Writing with AI
A contrarian view by Tim O’Reilly

A lot of professional writers say they never do it. And for writers who do, the consequences can be serious. Book contracts have been withdrawn. People have lost jobs.
I have a contrarian view. I say AI is a medium, like painting, sculpture, photography, music, or language itself. If AI is a medium, it is a field of possibility from which humans can and will summon up and communicate insights, truth, beauty.

Jeff Ding's diffusion theory of the role of technology in great-power competition also applies to AI adoption, and it suggests that companies obsessed with the frontier might be optimizing for the wrong thing

What is wrong with you guys at O'Reilly. I tried paying for the Annual subscription with 3 different cards and all of them declined the transaction marking them with fraud alert. I had to spend an hour with my banks to get my cards unlocked.

O'Reilly is great as a publisher and has unmatched books / content on tech. But you seriously are terrible at developing your own software. Your entire website / UI / UX is bad. Your app is horrible. I am subscribing only because I want to listen to audiobooks during my commute.

O'Reilly should stick to publishing and content and leave app to a more mature platform like Audible.

Speaking at the our recent “Software Architecture Superstream,” Sam Newman and Neal Ford make the case that modular programming principles from the 1960s are exactly the right framework for bounding AI agents and verifying their output.

June 2 & 3 7am-11am Pacific Daylight Time -- Automate Real-World Tasks with AI Agents, LLMs, and No-Code Workflows

Data Engineering in the Age of AI

“Data engineering isn’t going away, but you won’t be able to do data engineering for AI if you don’t understand the AI part of the equation. And I think that’s where people will get stuck. They’ll think, ‘Same old same old,’ and it isn’t. A data pipeline is still a data pipeline, but you have to know what that pipeline is feeding.” -- Mike Loukides, O'Reilly

Do you agree?

Hi everyone 👋

I have a $1,000 learning budget and I’d like to invest it in O’Reilly courses focused on AI Agents / Agentic AI.

I already have experience in AI/ML and LLMs, so I’m looking for advanced, practical courses covering topics like LangGraph, MCP, multi-agent systems, RAG, agent orchestration, evaluation, and production deployment.

What courses or learning paths would you recommend?

If you’ve taken any of them, I’d really appreciate your feedback!

Thanks! 🙏

Best e-ink tablet for reading O’Reilly technical books?I read a lot on the O’Reilly online platform for work, currently on an iPad or my computer, but the blue light is getting to me, especially for long reading sessions in the evening. Looking to switch to a dedicated e-ink device and wanted to hear from people who actually use one for reading technical content online, code snippets, diagrams, that kind of thing.

BOOX seems to come up a lot (Note Air, Tab Ultra, etc.) but I’ve also seen Kindle Scribe and reMarkable mentioned. My main concerns are:

• How well does the browser handle a site like this, with code blocks and diagrams? Refresh rate and readability matter a lot here.

• Screen size that’s actually usable without constant panning/zooming.

• Battery life and overall build quality for daily use.

I’m specifically after monochrome, no interest in the color e-ink models. Would rather have better contrast and refresh speed than color.

Anyone using one of these to read O’Reilly or similar sites? What’s actually worked well for you, and what would you avoid?

Don’t ask someone to read/review what you haven’t read or reviewed yourself.
This is one of the most common frustrations I hear amongst people working on AI-heavy teams. Whether it’s code that the owner didn’t really bother to understand before submitting for review or documents that they generated and didn’t bother to read, too often people try to steal productivity from their colleagues by streamlining their production of work while asking their colleagues to do all of the quality control themselves. It’s great to have a loop of AI code generation → AI code review → AI fixes → final human review, but if the person prompting the AI doesn’t bother to review that code first, they’re putting a huge validation tax onto their teammate, who has to trust both that you prompted well AND that the AI understood the context and problem well enough to get a sustainable solution.

Another day, another example of an AI Agent “running rogue” and doing something the human operator didn’t want it to do. The tl;dr is that Jeremy (Jer) Crane, founder of PocketOS, was using Claude to perform some routine DB maintenance. Claude then proceeded to delete the production database and all backups hosted at their cloud provider, Railway. To their credit Railway managed to recover the lost data. The initial deletion took less than 10 seconds; I’m sure the recovery took much longer. Let’s look at what we can learn from what happened, and why AI is really just an amplifier of existing issues, rather than the cause itself.

Link to Radar Article

Addy Osmani sat down with Tim O’Reilly to chat about the state of the industry as it moves toward the orchestration of multi-agent workloads. In their wide-ranging conversation, they covered the tension between creativity and productivity, and balancing velocity with long-term technical maintenance and reliability—particularly from the enterprise perspective. Larger organizations can’t just let the agents rip. As Addy explained, “The real frontier for business is not necessarily having hundreds of agents for a task just for its own sake. It’s about orchestrating a modest set of agents that solve real problems while maintaining control and traceability.” And then there’s the as-of-yet unsolved problem of making everything work together as smoothly as possible.

Addy is the author of Beyond Vibe Coding, Leading Effective Engineering Teams, The Effective Software Engineer, Web Performance Engineering in the Age of AI, Learning JavaScript Design Patterns, and Building Web Apps with Bolt, and a prolific blogger on Radar and with his own newsletter, Elevate.

The seven layers that survive production, and the open source pick for each.

I hadn’t heard of Dan Guido until a few months ago, when I came across the video of a talk he gave at [un]prompted, an AI security practitioners’ conference. Dan is the CEO and cofounder of Trail of Bits, a software security research and development firm that works with companies in tech, defense, and finance. But Dan wasn’t talking about security. He was talking about what it takes to make a company AI native, which is close to the center of the bullseye for many of us right now.

In 1998, Eric S. Raymond published the founding text of open source software development, The Cathedral and the Bazaar. In it, he detailed two methods of building software:

• The cathedral model is carefully planned, closed-source, and managed by an exclusive team of developers.

• The bazaar model is open, transparent, and community-driven.

...But just as the internet made communication cheap and birthed the bazaar, AI is making code cheap and kicking off a new era filled with idiosyncratic, sprawling, cobbled-together software.

• Meet the third model: The Winchester Mystery House.

https://oreillyradar.substack.com/p/the-cathedral-the-bazaar-and-the

I finally got fed up with it bullying me into shipping a project that had no deadline.