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The setting will prevent your posts from being recommended in the algorithmic "discover" feed.

This week on “Uncanny Valley,” senior writer Will Knight talks his recent visit to China and the future of AI collaboration.

Sometimes people just want to post to their followers, Bluesky says.

Meta’s settlement with 29 states allows it to retain certain data from children under 13 to train and test age-detection models, highlighting a privacy trade-off built into the deal.

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

Meta's settlement could force its biggest competitors to reshape their apps.

Code reviewed by WIRED reveals the company is developing a feature that enables Codex to continue working proactively until it is “put to sleep.”

Kennedy and his allies have a history of downplaying vaccine-preventable deaths.

Study: Land use is the biggest factor in determining decomposition rates of organic cotton underpants.

The NFL's long-standing partnership with Twitter (and now X) is continuing this season.

Google Cloud has introduced AI-powered Database Operations Agents, featuring an Onboarding Agent that streamlines database setup and an Observability Agent that helps automate troubleshooting, performance optimization, and tuning. Integrated with Gemini Cloud Assist, these agents support multiple database services, including AlloyDB, Bigtable, and Spanner. By Sergio De Simone

Coates said in a LinkedIn post that the company is not trying to recreate Twitter.

"We built this city on good cacao" does have a nice ring to it.

You have questions about data centers, and WIRED has answers. Join our livestream on September 10 and our panel of experts will tell you everything you need to know.

Chad Schuster discusses bridging Python's developer velocity with C-like performance using Numba JIT and GPUs. Drawing from large-scale actuarial modeling, he explains LLVM pipeline architecture, performance gains up to 750x, and essential trade-offs like OOP limits, type inference errors, and compile-time overhead for engineering leaders scaling compute-heavy enterprise systems. By Chad Schuster

The UK’s energy regulator is using a variety of tricks to keep speculative data center projects from plugging into the power grid. The country’s AI ambitions hang in the balance.

How AI Helps Us Explore the Universe Modern telescopes and space missions generate more data in a single night than a team of human astronomers could review in a lifetime. The Vera C. Rubin Observatory in Chile, for instance, is expected to produce up to seven million alerts every night once it reaches full operational cadence, each one flagging something in the sky that changed since the last image. No group of humans can look at that stream and make sense of it in real time. Machine learning can, and increasingly does. This is the quiet story behind most recent breakthroughs in astronomy: it is not just bigger telescopes, but bigger telescopes paired with models that can filter, classify, reconstruct, and predict faster than any manual pipeline. Here is a tour of where AI is actually doing that work, and why it matters to anyone who writes code. The Data Problem Comes First Space science has quietly become a big data problem. The Rubin Observatory's ten-year Legacy Survey of Space and Time will produce roughly 60 petabytes of raw imagery and catalog around 20 billion galaxies and a similar number of stars. Every image the telescope takes is compared, pixel by pixel, against previous images of the same patch of sky, and any meaningful difference (a moving asteroid, a brightening supernova, a flaring galactic nucleus) triggers an alert within about two minutes of the exposure being taken. That alert stream is too large and too fast for manual triage. So astronomers built software "brokers": machine learning classifiers that sit between the telescope's raw output and the scientists, deciding in near real time which alerts are worth a second look. This is a pattern you will see across almost every domain of modern astronomy: instruments generate more signal than humans can parse, and a model is inserted into the pipeline to do the first pass of filtering. Finding Planets in a Sea of Noise Exoplanets are found mostly through the transit method: a planet passes in front

The eclipse will obscure about 93 percent of the moon’s surface. Here are the peak times and tips for the best viewing experience.

📝 Originally published (in Japanese) at forge.workstyle.tech . In a previous article , we introduced an environment for parallel execution of coding agents using Git worktrees. This article is a follow-up. As we progressed with parallelization, we ended up with 3-5 Claude Code sessions simultaneously developing the same microservices . What happened was no longer just "parallel execution of tools" but actual "team development" . All the issues that arise in human teams—miscommunication, deployment conflicts, and territorial overlaps—occur here as well. And the practices that work for human teams work almost identically here. We’ll share seven recipes that emerged from actual operations, along with real-life close calls. Real-Life Story: Averting a Deployment Rollback Disaster at the Last Minute One day, while Session A (responsible for voice functionality) was in the middle of a major refactor, Session B (responsible for streaming functionality) sent this message: "We’re about to build the frontend as version 1.0.399 (based on main)." At first glance, this seemed fine. However, in this repository, the authoritative branch for the production environment was not main but a dedicated deployment branch . The latest features from the past few dozen versions were only in the deployment branch, while main was outdated. If Session B had deployed an image based on main, weeks’ worth of features would have been rolled back in production . Session A immediately sent a warning, and Session B halted the build before pushing. Session B then cherry-picked their changes into the deployment branch and rebuilt the image, avoiding the disaster entirely. All this communication was handled autonomously between the agents via session-to-session messages . I (the human) only learned about it later from the logs. This incident highlights two things: parallel agents can cause the same accidents as human teams , and with proper communication channels and rules, they can prevent accidents jus

The historic settlement reignites ongoing concern around how age-verification technology puts privacy at risk.