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i'm building a web browser for reverse engineers! * identify calls to common fingerprinting APIs * decode/decrypt known data collector payloads * override / hook things without leaving a trace * detect obfuscated scripts & deobfuscate + more

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131,926 görüntüleme • 10 ay önce •via X (Twitter)

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Sensitive data, including students' personal information, was processed by Google's Gemini through automation scripts prepared by quality assurance engineers at COEMPT Eduteck, CBSE's tech vendor. Ethical hacker Nisarga Adhikary (nisarga), who exposed #CBSE, calls it "scary" and "sad" that a third-party company allegedly sent such data to the U.S. for processing. "Data Privacy Laws are not respected, and they should get sued for doing this without student consent," Adhikary says in conversation with Maitri Porecha (Maitri Porecha) and John Xavier (John Xavier).
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Sensitive data, including students' personal information, was processed by Google's Gemini through automation scripts prepared by quality assurance engineers at COEMPT Eduteck, CBSE's tech vendor. Ethical hacker Nisarga Adhikary (nisarga), who exposed #CBSE, calls it "scary" and "sad" that a third-party company allegedly sent such data to the U.S. for processing. "Data Privacy Laws are not respected, and they should get sued for doing this without student consent," Adhikary says in conversation with Maitri Porecha (Maitri Porecha) and John Xavier (John Xavier).

The Hindu

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Dude... This is ridiculously useful 🤯 A site that's like a swiss army knife for devs & designers. Which contains a collection of small, browser-based tools across different categories that work without logins, tracking, or data collection. Nice to bookmark if you like fast, no-BS tools that just work :) Source 🔗: tools . rmv . fyi Follow me for more amazing AI, Coding & Web Dev insights 💎
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Dude... This is ridiculously useful 🤯 A site that's like a swiss army knife for devs & designers. Which contains a collection of small, browser-based tools across different categories that work without logins, tracking, or data collection. Nice to bookmark if you like fast, no-BS tools that just work :) Source 🔗: tools . rmv . fyi Follow me for more amazing AI, Coding & Web Dev insights 💎

Ram Maheshwari

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Just upgraded our Dutch customers with a new Uniclient experience. Uniclient is a major part of our L2 stack. It allows our engineers to build beautiful, pixel-perfect experiences across multiple platforms using a single codebase and a rich framework that automatically adjusts to locales, screen sizes, device types, and more. We used to need separate engineering teams to support our iOS, Android, mobile web, and desktop web experiences. No more. Uniclient makes our engineering team exponentially faster when building frontend experiences for onboarding, policy management, and claims. In the vid you can see Uniclient running in a desktop browser. It's our Dutch product displayed in English (great for expats).
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Just upgraded our Dutch customers with a new Uniclient experience. Uniclient is a major part of our L2 stack. It allows our engineers to build beautiful, pixel-perfect experiences across multiple platforms using a single codebase and a rich framework that automatically adjusts to locales, screen sizes, device types, and more. We used to need separate engineering teams to support our iOS, Android, mobile web, and desktop web experiences. No more. Uniclient makes our engineering team exponentially faster when building frontend experiences for onboarding, policy management, and claims. In the vid you can see Uniclient running in a desktop browser. It's our Dutch product displayed in English (great for expats).

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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!
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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!

DeepLearning.AI

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Introducing the quickest way to get data from anywhere on the internet, all without a single browser. We allow you to turn any website into an API in seconds, meaning you can get data faster and cheaper than ever before. Comment what sites you want to see Parse working on and I'll start pushing more demos soon! Super excited to be building at Founders Inc for the summer, join the waitlist at the link 😁
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Introducing the quickest way to get data from anywhere on the internet, all without a single browser. We allow you to turn any website into an API in seconds, meaning you can get data faster and cheaper than ever before. Comment what sites you want to see Parse working on and I'll start pushing more demos soon! Super excited to be building at Founders Inc for the summer, join the waitlist at the link 😁

Alex

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Announcing Chrome DevTools MCP! 🚀 Connect your AI coding agent to Chrome's powerful automation & debugging capabilities with ease. Key features: ✅ Reliable automation: It can programmatically handle clicks, form fills, dialogs, and page navigation with ease. ✅ Performance insights: Go beyond simple audits. Instruct your agent to record a performance trace and extract actionable insights to optimize your web apps. ✅ Advanced debugging: Empower your AI to analyze network requests, list console messages, take screenshots, and even evaluate scripts in the browser context. ✅ Browser emulation: Easily test different conditions by emulating CPU slowdowns, network throttling, or various screen sizes. Works well with modern web apps and believe this will unlock new workflows for automated testing, AI-driven debugging, and interactive web development. And there's much more to come!
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Announcing Chrome DevTools MCP! 🚀 Connect your AI coding agent to Chrome's powerful automation & debugging capabilities with ease. Key features: ✅ Reliable automation: It can programmatically handle clicks, form fills, dialogs, and page navigation with ease. ✅ Performance insights: Go beyond simple audits. Instruct your agent to record a performance trace and extract actionable insights to optimize your web apps. ✅ Advanced debugging: Empower your AI to analyze network requests, list console messages, take screenshots, and even evaluate scripts in the browser context. ✅ Browser emulation: Easily test different conditions by emulating CPU slowdowns, network throttling, or various screen sizes. Works well with modern web apps and believe this will unlock new workflows for automated testing, AI-driven debugging, and interactive web development. And there's much more to come!

Addy Osmani

268,896 görüntüleme • 10 ay önce

I just built an AI agent that’s 10x smarter than anything using basic search APIs. Here’s what nobody’s telling you about AI development right now. Most developers are stuck using limited search APIs. They’re missing social media data, forums, live news, and answer engines. Their AI is effectively blind to 90% of the public web. The result: Stale data. Weak responses. And endless engineering overhead just to stitch everything together. What changed everything for me was Bright Data’s Web Discovery platform. Instead of juggling multiple APIs and unreliable sources, I got real-time access to every public data source through one unified API. Google. Bing. Twitter. Reddit. Instagram. TikTok. ChatGPT. Perplexity. Even historical web archives going back years. Here’s why this actually matters in practice: • One API instead of 10+ fragmented integrations • Real-time, constantly refreshed public web data • Coverage across search engines, social platforms, forums, and answer engines • Consistent data structure that just works • Way less time fighting data plumbing, way more time building intelligence I used it to build a real-time pricing monitor that tracks competitor pricing, social sentiment, and trending topics at the same time. Something that would’ve taken weeks of integration work happened in a single afternoon. The real breakthrough isn’t just access. It’s consistency. Reliability. And freedom. If you’re building search agents, RAG pipelines, or any AI-driven product, you’re handicapping yourself without comprehensive web data. Check the link in the comments to try it yourself. They’re offering trial credits, and the documentation is actually solid. This is the difference between AI products that work and AI products that dominate. Check it out here:
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I just built an AI agent that’s 10x smarter than anything using basic search APIs. Here’s what nobody’s telling you about AI development right now. Most developers are stuck using limited search APIs. They’re missing social media data, forums, live news, and answer engines. Their AI is effectively blind to 90% of the public web. The result: Stale data. Weak responses. And endless engineering overhead just to stitch everything together. What changed everything for me was Bright Data’s Web Discovery platform. Instead of juggling multiple APIs and unreliable sources, I got real-time access to every public data source through one unified API. Google. Bing. Twitter. Reddit. Instagram. TikTok. ChatGPT. Perplexity. Even historical web archives going back years. Here’s why this actually matters in practice: • One API instead of 10+ fragmented integrations • Real-time, constantly refreshed public web data • Coverage across search engines, social platforms, forums, and answer engines • Consistent data structure that just works • Way less time fighting data plumbing, way more time building intelligence I used it to build a real-time pricing monitor that tracks competitor pricing, social sentiment, and trending topics at the same time. Something that would’ve taken weeks of integration work happened in a single afternoon. The real breakthrough isn’t just access. It’s consistency. Reliability. And freedom. If you’re building search agents, RAG pipelines, or any AI-driven product, you’re handicapping yourself without comprehensive web data. Check the link in the comments to try it yourself. They’re offering trial credits, and the documentation is actually solid. This is the difference between AI products that work and AI products that dominate. Check it out here:

Hasan Toor

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27:27

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,017 görüntüleme • 4 ay önce

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:
2:06

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,133 görüntüleme • 1 yıl önce

Engineers, creatives, designers - lend me your ears, and eyes, to check my🥇1st tech demo of 2025: A #WebAI Agent running entirely client side in the browser, that's capable of controlling a flight search webpage, to get the job done! I'm using Google's Gemma 2 (2B) model in #JavaScript via #WebGPU thanks to the MediaPipe Web LLM library, combined with some extra function calling logic to enable advanced user experiences. If you like what you see, give this post a share, as I'm just one guy trying to show the world the power of Web AI to do great things. Try it for yourself using the link below after watching the video, but do so on a modernish machine (I have verified it runs on my 6 year old Dell XPS with only an Intel integrated GPU) but use a modern browser like Chrome as it needs WebGPU to run. Want me to make a demo for your industry? Tell me what you need / want to see next! Let me know what you think, and if you have any questions, then drop them in the comments below! YouTube Video Link:
5:52

Engineers, creatives, designers - lend me your ears, and eyes, to check my🥇1st tech demo of 2025: A #WebAI Agent running entirely client side in the browser, that's capable of controlling a flight search webpage, to get the job done! I'm using Google's Gemma 2 (2B) model in #JavaScript via #WebGPU thanks to the MediaPipe Web LLM library, combined with some extra function calling logic to enable advanced user experiences. If you like what you see, give this post a share, as I'm just one guy trying to show the world the power of Web AI to do great things. Try it for yourself using the link below after watching the video, but do so on a modernish machine (I have verified it runs on my 6 year old Dell XPS with only an Intel integrated GPU) but use a modern browser like Chrome as it needs WebGPU to run. Want me to make a demo for your industry? Tell me what you need / want to see next! Let me know what you think, and if you have any questions, then drop them in the comments below! YouTube Video Link:

Jason Mayes

42,895 görüntüleme • 1 yıl önce

One of the interesting things about being on-chain is how easy it is for anyone to trace your activity. For casual users it may not matter much, but for serious traders or builders it quickly becomes a concern. Anyone can follow your wallet activity, trading patterns, and transaction history. As the space grows, tools that allow users to interact with DeFi without exposing everything will matter a lot more. So, in the next few months, I'm going to be educating on how Houdini Swap approaches that. Follow me & turn on notifications. It's about to be a long ride from here!
1:25

One of the interesting things about being on-chain is how easy it is for anyone to trace your activity. For casual users it may not matter much, but for serious traders or builders it quickly becomes a concern. Anyone can follow your wallet activity, trading patterns, and transaction history. As the space grows, tools that allow users to interact with DeFi without exposing everything will matter a lot more. So, in the next few months, I'm going to be educating on how Houdini Swap approaches that. Follow me & turn on notifications. It's about to be a long ride from here!

Sir Khaycee

16,450 görüntüleme • 4 ay önce

Here is why Nimble has 1.1M Claude plugin installs… I tested the Nimble Studio for a few minutes and immediately understood the appeal. Instead of manually scraping sites or fighting APIs, Nimble lets AI agents pull structured live data from platforms like TikTok, YouTube, Instagram, Reddit, and more. I tried pulling trending AI tool content and it turned messy social pages into a clean dataset almost instantly, complete with engagement metrics, creators, hashtags, and trends. The interesting part is how natural it feels. You’re basically giving AI agents the ability to interact with live web data in a usable format instead of static search results. That’s why the 1.1M installs matter. It solves a real problem for developers, researchers, marketers, and teams building AI workflows around real-time internet data.
0:27

Here is why Nimble has 1.1M Claude plugin installs… I tested the Nimble Studio for a few minutes and immediately understood the appeal. Instead of manually scraping sites or fighting APIs, Nimble lets AI agents pull structured live data from platforms like TikTok, YouTube, Instagram, Reddit, and more. I tried pulling trending AI tool content and it turned messy social pages into a clean dataset almost instantly, complete with engagement metrics, creators, hashtags, and trends. The interesting part is how natural it feels. You’re basically giving AI agents the ability to interact with live web data in a usable format instead of static search results. That’s why the 1.1M installs matter. It solves a real problem for developers, researchers, marketers, and teams building AI workflows around real-time internet data.

Kaitee

28,590 görüntüleme • 2 ay 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

29,659 görüntüleme • 1 ay önce

I think the fact is there are only going to be four or five of these companies that are able to raise the billions required to build the infrastructure to support these models. It's a global competition, and particularly against the Chinese right now because they're trying to beat us on AI, because the country with the best AI is gonna win all the wars. That's what's gonna happen. I think at the end of the day you want diversification in your portfolio, not just one stack. For me, I'm playing the picks and shovels. I'm building energy because you need energy for this stuff, and I'm building data centers. Now, there are different things, but right now we need more electrical power in this country beyond just data centers. We just need power because we got a booming economy.
2:33

I think the fact is there are only going to be four or five of these companies that are able to raise the billions required to build the infrastructure to support these models. It's a global competition, and particularly against the Chinese right now because they're trying to beat us on AI, because the country with the best AI is gonna win all the wars. That's what's gonna happen. I think at the end of the day you want diversification in your portfolio, not just one stack. For me, I'm playing the picks and shovels. I'm building energy because you need energy for this stuff, and I'm building data centers. Now, there are different things, but right now we need more electrical power in this country beyond just data centers. We just need power because we got a booming economy.

Kevin O'Leary aka Mr. Wonderful

13,143 görüntüleme • 2 ay 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

39,959 görüntüleme • 1 ay önce

By combining over 20 million km² of PlanetScope near-daily oceanic imagery, advanced artificial intelligence, and AIS shipping data, our partner SynMax Maritime has created Theia, a maritime domain awareness solution. This intelligence-driven approach helps detect dark vessels, monitor ship-to-ship transfers, and identify illicit maritime activities, like illegal fishing. "It's Planet’s broad area monitoring that's key for Theia to do what it does,” SynMax’s CEO Brendan Moore. “We couldn’t be picking up tens of thousands of vessels a day over millions of square kilometers without that breadth that Planet provides.” Read more about our partnership: Watch to learn about this solution:
2:13

By combining over 20 million km² of PlanetScope near-daily oceanic imagery, advanced artificial intelligence, and AIS shipping data, our partner SynMax Maritime has created Theia, a maritime domain awareness solution. This intelligence-driven approach helps detect dark vessels, monitor ship-to-ship transfers, and identify illicit maritime activities, like illegal fishing. "It's Planet’s broad area monitoring that's key for Theia to do what it does,” SynMax’s CEO Brendan Moore. “We couldn’t be picking up tens of thousands of vessels a day over millions of square kilometers without that breadth that Planet provides.” Read more about our partnership: Watch to learn about this solution:

Planet

14,135 görüntüleme • 1 yıl önce

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.
0:31

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,428 görüntüleme • 11 ay önce

GeoLibre v2.4.0 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 is about reaching more data and moving through it: new browsers for STAC, NASA Earthdata, and Hugging Face, a flight simulator, a Time Slider that animates tiled data and data cubes, and a live API for pages that embed the map. What's new in v2.4.0 - STAC catalogs browser: discover public catalogs from STAC Index, connect to static catalogs and STAC APIs, search a collection's items, and add any visualizable asset to the map. No more copying COG URLs by hand. - Earthdata GIS browser: search NASA's Earthdata GIS portal and add its imagery, map, and feature services, and its published web maps, as first-class layers. - Hugging Face on the map: search the Hub, walk a dataset repo's folders, and add its vector and raster files. You can create a dataset repo and upload layers to it too. - Flight Simulator: steer a continuous free-flight camera over terrain and 3D layers from the keyboard, instead of declaring a destination and watching a scripted flight. - Time Slider for tiled data and data cubes: bind vector tiles, PMTiles, and MBTiles to the timeline and animate them over their full extent, and let a Zarr store's own time dimension join the shared timeline. - Zarr gets a real Add Data path: open a remote store or a folder on disk, and pick variables and dimensions by their actual coordinate values rather than raw indices. - Live embed API: a versioned postMessage protocol so a host page can load a project, move the camera, highlight features, and open a tool at runtime, with events coming back out. - OGC API - Features: add collections as vector layers from whatever URL you have in hand, whether that is a landing page, /collections, or a full items URL. - H3 everywhere: a new hexagonal grid plugin that renders and inspects H3 cells and exports them as GeoJSON or CSV, plus typing an H3 index into the search box to fly straight to that cell. - Jupyter from outside the app: attach VS Code's Jupyter extension or jupyter console to the desktop app's server and your notebook cells drive the map. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #GeoLibre
1:04

GeoLibre v2.4.0 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 is about reaching more data and moving through it: new browsers for STAC, NASA Earthdata, and Hugging Face, a flight simulator, a Time Slider that animates tiled data and data cubes, and a live API for pages that embed the map. What's new in v2.4.0 - STAC catalogs browser: discover public catalogs from STAC Index, connect to static catalogs and STAC APIs, search a collection's items, and add any visualizable asset to the map. No more copying COG URLs by hand. - Earthdata GIS browser: search NASA's Earthdata GIS portal and add its imagery, map, and feature services, and its published web maps, as first-class layers. - Hugging Face on the map: search the Hub, walk a dataset repo's folders, and add its vector and raster files. You can create a dataset repo and upload layers to it too. - Flight Simulator: steer a continuous free-flight camera over terrain and 3D layers from the keyboard, instead of declaring a destination and watching a scripted flight. - Time Slider for tiled data and data cubes: bind vector tiles, PMTiles, and MBTiles to the timeline and animate them over their full extent, and let a Zarr store's own time dimension join the shared timeline. - Zarr gets a real Add Data path: open a remote store or a folder on disk, and pick variables and dimensions by their actual coordinate values rather than raw indices. - Live embed API: a versioned postMessage protocol so a host page can load a project, move the camera, highlight features, and open a tool at runtime, with events coming back out. - OGC API - Features: add collections as vector layers from whatever URL you have in hand, whether that is a landing page, /collections, or a full items URL. - H3 everywhere: a new hexagonal grid plugin that renders and inspects H3 cells and exports them as GeoJSON or CSV, plus typing an H3 index into the search box to fly straight to that cell. - Jupyter from outside the app: attach VS Code's Jupyter extension or jupyter console to the desktop app's server and your notebook cells drive the map. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #GeoLibre

Qiusheng Wu

13,569 görüntüleme • 6 gün önce

Chills! 🥶 Michael Buffer’s introduction for JAY-Z at MSG🐐 “There's never been a nigga this good for this long, This hood or this pop, this hot or this strong, With so many different flows, this one's for this song, The next one I switch up, this one will get bit up, These fucks, too lazy to make up shit, they crazy, They don't, paint pictures, they just, trace me, You know what? Soon they forget where they plucked, Their whole style from then try to reverse the outcome, I'm like, "TAA!" I'm not a biter, I'm a writer for myself and others, I say a BIG verse, I'm only biggin' up my brother, Biggin' up my borough, I'm big enough to do it, I'm that thorough, plus I know my own flow is foolish So them rings and things you sing about, bring 'em out, It's hard to yell when the barrel's in your mouth, I'm in, new sneakers, dual-seaters, few divas, What more can I tell you? Let me spell it for you: W-I, double-L, I-E, Nobody truer than H-O-V, And I'm back for more, New York's ambassador, Prime Minister, back to finish my business up”
2:01

Chills! 🥶 Michael Buffer’s introduction for JAY-Z at MSG🐐 “There's never been a nigga this good for this long, This hood or this pop, this hot or this strong, With so many different flows, this one's for this song, The next one I switch up, this one will get bit up, These fucks, too lazy to make up shit, they crazy, They don't, paint pictures, they just, trace me, You know what? Soon they forget where they plucked, Their whole style from then try to reverse the outcome, I'm like, "TAA!" I'm not a biter, I'm a writer for myself and others, I say a BIG verse, I'm only biggin' up my brother, Biggin' up my borough, I'm big enough to do it, I'm that thorough, plus I know my own flow is foolish So them rings and things you sing about, bring 'em out, It's hard to yell when the barrel's in your mouth, I'm in, new sneakers, dual-seaters, few divas, What more can I tell you? Let me spell it for you: W-I, double-L, I-E, Nobody truer than H-O-V, And I'm back for more, New York's ambassador, Prime Minister, back to finish my business up”

💎🍾

30,029 görüntüleme • 9 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