Some of the most eye-catching claims about AI may be performative; less about securing actual investment, and more about shaping perception

One of the principles in our upcoming book Architecture as Code is the ability for architects to design automated governance checks for important architectural

Chris Lattner on Software Craftsmanship and AI

Using ChatGPT, Copilot Pro, and AI add-ins to save time and work more efficiently

Editor's Note: This article launched an ongoing series exploring the Sens-AI Framework—practical habits for learning and coding with AI. The framework is

What Is a Semantic Layer, and Why Is It Important?

How Top Grads Strategically Use AI to Improve Their Chances of Success

From data readiness to infrastructure changes, and from ethical concerns to automation trends, AI is transforming how organizations design, build, and evolve their architectures.

The following originally appeared on Asimov's Addendum and is being republished here with the author's permission.The other day, I was looking for parking

Why Treating Cursor or Copilot like a Coworker Will Burn You

We’ve been bombarded with claims about how much generative AI improves software developer productivity: It turns regular programmers into 10x programmers, and

Become an expert on data science and software engineering for this library of ebooks from O’Reilly! All purchases support Code for America!

If the assumption that we've lived with for years that scale brings lower marginal costs isn’t true, we really have to rethink a lot of things. Anjali Shrivastava is an independent researcher and data scientist looking into the unit economics of AI—probing how the seemingly simple token unit of cost for generative AI services is actually highly variable, leading to hidden margin risks that undermine conventional subscription or usage pricing models. Anjali joined Tim to talk through her recent work, particularly as it relates to code-generation tools like Cursor and Claude Code. Over the course of a very interesting hour, they got into the distinctions between input tokens and output tokens and between reasoning tokens and output tokens, and why they matter; the challenges providers face in instituting systems to offer visibility for users, and even for themselves—and why it’s not only a business problem to solve but one at the heart of AI products as they currently exist; how AI service providers differ from traditional SaaS businesses in ways that make the latter’s pricing model obsolete (even though as of now, it’s the model that AI providers continue to use); and how AI providers can balance, and even temper, demand as use scales. They also discussed the efficiency opportunities new hardware may provide, one potential future where AI gets much pricier, why efficiency alone won’t solve the pricing issue, how external bottlenecks, like electricity, influence pricing, and much more. Watch it now. Follow O'Reilly on: LinkedIn: https://www.linkedin.com/company/oreilly/ Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia BlueSky: https://bsky.app/profile/oreilly.bsky.social

We've been watching enterprises struggle with the same customer service paradox for years: They have all the technology in the world, yet a simple address

In today’s fast-paced business environment, the ultimate goal of any data effort is to enable better decisions and drive meaningful organizational outcomes.

Tools, Techniques, and Best Practices for Supporting AI and ML Use Cases

Much like the introduction of the personal computer, the Internet, and the iPhone into the public sphere, recent developments in the AI space, from Generative

Ask Lee Atchison and F5 what's important for the foundational technology stack in the AI era.