and other experiments over a period of four decades without their informed consent. This is only what they admit to, then programs get put under higher classification, or under the guise of "national security," or go black so even members of Congress might not even get access to such programs. Mk Ultra as well, back in the 1960's used over 4,000 military service men without their consent for human experiments such as mind control. No one to this day has been held accountable for these experiments on our military and American citizens. Since the mid 1980's, radio frequency energy has been mastered by professionals in the field and graduates of Yale University and others and have been working directly with the military on mind control operations. The equipment being used needs no implant device, mechanical, or electronic device attached to the human being to be able to induce specific behaviors, moods, even V2K which is "voice-to-skull" technology and the CIA, NSA, DARPA, and our military are extremely interested in this technology Following MK Ultra, attention shifted toward electromagnetic technologies, including extremely low frequency (ELF) and radio frequency (RF) waves, which can influence emotions and project voices into the human mind. The "Frey Effect," discovered in the 1960s, demonstrated that pulsed microwaves could produce audible effects or voices in a subject's head, also known as "voice-to-skull" (V2K). Declassified documents show the U.S. military and intelligence agencies explored and used this tech during the Gulf War in the 1990s. DARPA—established in 1958 to advance cutting-edge military technology—began funding projects that bridged neuroscience and electronics, laying the groundwork for brain-computer interfaces (BCIs). DARPA’s role expanded significantly in the 21st century with programs like the Next-Generation Nonsurgical Neurotechnology (N3) initiative, launched in 2018. N3 aims to develop non-invasive BCIs that allow soldiers to control drones or robots with their thoughts, using techniques like ultrasound, magnetic fields, and nanoparticles to interface with the brain. This builds on earlier DARPA efforts, such as the Revolutionizing Prosthetics program, which enabled thought-controlled artificial limbs. Concurrently, the Obama administration’s BRAIN Initiative, announced in 2013, sought to map the brain’s neural circuits and accelerate neurotechnology development. While framed as a scientific endeavor to treat neurological disorders, its overlap with DARPA funding—$225 million by 2015—imply heavy military applications, including behavior modification and thought decoding. The integration of ELF/RF frequencies with 5G and AI represents the latest frontier. 5G’s high-frequency millimeter waves and dense network of transmitters could, in theory, enhance the precision and range of electromagnetic signals targeting the brain. Paired with AI, which can analyze vast datasets of neural activity in real time, this technology enables remote behavior control and thought reading. DARPA’s N3 program, for instance, envisions two-way communication between brains and machines, potentially allowing external systems to interpret intentions or implant sensory feedback—like voices or emotions. Speculative claims suggest that military and intelligence agencies are weaponizing these tools to suppress fear in soldiers, influence adversaries’ decisions, or surveil thoughts. Today, these technologies raise profound ethical and practical questions. The line between enhancement and control blurs when AI-driven systems could override human autonomy. Reports of "targeted individuals" experiencing voice projection or emotional manipulation fuel speculation and aren't looking unrealistic after all. Arguments that the secrecy surrounding DARPA and CIA projects—echoing Mk Ultra’s hidden abuses—obscures the true extent of weaponization, while proponents frame advancements vital, for national security. The progression from Mk Ultra’s drug experiments and other crude techniques to master mind control have surpassed ELF/RF explorations, and now to DARPA’s AI-enhanced BCIs under the BRAIN Initiative reflects a trajectory toward increasingly sophisticated mind control capabilities. The addition of 5G’s infrastructure and other radar installations around the world amplifies the potential for remote influence, suggesting a future where thoughts, emotions, and behaviors could be manipulated, monitored, and induced. The WEF makes their goals for this tech more than clear on their own website. Privacy will no longer exist. While the military and intelligence agencies undeniably pursue these technologies for strategic advantage, the full scope of their operational use and the line between science fiction and reality is diminishing by the day now that these technologies are fully operational and in use today and have now entered the advertising industry and big tech. I'm sure you can figure out where this is all going, and honestly, it's not good.
12:36

DARPA's NEW TECH that TERRIFIED CONGRESS. Understand DARPA's Nonsurgical Wireless Brain Computer Remote Interface Neurotechnology that uses light and sounds as weapons that can induce behavior, emotions, read and implant thoughts, pictures, etc. Now, combining this tech with A.I., we have reached unbelievable new levels of science that have opened pandora's box, and some of the things in pandora's box are extremely concerning for the future of humanity. For the past COUPLE of DECADES, DARPA, the DoD, military contractors, and leading universities have been constructing and implementing wireless, invisible weapons against the people right in front of you, and nobody even noticed. The weaponization of this tech is so dangerous that even the U.N. is sounding the alarm because of it's untraceable and unethical capabilities. This is an introduction and overview. Pay Attention. I show you things for a reason. Secretary of Energy Hazel Reid O'Leary, who served under the Clinton administration stated that over 500,000 thousand Americans have been used in human experiments, including mind control and other experiments over a period of four decades without their informed consent. This is only what they admit to, then programs get put under higher classification, or under the guise of "national security," or go black so even members of Congress might not even get access to such programs. Mk Ultra as well, back in the 1960's used over 4,000 military service men without their consent for human experiments such as mind control. No one to this day has been held accountable for these experiments on our military and American citizens. Since the mid 1980's, radio frequency energy has been mastered by professionals in the field and graduates of Yale University and others and have been working directly with the military on mind control operations. The equipment being used needs no implant device, mechanical, or electronic device attached to the human being to be able to induce specific behaviors, moods, even V2K which is "voice-to-skull" technology and the CIA, NSA, DARPA, and our military are extremely interested in this technology Following MK Ultra, attention shifted toward electromagnetic technologies, including extremely low frequency (ELF) and radio frequency (RF) waves, which can influence emotions and project voices into the human mind. The "Frey Effect," discovered in the 1960s, demonstrated that pulsed microwaves could produce audible effects or voices in a subject's head, also known as "voice-to-skull" (V2K). Declassified documents show the U.S. military and intelligence agencies explored and used this tech during the Gulf War in the 1990s. DARPA—established in 1958 to advance cutting-edge military technology—began funding projects that bridged neuroscience and electronics, laying the groundwork for brain-computer interfaces (BCIs). DARPA’s role expanded significantly in the 21st century with programs like the Next-Generation Nonsurgical Neurotechnology (N3) initiative, launched in 2018. N3 aims to develop non-invasive BCIs that allow soldiers to control drones or robots with their thoughts, using techniques like ultrasound, magnetic fields, and nanoparticles to interface with the brain. This builds on earlier DARPA efforts, such as the Revolutionizing Prosthetics program, which enabled thought-controlled artificial limbs. Concurrently, the Obama administration’s BRAIN Initiative, announced in 2013, sought to map the brain’s neural circuits and accelerate neurotechnology development. While framed as a scientific endeavor to treat neurological disorders, its overlap with DARPA funding—$225 million by 2015—imply heavy military applications, including behavior modification and thought decoding. The integration of ELF/RF frequencies with 5G and AI represents the latest frontier. 5G’s high-frequency millimeter waves and dense network of transmitters could, in theory, enhance the precision and range of electromagnetic signals targeting the brain. Paired with AI, which can analyze vast datasets of neural activity in real time, this technology enables remote behavior control and thought reading. DARPA’s N3 program, for instance, envisions two-way communication between brains and machines, potentially allowing external systems to interpret intentions or implant sensory feedback—like voices or emotions. Speculative claims suggest that military and intelligence agencies are weaponizing these tools to suppress fear in soldiers, influence adversaries’ decisions, or surveil thoughts. Today, these technologies raise profound ethical and practical questions. The line between enhancement and control blurs when AI-driven systems could override human autonomy. Reports of "targeted individuals" experiencing voice projection or emotional manipulation fuel speculation and aren't looking unrealistic after all. Arguments that the secrecy surrounding DARPA and CIA projects—echoing Mk Ultra’s hidden abuses—obscures the true extent of weaponization, while proponents frame advancements vital, for national security. The progression from Mk Ultra’s drug experiments and other crude techniques to master mind control have surpassed ELF/RF explorations, and now to DARPA’s AI-enhanced BCIs under the BRAIN Initiative reflects a trajectory toward increasingly sophisticated mind control capabilities. The addition of 5G’s infrastructure and other radar installations around the world amplifies the potential for remote influence, suggesting a future where thoughts, emotions, and behaviors could be manipulated, monitored, and induced. The WEF makes their goals for this tech more than clear on their own website. Privacy will no longer exist. While the military and intelligence agencies undeniably pursue these technologies for strategic advantage, the full scope of their operational use and the line between science fiction and reality is diminishing by the day now that these technologies are fully operational and in use today and have now entered the advertising industry and big tech. I'm sure you can figure out where this is all going, and honestly, it's not good.

The SCIF

163,123 просмотров • 1 год назад

DARPA and the CIA's neurological electronic weapons are absolutely terrifying and are the biggest story that nobody is talking about. In the 1950s, the CIA launched Project MKUltra, a covert program aimed at developing techniques to manipulate mental states and brain functions for interrogation and psychological operations. Now there's MK Ultra 3.0. Following MK Ultra, attention shifted toward electromagnetic technologies, including extremely low frequency (ELF) and radio frequency (RF) waves, which can influence emotions and project voices into the human mind. The "Frey Effect," discovered in the 1960s, demonstrated that pulsed microwaves could produce audible effects or voices in a subject's head, also known as "voice-to-skull" (V2K). Declassified documents show the U.S. military and intelligence agencies explored and used this tech during the Gulf War in the 1990s. DARPA—established in 1958 to advance cutting-edge military technology—began funding projects that bridged neuroscience and electronics, laying the groundwork for brain-computer interfaces (BCIs). DARPA’s role expanded significantly in the 21st century with programs like the Next-Generation Nonsurgical Neurotechnology (N3) initiative, launched in 2018. N3 aims to develop non-invasive BCIs that allow soldiers to control drones or robots with their thoughts, using techniques like ultrasound, magnetic fields, and nanoparticles to interface with the brain. This builds on earlier DARPA efforts, such as the Revolutionizing Prosthetics program, which enabled thought-controlled artificial limbs. Concurrently, the Obama administration’s BRAIN Initiative, announced in 2013, sought to map the brain’s neural circuits and accelerate neurotechnology development. While framed as a scientific endeavor to treat neurological disorders, its overlap with DARPA funding—$225 million by 2015—imply heavy military applications, including behavior modification and thought decoding. The integration of ELF/RF frequencies with 5G and AI represents the latest frontier. 5G’s high-frequency millimeter waves and dense network of transmitters could, in theory, enhance the precision and range of electromagnetic signals targeting the brain. Paired with AI, which can analyze vast datasets of neural activity in real time, this technology enables remote behavior control and thought reading. DARPA’s N3 program, for instance, envisions two-way communication between brains and machines, potentially allowing external systems to interpret intentions or implant sensory feedback—like voices or emotions. Speculative claims suggest that military and intelligence agencies are weaponizing these tools to suppress fear in soldiers, influence adversaries’ decisions, or surveil thoughts. Today, these technologies raise profound ethical and practical questions. The line between enhancement and control blurs when AI-driven systems could override human autonomy. Reports of "targeted individuals" experiencing voice projection or emotional manipulation fuel speculation and aren't looking unrealistic after all. Arguments that the secrecy surrounding DARPA and CIA projects—echoing MKUltra’s hidden abuses—obscures the true extent of weaponization, while proponents frame advancements vital, for national security. The progression from MKUltra’s crude drug experiments to ELF/RF explorations, and now to DARPA’s AI-enhanced BCIs under the BRAIN Initiative reflects a trajectory toward increasingly sophisticated mind control capabilities. The addition of 5G’s infrastructure amplifies the potential for remote influence, suggesting a future where thoughts, emotions, and behaviors could be manipulated or monitored. While the military and intelligence agencies undeniably pursue these technologies for strategic advantage, the full scope of their operational use and the line between what the public believes is even possible is diminishing by the day now that these technologies and operations are fully functional. You were warned.
14:22

DARPA and the CIA's neurological electronic weapons are absolutely terrifying and are the biggest story that nobody is talking about. In the 1950s, the CIA launched Project MKUltra, a covert program aimed at developing techniques to manipulate mental states and brain functions for interrogation and psychological operations. Now there's MK Ultra 3.0. Following MK Ultra, attention shifted toward electromagnetic technologies, including extremely low frequency (ELF) and radio frequency (RF) waves, which can influence emotions and project voices into the human mind. The "Frey Effect," discovered in the 1960s, demonstrated that pulsed microwaves could produce audible effects or voices in a subject's head, also known as "voice-to-skull" (V2K). Declassified documents show the U.S. military and intelligence agencies explored and used this tech during the Gulf War in the 1990s. DARPA—established in 1958 to advance cutting-edge military technology—began funding projects that bridged neuroscience and electronics, laying the groundwork for brain-computer interfaces (BCIs). DARPA’s role expanded significantly in the 21st century with programs like the Next-Generation Nonsurgical Neurotechnology (N3) initiative, launched in 2018. N3 aims to develop non-invasive BCIs that allow soldiers to control drones or robots with their thoughts, using techniques like ultrasound, magnetic fields, and nanoparticles to interface with the brain. This builds on earlier DARPA efforts, such as the Revolutionizing Prosthetics program, which enabled thought-controlled artificial limbs. Concurrently, the Obama administration’s BRAIN Initiative, announced in 2013, sought to map the brain’s neural circuits and accelerate neurotechnology development. While framed as a scientific endeavor to treat neurological disorders, its overlap with DARPA funding—$225 million by 2015—imply heavy military applications, including behavior modification and thought decoding. The integration of ELF/RF frequencies with 5G and AI represents the latest frontier. 5G’s high-frequency millimeter waves and dense network of transmitters could, in theory, enhance the precision and range of electromagnetic signals targeting the brain. Paired with AI, which can analyze vast datasets of neural activity in real time, this technology enables remote behavior control and thought reading. DARPA’s N3 program, for instance, envisions two-way communication between brains and machines, potentially allowing external systems to interpret intentions or implant sensory feedback—like voices or emotions. Speculative claims suggest that military and intelligence agencies are weaponizing these tools to suppress fear in soldiers, influence adversaries’ decisions, or surveil thoughts. Today, these technologies raise profound ethical and practical questions. The line between enhancement and control blurs when AI-driven systems could override human autonomy. Reports of "targeted individuals" experiencing voice projection or emotional manipulation fuel speculation and aren't looking unrealistic after all. Arguments that the secrecy surrounding DARPA and CIA projects—echoing MKUltra’s hidden abuses—obscures the true extent of weaponization, while proponents frame advancements vital, for national security. The progression from MKUltra’s crude drug experiments to ELF/RF explorations, and now to DARPA’s AI-enhanced BCIs under the BRAIN Initiative reflects a trajectory toward increasingly sophisticated mind control capabilities. The addition of 5G’s infrastructure amplifies the potential for remote influence, suggesting a future where thoughts, emotions, and behaviors could be manipulated or monitored. While the military and intelligence agencies undeniably pursue these technologies for strategic advantage, the full scope of their operational use and the line between what the public believes is even possible is diminishing by the day now that these technologies and operations are fully functional. You were warned.

The SCIF

72,635 просмотров • 1 год назад

GPT-5 Latest Rumors and Leaks - Scraping Together All the Evidence! TLDR: It's going to be an "all-in-one" model across all current modalities and likely the first fully agentic foundation model! It's also likely less than a month or two away! More details to follow: GPT-5's release appears imminent based on cryptic hints from OpenAI researchers and industry rumors, with most consensus pointing to a July or August 2025 launch date, though some suggest it could be delayed until December due to training requirements. Sam Altman previously indicated GPT-5 would follow GPT-4.5 in "months not weeks," suggesting a potential release window anywhere from early 2025 to February 2026 at the latest. The anticipation builds on OpenAI's pattern of sudden major releases rather than gradual rollouts. The expected capabilities represent a paradigm shift beyond incremental improvements, featuring enhanced reasoning capabilities that OpenAI is integrating across all their models, improved reliability with reduced hallucination rates hopefully dropping below 15%, and substantially better coding performance that has already reached the point where OpenAI employees rely on their own tools for development work. GPT-5 is expected to excel in mathematics, context management with better memory systems, and feature all-in-one modality including native voice, high-quality image processing, and likely video generation and understanding, though real-time video streaming may wait until GPT-5.5. Parameter count speculation ranges wildly from conservative estimates of 5-50 trillion parameters to more ambitious predictions of up to one quadrillion parameters, representing a potential 1000x jump from GPT-4's estimated 1-1.5 trillion parameters. However, the focus has shifted from pure parameter scaling to inference-time compute and architectural innovations, with mixture of experts architecture expected to continue and new emphasis on tokenizing diverse data formats and streaming capabilities. GPT-5 is anticipated to enable fully autonomous agents with workflow management, API integration, and real-world actions, building on current tools like CodeX and Deep Research. Computer-using agents utilizing keyboard, video, and mouse interfaces represent the next major breakthrough, potentially accessing virtually any digital task or service. The integration covers everything from playing video games to editing videos to writing books, with the total addressable market being incalculable given the ubiquity of computer interfaces. Benchmark expectations include MMLU saturation at 95% accuracy, SWEBench jumping from 32% to 85% (indicating mastery of coding tasks), advanced math benchmarks reaching 40-50% accuracy, and multimodal tasks achieving 90% success rates. Context windows are expected to be substantially larger than current speculation suggests, potentially reaching 1-2 million tokens rather than the rumored 64,000-256,000 tokens, especially given that competitors like Google have already achieved massive context windows. The timeline predictions from major institutes like Brookings, McKinsey, and MIT are considered overly conservative, with the real progression likely occurring much faster. While consensus suggests agent deployment dawn in 2025, infrastructure scaling in 2026, autonomous ecosystems in 2027, and hybrid teams by 2028, the reality is that many of these developments are already happening or will occur 1-2 years earlier than predicted. We're currently at OpenAI's "reasoners" stage and approaching level 3 agents, with level 4 innovators expected by 2026 and level 5 organizations by 2027-2028, representing a much more accelerated path to artificial general intelligence than mainstream forecasts suggest.
24:39

GPT-5 Latest Rumors and Leaks - Scraping Together All the Evidence! TLDR: It's going to be an "all-in-one" model across all current modalities and likely the first fully agentic foundation model! It's also likely less than a month or two away! More details to follow: GPT-5's release appears imminent based on cryptic hints from OpenAI researchers and industry rumors, with most consensus pointing to a July or August 2025 launch date, though some suggest it could be delayed until December due to training requirements. Sam Altman previously indicated GPT-5 would follow GPT-4.5 in "months not weeks," suggesting a potential release window anywhere from early 2025 to February 2026 at the latest. The anticipation builds on OpenAI's pattern of sudden major releases rather than gradual rollouts. The expected capabilities represent a paradigm shift beyond incremental improvements, featuring enhanced reasoning capabilities that OpenAI is integrating across all their models, improved reliability with reduced hallucination rates hopefully dropping below 15%, and substantially better coding performance that has already reached the point where OpenAI employees rely on their own tools for development work. GPT-5 is expected to excel in mathematics, context management with better memory systems, and feature all-in-one modality including native voice, high-quality image processing, and likely video generation and understanding, though real-time video streaming may wait until GPT-5.5. Parameter count speculation ranges wildly from conservative estimates of 5-50 trillion parameters to more ambitious predictions of up to one quadrillion parameters, representing a potential 1000x jump from GPT-4's estimated 1-1.5 trillion parameters. However, the focus has shifted from pure parameter scaling to inference-time compute and architectural innovations, with mixture of experts architecture expected to continue and new emphasis on tokenizing diverse data formats and streaming capabilities. GPT-5 is anticipated to enable fully autonomous agents with workflow management, API integration, and real-world actions, building on current tools like CodeX and Deep Research. Computer-using agents utilizing keyboard, video, and mouse interfaces represent the next major breakthrough, potentially accessing virtually any digital task or service. The integration covers everything from playing video games to editing videos to writing books, with the total addressable market being incalculable given the ubiquity of computer interfaces. Benchmark expectations include MMLU saturation at 95% accuracy, SWEBench jumping from 32% to 85% (indicating mastery of coding tasks), advanced math benchmarks reaching 40-50% accuracy, and multimodal tasks achieving 90% success rates. Context windows are expected to be substantially larger than current speculation suggests, potentially reaching 1-2 million tokens rather than the rumored 64,000-256,000 tokens, especially given that competitors like Google have already achieved massive context windows. The timeline predictions from major institutes like Brookings, McKinsey, and MIT are considered overly conservative, with the real progression likely occurring much faster. While consensus suggests agent deployment dawn in 2025, infrastructure scaling in 2026, autonomous ecosystems in 2027, and hybrid teams by 2028, the reality is that many of these developments are already happening or will occur 1-2 years earlier than predicted. We're currently at OpenAI's "reasoners" stage and approaching level 3 agents, with level 4 innovators expected by 2026 and level 5 organizations by 2027-2028, representing a much more accelerated path to artificial general intelligence than mainstream forecasts suggest.

David Shapiro (L/0)

17,654 просмотров • 1 год назад

The U.S. MUST win the AI race We’ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actors—who will naturally prioritize expert tasks aligned with their own interests—to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Here’s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeek’s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. 🇺🇸
0:48

The U.S. MUST win the AI race We’ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actors—who will naturally prioritize expert tasks aligned with their own interests—to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Here’s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeek’s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. 🇺🇸

Ali Ansari

396,789 просмотров • 7 месяцев назад

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼
51:23

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

JJ Englert

572,438 просмотров • 5 месяцев назад