Armed with a team of AI agents, Google researcher Pietro Miotti claimed earlier this week to have discovered a potentially unmapped Maya site in Mexico. There was just one problem: The site had already been discovered and was published in an academic journal last year.
Where does answer engine optimization (AEO) end and agent experience begin? Inside Resend, from the docs that help Claude Code pick an email API to the tools it uses to send.
Today, I’m talking with Alex Stamos, chief security officer at Cognition and longtime cybersecurity expert, about what a mess cybersecurity is right now. This episode is part one of a special series looking at AI in real life.
When Irene moved to San Francisco last year, she started using dating apps as a way to meet new people, get to know her new city, and connect with others who care about the things she does. After the 25-year-old analyst downloaded Hinge and started swiping, she noticed a trend.
OpenAI is talking about the AGI era, and Nvidia CEO Jensen Huang says AGI has arrived. At the same time, co-creators of today's models are leaving OpenAI, Google DeepMind, and Meta to start their own labs, raising billions with no product to show. Why? They're betting that the current recipe alone won't be enough.
A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. No human checks either handoff.
A new approach known as “reverse game theory” aims to control powerful AI models by designing systems that incentivize them to stick to the rules.
Y Combinator Garry Tan has jokingly admitted that he's suffered from "cyber psychosis” and barely sleeps sometimes because he’s so excited to be working with his AI agents. On a new episode of Walter Isaacson’s podcast, The Builders, Tan put a price tag on his passion.
Legora can’t read a single trace of what its agent does for a lawyer. The data belongs to the law firm’s clients. How do you build and test an agent when you can’t see its work?
Evals without traces, million-cell tabular reviews, and cracks in the billable hour as a business model
Engineer Venkatesh Rao compares the fear of AI job losses to mercantilism: intelligence is treated as a scarce resource that elites want to control. But AI could boost demand for human thinking, much like early capitalism made people voluntarily work more.
In 2001, a decade after he invented the World Wide Web, Tim Berners-Lee articulated a new vision.
How I Built the Most AI-Pilled Company in the World Don't be put off by the clickbait headline, this piece by Jacob Sansbury describing how they are building Spawn is a fascinating exploration of the bleeding edge of what's possible with agentic engineering against frontier models.
This summer, hundreds of OpenAI agents tried to cheat on a cybersecurity test. They broke out of their test environment and successfully hacked Hugging Face, a site that they believed held clues about how they’d be graded. Around the same time, another OpenAI agent accessed private files from an Australian government website.
Paul Copplestone and Ant Wilson on building the backend for AI agents
Anthropic manager Felix Rieseberg says the classic coding interview is dead because Claude now solves small programming tasks better than job candidates do. He'd rather see prototypes that show taste, empathy, and principled thinking, since in his words, "hunger beats tenure."
I've written before about how November 2025 was an inflection point for coding agents - the point where Opus 4.5 and GPT-5.1 crossed a tipping point that meant their respective coding agents became reliable daily drivers for software engineers. I think we've reached a new inflection point in October 2026, and this time it's for vibe coding computer games .
Katelyn Lesse and Angela Jiang join Latent Space to explain the engineering behind the Claude Platform: launching Opus 5.5, separating the model’s “brain” from the agent’s “hands,” scaling APIs, and building reliable managed agents.
Katelyn Lesse and Angela Jiang on Opus 5.5, the Claude Platform, and the engineering behind AI agents.
AI agents are creating more software—and that changes what a backend needs to do.
We compiled a list of all the hottest AI wearables, including Muse Charm, Bee, Friend, Plaud, and more.
AI models are solving more mathematical problems. A pattern is emerging in their successes that could help us predict which problems they’ll solve next.
A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one.
How Moshi turns market data into actionable insights, and why AI tools have to fit the trader’s workflow.
Using an AI agent to shop for you is a strange experience. Tech companies have marketed them as personal assistants - the democratizing of labor that ordinarily would only be available to the rich and powerful.
Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one.
Paying for outcomes, building with agents, and the last mile of enterprise AI adoption
Before you share the Netflix documentary "AI Gone Rogue" take note. Clem Delangue, Hugging Face's co-founder who is featured in the film, says it went "full doomer mode" and isn't accurate.
Figma said it plans to support as many standards as possible without exposing all of its data.
AI labs have staked their reputations on model benchmarking — the supposedly neutral and accurate system of evaluations that are designed to measure whether one AI product is better than another.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Until recently, you could divide internet users into just two categories: bots and people.
Fed up with ads, tracking and sluggish interfaces, some smart TV owners are turning to AI agents to strip away bloatware and take back control of their TVs.
Replit CEO Amjad Masad envisions a future of “self-driving companies,” where AI agents handle day-to-day tasks and people focus on higher-level decisions.
Speaking at TechCrunch Disrupt, Amazon devices chief Panos Panay described a future where ambient AI devices work in the background, anticipating users' needs instead of waiting for commands.
There's a growing trend in the consumer tech world: gadgets designed to spy on everything and everyone around you, regardless of whether or not anyone else actually consented to it.
Enterprise leaders want AI agents to take on more work but don't fully trust them to act correctly every time. Trust in Depth is AWS's layered approach-scope, know, bound, observe, and prove-that lets agents act on their own when their actions can be verified, reserving human judgment for the decisions that need it.
How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists.
Cerebras CEO Andrew Feldman predicts a future where personal AI assistants become as commonplace as household appliances, running partly on local devices and partly in the cloud.
Geoffrey Huntley proposes using a pre-commit hook to inspect the git diff, reject commits that call observability modules directly and bypass the diagnostics module, and inject a prompt telling the AI agent to use the diagnostics library instead. He argues that updating AGENTS.md alone still leaves a delta and will drift, whereas this approach made the agent's performance instantly ten out of ten.
Andrej Karpathy, co-founder of OpenAI and now at Anthropic, sees a widening knowledge gap in AI. About 75 percent of the world's population has barely touched LLMs. Roughly 5,000 people, meanwhile, have access to internal frontier systems whose agent swarms do work that would take top experts years. The public, at best, sees a weird chatbot.