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❌ Conditional rendering loses form state in React. ✅ React 19's Activity API preserves it. e.g. Switch between steps → your data stays. Effects cleanup automatically → no wasted resources.

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George Moller

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74,081 görüntüleme • 6 ay önce •via X (Twitter)

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🎉Super excited to introduce React Internals Explorer which visualize how React works internally automatically! No more guessing on how it works! Inspect it! In the video I demoed how we can easily see how React@19 and 18 handle siblings differently when a promise is thrown. 😳 So far it only supports Fiber Tree, I'll add more capabilities to it. Looking for your feedback or bug reports!
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🎉Super excited to introduce React Internals Explorer which visualize how React works internally automatically! No more guessing on how it works! Inspect it! In the video I demoed how we can easily see how React@19 and 18 handle siblings differently when a promise is thrown. 😳 So far it only supports Fiber Tree, I'll add more capabilities to it. Looking for your feedback or bug reports!

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For our new product, we built a chat in React Native that feels like iMessage Here's how we did it: ① Fabric Swift View ② UICollectionView ③ SwiftUI cells via UIHostingConfiguration React is responsible for the state - mutations, fetching, infinite loading - all via React Query. The native Fabric view is purely for rendering. React decides what to show, native decides how to show it The bridge between three frameworks was tricky but worth it. UICollectionView animations come for free and scroll performance is native-smooth No blank spaces and no FPS drops - all powered by the New Architecture Who wants to see the code?

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📈 Meet Your New AI-Powered Prediction Market Agent - PrediMarket Agent! Introducing our all-in-one Prediction Market Agent—designed to make trading smarter, faster, and more profitable for EVERYONE: ✅ Smart Market Recommendations: AI auto-suggests trending markets based on real-time trends + your preferences. Skip the filtering, grab opportunities instantly. ✅ 360° Market Analysis: Dives deep into 3 core pillars—context, win rate, and profit optimization (because high win rates ≠ high gains!). AI balances risk/reward for smarter strategies. ✅ One-Click Smart Trading: Connect your API Key, and let the Agent handle orders automatically. Fewer steps, faster executions. ✅ Dual Interaction Modes: Trade via natural language prompts OR clickable cards—fits your workflow, whatever it is. No more guesswork. No more tedious steps. Just data-driven decisions and bigger wins. Ready to level up your prediction market game? Coming soon!🚀 #ChainOperaAI #AIPrediction #SmartTrading #MarketAnalysis #Web3Tools #Polymarket
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📈 Meet Your New AI-Powered Prediction Market Agent - PrediMarket Agent! Introducing our all-in-one Prediction Market Agent—designed to make trading smarter, faster, and more profitable for EVERYONE: ✅ Smart Market Recommendations: AI auto-suggests trending markets based on real-time trends + your preferences. Skip the filtering, grab opportunities instantly. ✅ 360° Market Analysis: Dives deep into 3 core pillars—context, win rate, and profit optimization (because high win rates ≠ high gains!). AI balances risk/reward for smarter strategies. ✅ One-Click Smart Trading: Connect your API Key, and let the Agent handle orders automatically. Fewer steps, faster executions. ✅ Dual Interaction Modes: Trade via natural language prompts OR clickable cards—fits your workflow, whatever it is. No more guesswork. No more tedious steps. Just data-driven decisions and bigger wins. Ready to level up your prediction market game? Coming soon!🚀 #ChainOperaAI #AIPrediction #SmartTrading #MarketAnalysis #Web3Tools #Polymarket

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Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the form’s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKit’s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasn’t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️
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Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the form’s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKit’s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasn’t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️

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How should you search, filter, and paginate data with Next.js? This demo has 50,000 books in a Postgres database. • Page Load: When the page loads, we see the React Suspense fallback. This loading skeleton is displayed until the first page of books is retrieved from the database. • Searching: The search input has a 200ms debounce. After 200ms of inactivity, the form submits, updating the URL state with `?q={search}`. The Server Component reads `searchParams` and queries the database. On form submission, a React transition starts, allowing us to read the pending status with `useFormStatus` to display an inline loading state. • State Preservation: Navigating to an individual book page retains the search input state. Reloading the page or sharing the link preserves the search results. • Client-side Filtering: Filtering authors in the left sidebar is done client-side. Authors are fetched by a Server Component and passed as props to the sidebar. Changing the input value updates React state and re-renders the sidebar. • Optimistic Updates: The sidebar’s selected authors are optimistically updated with `useOptimistic`. Checkbox selections update instantly without waiting for the URL to change. • State Preservation: Navigating to an individual book page retains the sidebar filter input and selected author state across navigations, giving it an app-like feel. • Pagination: Navigating between pages updates the URL state, triggering the Server Component to query the database for the specific page of books. We also fetch the total book count to show the total number of pages. This demo isn't perfect yet (still working on it) but it's been a fun playground for some of these patterns. You can imagine a similar experience for thousands of movies, cars, products, or any other very large dataset. Demo → Code →

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“Never did we dreamt”? 😳 Mr. President, with all due respect — Chicago State University did not train you for this level of pedestrian grammar. When you say “Never did we dreamt”, that’s like saying “Did you went there?”❌ Wrong. ✅ It should be “Never did we dream” — because “did” already handles the past tense. Grammar 101: After “did,” your main verb stays in its base form. I am just trying to help you make your speeches as ‘authentic’ as your diploma. 🫢

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Following ☄︎ work, I'm experimenting with some react three fiber with #threejs webGPU. We no longer need an EffectComposer to use post-processing effects in #r3f. Now, everything is done by composing TSL nodes. I created a small component that helps in rendering post-processing effects. All you need to do is import the effects you want and add them to the final composition. I will share the link to the demo and source code on Monday 😍. Another thing I want to highlight is how amazing the Screen Space Reflection effect is. It generates reflections not only on the ground, as we had before in r3f in webgl. It is able to calculate the reflections between the meshes. Look how beautiful the reflection of the lightsaber on Darth Vader's mask is. This is not something that is easy to calculate and this is exactly where WebGPU in threejs shines! Congratulations to everyone on the threejs team. I am increasingly impressed with WebGPU and TSL.

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HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

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I am very excited to release GeoLibre v1.0 🌍 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. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre
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I am very excited to release GeoLibre v1.0 🌍 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. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre

Qiusheng Wu

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Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
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Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

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PREDICTSCREENER - OPINION BUILDERS PROGRAM PARTICIPANT 🔥 Your feedback and reviews are very important 🙏 All modules are running 24/7 in full LIVE mode 🖥️ All Markets Data List of all available markets with Chance and 1h/6h/24h Deltas updating EVERY 5 SECONDS. Sorting options and a keyword search form included. 📈 Trending The "Hottest" Top 5 markets by number of trades over the last 1h/24h. Updates every 5 minutes. 🐋 Whales Stream FULL REAL-TIME trade feed with filtering by amount. Storage of historical data for the past days and weeks. 🌊 Whale Activity Total number of trades with filtering by amount over the last 1h/24h. Updates every 5 minutes. Stack for maximum performance: React 19, Zustand, Tailwind on the frontend + Node.js, Socket io, PostgreSQL and Redis on the backend 🚀 The community keeps building 💪

AVZ

14,840 görüntüleme • 5 ay önce

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.
8:08

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.

Julian Goldie SEO

25,880 görüntüleme • 4 ay önce

Leonardo’s Omni Editing is here — powered by FLUX.1 Kontext and GPT-Image-1 You can now edit any image right inside the viewer — with a simple prompt. No switching tools. No breaking flow. This launch is powered by two advanced Omni models: ✅ GPT-Image-1 — for multi-reference creativity and context-aware generation ✅ FLUX.1 Kontext — developed by Black Forest Labs, with Leonardo as an official launch partner. Built for fast, controlled edits that preserve key details. What’s new: • Inline prompt bar built into every generated image • Make fast, precise edits: lighting, objects, layout, even in-image text • Switch between models based on your creative needs • Stay in one workflow — no extra steps, no restarts Try it now 🔥
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Leonardo’s Omni Editing is here — powered by FLUX.1 Kontext and GPT-Image-1 You can now edit any image right inside the viewer — with a simple prompt. No switching tools. No breaking flow. This launch is powered by two advanced Omni models: ✅ GPT-Image-1 — for multi-reference creativity and context-aware generation ✅ FLUX.1 Kontext — developed by Black Forest Labs, with Leonardo as an official launch partner. Built for fast, controlled edits that preserve key details. What’s new: • Inline prompt bar built into every generated image • Make fast, precise edits: lighting, objects, layout, even in-image text • Switch between models based on your creative needs • Stay in one workflow — no extra steps, no restarts Try it now 🔥

Leonardo.Ai

33,908 görüntüleme • 1 yıl önce

💥Simple guide on Node Delegator Program & 50 WL 💰Funding - $9M 💵$35 - $175 🤔What is Node Delegator Program? ✅U can buy node licenses & delegate it to supported Node Infra project 🤔How is it different ⚠️Other licenses u need to buy & run Node in VPS or your PC ✅In Mira, u just buy & delegate the license to earn rewards ❌No node setup ❌No vps cost ❌No maintenance, updates etc ✅Just buy & delegate 🤔What's the price ✅U can contribute anywhere between $35 - $175 👇Steps to join 1️⃣Wallet Whitelist 🔗 ✅U need WL code to join 🎟️Exclusive code for first 50 Members - CTELU ✅Paste ur address & WL ⚠️This code works only for first 50 ⚠️This is not Referal code, I don't get any Referal earnings ✅U can join project Discord for more codes 2️⃣Buying the license ✅This week project might drop a link for licenses purchase ✅Only whitelisted address can join this ⚠️Exact date & time not announced, so be active to grab those licenses 3️⃣Other details ✅One license per wallet ✅Only limited slots available ✅I am expecting around 3000-5000 slots in first phase ✅There might be few more phases, so if u missed this one u might get chance in next one 😁More details r covered in my yesterday's YouTube video, links in bio 📸Post your screenshot if u were one of those first 50 Members to grab the WL 💙Like 🔁RT
0:22

💥Simple guide on Node Delegator Program & 50 WL 💰Funding - $9M 💵$35 - $175 🤔What is Node Delegator Program? ✅U can buy node licenses & delegate it to supported Node Infra project 🤔How is it different ⚠️Other licenses u need to buy & run Node in VPS or your PC ✅In Mira, u just buy & delegate the license to earn rewards ❌No node setup ❌No vps cost ❌No maintenance, updates etc ✅Just buy & delegate 🤔What's the price ✅U can contribute anywhere between $35 - $175 👇Steps to join 1️⃣Wallet Whitelist 🔗 ✅U need WL code to join 🎟️Exclusive code for first 50 Members - CTELU ✅Paste ur address & WL ⚠️This code works only for first 50 ⚠️This is not Referal code, I don't get any Referal earnings ✅U can join project Discord for more codes 2️⃣Buying the license ✅This week project might drop a link for licenses purchase ✅Only whitelisted address can join this ⚠️Exact date & time not announced, so be active to grab those licenses 3️⃣Other details ✅One license per wallet ✅Only limited slots available ✅I am expecting around 3000-5000 slots in first phase ✅There might be few more phases, so if u missed this one u might get chance in next one 😁More details r covered in my yesterday's YouTube video, links in bio 📸Post your screenshot if u were one of those first 50 Members to grab the WL 💙Like 🔁RT

CryptoTelugu

95,853 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

39,608 görüntüleme • 1 ay önce

🌟Simple guide to DAC | Quantum Chain L1 Testnet 🪂 💰Cost: $0 💵Rewards: Confirmed DAC is a quantum-proof L1 chain built for RWA, DeFi, AI, and secure data validation Steps: 🔗 ✅ Connect MetaMask wallet ✅ Add DAC network to wallet ✅ Claim Gold early badge ( 4 days left ) ✅ Click on Faucet ✅ Claim DACC faucet ✅ You can claim faucet once every 8 hrs ✅ It might take time to receive tokens in your wallet ✅ Click Home ✅ Click View all tasks ✅ Click Social and complete tasks ✅ It may take an hour to receive confirmation mail ✅ ✅ Join Discord ✅ Verify on social tasks page ✅ Complete all the remaining tasks ✅ It will automatically verify after a few minutes ✅ Click on My Activity ✅ Click On-chain ✅ Complete on-chain tasks ✅ Completing these tasks requires DACC tokens ✅ Complete tasks after you get DACC tokens in your wallet ✅ Claim Badges ✅ Claim all the badges by completing tasks ✅ Click on Quantum Crate ✅ You can open crates 5/day ✅ Each crate costs 150 QE ✅ Crates contain DACC, QE, Multipliers ✅ Click on QE Pool ✅ You can earn 1000 QE for burning 1 DACC ✅ Or you can stake DACC and earn QE rewards ✅ Done Keep stacking QE by completing daily check-ins, tasks, and claim badges whenever they are available That's all for now and will update if new tasks or updates appear Follow and turn on notifications for updates 🤍Like 🔁Retweet
3:28

🌟Simple guide to DAC | Quantum Chain L1 Testnet 🪂 💰Cost: $0 💵Rewards: Confirmed DAC is a quantum-proof L1 chain built for RWA, DeFi, AI, and secure data validation Steps: 🔗 ✅ Connect MetaMask wallet ✅ Add DAC network to wallet ✅ Claim Gold early badge ( 4 days left ) ✅ Click on Faucet ✅ Claim DACC faucet ✅ You can claim faucet once every 8 hrs ✅ It might take time to receive tokens in your wallet ✅ Click Home ✅ Click View all tasks ✅ Click Social and complete tasks ✅ It may take an hour to receive confirmation mail ✅ ✅ Join Discord ✅ Verify on social tasks page ✅ Complete all the remaining tasks ✅ It will automatically verify after a few minutes ✅ Click on My Activity ✅ Click On-chain ✅ Complete on-chain tasks ✅ Completing these tasks requires DACC tokens ✅ Complete tasks after you get DACC tokens in your wallet ✅ Claim Badges ✅ Claim all the badges by completing tasks ✅ Click on Quantum Crate ✅ You can open crates 5/day ✅ Each crate costs 150 QE ✅ Crates contain DACC, QE, Multipliers ✅ Click on QE Pool ✅ You can earn 1000 QE for burning 1 DACC ✅ Or you can stake DACC and earn QE rewards ✅ Done Keep stacking QE by completing daily check-ins, tasks, and claim badges whenever they are available That's all for now and will update if new tasks or updates appear Follow and turn on notifications for updates 🤍Like 🔁Retweet

CCT

33,435 görüntüleme • 2 ay önce

Hollywood has a dirty secret. That perfectly clean green screen shot in your favorite Marvel movie? A human being sat in a dark room for 6 hours fixing it frame by frame. The AI keyer got the body. A person painted every strand of hair. By hand. At 2 AM. For 400 frames. The software costs $5,000 a year. And it still cannot key hair. Nuke: $4,988/year. Cannot key hair in motion blur. After Effects: $264/year. Cannot key transparent glass. Boris FX: $1,865. Cannot key fine edges without haloing. The industry's solution for 30 years has been the same: pay for expensive software, then pay a human to fix what the software couldn't. The YouTubers behind Corridor Crew looked at this and asked a different question. What if the AI didn't try to remove the green? What if it figured out what color was actually there before the green contaminated it? They trained a neural network on synthetic 3D data. Not scraped footage. Not stolen clips. Perfectly rendered scenes where every pixel's true color was already known. Hair strands. Motion blur. Transparent glass. All simulated with known ground truth. Then they fed it real green screen footage. It worked. They called it CorridorKey. Then they open sourced it. → Feed it raw green screen footage → AI reconstructs the true foreground color for every pixel → Hair stays perfect. Every strand. → Motion blur stays intact. Every frame. → Transparent glass stays transparent. → 16-bit and 32-bit EXR output. Nuke-ready. Resolve-ready. → Handles 4K natively → Runs on consumer GPUs. 6 GB VRAM minimum. → Runs on Apple Silicon via MLX → Auto-detects green or blue screen → Removes tracking markers automatically → DaVinci Resolve plugin live → Standalone GUI for non-technical users → One-click installer. No Python setup. Here's the wildest part: Within 2.5 months: 13,000+ GitHub stars. 787 forks. Active Discord. Community built a cloud render farm so you can process footage without owning a GPU. DaVinci Resolve plugin shipped. Nuke and After Effects plugins in development. VFX freelancers are already offering CorridorKey-powered services to production companies. One person. One GPU. Hollywood-quality keys. A business built on free software. Nuke: $4,988/year. Still needs manual cleanup. After Effects: $264/year. Still needs manual cleanup. Rotoscope artist: $50 to $150/hour. For the cleanup. CorridorKey: $0. No cleanup needed. One honest flag: the license is CC BY-NC-SA 4.0. Free forever for personal projects, students, indie films, and learning. Commercial use requires permission from Corridor Digital. They did not pretend it was MIT. A problem that plagued Hollywood for 30 years. Solved by YouTubers. Open sourced for free. 13,000+ stars. 787 forks. CC BY-NC-SA 4.0. Your footage. Your keys. No rotoscoping.
1:14

Hollywood has a dirty secret. That perfectly clean green screen shot in your favorite Marvel movie? A human being sat in a dark room for 6 hours fixing it frame by frame. The AI keyer got the body. A person painted every strand of hair. By hand. At 2 AM. For 400 frames. The software costs $5,000 a year. And it still cannot key hair. Nuke: $4,988/year. Cannot key hair in motion blur. After Effects: $264/year. Cannot key transparent glass. Boris FX: $1,865. Cannot key fine edges without haloing. The industry's solution for 30 years has been the same: pay for expensive software, then pay a human to fix what the software couldn't. The YouTubers behind Corridor Crew looked at this and asked a different question. What if the AI didn't try to remove the green? What if it figured out what color was actually there before the green contaminated it? They trained a neural network on synthetic 3D data. Not scraped footage. Not stolen clips. Perfectly rendered scenes where every pixel's true color was already known. Hair strands. Motion blur. Transparent glass. All simulated with known ground truth. Then they fed it real green screen footage. It worked. They called it CorridorKey. Then they open sourced it. → Feed it raw green screen footage → AI reconstructs the true foreground color for every pixel → Hair stays perfect. Every strand. → Motion blur stays intact. Every frame. → Transparent glass stays transparent. → 16-bit and 32-bit EXR output. Nuke-ready. Resolve-ready. → Handles 4K natively → Runs on consumer GPUs. 6 GB VRAM minimum. → Runs on Apple Silicon via MLX → Auto-detects green or blue screen → Removes tracking markers automatically → DaVinci Resolve plugin live → Standalone GUI for non-technical users → One-click installer. No Python setup. Here's the wildest part: Within 2.5 months: 13,000+ GitHub stars. 787 forks. Active Discord. Community built a cloud render farm so you can process footage without owning a GPU. DaVinci Resolve plugin shipped. Nuke and After Effects plugins in development. VFX freelancers are already offering CorridorKey-powered services to production companies. One person. One GPU. Hollywood-quality keys. A business built on free software. Nuke: $4,988/year. Still needs manual cleanup. After Effects: $264/year. Still needs manual cleanup. Rotoscope artist: $50 to $150/hour. For the cleanup. CorridorKey: $0. No cleanup needed. One honest flag: the license is CC BY-NC-SA 4.0. Free forever for personal projects, students, indie films, and learning. Commercial use requires permission from Corridor Digital. They did not pretend it was MIT. A problem that plagued Hollywood for 30 years. Solved by YouTubers. Open sourced for free. 13,000+ stars. 787 forks. CC BY-NC-SA 4.0. Your footage. Your keys. No rotoscoping.

Nav Toor

295,185 görüntüleme • 2 ay önce

New 2026 lawsuit shows companies, including Fortune 500 companies are using an AI software that automatically filters out job applicants based on personal data The AI takes your location info, social media posts, even web searches and automatically rejects your job application (INSANE) “If you've been applying to any corporate jobs lately, the reason you got rejected or never even received a response is far crazier than you ever could have thought. A lawsuit just exposed Eightfold AI, the company Microsoft and many other giants use to score your application. And in that lawsuit, they're saying that Eightfold AI is not doing what we all thought, which was just kind of processing your application and giving you a little score. They're actually taking all the data they can find about you; your location history, your web searches, your social profiles, files, combining all of it and giving you a score, 0 out of 5. And if you have a low one, they're not ever gonna look at you. So yeah, now we have a social credit score for applications” Here’s more information: It’s a real class-action lawsuit filed in 2026 against Eightfold AI. They are a major AI-powered hiring platform used by companies like Microsoft, PayPal, Morgan Stanley, Starbucks, Chevron, Bayer and more Secret scoring system works like this Eightfold’s tools allegedly generate a “Match Score” or “likelihood of success” score from 0 to 5 for applicants Low-scoring candidates are often automatically filtered out automatically before any human recruiter sees their application The AI supposedly uses extensive data collections and aggregates far more than just your resume and over letter. This includes: - Social media profiles (e.g., LinkedIn) - Location and history data - Internet activity including web searches - Other third-party tracking data, the suit mentions vast datasets with 1.5+ billion data points There is no transparency or recourse Applicants allegedly aren’t notified, given a chance to review/correct the data, or informed of adverse actions The plaintiffs argue violates the Fair Credit Reporting Act (FCRA) and California’s Investigative Consumer Reporting Agencies Act This should definitely be banned
1:54

New 2026 lawsuit shows companies, including Fortune 500 companies are using an AI software that automatically filters out job applicants based on personal data The AI takes your location info, social media posts, even web searches and automatically rejects your job application (INSANE) “If you've been applying to any corporate jobs lately, the reason you got rejected or never even received a response is far crazier than you ever could have thought. A lawsuit just exposed Eightfold AI, the company Microsoft and many other giants use to score your application. And in that lawsuit, they're saying that Eightfold AI is not doing what we all thought, which was just kind of processing your application and giving you a little score. They're actually taking all the data they can find about you; your location history, your web searches, your social profiles, files, combining all of it and giving you a score, 0 out of 5. And if you have a low one, they're not ever gonna look at you. So yeah, now we have a social credit score for applications” Here’s more information: It’s a real class-action lawsuit filed in 2026 against Eightfold AI. They are a major AI-powered hiring platform used by companies like Microsoft, PayPal, Morgan Stanley, Starbucks, Chevron, Bayer and more Secret scoring system works like this Eightfold’s tools allegedly generate a “Match Score” or “likelihood of success” score from 0 to 5 for applicants Low-scoring candidates are often automatically filtered out automatically before any human recruiter sees their application The AI supposedly uses extensive data collections and aggregates far more than just your resume and over letter. This includes: - Social media profiles (e.g., LinkedIn) - Location and history data - Internet activity including web searches - Other third-party tracking data, the suit mentions vast datasets with 1.5+ billion data points There is no transparency or recourse Applicants allegedly aren’t notified, given a chance to review/correct the data, or informed of adverse actions The plaintiffs argue violates the Fair Credit Reporting Act (FCRA) and California’s Investigative Consumer Reporting Agencies Act This should definitely be banned

Wall Street Apes

214,547 görüntüleme • 6 gün önce

GeoLibre v1.1 is out! 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 builds on the v1.0 foundation with features that make day-to-day mapping smoother: - Attribute table management — rename, delete, hide/show, and reorder fields right in the table, with the layout saved to your project. - Data-driven symbology for Add Vector Layer — single, categorized, graduated, and expression-based styling now applies to maplibre-gl-vector layers. - Atmosphere Effects plugin — a deep-space backdrop, parallax starfield, comets, and a glowing globe halo at low zoom (toggle it from the Controls menu). - Layer panel upgrades — rename layers, open the attribute table, and export straight from the layer actions menu. - Auto Refresh extended to Add Vector Layer URL layers. - In-browser GeoPandas via Pyodide — run the vector tools with no Python sidecar, same results. - Plugin API — external plugins can now render on the host's shared instance via app.getDeckGL(). Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre
0:55

GeoLibre v1.1 is out! 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 builds on the v1.0 foundation with features that make day-to-day mapping smoother: - Attribute table management — rename, delete, hide/show, and reorder fields right in the table, with the layout saved to your project. - Data-driven symbology for Add Vector Layer — single, categorized, graduated, and expression-based styling now applies to maplibre-gl-vector layers. - Atmosphere Effects plugin — a deep-space backdrop, parallax starfield, comets, and a glowing globe halo at low zoom (toggle it from the Controls menu). - Layer panel upgrades — rename layers, open the attribute table, and export straight from the layer actions menu. - Auto Refresh extended to Add Vector Layer URL layers. - In-browser GeoPandas via Pyodide — run the vector tools with no Python sidecar, same results. - Plugin API — external plugins can now render on the host's shared instance via app.getDeckGL(). Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre

Qiusheng Wu

20,691 görüntüleme • 1 ay önce

OpenClaw has 186K GitHub stars and 1.5M compromised API keys. I needed a secure alternative. So, I built it with n8n and Claude Opus 4.6. It can already: - Reply to your Telegram messages - Access selected folders from your laptop - Access Gmail, Drive, Notion, Linear, etc. - Install new local tools in a sandbox - Run autonomously for hours - Create multiple subagents - Learn from experience - Wake up regularly But, unlike OpenClaw, it: - Can't access your API keys - Can't modify its environment - Can't access folders you haven't shared - Can't access tools you haven't approved - Must get your confirmation, e.g., when sending emails These aren’t prompt instructions. They’re hard architectural boundaries — Docker isolation, mounted folder permissions, n8n’s tool approval system. Key components: ✅ The VPS on Hostinger hosts n8n and a sandbox container. Agents can also connect to my laptop's sandbox via a Claudeflare tunnel + Desktop Commander MCP. ✅ The Manager agent is the brain. It plans, decides, delegates, and talks to the user. It never touches files. It never runs scripts. It works entirely from executor summaries. ✅ The Executor agents are the hands. Each receives a task (what to do + why it matters), decides how to execute it, and reports back. They can install new tools and execute code only in their dedicated sandboxes. ✅ Data Tables in n8n store both memories and sessions — no external database, no vector store, no infrastructure. Just rows in a table. Turns out, that's enough. Two memory types: - Manager memory: user preferences, facts, corrections, relationship, skills, context - Executor memory: what tools are installed, what’s broken, workarounds ✅ Sessions are short-term state for multi-step tasks. Original request, plan, assumptions, and what happened so far. When the Manager loops with fresh context, the session is all it gets. That's a Ralph Wiggum loop. I've been using it for 5 days. And already can't imagine not having it on my phone. What's next: - Heartbeat via Cron (a scheduled prompt) - Civic Nexus governance + MCPs - Supermemory integration - WhatsApp as an additional surface - Hardening The architecture supports all of it. OpenClaw proved people want personal AI agents. It also proved that 'just trust the prompt' isn't a security model. Docker isolation, mounted folder permissions, tool approval — none of this is new technology. It's just discipline. You can easily do this even with n8n — no coding required. --- Want to try it or read more? More, what I learned, and a setup guide: productcompass[.]pm
0:58

OpenClaw has 186K GitHub stars and 1.5M compromised API keys. I needed a secure alternative. So, I built it with n8n and Claude Opus 4.6. It can already: - Reply to your Telegram messages - Access selected folders from your laptop - Access Gmail, Drive, Notion, Linear, etc. - Install new local tools in a sandbox - Run autonomously for hours - Create multiple subagents - Learn from experience - Wake up regularly But, unlike OpenClaw, it: - Can't access your API keys - Can't modify its environment - Can't access folders you haven't shared - Can't access tools you haven't approved - Must get your confirmation, e.g., when sending emails These aren’t prompt instructions. They’re hard architectural boundaries — Docker isolation, mounted folder permissions, n8n’s tool approval system. Key components: ✅ The VPS on Hostinger hosts n8n and a sandbox container. Agents can also connect to my laptop's sandbox via a Claudeflare tunnel + Desktop Commander MCP. ✅ The Manager agent is the brain. It plans, decides, delegates, and talks to the user. It never touches files. It never runs scripts. It works entirely from executor summaries. ✅ The Executor agents are the hands. Each receives a task (what to do + why it matters), decides how to execute it, and reports back. They can install new tools and execute code only in their dedicated sandboxes. ✅ Data Tables in n8n store both memories and sessions — no external database, no vector store, no infrastructure. Just rows in a table. Turns out, that's enough. Two memory types: - Manager memory: user preferences, facts, corrections, relationship, skills, context - Executor memory: what tools are installed, what’s broken, workarounds ✅ Sessions are short-term state for multi-step tasks. Original request, plan, assumptions, and what happened so far. When the Manager loops with fresh context, the session is all it gets. That's a Ralph Wiggum loop. I've been using it for 5 days. And already can't imagine not having it on my phone. What's next: - Heartbeat via Cron (a scheduled prompt) - Civic Nexus governance + MCPs - Supermemory integration - WhatsApp as an additional surface - Hardening The architecture supports all of it. OpenClaw proved people want personal AI agents. It also proved that 'just trust the prompt' isn't a security model. Docker isolation, mounted folder permissions, tool approval — none of this is new technology. It's just discipline. You can easily do this even with n8n — no coding required. --- Want to try it or read more? More, what I learned, and a setup guide: productcompass[.]pm

Paweł Huryn

53,999 görüntüleme • 5 ay önce