The pilot stage is the best time to consider the implications of architecture, security, and operations for running AI systems in production.

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A programmer on your team is great at maintaining the old system. But the company has switched to a new platform. What do you do with the no-longer-effective developer? This story is not hypothetical, though I've changed the names and technologies to protect identities. Based on the response I got when I asked dozens of developers and managers how they'd handle the situation, the situation is also not at all unique. See how you'd respond. (Warning: This is part 1 of 2.)

The new algebraic methods allow optimizations on floating-point addition, subtraction, multiplication, division, and remainder operations using the algebraic properties of real numbers.

Google adds pay-as-you-go Gemini pricing as enterprises seek control over AI spending ... startup Doozer AI, pointed out. “When a pilot requires a license ... generative AI. He writes for CIO, InfoWorld, Computerworld, and Network ...

Podcaster John C. Dvorak, who wrote for publications including InfoWorld, PC Magazine and the San Francisco Examiner, was known for his skepticism of early computer technology.

Open-weight AI models are a big deal, as Nvidia and more than 200 other companies and organizations attested in their “Open Weights and American AI Leadership” letter in July. The question is who will pay for them. Why are they a big deal? Well, former Red Hat CEO Jim Whitehurst will tell you that “open weights can play the same catalytic role [as open source]” to drive innovation in AI. This isn’t to suggest that open source, or open weights, will “win” in some absolute sense. Linux, for example, has become essential to the fabric of enterprise computing, but there are still plenty of Windows servers running. So open-weight models like Kimi or Nemotron aren’t going to topple Anthropic or OpenAI anytime soon. However, according to Whitehurst, they can “create a broader competitive landscape where innovation happens faster, and the power of AI is broadly shared.” Again, only if we can figure out how to pay for them. Betting on self-interest That was the concern I expressed to Whitehurst, much more politely than when he became Red Hat CEO back in 2007, and it’s something the industry must confront. Or maybe not. Open source, for example, has always depended on...

DuckDB, the analytics database with high-end features in a single easily deployed binary, is due to release a major update, version 2.0, this fall. In a blog post on the DuckDB site, the project’s maintainers outlined several key features that motivated a major version bump. The single biggest is a client/server mode, allowing DuckDB to function both as a compact, in-process database for a single client (its original use case), and as a service for multiple clients. The previously released quack extension for DuckDB made this possible on an experimental basis; now quack is being promoted to a stable release with DuckDB 2.0. The quack extension also makes it easier to remotely query other supported databases such as PostgreSQL and MySQL. Many of the other major changes in DuckDB 2.0 are internal, but with big implications for end users. The data engine beneath DuckDB now works asynchronously, allowing the I/O layer and the query processing layer to scale separately and make better use of parallelism. This, plus other internal changes, means faster queries overall, with the speedups ranging up to 40 times faster depending on the kind of workoad involved. A new storage format for the database means databases with...

Today’s programming languages allow developers to make sure our agents are producing quality code. Before long, they’ll be unnecessary.

My current model-selection strategy is embarrassingly simple… and wrong: I choose the most capable (expensive) model because I’m worried the cheaper one might get something wrong. That makes me spend too much, of course, which is why I (like you, perhaps?) continue to struggle with one of the most fundamental challenges in AI: How can I know when a cheaper model is sufficient for a task? Or, really, which model should I use at all? I asked a friend, Leo Zheng, who leads marketing for Fireworks AI, an AI infrastructure company that runs and improves open-weight models. Surely it was his job to know? His answer surprised me. I thought the answer would come down to models, but it doesn’t. Fireworks, he said, wants to “enable every company to own the continual learning loop within their four walls.” The idea is to abstract away model updates while companies feed the system new signals as customer behavior changes. That’s when it clicked. I was asking how to automate model choice, but the harder problem is building a feedback loop that tells a company what worked. Model choice then becomes simply one important action the system can take, not the be-all and...

Akamai Security Research https://www.akamai.com/blog/security-research/rails-active-storage-rce-cve-2026-66066 Rapid7 Emergent Threat Response https://www.rapid7.com/blog/post/etr-kindarails2shell-cve-2026-66066-critical-arbitrary-file-read-and-possible-remote-code-execution-in-ruby-on-rails/ HeroDevs Security Blog https://www.herodevs.com/blog-posts/cve-2026-66066-rails-active-storage-arbitrary-file-read-and-rce InfoWorld News https://www.infoworld.com/article/4205407/ruby-on-rails-critical-bug-puts-every-image-upload-under-scrutiny-2.html

Podcaster John C. Dvorak, who wrote for publications including InfoWorld, PC Magazine and the San Francisco Examiner, was known for his skepticism of early computer technology.

Podcaster John C. Dvorak, who wrote for publications including InfoWorld, PC Magazine and the San Francisco Examiner, was known for his skepticism of early computer technology.