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🚀 SamGeo v0.14.0 is here! This release brings interactive segmentation for remote sensing imagery powered by Meta’s SAM3. 🟦 Draw a bounding box to automatically detect all similar objects across the image. 📝 Or just type a text prompt to segment objects instantly. No complex setup. No manual labeling....

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Qiusheng Wu

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33,558 görüntüleme • 7 ay önce •via X (Twitter)

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🚀 A brand-new feature is coming to the SAMGeo Python package: interactive remote-sensing image segmentation with text prompts powered by SAM3. This update makes geospatial segmentation even more intuitive: just describe what you want to detect, and let GeoAI do the rest. ✨ GitHub PR: #GeoAI #SAM3 #OpenSource #Python
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🚀 A brand-new feature is coming to the SAMGeo Python package: interactive remote-sensing image segmentation with text prompts powered by SAM3. This update makes geospatial segmentation even more intuitive: just describe what you want to detect, and let GeoAI do the rest. ✨ GitHub PR: #GeoAI #SAM3 #OpenSource #Python

Qiusheng Wu

32,922 görüntüleme • 7 ay önce

QGIS GeoAI Plugin v0.5.0 is out! I just released v0.5.0 of the QGIS GeoAI Plugin, bringing interactive image segmentation powered by DeepForest ( to your desktop GIS workflow. With this update, you can load pretrained DeepForest models to segment real-world objects such as trees, birds, livestock, dead trees, and more, directly inside QGIS. Try it out: Documentation: GitHub: #deepforest #geoai #QGIS #geospatial #OpenSource
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QGIS GeoAI Plugin v0.5.0 is out! I just released v0.5.0 of the QGIS GeoAI Plugin, bringing interactive image segmentation powered by DeepForest ( to your desktop GIS workflow. With this update, you can load pretrained DeepForest models to segment real-world objects such as trees, birds, livestock, dead trees, and more, directly inside QGIS. Try it out: Documentation: GitHub: #deepforest #geoai #QGIS #geospatial #OpenSource

Qiusheng Wu

21,385 görüntüleme • 5 ay önce

🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI
1:16

🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI

Qiusheng Wu

12,831 görüntüleme • 9 ay önce

🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing
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🚀 I am very excited to release the SamGeo QGIS plugin for geospatial image segmentation, powered by Meta’s Segment Anything Model (SAM 3) In this full tutorial, I’ll walk you through how to install, configure, and start segmenting satellite imagery in QGIS without writing a single line of code! 👉 Download the plugin here: 💻 Full video tutorial: #QGIS #SegmentAnything #SAM #GeoAI #RemoteSensing

Qiusheng Wu

29,139 görüntüleme • 7 ay önce

🚀 GeoAI Tutorial 23: Building Detection from Aerial Imagery and LiDAR Data 🏙️🛰️ In this tutorial, I demonstrate how to train a deep learning segmentation model for building detection by combining aerial/satellite imagery with LiDAR data. You'll learn how to fuse multi-source remote sensing data to improve model accuracy and robustness. 🔗 Tutorial video: 📓 Notebook example: 💻 GitHub repo: 📚 Documentation: 🎥 Full playlist: #GeoAI #geospatial #AI #Python #DeepLearning
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🚀 GeoAI Tutorial 23: Building Detection from Aerial Imagery and LiDAR Data 🏙️🛰️ In this tutorial, I demonstrate how to train a deep learning segmentation model for building detection by combining aerial/satellite imagery with LiDAR data. You'll learn how to fuse multi-source remote sensing data to improve model accuracy and robustness. 🔗 Tutorial video: 📓 Notebook example: 💻 GitHub repo: 📚 Documentation: 🎥 Full playlist: #GeoAI #geospatial #AI #Python #DeepLearning

Qiusheng Wu

12,244 görüntüleme • 1 yıl önce

🚀 Visualizing Planet’s Tanager Hyperspectral Data in 3D! 🌍🛰️ Create an interactive image cube to explore the internal structure of all 426 spectral bands captured by Planet’s Tanager mission — going far beyond the traditional RGB view of desktop GIS tools. This approach allows for a deeper comparison of spectral characteristics across different land cover types and supports more advanced geospatial analysis. 📓 Try the notebook example: 💻 Explore the code on GitHub: #Hyperspectral #Geospatial #Tanager #Python #DataScience #OpenSource
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🚀 Visualizing Planet’s Tanager Hyperspectral Data in 3D! 🌍🛰️ Create an interactive image cube to explore the internal structure of all 426 spectral bands captured by Planet’s Tanager mission — going far beyond the traditional RGB view of desktop GIS tools. This approach allows for a deeper comparison of spectral characteristics across different land cover types and supports more advanced geospatial analysis. 📓 Try the notebook example: 💻 Explore the code on GitHub: #Hyperspectral #Geospatial #Tanager #Python #DataScience #OpenSource

Qiusheng Wu

11,341 görüntüleme • 1 yıl önce

🎁 A New Year’s gift to the geospatial community: a brand-new QGIS plugin that unlocks access to 80+ petabytes of satellite imagery and geospatial datasets with zero coding required. In my first video of 2026, I’m excited to share what might be one of the most powerful QGIS plugins I’ve built so far. This plugin makes it incredibly easy to explore the official Earth Engine Data Catalog and the Awesome GEE Community Catalog directly inside QGIS. With just a few clicks, you can browse massive Earth Engine datasets, visualize them on the map, generate time series from ImageCollections, export data for further analysis, and even convert Earth Engine JavaScript workflows to Python, all without writing a single line of code. If you’ve ever wanted the power of Earth Engine inside QGIS without the learning curve, this plugin is for you. 👉 Download the plugin and follow the full step-by-step video tutorial to get started today: QGIS Plugin Page: GitHub Repository: Video Tutorial: Happy New Year, and I hope this tool helps you do more with geospatial data in 2026 🚀 #QGIS #geospatial #EarthEngine #Python #datascience
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🎁 A New Year’s gift to the geospatial community: a brand-new QGIS plugin that unlocks access to 80+ petabytes of satellite imagery and geospatial datasets with zero coding required. In my first video of 2026, I’m excited to share what might be one of the most powerful QGIS plugins I’ve built so far. This plugin makes it incredibly easy to explore the official Earth Engine Data Catalog and the Awesome GEE Community Catalog directly inside QGIS. With just a few clicks, you can browse massive Earth Engine datasets, visualize them on the map, generate time series from ImageCollections, export data for further analysis, and even convert Earth Engine JavaScript workflows to Python, all without writing a single line of code. If you’ve ever wanted the power of Earth Engine inside QGIS without the learning curve, this plugin is for you. 👉 Download the plugin and follow the full step-by-step video tutorial to get started today: QGIS Plugin Page: GitHub Repository: Video Tutorial: Happy New Year, and I hope this tool helps you do more with geospatial data in 2026 🚀 #QGIS #geospatial #EarthEngine #Python #datascience

Qiusheng Wu

84,918 görüntüleme • 6 ay önce

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 görüntüleme • 1 ay önce

OpenClaw just got a free image generator built directly inside it. And it's powered by Google's fastest model ever. Here's what you can do now: → Describe a thumbnail and OpenClaw generates it instantly. → Post it straight to Discord or Telegram automatically. → Create a dedicated channel just for generating visuals on demand. → Build ads, social content, and landing page graphics without switching apps. → Set it up with one API key and a single skill file. No clicking between tools. No switching apps. No waiting. You describe it. OpenClaw builds it. Done. Most people are still bouncing between 5 different image tools every day. The 1% just automated the whole thing inside their AI agent. Want the skill file and exact setup prompt? Drop a comment below. 👇
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OpenClaw just got a free image generator built directly inside it. And it's powered by Google's fastest model ever. Here's what you can do now: → Describe a thumbnail and OpenClaw generates it instantly. → Post it straight to Discord or Telegram automatically. → Create a dedicated channel just for generating visuals on demand. → Build ads, social content, and landing page graphics without switching apps. → Set it up with one API key and a single skill file. No clicking between tools. No switching apps. No waiting. You describe it. OpenClaw builds it. Done. Most people are still bouncing between 5 different image tools every day. The 1% just automated the whole thing inside their AI agent. Want the skill file and exact setup prompt? Drop a comment below. 👇

Julian Goldie SEO

11,846 görüntüleme • 4 ay önce

GeoLibre v2.1 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release adds a QGIS-style Browser panel for exploring data from one place, animated route playback with video export, Wikipedia knowledge cards, and Mapillary street-level images. What's new in v2.1.0 - Browser panel (Data Source Manager): explore and add data from one place. Browse map services, connect to PostGIS databases and drill into their schemas and tables, open local files, and save Favorites, all with full keyboard navigation. - Route animation: send a marker along any line layer, follow the track in 3D with camera controls, and export the whole animation as an MP4 video. - In-browser object detection: run ONNX/YOLO models directly in the webview, with no server or Python required. - Map recording: capture the map canvas, or a drawn bounding box, straight to a video file from the browser. - Native-resolution photo viewer: view geotagged photos at full detail right on the map. - Wikipedia knowledge cards: click any place to pull up its Wikipedia summary and info card. - More planetary basemaps: USGS imagery for nine more celestial bodies, plus a new OpenAerialMap imagery search plugin. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre
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GeoLibre v2.1 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release adds a QGIS-style Browser panel for exploring data from one place, animated route playback with video export, Wikipedia knowledge cards, and Mapillary street-level images. What's new in v2.1.0 - Browser panel (Data Source Manager): explore and add data from one place. Browse map services, connect to PostGIS databases and drill into their schemas and tables, open local files, and save Favorites, all with full keyboard navigation. - Route animation: send a marker along any line layer, follow the track in 3D with camera controls, and export the whole animation as an MP4 video. - In-browser object detection: run ONNX/YOLO models directly in the webview, with no server or Python required. - Map recording: capture the map canvas, or a drawn bounding box, straight to a video file from the browser. - Native-resolution photo viewer: view geotagged photos at full detail right on the map. - Wikipedia knowledge cards: click any place to pull up its Wikipedia summary and info card. - More planetary basemaps: USGS imagery for nine more celestial bodies, plus a new OpenAerialMap imagery search plugin. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre

Qiusheng Wu

11,627 görüntüleme • 13 gün önce

GeoLibre v1.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings field data collection, image georeferencing, and a full Jupyter notebook experience right next to the map. What's new in v1.4.0 - Jupyter beside the map: Run notebooks alongside your map (JupyterLite in the browser, JupyterLab on the desktop). - Field data collection: Capture point observations in the field with custom, configurable forms. - Georeferencer: Pin a scanned map or image to the map using ground control points. - Spectral Index toolbox: Compute NDVI, NDWI, EVI, and more directly from your rasters. - Better internationalization: Correct rendering of right-to-left basemap labels (Arabic, Hebrew). - More reliable data handling: Render GeoPackages without GDAL's WASM thread crash. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python
1:12

GeoLibre v1.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings field data collection, image georeferencing, and a full Jupyter notebook experience right next to the map. What's new in v1.4.0 - Jupyter beside the map: Run notebooks alongside your map (JupyterLite in the browser, JupyterLab on the desktop). - Field data collection: Capture point observations in the field with custom, configurable forms. - Georeferencer: Pin a scanned map or image to the map using ground control points. - Spectral Index toolbox: Compute NDVI, NDWI, EVI, and more directly from your rasters. - Better internationalization: Correct rendering of right-to-left basemap labels (Arabic, Hebrew). - More reliable data handling: Render GeoPackages without GDAL's WASM thread crash. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python

Qiusheng Wu

29,386 görüntüleme • 1 ay önce

The future is here. 🚀 Introducing Nwiro | Your Unreal Engine AI Assistant, a new AI powered PCG environment creation tool by Leartes. nwiro.ai Turn a simple text prompt into fully generated Unreal Engine environments in minutes. Intelligent asset placement. Automated PCG workflows. Faster, smarter world building. From idea to immersive world, instantly. This is an Alpha release, and we are just getting started. Download Nwiro and get 3 free credits to start creating right away. Create beyond limits. Create with Nwiro. 🌍✨ Get Nwiro on Fab for free and give feedback to unlock more features! 🔗 🔗 Check out the Nwiro website #Nwiro #Leartes #UnrealEngine #PCG #GameDev #AI #WorldBuilding #Tool
1:36

The future is here. 🚀 Introducing Nwiro | Your Unreal Engine AI Assistant, a new AI powered PCG environment creation tool by Leartes. nwiro.ai Turn a simple text prompt into fully generated Unreal Engine environments in minutes. Intelligent asset placement. Automated PCG workflows. Faster, smarter world building. From idea to immersive world, instantly. This is an Alpha release, and we are just getting started. Download Nwiro and get 3 free credits to start creating right away. Create beyond limits. Create with Nwiro. 🌍✨ Get Nwiro on Fab for free and give feedback to unlock more features! 🔗 🔗 Check out the Nwiro website #Nwiro #Leartes #UnrealEngine #PCG #GameDev #AI #WorldBuilding #Tool

Leartes

68,791 görüntüleme • 4 ay önce

Introducing the New Silencio Map Explorer. Explore real-world noise levels across streets, hotels and restaurants. All powered by community-contributed data from over 1 million sensors. This isn't just a map; it's a window into community-powered data that allows you to: • Assess outdoor noise levels globally using Street, Hotel, and Restaurant data layers. • Identify potentially quieter areas for living or working. • Observe noise level trends within specific locations. We’re making decentralized data both accessible and actionable. And this is just the beginning. Coming soon: • AI-driven analysis for deeper environmental understanding. • Noise source identification capabilities. • Downloadable, detailed environmental reports for specific areas. This is what DePIN should feel like. No speculation. Just real-world tools powered by real people. Start exploring:
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Introducing the New Silencio Map Explorer. Explore real-world noise levels across streets, hotels and restaurants. All powered by community-contributed data from over 1 million sensors. This isn't just a map; it's a window into community-powered data that allows you to: • Assess outdoor noise levels globally using Street, Hotel, and Restaurant data layers. • Identify potentially quieter areas for living or working. • Observe noise level trends within specific locations. We’re making decentralized data both accessible and actionable. And this is just the beginning. Coming soon: • AI-driven analysis for deeper environmental understanding. • Noise source identification capabilities. • Downloadable, detailed environmental reports for specific areas. This is what DePIN should feel like. No speculation. Just real-world tools powered by real people. Start exploring:

Silencio | Voice Data for AI

15,221 görüntüleme • 1 yıl önce

If you can text, you can now edit videos. Introducing our new chat-based editor. Just type the edits you want to see and our AI does it for you. No learning curve or special skills required. This editor doesn’t only follow your direction, but has taste and opinions. Every graphic, zoom, or transition is applied in a way that quietly boosts engagement. To show you what this means, we pitted an editor against the prompt-box. Try it for yourself in Captions by Mirage
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If you can text, you can now edit videos. Introducing our new chat-based editor. Just type the edits you want to see and our AI does it for you. No learning curve or special skills required. This editor doesn’t only follow your direction, but has taste and opinions. Every graphic, zoom, or transition is applied in a way that quietly boosts engagement. To show you what this means, we pitted an editor against the prompt-box. Try it for yourself in Captions by Mirage

Gaurav Misra

59,995 görüntüleme • 10 ay önce

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 görüntüleme • 10 ay önce

GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography
0:38

GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

Qiusheng Wu

15,397 görüntüleme • 1 ay önce

We are unlocking something big 🔥 With Nexera Chain now live on mainnet, we’re bringing a completely new experience to the market, one that lowers the barrier to ZERO and opens the door to anyone wanting to enter tokenization. No blockchain knowledge. No dev skills. Just an idea and a simple AI prompt. From that prompt, you instantly get a fully functional landing page and a tokenized marketplace. All powered by Nexera Chain. This changes everything. It means Web2 creators, companies, and entrepreneurs can now launch tokenized products and markets without ever touching a line of code. This is how Nexera Chain will drive real adoption. This is how we onboard the next wave of TVL. This is how we turn curious users into real builders. We're not just enabling new use cases. We're creating a new standard. Nexera Chain is here. And it's about to get wild. $NXRA 🚀
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We are unlocking something big 🔥 With Nexera Chain now live on mainnet, we’re bringing a completely new experience to the market, one that lowers the barrier to ZERO and opens the door to anyone wanting to enter tokenization. No blockchain knowledge. No dev skills. Just an idea and a simple AI prompt. From that prompt, you instantly get a fully functional landing page and a tokenized marketplace. All powered by Nexera Chain. This changes everything. It means Web2 creators, companies, and entrepreneurs can now launch tokenized products and markets without ever touching a line of code. This is how Nexera Chain will drive real adoption. This is how we onboard the next wave of TVL. This is how we turn curious users into real builders. We're not just enabling new use cases. We're creating a new standard. Nexera Chain is here. And it's about to get wild. $NXRA 🚀

Nexera

32,105 görüntüleme • 1 yıl önce

GeoLibre v1.2.0 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 35+ pull requests of new capabilities. A few highlights: - Run SQL right in the browser. The SQL Workspace pairs DuckDB Spatial with a new in-browser PostGIS engine (PGlite), so you can query layers, local files, and remote URLs without a server. - A smarter attribute table. Add fields, run a field calculator, and explore your data with a built-in Charts panel (histogram, scatter, bar, line, and box plots). - More ways to add data. OpenStreetMap PBF extracts, Cloud-Optimized NetCDF/HDF via kerchunk, georeferenced video overlays, authenticated 3D Tiles, and a Layer builder for custom overlays. - Better visualization. Heatmap rendering, point clustering, and H3 hexagonal grids for spatial binning. - New analysis and routing. A Directions plugin, plus Spatial Join, Select by Value, and Select by Location vector tools. - Print and share. A print layout composer that exports your map to PNG or PDF. - Work faster. A command palette (Ctrl/Cmd + K), global keyboard shortcuts, and undo/redo for layer and style operations. - Built for everyone. New internationalization framework, an accessibility pass with automated axe checks, an installable offline-capable PWA web build, React error boundaries, and Playwright end-to-end tests. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre
1:25

GeoLibre v1.2.0 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 35+ pull requests of new capabilities. A few highlights: - Run SQL right in the browser. The SQL Workspace pairs DuckDB Spatial with a new in-browser PostGIS engine (PGlite), so you can query layers, local files, and remote URLs without a server. - A smarter attribute table. Add fields, run a field calculator, and explore your data with a built-in Charts panel (histogram, scatter, bar, line, and box plots). - More ways to add data. OpenStreetMap PBF extracts, Cloud-Optimized NetCDF/HDF via kerchunk, georeferenced video overlays, authenticated 3D Tiles, and a Layer builder for custom overlays. - Better visualization. Heatmap rendering, point clustering, and H3 hexagonal grids for spatial binning. - New analysis and routing. A Directions plugin, plus Spatial Join, Select by Value, and Select by Location vector tools. - Print and share. A print layout composer that exports your map to PNG or PDF. - Work faster. A command palette (Ctrl/Cmd + K), global keyboard shortcuts, and undo/redo for layer and style operations. - Built for everyone. New internationalization framework, an accessibility pass with automated axe checks, an installable offline-capable PWA web build, React error boundaries, and Playwright end-to-end tests. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre

Qiusheng Wu

39,890 görüntüleme • 1 ay önce

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):
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I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):

Meer | AI Tools & News

17,593 görüntüleme • 7 ay önce

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:
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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:

Vantor

16,231 görüntüleme • 5 ay önce