Reproducibility can decide whether an AI workflow scales or quietly breaks.

The April stream of Numerically Speaking LIVE explored Python environment management, including where teams run into trouble and how to build more reliable environments: https://www.youtube.com/watch?v=fV8_pmU5Rqs

Do you know what your AI is costing you? Or every AI tool in your environment?

Gartner® identified 7 AI-specific controls that orgs need to enforce AI governance. Our corresponding list with 7 questions helps to determine whether your org is up to par: https://bit.ly/4cfL8ZW

We’re at PyTexas in Austin, today through April 19! Catch Anaconda’s Dawn Wages’ keynote and James Bednar’s session — both happening tomorrow, April 18.

If you’re attending, stop by our booth and say hello, we’d love to meet you! https://bit.ly/4cbd0go

AI coding assistants often guess your setup.

With Anaconda MCP Server, Claude can actually see your machine, create environments with the right tools, and review what’s installed across projects: https://bit.ly/3Qb9SJZ

Most AI pilots don’t fail because of bad models, but because of gaps in infrastructure, governance, and planning. ❌

In this on-demand webinar, Anaconda’s Steve Croce breaks down why 95% never reach production and how to be part of the 5% that do: https://bit.ly/4tk5g2p

If you're using a list comprehension just to pass results into functions like sum() or max(), those brackets might be unnecessary.

Dropping them turns it into a generator, so Python processes each value on demand instead of storing everything in memory.

Anaconda will be at PyCon DE & PyData 2026 in Germany 4/14–4/16! 🇩🇪

Hear from Anaconda software engineers on 4/14 in sessions exploring Census/OpenStreetMap data and mixing conda + pip without breaking environments.

Stop by our booth to meet our team: https://bit.ly/41qvQLE

Anaconda’s audit trail makes it easier to understand how your packages change over time.

Every addition and removal is automatically recorded with a timestamp and reason. ✍️

Get a searchable history that shows exactly what changed, when, and why: https://bit.ly/4trT7IZ

Zempler Bank’s Head of Data Science and Engineering, James Coveney, shares how his team moved to Python to build more advanced fraud models without compromising security.

Discover how Zempler cut fraud by 90% while maintaining a smooth customer experience: https://bit.ly/4dS7S30

Now available: early access to the first *eight* chapters of AI Agents in Action, Second Edition! 📖

This comprehensive guide walks through building real-world agent systems with LLMs, MCP, and emerging patterns like multi-agent collaboration: https://bit.ly/45prHd8

You just wanted pandas and numpy… and suddenly conda’s solver is questioning your life choices.

When dependency conflicts hit, you might end up staring at a 'LibMambaUnsatisfiableError' for an eternity. ⚠️

We've got a tutorial to help fix dependency conflicts: https://bit.ly/4bQV6AM

The news of OpenAI acquiring Astral has sparked both excitement and caution across the Python community.

David DeSanto and Peter Wang dive deep into how open-source trust can shift when priorities change—and how Anaconda is responding: https://bit.ly/4c632wO

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