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Project: #FakeNews Detector Using #Python and #ML. Models: DecisionTree Classifiers. Logistic Regression Model. Random Forest Classification ID: FE/23/68871350 #3MTTLearningCommunity #My3MTT 3MTT Nigeria Github Repo to project:

Yusuf Nasir Ahmad

1,408 subscribers

13,193 Aufrufe • vor 2 Jahren •via X (Twitter)

Nachrichten & Politik #FakeNews #Python #ML #3MTTLearningCommunity #My3MTT

Anya Rossi• Live Now

Private livecam show

10 Kommentare

Profilbild von Shamsuddeen Jibril Bagaldi

Shamsuddeen Jibril Bagaldivor 2 Jahren

@3MTTNigeria I wish you all the best sir

Profilbild von Yusuf Nasir Ahmad

Yusuf Nasir Ahmadvor 2 Jahren

@3MTTNigeria Thank you so much Drone Master

Profilbild von Th3Cl0udGuy ☁️💚🌙

Th3Cl0udGuy ☁️💚🌙vor 2 Jahren

@3MTTNigeria May Almighty Allah put Barakah in it brother (ameen)

Profilbild von Yusuf Nasir Ahmad

Yusuf Nasir Ahmadvor 2 Jahren

@3MTTNigeria Cyber Security, check the work on Github and check how Fake News breed security threats in Nigeria. I am open for Colab sir.

Profilbild von Yusuf Hamisu

Yusuf Hamisuvor 2 Jahren

@3MTTNigeria Best wishes, Nasiru

Profilbild von Yusuf Nasir Ahmad

Yusuf Nasir Ahmadvor 2 Jahren

@3MTTNigeria Thanks so much Mr. Opportunity. Kindly have time to review the Project on Github.

Profilbild von Abdullahi Salihu

Abdullahi Salihuvor 2 Jahren

@3MTTNigeria Nice one Nasir, keep it up

Profilbild von Yusuf Nasir Ahmad

Yusuf Nasir Ahmadvor 2 Jahren

@3MTTNigeria Dr. I hope you will get to patronize the project and build on it. It is available on Github.

Profilbild von GieDee

GieDeevor 2 Jahren

@3MTTNigeria More power to your elbow Champ!

Profilbild von Yusuf Nasir Ahmad

Yusuf Nasir Ahmadvor 2 Jahren

@3MTTNigeria Thanks Boss

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Marlow

33,451 Aufrufe • vor 6 Monaten

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Riley Brown

367,311 Aufrufe • vor 1 Jahr

I'm open-sourcing the entire ComfyDeploy platform again. Yes, our entire YC company. And we are officially moving on from ComfyUI. The community will decide what's next for ComfyDeploy. Below is an abstract; for more, head to the open source repo below. Existing customers will not be affected; more details are in the repo. The service will continue to run until the last customer remains. For those who are new, ComfyDeploy is a cloud service that deploys ComfyUI, provides a simplified interface, and an API to creative teams. How did we get here? In late 2023, I started ComfyDeploy as an open source project while I was working at my previous company. We had a problem deploying ComfyUI to our production server because of the complexity involved in integrating it into a serverless environment. I posted here about this little project that I was working on as an indie hacker, and it blew up overnight. I woke up to 100k impressions on the post. I put up my cal link, and people started scheduling calls. I had the opportunity to speak with numerous individuals worldwide, including those who reached out to help or potentially utilize ComfyDeploy. We got into YC with ComfyDeploy around 2.5K MRR. Around the same timeframe, ComfyOrg was introduced, Stability collapsed, and Flux just came out. We continued building ComfyDeploy for months, keeping things going. The company was growing, but very slowly. We realized the biggest issue is that we are still really early, and it takes time for businesses and enterprises to really adopt such a niche tool. And we are not ComfyOrg. Meanwhile, closed-source models dropped, and many workflows we knew became no longer useful. Coming from a game developer background, I saw huge potential with ComfyUI at first. Still, I never would have imagined that one giant model could do precisely what you put into words, and you still need workflows to fine-tune and control the exact outputs. ComfyDeploy made it possible for teams to experiment with this. But we were stuck in the middle. First, we are not ComfyOrg; second, closed-source models were doing things way better and slowly eating up the market. As of today, ComfyDeploy is doing $29k MRR, and our last 30 days' revenue was $50k processed. Which is the highest we have ever got, but also the most depressing day I have ever had..... More in the GitHub repo.

I'm open-sourcing the entire ComfyDeploy platform again. Yes, our entire YC company. And we are officially moving on from ComfyUI. The community will decide what's next for ComfyDeploy. Below is an abstract; for more, head to the open source repo below. Existing customers will not be affected; more details are in the repo. The service will continue to run until the last customer remains. For those who are new, ComfyDeploy is a cloud service that deploys ComfyUI, provides a simplified interface, and an API to creative teams. How did we get here? In late 2023, I started ComfyDeploy as an open source project while I was working at my previous company. We had a problem deploying ComfyUI to our production server because of the complexity involved in integrating it into a serverless environment. I posted here about this little project that I was working on as an indie hacker, and it blew up overnight. I woke up to 100k impressions on the post. I put up my cal link, and people started scheduling calls. I had the opportunity to speak with numerous individuals worldwide, including those who reached out to help or potentially utilize ComfyDeploy. We got into YC with ComfyDeploy around 2.5K MRR. Around the same timeframe, ComfyOrg was introduced, Stability collapsed, and Flux just came out. We continued building ComfyDeploy for months, keeping things going. The company was growing, but very slowly. We realized the biggest issue is that we are still really early, and it takes time for businesses and enterprises to really adopt such a niche tool. And we are not ComfyOrg. Meanwhile, closed-source models dropped, and many workflows we knew became no longer useful. Coming from a game developer background, I saw huge potential with ComfyUI at first. Still, I never would have imagined that one giant model could do precisely what you put into words, and you still need workflows to fine-tune and control the exact outputs. ComfyDeploy made it possible for teams to experiment with this. But we were stuck in the middle. First, we are not ComfyOrg; second, closed-source models were doing things way better and slowly eating up the market. As of today, ComfyDeploy is doing $29k MRR, and our last 30 days' revenue was $50k processed. Which is the highest we have ever got, but also the most depressing day I have ever had..... More in the GitHub repo.

BennyKok

147,457 Aufrufe • vor 10 Monaten

Introducing BioCLIP: A Vision Foundation Model for the Tree of Life A foundation model that strongly generalizes on the tree of life (2M+ species), outperforming OpenAI CLIP by 18% in zero-shot classification, and supports open-ended classification over almost the entire tree of life What's the secrete ingredients? > Data: we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of organism images to date. It contains 10.4M images for over 450K taxa, sourced from iNaturalist, BIOSCAN, and Encyclopedia of Life. > Modeling: we creatively repurposes CLIP's multimodal contrastive learning objective for hierarchical image classification. The autoregressive language model naturally encodes the hierarchy of the tree of life taxonomy, which in turn bakes the hierarchical representation into the vision transformer encoder. Key results > Strong zero/few-shot classification for animals/plants/fungi, including rare species, outperforming CLIP by avg 16-18% absolute. > T-sne visualization shows that BioCLIP's vision encoder has captued the fine-grained hierarchical structure of the tree of life > BioCLIP is a kind of universal classifier for the tree of life. Just give it an organism image and it will likely find the correct species (among top 5)! But use it with caution; it's not perfect yet.. Final remarks > AI for Science is really hard but extremely rewarding! It took us a ton of time (1+ year) and frustration trying to find a plausible way to integrate the tree of life taxonomy into foundation model training. But when the "Eureka!" moment came and the idea hit us (by the great Wei-Lun Chao) that CLIP's multimodal contrastive learning objective can be repurposed for that, everything just follows naturally. It was truly a moment of joy and excitement! > BioCLIP is our first attempt at foundation models for biology, but it certainly won't be the last! There's so much more to do at the intersection of one of the oldest scientific disciplines and the young but thriving field of AI. Biological intelligence is the foundation for artificial intelligence, and artificial intelligence will in turn become the most important tool for us to unraval the mysteries of biological intelligence. We are hiring postdocs and PhDs in the NSF Imageomics Institute institute to explore this exciting field! Drop us an email. also happy to chat about it at #NeurIPS2023 with any of Tanya, Wei-Lun Chao, or me. - paper: - project: - demo: - model: - data (TreeOfLife-10M): to be released on Hugging Face soon joint work with the amazing Imageomics Institute team: @samstevens6860 Lisa Wu, Matt Thompson, Elizabeth Campolongo Chan Hee (Luke) Song David Carlyn Li Dong Wasila Dahdul Chuck Stewart, Tanya Berger-Wolf Wei-Lun Chao Yu Su

Introducing BioCLIP: A Vision Foundation Model for the Tree of Life A foundation model that strongly generalizes on the tree of life (2M+ species), outperforming OpenAI CLIP by 18% in zero-shot classification, and supports open-ended classification over almost the entire tree of life What's the secrete ingredients? > Data: we curate and release TreeOfLife-10M, the largest and most diverse ML-ready dataset of organism images to date. It contains 10.4M images for over 450K taxa, sourced from iNaturalist, BIOSCAN, and Encyclopedia of Life. > Modeling: we creatively repurposes CLIP's multimodal contrastive learning objective for hierarchical image classification. The autoregressive language model naturally encodes the hierarchy of the tree of life taxonomy, which in turn bakes the hierarchical representation into the vision transformer encoder. Key results > Strong zero/few-shot classification for animals/plants/fungi, including rare species, outperforming CLIP by avg 16-18% absolute. > T-sne visualization shows that BioCLIP's vision encoder has captued the fine-grained hierarchical structure of the tree of life > BioCLIP is a kind of universal classifier for the tree of life. Just give it an organism image and it will likely find the correct species (among top 5)! But use it with caution; it's not perfect yet.. Final remarks > AI for Science is really hard but extremely rewarding! It took us a ton of time (1+ year) and frustration trying to find a plausible way to integrate the tree of life taxonomy into foundation model training. But when the "Eureka!" moment came and the idea hit us (by the great Wei-Lun Chao) that CLIP's multimodal contrastive learning objective can be repurposed for that, everything just follows naturally. It was truly a moment of joy and excitement! > BioCLIP is our first attempt at foundation models for biology, but it certainly won't be the last! There's so much more to do at the intersection of one of the oldest scientific disciplines and the young but thriving field of AI. Biological intelligence is the foundation for artificial intelligence, and artificial intelligence will in turn become the most important tool for us to unraval the mysteries of biological intelligence. We are hiring postdocs and PhDs in the NSF Imageomics Institute institute to explore this exciting field! Drop us an email. also happy to chat about it at #NeurIPS2023 with any of Tanya, Wei-Lun Chao, or me. - paper: - project: - demo: - model: - data (TreeOfLife-10M): to be released on Hugging Face soon joint work with the amazing Imageomics Institute team: @samstevens6860 Lisa Wu, Matt Thompson, Elizabeth Campolongo Chan Hee (Luke) Song David Carlyn Li Dong Wasila Dahdul Chuck Stewart, Tanya Berger-Wolf Wei-Lun Chao Yu Su

Yu Su

80,660 Aufrufe • vor 2 Jahren

10 repos that mass replace a $100,000/year football analytics department. all free. all open source. -> replaces Hawkeye and Second Spectrum YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. calculates speed, distance, possession. from a TV feed. no sensors. -> replaces entire quant sports desk stacked ensemble: LightGBM + XGBoost + Neural Networks + Random Forest. scrapes FBRef automatically. ELO with dynamic K-factor. Poisson xG. MongoDB backend. the most complete open-source football prediction pipeline on GitHub. -> replaces paid prediction platforms ($30/mo) full GUI app. 7 ML algorithms. downloads data from football-data. co. uk. predicts upcoming fixtures. exports to Excel. one click. -> replaces manual feature engineering XGBoost with 354 hand-crafted features. works for any European league. data straight from football-data. co. uk. plug and predict. -> replaces value bet scanners ($50/mo) ELO + expected goals + offensive/defensive ratings. compares model probability vs Vegas lines. flags when you have edge. -> replaces bookmaker calibration tools Gradient Boosting tuned to output probabilities that match real bookmaker odds. not just accuracy - calibrated confidence. -> replaces StatsBomb xG subscription xG model from KU Leuven researchers. LogReg + XGBoost pipelines. supports Wyscout, StatsBomb, Opta data. academic grade. -> replaces xG analytics dashboards xG on StatsBomb open data. SHAP explanations for every prediction. proper calibration. tested on FIFA World Cup 2022. -> replaces basic prediction models Poisson distribution for goal simulation. the classical approach that still beats most ML models on draw prediction. -> replaces Premier League prediction services XGBoost + AdaBoost + SVM on EPL data. detailed EDA. confusion matrices. honest 56% accuracy - because football is hard. like + bookmark you'll need this when you build your first football prediction bot

10 repos that mass replace a $100,000/year football analytics department. all free. all open source. -> replaces Hawkeye and Second Spectrum YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. calculates speed, distance, possession. from a TV feed. no sensors. -> replaces entire quant sports desk stacked ensemble: LightGBM + XGBoost + Neural Networks + Random Forest. scrapes FBRef automatically. ELO with dynamic K-factor. Poisson xG. MongoDB backend. the most complete open-source football prediction pipeline on GitHub. -> replaces paid prediction platforms ($30/mo) full GUI app. 7 ML algorithms. downloads data from football-data. co. uk. predicts upcoming fixtures. exports to Excel. one click. -> replaces manual feature engineering XGBoost with 354 hand-crafted features. works for any European league. data straight from football-data. co. uk. plug and predict. -> replaces value bet scanners ($50/mo) ELO + expected goals + offensive/defensive ratings. compares model probability vs Vegas lines. flags when you have edge. -> replaces bookmaker calibration tools Gradient Boosting tuned to output probabilities that match real bookmaker odds. not just accuracy - calibrated confidence. -> replaces StatsBomb xG subscription xG model from KU Leuven researchers. LogReg + XGBoost pipelines. supports Wyscout, StatsBomb, Opta data. academic grade. -> replaces xG analytics dashboards xG on StatsBomb open data. SHAP explanations for every prediction. proper calibration. tested on FIFA World Cup 2022. -> replaces basic prediction models Poisson distribution for goal simulation. the classical approach that still beats most ML models on draw prediction. -> replaces Premier League prediction services XGBoost + AdaBoost + SVM on EPL data. detailed EDA. confusion matrices. honest 56% accuracy - because football is hard. like + bookmark you'll need this when you build your first football prediction bot

zostaff

221,971 Aufrufe • vor 2 Monaten

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

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 1 Monat

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

Paweł Huryn

219,498 Aufrufe • vor 4 Monaten

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 diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

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 diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Akshay 🚀

33,358 Aufrufe • vor 7 Monaten