Tune in for a brand new Numerically Speaking LIVE this Friday at 11AM PT! 📺 In this episode, we dive deep into #Python environment management and run through demos on Jupyter, organizational workflows, and a real-world CFD use case: https://bit.ly/4mbXMwf

95% of AI pilots never reach production due to gaps in infrastructure, governance, and execution. In this on-demand session, Anaconda’s Steve Croce shares common mistakes, practical frameworks, and why testing and measurement matter more than model choice: https://bit.ly/4tk5g2p

Many orgs say they have an AI strategy, but few enforce it where AI actually runs. 🚩 Research shows the gap isn’t policy, but infrastructure. Governance built at the organizational level doesn’t reach the application or model layers. Learn more: https://bit.ly/3Q3EDQU

Skill files are Markdown docs you give an LLM before it runs a task, so it understands your conventions and expectations. Write your own, or use existing ones. Anthropic has a repo with skill files for things like reading PDFs. There’s even one for creating more skill files. 🪆

Open source AI adoption is accelerating, but poor governance stalls projects. Discover how to scale open-source AI with secure infrastructure, automated policy enforcement, and cost-efficient techniques like quantization (without slowing innovation) → https://bit.ly/4bAjQgj

Evaluating models for production used to take weeks. 🐌 AI Catalyst cuts that work down to hours by bringing everything into one place, filtering by use case, surfacing hardware requirements, and showing benchmarks built for real workloads: https://bit.ly/3PvMOW5

Only 15% of IT leaders strongly believe they have the right governance models to manage their AI agents in their enterprise applications. Discover actionable frameworks for modernizing your AI governance in this Gartner® report: https://bit.ly/4cDeUYX

This March Madness, we’re presenting the Elite Eight of Python for Data Science: Pandas 🐼 vs Polars ❄️ NumPy 🔢 vs PyTorch 🔥 Scikit-learn 🤖 vs TensorFlow 🧠 Matplotlib 📊 vs Plotly ✨ Which Python library is cutting down the net? Drop your championship pick below. 👇

Setting up GPU environments has become a major source of friction. CUDA alone spans 900+ components. Conda simplifies setup by handling driver detection, dependency resolution, and environment isolation. Learn more: https://bit.ly/4lSffJY *Comparison concept inspired by an NVIDIA GTC presentation.

A Stanford and Berkeley study showed models retrieve answers best at the start or end of context and struggle in the middle—sometimes worse than if they had no context at all. This week’s #PythonTips breaks down the research (links in the comments) and how to write prompts.

We’re so excited about the recent recognitions that reflect Anaconda’s momentum! 🏆 Fast Company’s Most Innovative Companies 🏆 G2's Best Software Awards 🏆 theCUBE's Tech Innovation CUBEd Awards 🏆 And more! Explore all of our recent accolades: https://bit.ly/47Ziolh

A churn model that worked perfectly in notebooks crashed in production because of unexpected null values. The root cause: missing schema validation. ☑️ Discover structured data modeling and the best practices for scalable Python workflows: https://bit.ly/3NMechY

Anaconda is accelerating trusted AI development at scale with expanded capabilities across AI Catalyst and the Anaconda Platform. Enhanced model discovery, a new MCP server for a conda-aware AI coding assistant, & a full audit trail for package decisions: https://bit.ly/4lz860M

Stop spending days wrestling with GPU setup, and start actually building AI. 🚀 In this on-demand webinar with Omdia’s Torsten Volk and Anaconda’s Alan McCarty, discover the #1 blocker to GPU adoption and how you can eliminate it for good: https://bit.ly/4smNNqg

LLMs are trained on a snapshot of the world. Ask about internal docs, and they’ll likely guess. 🔎 RAG helps by pulling in relevant external context when queried. Learn how it works, why retrieval quality determines response quality, and when you’ll need it for your AI apps. 👇

Big news: Anaconda’s collaboration with #NVIDIA now spans the full enterprise AI stack—from GPU-accelerated Python environments to open models for agentic AI. Learn more about the expanded partnership: https://bit.ly/3Nmv1Qy

This on-demand webinar explores how Anaconda’s curated packages, embedded in @Snowflake, help teams move from experimentation to secure deployment. Learn more about secure package management and cohesive distribution for reliable enterprise AI → https://bit.ly/4l0bOjR

Many AI pilots stall before production due to security, legal, and compliance hurdles. ⛔️ Our webinar on 3/26 will walk through a practical framework for evaluating open-source models, manual curation, and how Anaconda AI Catalyst handles this at scale: https://bit.ly/40aOoPp

Does your AI need to access external data? MCP is a standard that lets models call external data sources when they need information beyond their training. Anaconda‘s Daina Bouquin is back with more #PythonTips to explain how MCP works and how to build your own MCP server.

Hyperbound reduced evaluation cycles from days to hours by moving their evaluation framework to Conda inside Docker 🚀 Hear directly from Hyperbound CTO and co-founder Atul Raghunathan on why consistent environments are key to reliable AI. The story is up on our YouTube: https://bit.ly/4lbUUic

Most IT leaders (68%) say they struggle to keep up with the pace of change as app vendors add new embedded AI features. 38% admit that their app vendors drive their GenAI strategy. This @Gartner_inc report helps IT teams actually drive their AI strategy: https://bit.ly/4cDeUYX

We’re excited to share that Anaconda has joined the OpenMP Architecture Review Board to drive the official extension of OpenMP to the Python language! 🎉 Explore what this news means for Anaconda and the future of Python-native OpenMP specifications: https://bit.ly/4rTwmgG

@NVIDIAGTC 2026 is only 11 days away! ⏳ And time is running out to request a meeting with our leadership team while they’re all in San Jose. Share your availability today and discover how to navigate the complexities of scaling enterprise AI: https://bit.ly/4aOlXws

Python the language is what we write. The interpreter (usually CPython) is the program that translates that code into machine instructions at runtime. Explore what interpreters and interpreted languages are and how they compare to compiled languages: https://bit.ly/4rIdm4K

Python ships with powerful tools you may not be using...yet! 👀 Discover built-in modules to pretty-print JSON, spin up a quick local web server, drop straight into the debugger, and more. No install required. There’s even a terminal calendar. Which of these are new to you?

@ZemplerBank’s data science team needed more advanced ML for fraud detection, but couldn’t compromise on security. Explore how the team reduced fraud by over 90% by using Anaconda to pre-screen open source packages and align development with production: https://bit.ly/3ZYSRoe

When AI works in your dev environment but breaks in production, the culprit is often complex package dependencies. ⚠️ In this on-demand webinar with #Anaconda and #Snowflake, learn why cohesive package distribution matters for enterprise AI applications: https://bit.ly/4l0bOjR

A major gap exists in implementing enterprise AI: 70% of IT leaders say they have a centralized AI strategy, but only 34% can actually apply it to their enterprise apps ⛓️‍💥 This new @Gartner_inc report offers a complete framework for bridging this gap: https://bit.ly/4cDeUYX

AI-assisted coding is bringing on a host of new types of comments. Anaconda’s Daina Bouquin walks through the familiar types: the prompt you forgot to delete, the paranoid one, the honest “I have no idea what changed,” and more. Which one are you? Discuss in the comments 👇

In 2026, AI will be judged on results. 📈 Startups will need to solve real problems, not just wrap model APIs. Investing in flexible, scalable building blocks will matter most as the AI stack evolves. Our CEO @ddesanto.bsky.social shares his predictions: https://bit.ly/3OAuHO9

We’re a 2026 G2 Best Software Award winner for Best AI Software Products! 🏆 This recognition is especially meaningful because it’s based on feedback from our incredible community. Thank you for trusting Anaconda to power your AI innovation. 💚 https://bit.ly/4rZ9hc5

Hyperbound needed their AI sales coach to behave like real prospects: skeptical, opinionated, and consistent. By standardizing on conda, they cut eval cycles from days to hours and proved that speed and security don’t have to be a trade-off: https://bit.ly/3Zef2pN

Get early access to the first FIVE chapters of AI Agents in Action, 2nd Edition, a hands-on guide to designing and deploying intelligent agent systems. The guide goes beyond demos to show how real-world agent workflows are built, orchestrated, and scaled: https://bit.ly/45prHd8

Small context switches add up. Dig into why Jupyter’s command and edit modes make keyboard-first work possible, how keeping your hands on the keyboard can help you stay in flow, and the shortcuts that can make the biggest difference: https://bit.ly/3ZBHzG6

Another week, another #PythonTipsTuesday. 🐍 Let’s break down common comments in the age of AI code: prompts meant to be deleted, the “just in case” graveyard, cautious notes, and more. Comments seem small, but they say a lot about us as developers. Which one are you? 👀

In Python environments, many “package vulnerabilities” actually hide in bundled binary libraries, not the Python code itself. Invisible dependencies = noisy alerts and slow fixes Discover how making binary dependencies explicit addresses this: bit.ly/4bmMcLp

Package installs don’t have to slow down as ecosystems grow. Anaconda’s Dan Yeaw shared insights in @TDScience, showing how sharded, content-addressed metadata lets package managers fetch only what’s needed, rather than parsing massive global indexes: https://bit.ly/4a8M3dj

Is the industry measuring AI ROI all wrong? Will Python remain the dominant language for AI? Find the answers in the January edition of Numerically Speak, as our CEO David DeSanto shares bold predictions for the year ahead. Check out the full edition: https://bit.ly/4a6nlt7

A bank’s data science team faced a familiar tradeoff: moving fast with modern ML vs. meeting strict security and compliance needs. By using Anaconda to govern open-source dependencies, they built and deployed more advanced fraud models with confidence: https://bit.ly/4qEAL6y

We’re back for another #PythonTips Tuesday! 🐍 This week, we dig deeper into LLM file names — specifically the B that stands for billions of parameters that act like tiny adjustable knobs in a neural network. Together these parameters shape how a model learns language. 💪

⏰ Happening today at 9 AM PST: If AI convos in your org feel stuck, today’s live session is just for you. This discussion, in partnership with @OReillyMedia, will explore how execs can make more confident, informed decisions about AI: https://bit.ly/4qFpAtN

Early access is open for the first 4 chapters of AI Agents in Action! Drawing on Michael Lanham’s experience shipping enterprise agents, the guide walks through architectures, tooling, and practical techniques to take agents from prototype to production: https://bit.ly/45prHd8

Honored to be named to @fastcompany.com's Most Innovative Companies list! This one's for our 45M users who trust us to democratize Python & AI. Thank you for building the future with us! #FCMostInnovative www.anaconda.com/press/anacon...

An idea for an #AI class for undergraduate students in a social science. With @anacondainc.bsky.social you can download AI models to a miniPC and then experiment with them off the Internet (and protect personal data). #academicbsky 1/2

Miniconda 25.1.1 has arrived - with Python, conda, and access to 33,000+ open-source AI & ML packages. 🔥 Grab Miniconda 25.1.1 and get coding: www.anaconda.com/blog/minicon...

Meet #Lumen, an open-source #AI tool that makes data science seamless. Automate data pipelines, create dashboards, and customize or share workflows effortlessly. From data to decisions, faster. 👇

Today is the last day #LoveDataWeek, and if there’s one thing we know, it’s that teams that share and collaborate on data create the best work. 🤝 Show your data some love with our Python in Excel Cheat Sheet—your perfect match for seamless workflows. 💘 #ValentinesDay

🚀 Accelerate your AI journey for free. Get access to Cloud Notebooks, AI tools, and full learning resources for educators & students. Sign up for free now: www.anaconda.com/blog/anacond... #DataScience #MachineLearning #AIinEducation

Tracking progress isn’t just about motivation—it’s about leveraging data to build better habits. We explore how #Python and AI-driven tracking tools can go beyond checklists, providing real-time insights, dynamic visualizations, and habit-forming intelligence.🎯

Tune in to our webinar to see how Snowflake Notebooks and Anaconda Python packages can automate your Go-To-Market analytics workflows—from growth accounting to sales forecasting. Register for a chance to win a pass to Snowflake Summit 2025: event.on24.com/wcc/r/482030...