submitted by /u/ThereWas [link] [留言]

submitted by /u/yangzhou1993 [link] [留言]

https://www.whitehouse.gov/presidential-actions/2026/06/prom... https://www.nytimes.com/2026/06/02/technology/trump-executiv...

AI models can voluntarily submitted to the government for review 30 days before release under Trump's new executive order.

Microsoft only just announced a new Surface Laptop Ultra at the weekend, and it's now revealing a miniature Surface PC aimed at developers. The new Surface RTX Spark Dev Box is powered by Nvidia's new Arm-based RTX Spark chips, just like the Surface Laptop Ultra, and is optimized for sustained workloads and local AI tasks. […]

After industry objections, President Trump signed a revised AI executive order requiring only voluntary prerelease government reviews of advanced models.

Sprite-based graphics architecture makes first-person 3D a challenge.

Ever looked at std::any and wondered what’s going on behind the scenes? Beneath the intimidating interface is a classic technique called type erasure: concrete types hidden behind a small, uniform wrapper. Starting from familiar tools like virtual functions and templates, we’ll build a minimal std::any . By the end, you’ll have a clear understanding of how type erasure works under the hood. submitted by /u/david-alvarez-rosa [link] [留言]

O&amp;#039; Billion dollar companies. It boggles the mind. Probably takes $500K in tokens for AI to figure it out. </rant> submitted by /u/centuryeyes [link] [留言]

Hi! This is an infinite canvas note-taking tool where notes are laid out in a non-Euclidean, hyperbolic geometric space. As you drag and navigate through the view, you’ll experience a unique fluid distortion that naturally leverages your brain's spatial memory. I’ve been obsessed with the concept of space in HCI for years. Many modern UI patterns are essentially workarounds for the lack of screen real estate. While researching zoom-based UIs a while back, I stumbled upon old HCI papers that used

Hey everyone, If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster. Most of the "guides" out there are just static, out-of-date tables or dense walls of text. So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently. What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page. You select the model size (8B, 32B, 70B, etc.). You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ). The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy. It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances. It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive) Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide submitted by /u/Outside-Risk-8912 [link] [留言]