Visualizing complex data in Python just got easier! Meet... Cosmograph for Python 🪐: The widget brings GPU-accelerated, interactive layout graph rendering right inside your Jupyter notebooks. Here’s why it’s a game-changer: ⚡ GPU-accelerated performance ⛓️ Interactive network exploration with pan, zoom, hover & selection ⚙️ Rich configuration APIs for layout, color, size & more 📦 Seamless notebook integration & easy Python installation But that’s not all: ✅ Force-directed simulations for dynamic layouts ✅ Smooth handling of large-scale networks ✅ Minimal setup—just pip install cosmograph Link to the repo in next tweet! ______ Follow us → Daily Dose of Data Science ✔️ For more insights & tutorials on AI and Machine Learning.show more

6. Explore the basics of Python in Harvard’s CS50... course—perfect for getting started with coding, data science, web development, and more.show more

Here’s a proof of concept showing how data calculated... in Python can be used in Blender. #Blender can process the received data with custom geometry nodes, custom shaders and full 3D interactivity. This minimal example uses @networkX in Project Jupyter. Learn more in this thread🧵show more

Next edit suggestions (Beta) just dropped in JetBrains AI... Assistant. Get AI-powered suggestions anywhere in your file, not just the next line. ✅ Available for Java, Kotlin, and Python 🧠 Powered by AI, enhanced by IDE-native actions 💎 Unlimited use with AI Pro and AI Ultimate subscriptions Learn more →show more

⚛️ In React, rendering large lists or tables can... be a drag on performance. The fix? Virtualization! the process of only rendering part of that large data set (just enough to fill the viewport). ✅ Improves rendering time significantly. ✅ Lowers memory consumption.show more

Introducing Snowflake Workspaces — the unified environment where developers... build, test, and collaborate on data and AI projects. Code in SQL, Python, or @Streamlit — all inside the Snowflake AI Data Cloud. Fast. Secure. Built for the way you work.show more

🚀 SMV2 Workflow Update Alert! ⏫Managing your engagement just... got easier. We’ve added more granular tracking in "My Stats" and deeper insights into your referral network performance. ✅ New Auto-updating status for Retweets ✅ Automatic Outdated Task Removal ✅ Advanced stats for invitees (Followers & Workchain) 🙇‍♂️Read more on our recent blog article and don't hesitate to check our Gitbook for more info and SMV2 improvements! #SocialMining #Web3show more

Introducing segment-geospatial, A Python package for segmenting geospatial data... with the Segment Anything Model (SAM) GitHub: Docs: Notebook: #segmentanything #deeplearning #geopythonshow more

We're now rolling out interactive images to the Google... Gemini for more visual and dynamic learning. It’s designed to help you visually explore complex academic concepts, turning studying from passive viewing into active exploration. Learn more →show more

Exciting news from HYPE3.cool! We’re teaming up with Chainbase... (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchainshow more

The internet is evolving - are you ready? 🌐⚡... At SUI DePIN, your data isn’t just collected - it’s owned by you. Monetize it. Secure it. Power the next generation of AI on the Sui! ✅ Own your data ✅ Earn real rewards ✅ Fuel AI innovation No middlemen. No limits. Just you and your data. Step into the future today 👉show more

A preview Into the future of #quantum-GPU computing: At... GTC Washington, D.C., we announced NVIDIA NVQLink, an open system architecture for tightly coupling the extreme performance of GPU computing with quantum processors to build accelerated quantum supercomputers. In the expo hall, accelerated quantum computing was a centerpiece of the NVIDIA booth. See our live blog:show more

Why do data professionals love PyCharm? 🚀 Discover how... Moritz from Scieneers tackles daily challenges with PyCharm: ✨ Refactoring capabilities ⚙️ Easy run configuration setup 🌍 Seamless remote development 🔄 Minimal context switching Watch the video:show more

1. Introduction to Computational Thinking and Data Science Designed... for students seeking Python experience and enhanced knowledge of computational complexity. Check here 👇show more

This is the coolest thing you'll see today! Drawdata... is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. Very handy for learning & understanding the behaviour of ML algorithms! Check this out👇show more

DAY19 College(9am-5pm) ✅ Gym(6pm-7pm) ✅ Cohort 4.1,4.2 ✅ Project(7pm-2am)... ✅ >Done with the WebSockets >Solo-leveling app getting cooked >Learning python for this -show more

10 years of "it can't be done..." 7 NVIDIA... GPU architectures... 5 years of RAPIDS AI... 3 years of Voltron Data... finally a petabyte-scale GPU-native engine that DOESN'T require you to change your data pipelines. Same code, same data formats, just modular, interoperable, composable, extensible... and of course ACCELERATED! Theseus is the Scalable Performant And Compute Efficient engine🔥🔥🔥 Check out our benchmarks and new webpage... and reach out if you're struggling with queries above 30TBs.show more

With uv (Astral ) and Zed, it is pretty... easy to get set up to work on an existing Python project: 1. Clone the repo 2. `uv sync` to install deps in a .venv 3. Select the .venv in Zed's toolchain selector We have plans to further improve our support of Python. Stay tuned.show more