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Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git...

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Akshay 🚀

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33,358 Aufrufe • vor 9 Monaten •via X (Twitter)

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By 2030, the data science field is projected to grow by 30% -- this is the tool I’d add to my learning path if I were re-skilling into data science: 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐦𝐢𝐭𝐨-𝐚𝐢 It’s an open-source toolkit that adds AI to your Jupyter Notebooks, and it’s surprisingly helpful for learning by doing. Try now It lets you: • Use natural language to generate full data workflows • Upload an Excel file and watch it convert to clean Python code • Work with top models (ChatGPT, Claude, Gemini) right inside your notebook with no setup One thing I really like about Mito AI: it shows you exactly what it’s doing. (Which is super helpful for both beginners and debugging.) -The agent explains its assumptions - Every result links back to editable, transparent code That makes it a great companion if you're: • Transitioning from Excel • Exploring data automation or dashboards • Or just brushing up on how real data science pipelines work
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Exciting update on PantheonOS: Introducing Pantheon-Notebook & Pantheon-CLI — the first fully open-source, Python-based agentic tools that go beyond Claude Code in the field of data analysis. Pantheon-CLI runs entirely on your computer or server, supports 60+ tools and 50+ databases, and can call any Python, R, or Julia package alongside natural language. Chat with your data directly. It look like python-claude-code, but more appreciate for data analysis. Pantheon-Notebook brings the same agentic framework into Jupyter! Not just for writing code, it can also run and revise code automatically to generate the correct result, and even operate on files and study from website — beyond what any other tool can do! With Pantheon, you mix natural language + programming in one workflow, focusing on discovery instead of syntax barriers. We've applied Pantheon in some real-world cases: finance (customer explore), biology (Seurat, cell segmentation, annotation), sociology (survey analysis), and drug discovery (molecular docking). Pantheon is not just a CLI or a plugin — it's an agentic operating system for science, spanning both terminal and notebook. Why not try it now? We are actively preparing publications from this series of projects. Major contributors will be recognized in our GitHub repository and listed as key authors in these manuscripts. Feel free to reach out for collaborations, research assistant positions, visiting opportunities, rotation project or future PhD projects.
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Exciting update on PantheonOS: Introducing Pantheon-Notebook & Pantheon-CLI — the first fully open-source, Python-based agentic tools that go beyond Claude Code in the field of data analysis. Pantheon-CLI runs entirely on your computer or server, supports 60+ tools and 50+ databases, and can call any Python, R, or Julia package alongside natural language. Chat with your data directly. It look like python-claude-code, but more appreciate for data analysis. Pantheon-Notebook brings the same agentic framework into Jupyter! Not just for writing code, it can also run and revise code automatically to generate the correct result, and even operate on files and study from website — beyond what any other tool can do! With Pantheon, you mix natural language + programming in one workflow, focusing on discovery instead of syntax barriers. We've applied Pantheon in some real-world cases: finance (customer explore), biology (Seurat, cell segmentation, annotation), sociology (survey analysis), and drug discovery (molecular docking). Pantheon is not just a CLI or a plugin — it's an agentic operating system for science, spanning both terminal and notebook. Why not try it now? We are actively preparing publications from this series of projects. Major contributors will be recognized in our GitHub repository and listed as key authors in these manuscripts. Feel free to reach out for collaborations, research assistant positions, visiting opportunities, rotation project or future PhD projects.

evo-devo

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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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I'm often asked for the best public example of AI evals done right for a real, production product. I finally have an answer. Teresa Torres shares how she shipped an AI interview coach, and used evals to rapidly squash bugs and improve the product. Teresa shows how she: 1. did error analysis FIRST to find real issues (instead of using generic metrics) 😍 2. used Jupyter notebooks to analyze errors 3. built custom annotation tools + custom widgets in notebooks 4. built a LLM-judge and assertions to test for specific errors 5. iterated through this feedback loop until it worked. 6. kept things simple the whole time It's also probably the best commercial for Jupyter notebooks you can imagine. 🥰 Chapter summary below. Link to YT in next thread 00:00:00 - Intro 00:01:45 - The Product: Building an AI Interview Coach 00:06:34 - The Problem: How Do I Know if My AI Coach is Any Good? 00:10:15 - Using Airtable for Traces and Annotation 00:12:15 - Discovering Jupyter Notebooks and Designing the First Evals 00:15:15 - Example Evals: LLM-as-Judge vs. Code-Based Assertions 00:21:00 - Learning Python with ChatGPT to Analyze Eval Results 00:31:00 - VS Code, Custom Tools, and an Eval Investigation Notebook 00:39:45 - Building a Custom Annotation Tool with Claude 00:41:00 - From Personal Project to Production App 00:46:02 - How Should PMs and Engineers Collaborate on AI Products? 00:55:45 - Q&A: Capturing Feedback and Annotations from End Users 00:58:11 - Q&A: Is a Technical Background Necessary to Build AI? 01:02:28 - Q&A: What's Next for Teresa? 01:03:13 - Q&A: Unpacking the Micro-Decisions of Building an AI App
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51,376 Aufrufe • vor 1 Jahr

In 2026, multi-player AI will eat single-player AI. Right now, most AI tools are built for one human + one model in a private workspace. ChatGPT, Cursor, Claude. Incredibly powerful, but currently optimized for individuals. The impact is massive: drafts, code, specs, campaigns, workflows. But almost none of it is shared, aligned, or contextualized across a team. We've seen this movie before. Cloud 1.0 was just "the same software, but online". The real breakthrough was collaboration. Google Docs beat Word. Figma beat Sketch. Notion beat Evernote. Every single-player tool lost to its multi-player counterpart. AI is about to go through the same transformation. AI 1.0 gave individuals superpowers. AI 2.0 will give teams superpowers. And just like cloud, single-player tools will go multi-player or get replaced. If you're building here, I'd love to talk.
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In 2026, multi-player AI will eat single-player AI. Right now, most AI tools are built for one human + one model in a private workspace. ChatGPT, Cursor, Claude. Incredibly powerful, but currently optimized for individuals. The impact is massive: drafts, code, specs, campaigns, workflows. But almost none of it is shared, aligned, or contextualized across a team. We've seen this movie before. Cloud 1.0 was just "the same software, but online". The real breakthrough was collaboration. Google Docs beat Word. Figma beat Sketch. Notion beat Evernote. Every single-player tool lost to its multi-player counterpart. AI is about to go through the same transformation. AI 1.0 gave individuals superpowers. AI 2.0 will give teams superpowers. And just like cloud, single-player tools will go multi-player or get replaced. If you're building here, I'd love to talk.

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

Qiusheng Wu

18,075 Aufrufe • vor 2 Monaten

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.
0:22

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

40,562 Aufrufe • vor 6 Monaten

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google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

87,207 Aufrufe • vor 1 Monat

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.
1:49

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

394,189 Aufrufe • vor 4 Monaten

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!
0:20

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Sumanth

10,483 Aufrufe • vor 5 Monaten

Wow! Someone open sourced 100+ full AI apps you can just clone and ship.. and it's sitting at 124,000 stars on github it's one repo, awesome-llm-apps. and the important part.. these aren't tutorials teaching you to build an app. they're the finished apps themselves. RAG systems, AI agents, multi-agent teams.. tested end to end, apache licensed, yours to copy. a few that are actually in there: - a fraud investigation agent that cross-references public records to flag things that don't add up - a VC due-diligence agent team that writes startup investment analysis - a financial coach that builds you a budget, debt and savings plan - "chat with anything".. your github, gmail, PDFs, arxiv papers, youtube videos - a deep research agent that runs full web research on its own here's how to actually use it, not just star it: clone the repo, open the one app closest to what you need it's a working template.. swap in your data, your API key, your use case ship it as your own thing, or bolt three of them together into one bigger product the license literally lets you take these, customize them, and sell them. that's not a loophole, that's the point of apache-2.0. repo:
0:56

Wow! Someone open sourced 100+ full AI apps you can just clone and ship.. and it's sitting at 124,000 stars on github it's one repo, awesome-llm-apps. and the important part.. these aren't tutorials teaching you to build an app. they're the finished apps themselves. RAG systems, AI agents, multi-agent teams.. tested end to end, apache licensed, yours to copy. a few that are actually in there: - a fraud investigation agent that cross-references public records to flag things that don't add up - a VC due-diligence agent team that writes startup investment analysis - a financial coach that builds you a budget, debt and savings plan - "chat with anything".. your github, gmail, PDFs, arxiv papers, youtube videos - a deep research agent that runs full web research on its own here's how to actually use it, not just star it: clone the repo, open the one app closest to what you need it's a working template.. swap in your data, your API key, your use case ship it as your own thing, or bolt three of them together into one bigger product the license literally lets you take these, customize them, and sell them. that's not a loophole, that's the point of apache-2.0. repo:

Axel Bitblaze 🪓

46,487 Aufrufe • vor 1 Monat

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
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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

15,473 Aufrufe • vor 2 Monaten

🚨BREAKING: Someone just built the open source Loom killer that turns your screen recordings into AI agents. It's called Bloom and it does something Loom never could. Your recordings don't just sit as files. They become queryable, searchable, agent-ready data the moment you stop recording. Here's what actually happens: You hit record. Bloom captures your screen, mic, and system audio locally. While you're still recording, it uploads chunks to the cloud in real time. The second you stop, it auto-generates a full transcript, creates visual embeddings, and indexes every spoken word. Then the wild part kicks in. You can query your recordings through APIs. You can hook Claude Code directly into your video library and ask it questions. Your screen recording just became a knowledge base. → Local-first, your files never get locked in → Cmd+Shift+R starts and stops from anywhere on your system → One-click shareable link for any recording → Chat directly with your video content through VideoDB Chat → Works with Claude Code and other agent frameworks out of the box Recordings are no longer files. They're inputs for AI. MIT License. 100% Opensource. Link in comments.
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🚨BREAKING: Someone just built the open source Loom killer that turns your screen recordings into AI agents. It's called Bloom and it does something Loom never could. Your recordings don't just sit as files. They become queryable, searchable, agent-ready data the moment you stop recording. Here's what actually happens: You hit record. Bloom captures your screen, mic, and system audio locally. While you're still recording, it uploads chunks to the cloud in real time. The second you stop, it auto-generates a full transcript, creates visual embeddings, and indexes every spoken word. Then the wild part kicks in. You can query your recordings through APIs. You can hook Claude Code directly into your video library and ask it questions. Your screen recording just became a knowledge base. → Local-first, your files never get locked in → Cmd+Shift+R starts and stops from anywhere on your system → One-click shareable link for any recording → Chat directly with your video content through VideoDB Chat → Works with Claude Code and other agent frameworks out of the box Recordings are no longer files. They're inputs for AI. MIT License. 100% Opensource. Link in comments.

Rimsha Bhardwaj

128,775 Aufrufe • vor 4 Monaten

The new Softr has arrived. Today, we're launching the first AI-native platform for building real business software. Not prototypes. Not demos. Software you can build on Monday morning and share with your team and clients before lunch. Describe what you need, and Softr’s new AI Co-Builder instantly creates the database, app, and business logic — connected, secure, and ready for real users. In less than 5 minutes! → Secure and fully-functional. Logins, user management, permissions, hosting — built-in and working from the moment you hit publish. → Switch between AI prompting and visual editing at any time. No credits burned just to add text or change a color. → Any non-technical team member can own it and iterate on it. Visual interface, database and workflows you can see and edit — no black box. No developer. Ever. 2025 was prototypes. 2026 is software that actually works. Start building with Softr for free →
1:39

The new Softr has arrived. Today, we're launching the first AI-native platform for building real business software. Not prototypes. Not demos. Software you can build on Monday morning and share with your team and clients before lunch. Describe what you need, and Softr’s new AI Co-Builder instantly creates the database, app, and business logic — connected, secure, and ready for real users. In less than 5 minutes! → Secure and fully-functional. Logins, user management, permissions, hosting — built-in and working from the moment you hit publish. → Switch between AI prompting and visual editing at any time. No credits burned just to add text or change a color. → Any non-technical team member can own it and iterate on it. Visual interface, database and workflows you can see and edit — no black box. No developer. Ever. 2025 was prototypes. 2026 is software that actually works. Start building with Softr for free →

Softr

33,254 Aufrufe • vor 4 Monaten

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.
0:46

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.

Ihtesham Ali

31,074 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 2 Monaten

GeoLibre v2.5.0 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 meeting you where your work already lives: open your QGIS and ArcGIS Pro projects directly, talk to your team on the map itself, explore hyperspectral imagery as a 3D cube, work in discrete global grids, and host sharing and live collaboration on your own server. It is also the most community-driven release so far: 121 pull requests and 52 issues closed in 8 days, from 14 contributors, 12 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.5.0 - Comments on the map: pin a comment to any location or feature, reply in a thread, resolve it when it is handled. - Live collaboration, on your own server: several people can open the same session and see each other's edits, cursors, and comments in real time. - QGIS and ArcGIS Pro project import: open a .qgs, .qgz, .aprx, or .mapx and get the layers. - Discrete global grids: three new DGGS plugins (A5, DGGRID, and DGGAL) render and identify cells over the current view, plus DGGS Generator, Binning, and Compact processing tools. - Hyperspectral data you can actually read: local NetCDF and HDF grids are colormapped in the browser, a cube gets an RGB band combination picked by wavelength. - Spectral profiles from a click: identify a pixel to chart its spectrum against wavelength. - Smart styling on add: every new layer arrives with its own color and geometry-appropriate sizing, and a Style suggestions strip offers one-click renderers. - Autosave, crash recovery, and project history, plus an Elements panel for managing every annotation from a list, a Layer Library for saving a fully configured layer and re-adding it to any later project, and project duplication and templates. - GeoLibre Desktop is on the Mac App Store, and Thai brings the shipped locales to 16 languages besides English, all at 100% coverage. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre
1:55

GeoLibre v2.5.0 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 meeting you where your work already lives: open your QGIS and ArcGIS Pro projects directly, talk to your team on the map itself, explore hyperspectral imagery as a 3D cube, work in discrete global grids, and host sharing and live collaboration on your own server. It is also the most community-driven release so far: 121 pull requests and 52 issues closed in 8 days, from 14 contributors, 12 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.5.0 - Comments on the map: pin a comment to any location or feature, reply in a thread, resolve it when it is handled. - Live collaboration, on your own server: several people can open the same session and see each other's edits, cursors, and comments in real time. - QGIS and ArcGIS Pro project import: open a .qgs, .qgz, .aprx, or .mapx and get the layers. - Discrete global grids: three new DGGS plugins (A5, DGGRID, and DGGAL) render and identify cells over the current view, plus DGGS Generator, Binning, and Compact processing tools. - Hyperspectral data you can actually read: local NetCDF and HDF grids are colormapped in the browser, a cube gets an RGB band combination picked by wavelength. - Spectral profiles from a click: identify a pixel to chart its spectrum against wavelength. - Smart styling on add: every new layer arrives with its own color and geometry-appropriate sizing, and a Style suggestions strip offers one-click renderers. - Autosave, crash recovery, and project history, plus an Elements panel for managing every annotation from a list, a Layer Library for saving a fully configured layer and re-adding it to any later project, and project duplication and templates. - GeoLibre Desktop is on the Mac App Store, and Thai brings the shipped locales to 16 languages besides English, all at 100% coverage. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre

Qiusheng Wu

26,788 Aufrufe • vor 21 Tagen

We just raised $30M at a $500M valuation, bringing our total funding to $47M. Led by Craft Ventures , with Pace Capital , Chemistry , TruArrow, and others. But before anything else: this belongs to the community. ComfyUI started as one developer and one open-source repo. No roadmap. No company. Just creators who wanted real control over how they built with AI. That community is now: → 4 million users → 60,000+ community-built nodes → 150,000+ daily downloads Every number traces back to people who built in the open, for anyone to use. Here's where the funding goes: → Comfy Cloud: for teams and studios that need security and scale → Collaborative workflows: versioning and iteration built for how studios actually work → A better local experience: more seamless, more stable → Ecosystem reliability: making 60,000+ community nodes more dependable → Day-one model support: every major release, compatible at launch We are not building a walled garden. We are building open infrastructure, built to last. Thank you genuinely, The ComfyUI Team
2:32

We just raised $30M at a $500M valuation, bringing our total funding to $47M. Led by Craft Ventures , with Pace Capital , Chemistry , TruArrow, and others. But before anything else: this belongs to the community. ComfyUI started as one developer and one open-source repo. No roadmap. No company. Just creators who wanted real control over how they built with AI. That community is now: → 4 million users → 60,000+ community-built nodes → 150,000+ daily downloads Every number traces back to people who built in the open, for anyone to use. Here's where the funding goes: → Comfy Cloud: for teams and studios that need security and scale → Collaborative workflows: versioning and iteration built for how studios actually work → A better local experience: more seamless, more stable → Ecosystem reliability: making 60,000+ community nodes more dependable → Day-one model support: every major release, compatible at launch We are not building a walled garden. We are building open infrastructure, built to last. Thank you genuinely, The ComfyUI Team

ComfyUI

964,014 Aufrufe • vor 4 Monaten