CUDA for AMD Lemonade, Intel Arc Pro Linux Gains, XPU Manager 2.0 Today's Highlights Today's top GPU news highlights include AMD's Lemonade SDK gaining NVIDIA CUDA support, significant performance improvements for Intel Arc Pro GPUs on Linux 7.1, and the major 2.0 overhaul of Intel's XPU Manager for better GPU management on both Windows and Linux. AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support (Phoronix) Source: https://www.phoronix.com/news/AMD-Lemonade-10.7-Released AMD has released a new version of its Lemonade SDK, a powerful local AI server solution designed to leverage AMD's diverse hardware ecosystem, including their CPUs, GPUs, and NPUs. The most significant update in this release is the addition of NVIDIA CUDA support. This integration allows developers to utilize NVIDIA GPUs within their Lemonade-powered local AI deployments, bridging a critical gap in cross-platform AI development. The inclusion of CUDA support is a strategic move, enabling Lemonade to tap into NVIDIA's extensive CUDA ecosystem and a vast array of pre-optimized models and libraries. This means that applications built with Lemonade can now seamlessly target a wider range of hardware, offering unprecedented flexibility for developers working with local AI. For users, it provides the choice to deploy their AI models on either AMD or NVIDIA hardware using a single, unified SDK, expanding the potential reach and efficiency of their AI workloads. Comment: This is a massive step for cross-vendor AI development. Being able to use AMD's Lemonade SDK to deploy local AI models and then seamlessly target NVIDIA GPUs via CUDA truly unifies the AI backend landscape for diverse hardware setups, making it incredibly practical for hybrid environments. Intel Arc Pro B70 Showing Off Some Performance Wins With Linux 7.1 (Phoronix) Source: https://www.phoronix.com/review/linux-71-arc-pro-b70 Recent testing by Phoronix indicates that Intel's Arc Pro B70 discrete GPUs are demonstrating notable perform

The ShinyHunters hacking gang claims to have compromised the Oracle PeopleSoft servers of more than 100 organizations, including many universities.

Lawsuit: "Police let an error-prone AI system stand in for an investigation."

Microsoft's Xbox division will be hit with significant layoffs next month, according to people familiar with Microsoft's plans. The company has been preparing for the layoffs internally for weeks, with Xbox CEO Asha Sharma hinting about "making hard choices" last month. Sources suggest the cuts could even involve a studio closure, or changes to the […]

Hello everyone, tomorrow I have a meeting with my dissertation supervisor and I wanted to have a dissertation proposal ready. Initially, I moved forward with the following proposal: "Interpreting the Routing Dynamics of Capsule Networks for Explainable AI." My first approach to this topic was to study the paper "Transforming autoencoders," which is the first paper about capsule networks. Next, I did a search on the state of the art of transforming autoencoders and only found 2 papers since 2011. I think I should take advantage of the work I have developed so far on transforming autoencoders and write a dissertation about them. If anyone could take a look at the readme and tell me what they think, I would appreciate it. What do you think? I should suggest another topic involving transforming autoencoders. There isn't much scientific research on them. The professor is approachable, and if I present a good new topic, he'll let me change it! submitted by /u/Future-Persimmon5393 [link] [留言]

I build agents for work so I'm clearly not anti-AI, but the numbers keep bothering me, concerning the environmental factors of it. Every datacenter is the same now, gigawatts of new demand, water for cooling, grids that weren't designed for any of this, and rising cost of water for cities. And the data centers keep using clean water because of lack of technology to turn dirty water into usable water for cooling. Then I see Elon talking about putting data centers in orbit, solar powered, radiating heat into space, no water needed. And while I do think its going to be the end solution, I do think we have much more demand for compute power then Elon can provide so far with the space data centers and I think the demand is growing faster than Elon can provide Is efficiency improving fast enough to outrun demand? Are space data centers a real answer or a distraction that will fail? And is anything happening right now (smaller models, better scheduling, offsets) that you'd call an actual solution rather than PR? That people can use today to make an impact submitted by /u/Swift_lunatic_2604 [link] [留言]

C# 14: The field Keyword — Cleaner Properties, Zero Boilerplate Every C# developer has been there. You start with a clean auto-property, then requirements change and you need to add a tiny bit of validation. Suddenly that one-liner explodes into six lines of boilerplate — a private backing field, a getter that just returns it, a setter that assigns it. The logic is two words. The ceremony is everything else. C# 14 fixes this with the field keyword: a contextual keyword that refers to the compiler-synthesized backing field of a property, letting you write custom accessor logic without ever declaring an explicit field. The Problem: Boilerplate Tax on Simple Properties Auto-properties are one of C#'s best quality-of-life features. This is clean: public string Username { get ; set ; } But the moment you need to trim whitespace on assignment, that cleanness evaporates: private string _username = string . Empty ; public string Username { get => _username ; set => _username = value . Trim (); } You now have six lines — and four of them exist only to hold the shape of the pattern together. The backing field _username is not carrying any meaningful design weight. Its only job is to be a storage slot that Username uses privately. You already know the compiler creates one for auto-properties. You are just forced to make it visible so you can reference it. This is the boilerplate tax. You pay it every time you add even the smallest piece of logic to a property. Why field Exists The C# language team has discussed this friction for years. The challenge was finding syntax that is: Unambiguous — no conflict with existing identifiers Familiar — consistent with how value works in setters Scoped — only meaningful inside a property accessor The solution landed in C# 14: the contextual keyword field . Just like value refers to the incoming assignment in a setter, field refers to the hidden backing storage the compiler manages for the property. It is contextual, which means it only acts

After 11 years building e-learning software at a major enterprise, here’s what surprised me when we started shipping GenAI features to real users. The Starting Point Nobody Talks About Most GenAI integration blog posts start with a clean slate — a greenfield app, a fresh codebase, a blank canvas. Real life is messier. When we began integrating generative AI into our e-learning authoring tool, we weren’t starting from scratch. We were dealing with a mature enterprise product — millions of users, legacy architecture decisions made years ago, compliance requirements from Fortune 500 customers, and LMS interoperability standards (SCORM, xAPI) that were designed long before anyone imagined AI-generated content. The challenge wasn’t “how do we call an AI API.” It was “how do we ship AI features into a product that thousands of instructional designers depend on daily, without breaking their workflows or their trust.” Here’s what I learned. Lesson 1: The AI Feature Your Users Want Is Not the One You Think When we first scoped out AI integration, the engineering team gravitated toward the flashy stuff — generate an entire course from a prompt, auto-create assessments, AI-powered slide design. Then we talked to actual users. Instructional designers didn’t want AI to replace their expertise. They wanted it to eliminate the tedious parts of their workflow: Reformatting content across different output types (responsive HTML5, PDF, SCORM packages) Generating alt-text for hundreds of images in accessibility-compliant courses Summarizing lengthy SME-provided documents into digestible learning chunks Suggesting quiz questions from existing content (not generating courses from nothing) The takeaway: don’t let engineering excitement drive your AI feature roadmap. Do 10 user interviews before writing a single line of integration code. The highest-impact GenAI features are usually the boring ones. Lesson 2: Prompt Engineering Is a Product Decision, Not an Engineering Task We initially t

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [留言]

“Defenders cannot afford to take weeks to patch,” one Cybersecurity and Infrastructure Security Agency official warned on Wednesday.

US lawmakers are alarmed that Bill Pulte, a housing official with no intelligence experience, is poised to take charge of one of the government's most powerful surveillance tools.

Turn photos into game assets Discussion | Link

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 […]