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Browse more SANS AI resources for ways to build, break, and defend AI in production

Meta's Muse Spark foundational model is now powering Meta AI across the company's services. The model enables faster voice responses, smarter shopping assistance, and real-time visual recognition through device cameras. The initial rollout targets users in the US and Canada.

Fast mode for Claude Opus 4.7 is now available in research preview in the API and Claude Code, and on Cursor, Emergent, Factory, v0, Warp, and Windsurf. Fast mode is currently opt-in, but it will eventually become the default. A link to join the waitlist for fast mode is available.

Google and SpaceX were reportedly discussing orbital data centers as part of broader efforts to expand AI compute infrastructure beyond Earth-based facilities.

Inference workloads are more variable and less predictable than training workloads. This makes them a natural fit for serverless computing. However, serverless computing only works if new replicas can be spun up as fast as demand changes. This article looks at how Modal took AI inference server scaling from multiple kiloseconds to just tens of seconds.

The AI infrastructure boom has driven increased demand for analog and power semiconductors, notably benefiting Multilayer Ceramic Capacitors, amidst a past supply glut and competition. Companies like Texas Instruments and NXP Semiconductors are avoiding capacity expansion, focusing instead on raising prices and improving profitability. The semiconductor supply chain, previously supporting EV and solar industries, is now being leveraged for AI-related demand growth.

Parameter Golf attracted over 1,000 participants and 2,000 submissions focused on minimizing loss on a dataset within strict constraints. Participants leveraged a range of techniques, including careful tuning, quantization, and novel modeling ideas, with AI coding agents playing a significant role. This challenge revealed new talent and highlighted the evolving role of AI agents in research competitions.

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Researchers derived compression-aware neural scaling laws by training nearly 1,300 models, revealing how bytes per token affect compute allocation. This challenges the heuristic that scales models by 20 tokens per parameter, showing it's due to specific tokenizers. The study suggests scaling should use bytes, not tokens, for better compute efficiency across diverse languages.

The article discusses using reinforcement learning to fine-tune 4B models as recursive language models (RLMs) for production, achieving efficient task-specific behavior at a lower cost. By training a shared policy for both parent and child RLMs, this approach maintains task performance and reduces the need for multiple models. In tests, this method matches the performance of larger models like Claude Sonnet 4.6 but operates with significantly reduced size and cost.

Cactus Needle is a 26M parameter Simple Attention Network distilled from Gemini 3.1 that can be finetuned locally on a Mac/PC. It runs on Cactus at 6,000 tokens per second prefill and 1,200 decode speed. The weights are fully open. Simple Attention Networks are aimed at redefining AI for consumer devices like phones, watches, and glasses.

OpenAI shared a Codex workflow for agents that iteratively review, repair, and validate outputs using structured feedback loops to improve reliability.

Today's LLMs may be commercially valuable, but predicting text alone will not lead to human-level intelligence because language is only a small fraction of how humans understand the world. Future AI systems will instead rely on “world models” that learn abstract representations of physics, causality, and consequences, enabling planning, reasoning, and adaptation in real-world environments like robotics, healthcare, factories, and industrial systems.

Google introduced new Gemini-powered Android features that can complete actions across apps, browse the web, fill out forms, and generate custom widgets through natural language prompts.

Granola is the call recorder works locally on Mac and iPhone, no awkward meeting bot required. Take shorthand notes - AI makes them awesome. 1 month free with code: TLDR1MO

This repository contains reference agents, skills, and data sectors for the legal workflows that Anthropic most commonly sees.

Agentic search models are specialized LLMs trained specifically on search.

TLDR's Applied AI team is tasked with making every process at TLDR legible to code, runnable by anyone, and composable into larger workflows. Join a small, fast moving team using the latest AI tools with an unlimited token budget. Learn more.

Mk1 is a video analysis AI model priced 80-90% cheaper than rivals like Anthropic, OpenAI, and Google.

The Qwen team released Qwen-Image-2.0, their latest multimodal image generation model, showing improved typography, instruction following, photorealism, and long-text rendering across generation and editing tasks.