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The following article originally appeared on Medium and is being republished here with the author’s permission. This post is a follow-up to a post from last week on the progress of logging. A colleague pushed back on the idea that we’d soon be running code we don’t fully understand. He was skeptical: “We’ll still be […]
The End of Debugging Read More »
Quantum computing (QC) and AI have one thing in common: They make mistakes. There are two keys to handling mistakes in QC: We’ve made tremendous progress in error correction in the last year. And QC focuses on problems where generating a solution is extremely difficult, but verifying it is easy. Think about factoring 2048-bit prime
Software 2.0 Means Verifiable AI Read More »
The market is betting that AI is an unprecedented technology breakthrough, valuing Sam Altman and Jensen Huang like demigods already astride the world. The slow progress of enterprise AI adoption from pilot to production, however, still suggests at least the possibility of a less earthshaking future. Which is right? At O’Reilly, we don’t believe in
What If? AI in 2026 and Beyond Read More »
In 2025 AI reshaped how teams think, build, and deliver software. We’re now at a point where “AI coding assistants have quickly moved from novelty to necessity [with] up to 90% of software engineers us[ing] some kind of AI for coding,” Addy Osmani writes. That’s a very different world to the one we were in
Software in the Age of AI Read More »
When AI systems were just a single model behind an API, life felt simpler. You trained, deployed, and maybe fine-tuned a few hyperparameters. But that world’s gone. Today, AI feels less like a single engine and more like a busy city—a network of small, specialized agents constantly talking to each other, calling APIs, automating workflows,
AI Agents Need Guardrails Read More »
The debate about open source AI has largely featured open weight models. But that’s a bit like arguing that in the PC era, the most important goal would have been to have Intel open source its chip designs. That might have been useful to some people, but it wouldn’t have created Linux, Apache, or the
What MCP and Claude Skills Teach Us About Open Source for AI Read More »
The debate about open source AI has largely featured open weight models. But that’s a bit like arguing that in the PC era, the most important goal would have been to have Intel open source its chip designs. That might have been useful to some people, but it wouldn’t have created Linux, Apache, or the
What MCP and Claude Skills Teach Us About Open Source for AI Read More »
The following article originally appeared on Medium and is being republished here with the author’s permission. Don’t get me wrong, I’m up all night using these tools. But I also sense we’re heading for an expensive hangover. The other day, a colleague told me about a new proposal to route a million documents a day
Job for 2027: Senior Director of Million-Dollar Regexes Read More »
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 information at Dulles International Airport, and was delighted with the conciseness and accuracy of Google’s AI overview. It was much more convenient than being told that the information could be found
AI Overviews Shouldn’t Be “One Size Fits All” Read More »
Headlines surfaced by a simple “job market” search describe it as “a humiliation ritual” or “hell” and “an emerging crisis for entry-level workers.” The unemployment rate in the US for recent graduates is at an “unusually high” 5.8%—even Harvard Business School graduates have been taking months to find work. Inextricable from this conversation is the
Countering a Brutal Job Market with AI Read More »
