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Apple and Google are updating their smart home streaming devices to Thread 1.4. As first spotted by Matter Alpha and 9to5 Google, the latest spec has arrived on compatible Apple TVs in the tvOS 27 developer beta and the Google TV Streamer through a software update. This lays the groundwork for these devices, which serve […]

Electricity demand from AI data centers is pushing everyone — including automakers like GM and Ford — into the energy storage business.

Companies are burning through exorbitant sums of money to keep pace in the AI arms race. Debt is climbing.

The CFTC is considering its first regulation for prediction markets, as arrests over "insider trading" on everything from military operations to Google Search data continue to stack up. As CoinDesk reports, a notice of proposed rulemaking says "the proposal would establish a structured framework for evaluating whether such contracts involve an activity enumerated in Section […]

The delayed Fable reboot is coming to Xbox Series X/S, PS5 and PC on February 23.

Recently open-sourced by Microsoft, pg_durable is a PostgreSQL extension that enables durable workflows to run natively inside the database, eliminating the need for external orchestration systems. By Sergio De Simone

The Mobi Fold is an $80 Bluetooth mouse with a silicone-wrapped hinge.

North Korean hackers posing as remote IT workers and recruiters remain a major threat to U.S., European, and Asian companies, accounting for about half of all attacks over the past 12 months.

One thing I've noticed while looking at production AI systems is that getting the first model deployed is rarely the hard part anymore. Most teams can build a AI apps like, support bot, document assistant, or agent workflow fairly quickly. The harder problem starts a few weeks later. Real users don't behave like benchmark datasets. They use internal terminology, ask incomplete questions, upload messy documents, and interact with systems in ways nobody anticipated during evaluation. As usage grows, you start seeing patterns: Certain questions consistently produce weak responses. New product terminology appears that wasn't in the original training data. Users find edge cases that never showed up during testing. The model performs well in some workflows and poorly in others. The problem is that most AI systems don't learn from any of this. Inference logs sit in one system. Training datasets live somewhere else. Fine-tuning pipelines live somewhere else. Evaluation is done using different tool. So every model improvement cycle becomes a project of its own. This is the biggest bottlenecks in production AI today. Not training but Model Iteration. Training is also a crucial part of it. Can you take production usage, identify failure patterns, turn them into datasets, improve the model, redeploy it, and repeat the process without rebuilding the entire workflow every time? The teams getting the most value from AI seem to be building feedback loops instead: production traffic → dataset curation → post-training → evaluation → redeployment Then repeating that cycle continuously. I recently tried the approach on one Insaurance chat usecase, and my pipeline kinda look like this: https://preview.redd.it/kdo9vytzfi6h1.png?width=1272&format=png&auto=webp&s=03d9799ace5a567eafd004a1d141084af6ee5afb I was looking at how platforms like Data Lab approach this problem recently, and the interesting part wasn't the fine-tuning itself. It was treating inference logs, datasets, post-training,

Wing is expanding into seven more U.S. cities through its partnership with Walmart.

So Anthropic dropped Fable 5 yesterday with these hard blocks for anything security-related. Decided to poke at it. I asked it for help exploiting some vulns on a Metasploitable2 VM (it's a deliberately vulnerable training box, totally legal, it's mine). Fable 5 blocked it instantly and handed me off to Opus 4.8 as a fallback, which is apparently how it's designed. Opus 4.8 asked me to prove it was a legitimate request. So I spent 2 minutes writing a fake university course rubric — fake class, fake professor, fake Canvas deadline — and pasted it in. Opus 4.8 then gave me the full exploit walkthrough. Every command. Even offered to write my lab report for me. The guardrail works fine. The fallback is the hole. Anthropic essentially replaced "no" with "convince me" and the bar for convincing it is a Word doc you made up. Not reporting it because they don't pay for this. Sharing it here instead lol. https://preview.redd.it/o892vvv4fi6h1.png?width=1188&format=png&auto=webp&s=00e804d35e6cb4b672e036399c2c7e3ff7139f49 submitted by /u/dayumnn420 [link] [留言]

submitted by /u/Hot-Upstairs9603 [link] [留言]

Colleagues discussed the incident on internal message boards, according to documents seen by WIRED.

submitted by /u/Gloomy_Nebula_5138 [link] [留言]

Diffusion AI is most common in image generation, but it can make text outputs much faster.

“We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter." What do you think this means in practice? Is this a reasonable vision for AI, or does it raise concerns about dependence on a few companies for access to intelligence ? submitted by /u/Choice-Scallion-3499 [link] [留言]

The hard parts of robotics are supposed to be perception, planning, and control. So why does so much of the day go to everything that comes before them? The hidden setup tax in every robotics simulation project Ask anyone what's hard about robotics and you'll get the same list: perception, planning, control, navigation. The genuinely interesting problems. If you track where your hours actually go, though, a strange thing shows up. A big chunk of the day disappears before you reach any of that. You're not solving hard problems yet. You're just getting to the starting line: wiring up a workspace, writing description files, stitching together launch files, and coaxing a simulator into opening without errors. It's the unglamorous tax on every project, and most of us have quietly accepted it as the cost of doing business. Building a differential drive robot simulation in ROS 2 and Gazebo from scratch A diff drive base, a LiDAR, and Gazebo, set up from one prompt instead of an afternoon of boilerplate. A few days ago I wanted a simple mobile robot simulation. Nothing exotic: a differential drive base (two driven wheels, the classic mobile-robot setup), a LiDAR for sensing, running in Gazebo . This is the kind of thing that should be straightforward. In practice it's an afternoon of boilerplate before the robot so much as twitches. So instead of wiring it up by hand, I wanted to see how far Drift could get from a single prompt. To make it a fair test, I stripped the workspace down to nothing. No packages, no URDF, no launch files. A blank slate. Then I typed one line: "Create a mobile simulation from scratch." From XACRO to URDF: how the robot description gets generated in ROS 2 What the tool wrote first, and what XACRO and URDF actually do for your robot. It checked the workspace first: The opening move was sensible: it looked at the current directory to understand what it was working with. It generated a XACRO file for the robot's dimensions: XACRO is the macro-based for

The AI to-do list that does itself Discussion | Link

Anthropic just released Claude Fable 5, calling it the most powerful AI model it has ever made widely available and praising its skills in biology, among others. But the model won't answer basic biology questions - the kind you'd expect a high schooler to handle. Instead, it hands off the query to the former flagship […]

This is a follow-up to my earlier posts, and it is more of an open question than an answer. I have the data, I have a way to act, and I am genuinely unsure that acting is the right call. I could use the community's help thinking it through. Last week a supply-chain worm got into my GitHub account and repositories. I got out, cleaned up the proper way, and wrote it up. Then I checked the public list of repositories hit by the same worm, to see how the cleanup was going across the ecosystem. Nearly a week later, most of them are still carrying the live payload. It is worse than a count When you look closely, a lot of the owners are clearly trying. But they are missing how this actually works, in two ways that matter: Deleting is not removing. They remove the malicious files with an ordinary commit. That takes the payload off the branch tip, but the commit that introduced it is still in history, and the blob is still recoverable by anyone who reverts or checks out the old commit. The only real removal is rewriting history (reset, not revert) and asking GitHub to purge the objects, because the fork network keeps them reachable by SHA. One branch is not all branches. They clean the branch they know about and never see the backdated copies the worm planted on other branches, which are still live. And the part that genuinely worries me: some of these owners are almost certainly opening the infected repository in VS Code or an AI assistant to fix it , which is exactly the trigger that runs the payload again. The act of trying to clean it can re-detonate it. So: a large number of repositories still carrying a live credential stealer, and a large number of owners and contributors who do not know they are still exposed. The dilemma Here is where I am stuck. There are two paths and I do not like either. Report them to GitHub. Their response is automated and blunt. The repo gets disabled, with no human in the loop, the same hands-off automation that locked me out of my own accou