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Planning irrigation and tractor routes without precise terrain data creates severe operational hazards. Google Earth now generates elevation profiles straight from the measure tool. Agricultural professionals can instantly map management zones, identify natural bowls prone to flooding, and flag rollover hazards for heavy machinery. This spatial intelligence ensures your... show more

Google Earth

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35,397 Aufrufe • vor 2 Monaten •via X (Twitter)

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Today, we’re thrilled to announce the launch of Shapefile and 3D model import support on Google Earth. Plus, we’ve also added elevation profiles to the measure tool just for… good measure! You’ve made it clear. You want to be able to bring more of your data and models to Google Earth’s real-world canvas. We're excited to take the next step in delivering on that promise with these new features: 🔶 Shapefiles (SHP): Render industry-standard geospatial data as performant, cloud-native layers. 💡 3D Models (GLB): Place custom architectural mock-ups, massings, and more in a visually rich context. 🗻 Elevation Profiles: View detailed terrain data in the measure tool you already know and love. These new features are live now on Google Earth!

Today, we’re thrilled to announce the launch of Shapefile and 3D model import support on Google Earth. Plus, we’ve also added elevation profiles to the measure tool just for… good measure! You’ve made it clear. You want to be able to bring more of your data and models to Google Earth’s real-world canvas. We're excited to take the next step in delivering on that promise with these new features: 🔶 Shapefiles (SHP): Render industry-standard geospatial data as performant, cloud-native layers. 💡 3D Models (GLB): Place custom architectural mock-ups, massings, and more in a visually rich context. 🗻 Elevation Profiles: View detailed terrain data in the measure tool you already know and love. These new features are live now on Google Earth!

Google Earth

324,673 Aufrufe • vor 2 Monaten

An AI assistant for Google Earth Engine is now available in QGIS! You can explore and visualize any dataset from the 80+ PB Earth Engine Data Catalog using natural language, and run scalable geospatial analysis on demand without writing code. Every interaction automatically generates reusable code, making it easy to reproduce results, customize workflows, and transition from no-code to full scripting when needed. Upgrade the plugin to get started and bring AI-powered geospatial analytics directly into your QGIS workflow. GitHub: QGIS plugin: GeoAgent: #EarthEngine #QGIS #OpenSource #GeoAI #Geospatial

An AI assistant for Google Earth Engine is now available in QGIS! You can explore and visualize any dataset from the 80+ PB Earth Engine Data Catalog using natural language, and run scalable geospatial analysis on demand without writing code. Every interaction automatically generates reusable code, making it easy to reproduce results, customize workflows, and transition from no-code to full scripting when needed. Upgrade the plugin to get started and bring AI-powered geospatial analytics directly into your QGIS workflow. GitHub: QGIS plugin: GeoAgent: #EarthEngine #QGIS #OpenSource #GeoAI #Geospatial

Qiusheng Wu

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🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

Galbot

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Type “building” and the model finds the buildings. This is what deploying Google Earth AI imagery models in advanced spatial intelligence systems looks like in practice. Inside Vantor Sentry, Google Earth AI’s Open Vocabulary Detection (OVD) models allow analysts to identify objects in high-resolution satellite imagery simply by typing what they’re looking for. No pre-training. No custom labeling workflow. Combined with the quality and accuracy of Vantor’s spatial foundation—including the satellite imagery you see here—this provides sovereign operators with an unparalleled capability. Deployed inside a secure, air-gapped environment, this capability accelerates: 🔒 Persistent site monitoring 🔄 Change detection 🏗️ Infrastructure analysis 🗺️ Mission planning And because these models are integrated into Tensorglobe, they don’t operate in isolation. They can be fine-tuned locally using Vantor satellite imagery, third-party commercial data, sovereign sensor feeds, and customer-owned private archives. This is how foundation models become operational tools. Learn more:

Type “building” and the model finds the buildings. This is what deploying Google Earth AI imagery models in advanced spatial intelligence systems looks like in practice. Inside Vantor Sentry, Google Earth AI’s Open Vocabulary Detection (OVD) models allow analysts to identify objects in high-resolution satellite imagery simply by typing what they’re looking for. No pre-training. No custom labeling workflow. Combined with the quality and accuracy of Vantor’s spatial foundation—including the satellite imagery you see here—this provides sovereign operators with an unparalleled capability. Deployed inside a secure, air-gapped environment, this capability accelerates: 🔒 Persistent site monitoring 🔄 Change detection 🏗️ Infrastructure analysis 🗺️ Mission planning And because these models are integrated into Tensorglobe, they don’t operate in isolation. They can be fine-tuned locally using Vantor satellite imagery, third-party commercial data, sovereign sensor feeds, and customer-owned private archives. This is how foundation models become operational tools. Learn more:

Vantor

16,231 Aufrufe • vor 4 Monaten

Gemini 3 Flash is starting to roll out as the default model for AI Mode in Search globally. AI Mode can now tackle your most complicated questions with greater precision — without compromising speed. ⚡ With this upgrade, AI Mode becomes an all-around more powerful tool, better at understanding your needs. You can ask more nuanced questions and it will consider each aspect to provide a thoughtful response. And as always, you’ll have access to real-time information and useful links from across the web.

Gemini 3 Flash is starting to roll out as the default model for AI Mode in Search globally. AI Mode can now tackle your most complicated questions with greater precision — without compromising speed. ⚡ With this upgrade, AI Mode becomes an all-around more powerful tool, better at understanding your needs. You can ask more nuanced questions and it will consider each aspect to provide a thoughtful response. And as always, you’ll have access to real-time information and useful links from across the web.

Google

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GeoLibre v0.9.0 is out. GeoLibre is a lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data across desktop and web environments, with a responsive layout for mobile screens. This release turns GeoLibre into a far more complete analysis environment. Highlights: - New cloud data panels for Microsoft Planetary Computer and Google Earth Engine, plus an Overture Maps plugin and federal Web Services plugins, including NASA Earthdata, USGS National Map, EPA EnviroAtlas, FEMA NFHL - Accessing 1000+ NASA Earthdata layers with a few simple clicks - Time Slider plugin for animating time series raster and vector data - SQL Workspace: run DuckDB Spatial SQL directly against loaded layers, local files, and remote URLs, with query history and one-click "add results to map" - Conversion tools: Vector to GeoParquet, CSV to GeoParquet, and Raster to COG, running in the browser with DuckDB-WASM - Controls menu with Measure, Bookmark, Minimap, View State, and Print Everything is open source. Try it in your browser, install the desktop app, or self-host it. Live demo: GitHub: Feedback and contributions are welcome. #GIS #Geospatial #OpenSource #MapLibre #DuckDB #WebGIS #GeoLibre

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Qiusheng Wu

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Foundation models reach their full potential when paired with data at planetary scale. By combining the capabilities of Google Earth AI imagery models with the scale, depth, and quality of Vantor’s satellite imagery, Vantor customers can rapidly unlock complex, high-value use cases. Running Earth AI embedding models on Vantor’s 30 cm-class satellite imagery allows analysts to instantly surface visually similar features, patterns, and objects across massive datasets. A single reference image becomes a search query. From there, the system can: • Identify similar vessels across thousands of square kilometers • Detect infrastructure patterns at scale • Accelerate damage assessment workflows • Narrow broad-area searches before human review Our agreement with Google marks the first commercial deployment of Earth AI imagery models against a dataset offering global 2D coverage of virtually all landmass, global-scale 3D terrain, a 20+ year high-resolution archive, and constellation revisit rates of up to 15 times per day. That combination matters. When advanced imagery models are applied to a globally consistent, temporally deep, and geometrically precise spatial foundation, insight shifts from experimental to operational. And when deployed within sovereign, air-gapped environments, these capabilities move beyond cloud-based analytics to deliver mission-ready intelligence. Learn more:

Foundation models reach their full potential when paired with data at planetary scale. By combining the capabilities of Google Earth AI imagery models with the scale, depth, and quality of Vantor’s satellite imagery, Vantor customers can rapidly unlock complex, high-value use cases. Running Earth AI embedding models on Vantor’s 30 cm-class satellite imagery allows analysts to instantly surface visually similar features, patterns, and objects across massive datasets. A single reference image becomes a search query. From there, the system can: • Identify similar vessels across thousands of square kilometers • Detect infrastructure patterns at scale • Accelerate damage assessment workflows • Narrow broad-area searches before human review Our agreement with Google marks the first commercial deployment of Earth AI imagery models against a dataset offering global 2D coverage of virtually all landmass, global-scale 3D terrain, a 20+ year high-resolution archive, and constellation revisit rates of up to 15 times per day. That combination matters. When advanced imagery models are applied to a globally consistent, temporally deep, and geometrically precise spatial foundation, insight shifts from experimental to operational. And when deployed within sovereign, air-gapped environments, these capabilities move beyond cloud-based analytics to deliver mission-ready intelligence. Learn more:

Vantor

17,050 Aufrufe • vor 4 Monaten

Most developers can't explain how Single Sign-On (SSO) works. This was one of my favorite questions during technical interviews. I love to ask about it because it's not a trivial topic. Here is a 5-minute overview of how Single Sign-On works. We all hate passwords; the less we use them, the better, and SSO helps with that. When you log in to Google once and visit YouTube, Gmail, Drive, and any other connected service without re-entering your password, three players are working behind the scenes: • A user trying to access an application. You, in this case. • The application you want to access. For example, YouTube. • An Identity Provider (IDP) that will verify your identity. Google, in this case. Here is what happens when you try to access one application for the first time: 1. You try to log in to YouTube, and the application redirects you to the Identity Provider (IDP) for authentication. 2. The IDP (Google) checks your credentials and confirms your identity. It creates a new session for you on its server and sets a session cookie in your browser. 3. The IDP also creates a token for YouTube—a small piece of data that contains information about your identity. 4. Your browser grabs the token and presents it to YouTube. 5. YouTube checks the token, and if it is valid, lets you in. But then you want to access Google Drive: 1. You go to Google Drive, and the application redirects you to the IDP. 2. The IDP recognizes that you are still logged in because you have the session cookie. It doesn't need to ask for your credentials. 3. Instead, the IDP generates a new token for Drive. 4. Your browser grabs the token and presents it to Google Drive. If the token is valid, Drive lets you in. You can now access multiple applications without re-entering your password. This is probably one of the best things we've invented since sliced bread! But, of course, implementing Single Sign-On is a nightmare! If you are a developer, don't try to reinvent the wheel. I've been implementing SSO since dinosaurs were around, and I can tell you you want to check out Auth0. Auth0 makes implementing SSO 100x easier. They just updated their free plan, and you get a lot without having to pay a single cent. 25,000 monthly active users, unlimited social connections, and you can go to production with custom domains. FOR FREE! They are sponsoring this post. To save your time, keep your sanity, and have a really solid and secure solution, head over to their website:

Most developers can't explain how Single Sign-On (SSO) works. This was one of my favorite questions during technical interviews. I love to ask about it because it's not a trivial topic. Here is a 5-minute overview of how Single Sign-On works. We all hate passwords; the less we use them, the better, and SSO helps with that. When you log in to Google once and visit YouTube, Gmail, Drive, and any other connected service without re-entering your password, three players are working behind the scenes: • A user trying to access an application. You, in this case. • The application you want to access. For example, YouTube. • An Identity Provider (IDP) that will verify your identity. Google, in this case. Here is what happens when you try to access one application for the first time: 1. You try to log in to YouTube, and the application redirects you to the Identity Provider (IDP) for authentication. 2. The IDP (Google) checks your credentials and confirms your identity. It creates a new session for you on its server and sets a session cookie in your browser. 3. The IDP also creates a token for YouTube—a small piece of data that contains information about your identity. 4. Your browser grabs the token and presents it to YouTube. 5. YouTube checks the token, and if it is valid, lets you in. But then you want to access Google Drive: 1. You go to Google Drive, and the application redirects you to the IDP. 2. The IDP recognizes that you are still logged in because you have the session cookie. It doesn't need to ask for your credentials. 3. Instead, the IDP generates a new token for Drive. 4. Your browser grabs the token and presents it to Google Drive. If the token is valid, Drive lets you in. You can now access multiple applications without re-entering your password. This is probably one of the best things we've invented since sliced bread! But, of course, implementing Single Sign-On is a nightmare! If you are a developer, don't try to reinvent the wheel. I've been implementing SSO since dinosaurs were around, and I can tell you you want to check out Auth0. Auth0 makes implementing SSO 100x easier. They just updated their free plan, and you get a lot without having to pay a single cent. 25,000 monthly active users, unlimited social connections, and you can go to production with custom domains. FOR FREE! They are sponsoring this post. To save your time, keep your sanity, and have a really solid and secure solution, head over to their website:

Santiago

204,892 Aufrufe • vor 1 Jahr

DIGITAL TWIN UPDATE: The Unreal Engine digital twin project is CANCELLED! WE ARE OPEN SOURCING THE WORK!!!!!!! Instead, I'm building an end-to-end land management platform I'm calling Mazzap because it's an app and it's a map and also I'm Mr. Mazza. I'm also not calling a digital twin anymore; I'm calling it a V.E.I.L. which stands for Virtually Embodied Intelligent Land which serves the dual purpose of sounding way cooler and also conveys the long-term aims of the prject way better. With Mazzap anyone can easily generate a veil with nothing but publicly available data (and some photogrammetry objects if you so choose). Everything is perfectly georefrenced and you can right-click anywhere on your veil to get coordinates you can plug into google maps yourself. The next step for this is going to be building a survey companion mobile app so you can get field data yourself complete with attribute tables to drop in to get even HIGHER fidelity data than USGS. My stretch goal is using something like SAM 3D (but better) to generate georefrenced 3d tree assets based on the real trees on your land (which will eventually lead to growth and fruiting simulators based on your soil and hydrology data). Then again maybe that's not the next step; maybe the next step is plumbing in IoT devices so you can view assets in real time on your land. GPS trackers on the sheep, or maybe data from your solar controller. Or maybe it's up to you, because it's completely open source and it has an agents markdown file ready for your own coding agent to read and adapt for your purposes (did I mention this is vibecloded slop? sowwyyyy) Watch demo below and see how you can go from zero to beautifully rendered 3d map of your property in less than 15 minutes! If you're coming in cold to all this, below in the QT is a nested thread of reverse chronological tweets of my work on this (in unreal) so far.

DIGITAL TWIN UPDATE: The Unreal Engine digital twin project is CANCELLED! WE ARE OPEN SOURCING THE WORK!!!!!!! Instead, I'm building an end-to-end land management platform I'm calling Mazzap because it's an app and it's a map and also I'm Mr. Mazza. I'm also not calling a digital twin anymore; I'm calling it a V.E.I.L. which stands for Virtually Embodied Intelligent Land which serves the dual purpose of sounding way cooler and also conveys the long-term aims of the prject way better. With Mazzap anyone can easily generate a veil with nothing but publicly available data (and some photogrammetry objects if you so choose). Everything is perfectly georefrenced and you can right-click anywhere on your veil to get coordinates you can plug into google maps yourself. The next step for this is going to be building a survey companion mobile app so you can get field data yourself complete with attribute tables to drop in to get even HIGHER fidelity data than USGS. My stretch goal is using something like SAM 3D (but better) to generate georefrenced 3d tree assets based on the real trees on your land (which will eventually lead to growth and fruiting simulators based on your soil and hydrology data). Then again maybe that's not the next step; maybe the next step is plumbing in IoT devices so you can view assets in real time on your land. GPS trackers on the sheep, or maybe data from your solar controller. Or maybe it's up to you, because it's completely open source and it has an agents markdown file ready for your own coding agent to read and adapt for your purposes (did I mention this is vibecloded slop? sowwyyyy) Watch demo below and see how you can go from zero to beautifully rendered 3d map of your property in less than 15 minutes! If you're coming in cold to all this, below in the QT is a nested thread of reverse chronological tweets of my work on this (in unreal) so far.

Zy

54,025 Aufrufe • vor 4 Monaten

Based on the urgent and grave warnings from Dr. Bret Weinstein, a dystopian medical future is being engineered right now. They are not done with you. The current push is to migrate "vaccines" onto the mRNA platform. If the concerns of many leading experts are valid, this will result in a significant, yet concealed, increase in severe heart pathologies. The mechanism for a cover-up is already in place, as adverse events can be and will be dismissed. This is not a social or political issue. It is a physiological one. The objective is to avoid catastrophic damage to your body and the bodies of your children and grandchildren. The playbook is clear: Introduce revised mRNA injections. Strong-arm compliance by tying it to your financial existence—your ability to work, travel, and participate in society. Preemptively shut down all discussion of the hazards as "misinformation." Your instinct is to look away because the truth is horrifying. But future you would scream at you to pay attention NOW. It only gets worse from here. The time to say no, to understand the risks, and to resist this coercion is before you are presented with a choice that is not a choice at all. Your financial freedom and your bodily autonomy are on the line. Do not surrender them.

Based on the urgent and grave warnings from Dr. Bret Weinstein, a dystopian medical future is being engineered right now. They are not done with you. The current push is to migrate "vaccines" onto the mRNA platform. If the concerns of many leading experts are valid, this will result in a significant, yet concealed, increase in severe heart pathologies. The mechanism for a cover-up is already in place, as adverse events can be and will be dismissed. This is not a social or political issue. It is a physiological one. The objective is to avoid catastrophic damage to your body and the bodies of your children and grandchildren. The playbook is clear: Introduce revised mRNA injections. Strong-arm compliance by tying it to your financial existence—your ability to work, travel, and participate in society. Preemptively shut down all discussion of the hazards as "misinformation." Your instinct is to look away because the truth is horrifying. But future you would scream at you to pay attention NOW. It only gets worse from here. The time to say no, to understand the risks, and to resist this coercion is before you are presented with a choice that is not a choice at all. Your financial freedom and your bodily autonomy are on the line. Do not surrender them.

Camus

52,000 Aufrufe • vor 10 Monaten

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!

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

263,255 Aufrufe • vor 1 Jahr

$This is the setup that changed everything for me, Claude can now browse the internet on its own. Not with your browser. Its own dedicated browser.. It can now login with your credentials, scrape data, monitors communities and generates leads.. all in the background while you sleep First how you can set it up, > Open Claude Desktop > Click the plus (+) icon in the chat bar and select Connectors (or "Manage Connectors") > Click Add Custom Connector and enter the following details - Name: Firecrawl - URL: dev/{FIRECRAWL_API_KEY}/v2/mcp - API Key: Paste your API key from the Firecrawl dashboard. > Click Add. then just enable and Authorize, > Once added, find "Firecrawl" in your connectors list and toggle it on. > Claude will ask for approval to use the connector; select Always Allow to let it run tasks without prompting you every time can start using it normally in chat now.. Prompt it like: "Go to [website], extract [data], and organize it" It opens its own browser, navigates, scrapes, and brings you clean results. will share how i use this setup in my next post, so be sure to follow me and turn post notifications on.$

This is the setup that changed everything for me, Claude can now browse the internet on its own. Not with your browser. Its own dedicated browser.. It can now login with your credentials, scrape data, monitors communities and generates leads.. all in the background while you sleep First how you can set it up, > Open Claude Desktop > Click the plus (+) icon in the chat bar and select Connectors (or "Manage Connectors") > Click Add Custom Connector and enter the following details - Name: Firecrawl - URL: dev/{FIRECRAWL_API_KEY}/v2/mcp - API Key: Paste your API key from the Firecrawl dashboard. > Click Add. then just enable and Authorize, > Once added, find "Firecrawl" in your connectors list and toggle it on. > Claude will ask for approval to use the connector; select Always Allow to let it run tasks without prompting you every time can start using it normally in chat now.. Prompt it like: "Go to [website], extract [data], and organize it" It opens its own browser, navigates, scrapes, and brings you clean results. will share how i use this setup in my next post, so be sure to follow me and turn post notifications on.

Axel Bitblaze 🪓

179,605 Aufrufe • vor 3 Monaten

SOMEONE VIBE CODED A TOOL THAT FINDS BUSINESSES, READS THEIR REVIEWS, AND WRITES COLD EMAILS BASED ON THEIR OWN CUSTOMERS' COMPLAINTS this is insane. you type in any business type and pick a city. it scrapes every matching business off Google Maps with 30+ data fields. then it visits their actual websites and pulls verified emails, phone numbers, and every social media profile they have scraped live, not from some outdated database like other tools then the AI reads up to 50 of their Google reviews and finds their exact pain points "clients complain photos don't show the real size of properties" or "listings take too long to sell" then you tell it what YOUR business does. it cross-references your offer with their specific problems and generates a fully personalized cold email for each business. send it in 2 clicks. one by one and not bulk so it lands in the primary inbox and all of those leads land on a GPS-mapped CRM where you can draw sales territories, optimize driving routes, track your team's activity in real time, and transcribe voice notes after meetings. works in 200+ countries and with any business type. if they're on Google Maps you can find them this is the most complete lead gen tool i've ever seen AND he vibe coded the whole thing with Claude Code in 2 weeks

Rahul

98,186 Aufrufe • vor 1 Monat

📢🚨 HEY MOTHER FAM 📢 🚨 🚀 I can’t begin to explain my interest and enthusiasm for POPOLOGY POPOLOGY® It is the next big thing for Citizen Journalism! Helps you identify and develop thought leaders without de-platforming bull. As we know AI has taken the advertising business by storm and we are in the middle of a creative resurgence. POPOLOGY® is a platform that will support all platforms, because, well it is all platforms! You can save time by automating your content discovery. Disseminate intelligence in a more impactful way. Education retention is better than ever for our scholars. Earn from your online data behavior and practice fair use doctrine supporting the measure of true popular, "one person at a time" globally! What particularly intrigues me is the ingenious placement of commercials within the user's chosen POPcast® stream. This innovative approach not only addresses brand retention challenges but also empowers individuals to endorse the products and services they genuinely believe in. It is a testament to the platform's ability to align with the digitally savvy nature of our society and oﬀer a much-needed solution to the criticisms often associated with capitalism and brand dominance.

Whale GuilD

24,790 Aufrufe • vor 5 Monaten

SOMEONE VIBE CODED A TOOL THAT FINDS BUSINESSES, READS THEIR REVIEWS, AND WRITES COLD EMAILS BASED ON THEIR OWN CUSTOMERS' COMPLAINTS this is insane. you type in any business type and pick a city. it scrapes every matching business off Google Maps with 30+ data fields. then it visits their actual websites and pulls verified emails, phone numbers, and every social media profile they have scraped live, not from some outdated database like other tools then the AI reads up to 50 of their Google reviews and finds their exact pain points "clients complain photos don't show the real size of properties" or "listings take too long to sell" then you tell it what YOUR business does. it cross-references your offer with their specific problems and generates a fully personalized cold email for each business. send it in 2 clicks. one by one and not bulk so it lands in the primary inbox and all of those leads land on a GPS-mapped CRM where you can draw sales territories, optimize driving routes, track your team's activity in real time, and transcribe voice notes after meetings. works in 200+ countries and with any business type. if they're on Google Maps you can find them this is the most complete lead gen tool i've ever seen AND he vibe coded the whole thing with Claude Code in 2 weeks

Om Patel

599,839 Aufrufe • vor 3 Monaten

This is big. NVIDIA and Apple just unlocked the next level for Vision Pro with CloudXR. Here’s what you need to know: It streams from a PC or the cloud directly to your Vision Pro. No cables. Up to 4K at 120fps. The technology is called dynamic foveated streaming. Your eyes only see in full resolution at the center of your gaze. CloudXR tracks exactly where you’re looking and delivers maximum resolution there. Everything in your periphery gets optimized. The stream stays efficient without you ever noticing a difference. Why does this matter? Vision Pro is already the most advanced spatial computer ever made. But standalone processing has a ceiling. There are workflows that need more compute than any headset can carry. CloudXR removes that ceiling by connecting Vision Pro to the full power of NVIDIA RTX in real time. This is not a workaround. It is a native visionOS integration. Also worth knowing. Gaze data never leaves your device. Not to the app. Not to the server. Developers get the full performance benefit of foveation without ever touching your raw eye tracking data. Privacy built into the architecture. Three industries are already on this: Kia, Rivian, and Volvo are running 1:1 scale design reviews with photorealistic accuracy. Full size vehicle models evaluated in spatial computing before a physical prototype exists. That is a fundamental shift in how design works. Foxconn is walking factory floors digitally, optimizing facilities before construction begins. Switch is managing data center infrastructure through a full digital twin. Companies using this approach are reporting up to 30% improvement in development processes. And sim enthusiasts finally get to cut the cord. iRacing and X-Plane 12 are the first titles. Full GeForce RTX power, streamed wirelessly to Vision Pro, inside your physical space. For developers: one Xcode template, one codebase, deployed across Vision Pro, iPhone, and iPad. And multiple headsets can share the same streamed environment at once. Some fully immersed, others on a tablet. That collaborative layer is what enterprise has been asking for. Coming this spring with visionOS 26.4. It’s wild to think about what this unlocks. The future of spatial computing is incredibly bright. Live from GTC. More coming soon.

This is big. NVIDIA and Apple just unlocked the next level for Vision Pro with CloudXR. Here’s what you need to know: It streams from a PC or the cloud directly to your Vision Pro. No cables. Up to 4K at 120fps. The technology is called dynamic foveated streaming. Your eyes only see in full resolution at the center of your gaze. CloudXR tracks exactly where you’re looking and delivers maximum resolution there. Everything in your periphery gets optimized. The stream stays efficient without you ever noticing a difference. Why does this matter? Vision Pro is already the most advanced spatial computer ever made. But standalone processing has a ceiling. There are workflows that need more compute than any headset can carry. CloudXR removes that ceiling by connecting Vision Pro to the full power of NVIDIA RTX in real time. This is not a workaround. It is a native visionOS integration. Also worth knowing. Gaze data never leaves your device. Not to the app. Not to the server. Developers get the full performance benefit of foveation without ever touching your raw eye tracking data. Privacy built into the architecture. Three industries are already on this: Kia, Rivian, and Volvo are running 1:1 scale design reviews with photorealistic accuracy. Full size vehicle models evaluated in spatial computing before a physical prototype exists. That is a fundamental shift in how design works. Foxconn is walking factory floors digitally, optimizing facilities before construction begins. Switch is managing data center infrastructure through a full digital twin. Companies using this approach are reporting up to 30% improvement in development processes. And sim enthusiasts finally get to cut the cord. iRacing and X-Plane 12 are the first titles. Full GeForce RTX power, streamed wirelessly to Vision Pro, inside your physical space. For developers: one Xcode template, one codebase, deployed across Vision Pro, iPhone, and iPad. And multiple headsets can share the same streamed environment at once. Some fully immersed, others on a tablet. That collaborative layer is what enterprise has been asking for. Coming this spring with visionOS 26.4. It’s wild to think about what this unlocks. The future of spatial computing is incredibly bright. Live from GTC. More coming soon.

Justin Ryan ᯅ

43,627 Aufrufe • vor 3 Monaten

Dr. Soon-Shiong's Immune Trinity: Sleep, Red Light, and The Right Food. Everything Else is Noise. Dr. Patrick Soon-Shiong reframes human biology: we are a dynamic interplay of neutrons, photons, and electrons. A living, breathing battery. Central to this system is the natural killer (NK) cell, a guardian of our immune defense. Each cell is a intelligence network with 30,000 receptors. But this army doesn't run on willpower. It demands specific, natural inputs: Sleep: Non-negotiable. This is when your NK cell arsenal is rebuilt and restored. Without quality rest, your primary defense force is left depleted. Sunlight: Our most overlooked medicine. Specifically, the red wavelength is critical for stimulating these cells. It's not a supplement; it's a fundamental requirement for immune activation. This explains the stark seasonal patterns in human health. Why we get sicker in winter. The data from cloud-covered cities doesn't lie. Nature is about light. Plants fail without it, and so do we. Furthermore, we must fuel this "battery" correctly. Your gut biome can either activate your immune system or immunosuppress it. The formula for resilience is clear: Sleep to recharge. Sunlight to stimulate. Nourishing food to activate. It’s a return to the most fundamental elements of life. It’s time we started listening.

Dr. Soon-Shiong's Immune Trinity: Sleep, Red Light, and The Right Food. Everything Else is Noise. Dr. Patrick Soon-Shiong reframes human biology: we are a dynamic interplay of neutrons, photons, and electrons. A living, breathing battery. Central to this system is the natural killer (NK) cell, a guardian of our immune defense. Each cell is a intelligence network with 30,000 receptors. But this army doesn't run on willpower. It demands specific, natural inputs: Sleep: Non-negotiable. This is when your NK cell arsenal is rebuilt and restored. Without quality rest, your primary defense force is left depleted. Sunlight: Our most overlooked medicine. Specifically, the red wavelength is critical for stimulating these cells. It's not a supplement; it's a fundamental requirement for immune activation. This explains the stark seasonal patterns in human health. Why we get sicker in winter. The data from cloud-covered cities doesn't lie. Nature is about light. Plants fail without it, and so do we. Furthermore, we must fuel this "battery" correctly. Your gut biome can either activate your immune system or immunosuppress it. The formula for resilience is clear: Sleep to recharge. Sunlight to stimulate. Nourishing food to activate. It’s a return to the most fundamental elements of life. It’s time we started listening.

Camus

115,228 Aufrufe • vor 9 Monaten

Wow, since the last post blew up, here is another fascinating insight from my years working with car-on-demand companies and more traditional automakers. Most people still think the entire automotive business is about selling or leasing vehicles. But there is a new market emerging that is absolutely massive: data Modern cars are packed with sensors and constantly collect real-time information. And this data is quickly becoming one of the most valuable revenue streams in the industry. Companies like Tesla, with their autonomous, sensor-rich fleets, are positioned to benefit enormously, but the same applies to many newer connected vehicles across all brands. What makes this so powerful is how diverse the use cases are. Cities, for example, can tap into aggregated vehicle data to understand exactly where they need to intervene. If thousands of cars detect the same irregular bump on the road, you instantly know there is a pothole at a precise location. Scale that across a whole city and you have a live map of infrastructure issues before residents even complain. The same data can help optimize traffic flow, identify congestion patterns in real time, or highlight zones where drivers consistently brake or accelerate suddenly, revealing potential safety problems. Automakers can also use this information to better understand how people actually drive in the real world, which directly influences design, durability testing, and product evolution And then there is the insurance angle. As driving behavior becomes measurable at scale, dynamic insurance pricing emerge. Acceleration habits, braking patterns, cornering, speed consistency, environmental context all of this will feed into future scoring models What we are seeing right now is only the beginning. With cars full of electronics, sensors, and especially autonomous vision systems, data is becoming one of the largest and most predictable revenue lines for automakers and mobility companies. Even traditional vehicles are now connected and constantly transmitting information that can be analyzed or monetized in multiple ways We are still just scratching the surface, but the shift is already underway. The value is no longer just in the vehicle itself. It is also in the billions of data points it generates every single day

Wow, since the last post blew up, here is another fascinating insight from my years working with car-on-demand companies and more traditional automakers. Most people still think the entire automotive business is about selling or leasing vehicles. But there is a new market emerging that is absolutely massive: data Modern cars are packed with sensors and constantly collect real-time information. And this data is quickly becoming one of the most valuable revenue streams in the industry. Companies like Tesla, with their autonomous, sensor-rich fleets, are positioned to benefit enormously, but the same applies to many newer connected vehicles across all brands. What makes this so powerful is how diverse the use cases are. Cities, for example, can tap into aggregated vehicle data to understand exactly where they need to intervene. If thousands of cars detect the same irregular bump on the road, you instantly know there is a pothole at a precise location. Scale that across a whole city and you have a live map of infrastructure issues before residents even complain. The same data can help optimize traffic flow, identify congestion patterns in real time, or highlight zones where drivers consistently brake or accelerate suddenly, revealing potential safety problems. Automakers can also use this information to better understand how people actually drive in the real world, which directly influences design, durability testing, and product evolution And then there is the insurance angle. As driving behavior becomes measurable at scale, dynamic insurance pricing emerge. Acceleration habits, braking patterns, cornering, speed consistency, environmental context all of this will feed into future scoring models What we are seeing right now is only the beginning. With cars full of electronics, sensors, and especially autonomous vision systems, data is becoming one of the largest and most predictable revenue lines for automakers and mobility companies. Even traditional vehicles are now connected and constantly transmitting information that can be analyzed or monetized in multiple ways We are still just scratching the surface, but the shift is already underway. The value is no longer just in the vehicle itself. It is also in the billions of data points it generates every single day

Aurelien

72,062 Aufrufe • vor 7 Monaten

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,441 Aufrufe • vor 1 Jahr