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Deepfakes of Venezuela’s ousted president, Nicolás Maduro, flooded the internet after his capture, in a new collision of breaking news and artificial intelligence.
A.I. Images of Maduro Spread Rapidly, Despite Safeguards Read More »
The following article originally appeared on Block’s blog and is being republished here with the author’s permission. Lately, I’ve seen more developers online starting to side eye MCP. There was a tweet by Darren Shepherd that summed it up well: Most devs were introduced to MCP through coding agents (Cursor, VS Code) and most devs struggle to
MCPs for Developers Who Think They Don’t Need MCPs Read More »
Despite reporting to the contrary, there’s evidence to suggest that Grok isn’t sorry at all about reports that it generated non-consensual sexual images of minors. In a post Thursday night (archived), the large language model’s social media account proudly wrote the following blunt dismissal of its haters: “Dear Community, Some folks got upset over an
No, Grok can’t really “apologize” for posting non-consensual sexual images Read More »
The following article originally appeared on Medium and is being republished here with the author’s permission. There’s a fake confidence you can carry around when you’re learning a new technology. You watch a few videos, skim some docs, get a toy example working, and tell yourself, “Yeah, I’ve got this.” I’ve done that. It never lasts. A
If You’ve Never Broken It, You Don’t Really Know It Read More »
The Model Context Protocol (MCP) is genuinely useful. It gives people who develop AI tools a standardized way to call functions and access data from external systems. Instead of building custom integrations for each data source, you can expose databases, APIs, and internal tools through a common protocol that any AI can understand. However, I’ve
Following the publication of his new book, Building Applications with AI Agents, I chatted with author Michael Albada about his experience writing the book and his thoughts on the field of AI agents. Michael’s a machine learning engineer with nine years of experience designing, building, and deploying large-scale machine learning solutions at companies such as
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
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
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
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
