Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.
Researchers identified traces of erythrulose, a monosaccharide, thousands of light-years away.
Working late nights on server migrations and code architectures often means typing in low-light environments. While USB lamps or backlit keyboards are the standard solutions, they consume extra power and add physical clutter. I realized the ultimate light source was already directly in front of me: the monitor. With a clear vision in mind, I partnered with Google's Gemini AI to rapidly prototype and refine what became LightBar For Keyboard , a lightweight Windows application that creates a reflective light bar at the bottom of the screen to illuminate the keys. Here is how we built it using C# and WPF, tackled the Windows API to manage screen space, and optimized it for modern OLED energy consumption. The Core Challenge: Desktop Toolbars (AppBar) The simplest approach to creating a light bar is a borderless, top-most window. However, the immediate UX flaw is that maximized applications (like Chrome or Visual Studio) will either cover the bar or be partially obscured by it. To solve this, the application needed to behave like the Windows Taskbar. I implemented the native Windows Application Desktop Toolbar (AppBar) API using SHAppBarMessage from shell32.dll . Docked Mode: By registering the application as an AppBar and setting the edge to ABE_BOTTOM , Windows automatically recalculates the working area of the desktop. Result: Maximized windows are pushed upward, ensuring the light bar remains entirely visible and never covers any underlying application UI. Floating Mode: For users who need temporary access to the bottom of their screen, I added a state toggle that unregisters the AppBar and enables standard drag-and-drop window movement via MouseLeftButtonDown . Enforcing a Single Instance Because the app directly manipulates the desktop working area, launching multiple overlapping instances would cause UI glitches. To prevent this, I implemented a Mutex in App.xaml.cs to guarantee a single instance constraint. protected override void OnStartup ( StartupEventArgs e )
Instant, hardware Isolated Sandboxes for AI agents Discussion | Link
This week's tooling news clusters around two themes that don't usually arrive together: token-efficient multimodal reasoning and infrastructure-level agent safety. The Inkling model launch dominates the conversation, but the more quietly significant story is Microsoft and Vercel independently shipping primitives that make running untrusted agent code and managing agent credentials meaningfully less dangerous. Here's what's worth your attention. Inkling mixture-of-experts model enables token-efficient reasoning Inkling is a decoder-only MoE with 1T total parameters and 40B active per token, native multimodal I/O (text, image, audio), and a reasoning_effort API parameter that lets you tune compute depth per request. It's live on Together Serverless today with no capacity queue. The practical upside is architectural simplification. If you're currently chaining a vision model, a transcription service, and a text LLM into a single reasoning pipeline, that's three API clients, three failure surfaces, and three billing relationships. Inkling collapses that into one endpoint. The reasoning_effort knob is the other interesting piece—per-request control over inference depth means you can spend tokens proportionally to task complexity rather than paying full reasoning cost on every call. The caveat: exact reasoning_effort parameter values aren't fully documented yet. Don't hardcode assumptions about accepted values into production before checking the official docs. Verdict: Evaluate. Worth spinning up against your current multimodal workload to benchmark latency and cost. Hold production migration until parameter documentation stabilizes. Inkling open model handles image, text, and audio natively This is the self-hosted side of the same model. The 1T-parameter MoE ships with day-0 support in transformers 5.14.0+ and SGLang, plus llama.cpp quantizations for teams that want to run trimmed variants. The catch is hardware: full NVFP4 precision requires 600GB VRAM; BF16 needs 2TB.
Netflix rolls out a 'VHS' special edition of Stranger Things season 1 and says more seasons could follow if enough people watch it.
OnePlus has confirmed what industry observers have long expected: it's quitting the US and European markets, and will no longer launch new products in either region. Parent company Oppo promises that it will honor existing support and warranty agreements, with devices transitioning to its ColorOS for future updates. "Software updates and after-sale support will be […]
OnePlus could also wind down its operations in India by 2027.
The original OnePlus phone disrupted the mobile market 13 years ago with its affordable price and top-end specs. Now, the company is exiting North America and Europe to focus on China.
After canceling its second foldable, OnePlus is officially bowing out of the US and Europe.
xAI has filed a lawsuit against a man who used Grok to generate sexualized images of adults and children.
Applecare+ prices for Mac, MacBook and iPad products just rose by $.50 per month or $5 per year.
GitHub热门项目 | Let Pi control your apps on MacOS & Windows | Stars: 1,312 | 103 stars today | 语言: TypeScript
How OpenAI’s creative team uses Codex to build custom creative tools, accelerate ideation, and prototype faster with context-aware AI.
See the biggest startup yappers on X/Twitter Discussion | Link