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About a quarter of PCs are still running Microsoft's previous operating system.

Agent-testing startup Patronus AI, founded by former Meta AI researchers, is experiencing nearly insatiable demand, its investor says.

I've been experimenting with a compiler/runtime project that I'm not entirely sure is a good idea, so I'd love some feedback from people who've worked on deployment systems. The idea is to compile an exported PyTorch model into a self-contained package that contains: graph binary weights backend kernels (currently WGSL) runtime metadata A lightweight runtime loads that package and executes it directly in the browser with WebGPU. No Python, no server inference, and no dependency on a heavyweight runtime. Right now the attached demos are just neural video representations because they were easy to test, but the motivation is actually operator networks and scientific ML, where I like the idea of distributing a single portable artifact. The repo is here: https://github.com/Slater-Victoroff/Kuma I'm mostly looking for architectural feedback. Some questions I'm wrestling with: Is embedding backend kernels in the artifact a terrible idea? Is this solving a real deployment problem or just reinventing ONNX Runtime? Are there existing systems I should study that take a similar approach? If you were designing a deployment format today, what would you change? I'd especially appreciate thoughts from people who've worked on ONNX, IREE, TVM, ExecuTorch, MLIR, or similar compiler/runtime projects. submitted by /u/svictoroff [link] [留言]

This week, Instagram launched a series of new features for its smart TV app that are all designed to get people to spend more time on the platform through the biggest screens in their homes. In addition to vertical Reels, Instagram for TV - which is currently available for Amazon Fire TV, Google TV, and […]

Carr cites screen time concerns, is accused of trying to be "the nation’s parent."

As far as prices go, Apple is kind of a reverse canary in the coal mine. With its famously generous margins and immense purchasing volume, it can afford to ride out price fluctuations in its supply chain in a way no other consumer tech company can. So when Apple raises prices across nearly all of […]

The prediction market giant Polymarket said it's refunding users who had funds stolen due to a third-party breach.

The company says the increases are being driven by rising memory and console storage prices, with costs more than 2.5x higher than previous levels.

Notion is "going all in on using agents to run your inbox."

Cloudflare has recently shared how they uncovered an issue in their Rust implementation of CUBIC, a congestion controller algorithm, which prevented it from recovering from a scenario of heavy packet loss at the start of a connection. By Gianmarco Nalin

Table of Contents Overview Southwest and AWS From On-Prem to Cloud AI Comes Into Play Kiro Why This Matters? Closing Thoughts Overview Southwest Airlines is one of the largest carriers in the world. Other than having by far my absolute favorite airplane livery, it's a massive enterprise based in Dallas, Texas, with more than 72,000 employees, over 4,000 daily flights during peak travel periods, roughly 134 million customers annually, and service across 120+ airports in 12 countries. At this scale, what really matters is operational reliability and speed. Because in aviation, slow operations cause delays, delays cause unhappy customers, and unhappy customers aren't particularly great for the company's revenue. A few minutes of latency in one system can turn into hours of disruption in the real world. So... can cloud and AI solve it? Absolutely, if done right. Southwest and AWS Southwest has selected Amazon Web Services (AWS) as its primary cloud partner to help modernize its technology stack and transform how the airline runs, develops systems, and serves its customers. Through this collaboration, Southwest plans to move away from a predominantly on-premises infrastructure toward a cloud-based, AI and agent-enabled architecture on AWS by 2028 . 2028 is not far away from now (Jun 25, 2026). This is very ambitious considering the amount of work that needs to be done. From On-Prem to Cloud Moving an enterprise of this size from on-prem infrastructure to the cloud is way more complex than it sounds. It doesn't sound easy either. It's one of the hardest things you can ever do as a software engineer, DevOps engineer, architect, engineering manager, or anyone else involved in it. It involves a lot of steps, including but definitely not limited to: Assessing existing systems, dependencies, and infrastructure to understand what needs to move and how Defining a migration strategy (lift-and-shift, replatforming, or full refactoring to cloud-native architecture) Designing and bu