The AI world's dancing between practical magic and cautious criticism today. While HARNESS is using LLMs to predict hazards at DOE sites, one 74-year-old engineer spent 18 months building a privacy-first AI platform that challenges the status quo.

The United States Marine Corps is now actively deploying their AI Implementation Plan, and it's not about Terminator-style battle bots. The Corps is taking a surprisingly nuanced approach to military AI, focusing on risk-managed implementation across intelligence, logistics, and decision support. Here's the deal: while most military AI headlines scream 'killer robots,' the Marines are actually building systems to handle the unglamorous but critical stuff - predictive maintenance that keeps helicopters flying, supply chain optimization that ensures ammo reaches the front lines, and intelligence processing that turns data firehoses into actionable insights. The plan emphasizes human-machine teaming rather than autonomous weapons systems. Plot twist: this might be the most thoughtful military AI roadmap we've seen yet, balancing innovation with established ethical frameworks. The Marines know they're competing with China's no-guardrails approach while navigating western ethical constraints - a classic 'move fast but don't break humanity' dilemma.

Fresh research dropped today (arXiv:2511.10788) revealing why some LLM APIs might be burning your tokens unnecessarily. Researchers found that adaptive reasoning—where models adjust their thinking depth based on task complexity—significantly outperforms the one-size-fits-all approach most APIs use. Translation: Current APIs are solving simple problems with excessive computation. Models that can dynamically shift between fast intuition and deep reasoning could slash token usage by up to 40% for everyday tasks while maintaining quality. If you're making API calls today, test OpenAI's o1 or Claude 3.5 Sonnet, which both show early implementations of adaptive reasoning patterns. Expect this capability to be the next major battleground for efficiency as compute costs remain the limiting factor for most AI products.

Peter Thiel's Thiel Macro LLC just revealed it completely divested from Nvidia in Q3, according to yesterday's 13F filing. The billionaire investor's complete exit from the AI chip titan raises eyebrows, especially as NVDA continues trading near all-time highs at $1,250 per share. This comes as JPMorgan announced this morning their tech deals backlog is at multi-year highs in Asia Pacific, suggesting institutional money sees growth ahead. The bottom line: Thiel's exit could signal belief that Nvidia's AI chip dominance may face challenges from competitors like AMD and Intel, who are aggressively ramping up production, or that custom silicon from big tech (Google TPUs, Microsoft's Azure chips) is eating into Nvidia's market share faster than expected. Smart money is diversifying its AI infrastructure bets beyond the obvious leader.

Launched today, Everdone is flipping the AI assistant model on its head with a 'Work-as-a-Service' platform. Their first product, CodeDoc, automatically documents your GitHub repos with usage-based pricing rather than subscription fees. Think of it as hiring AI workers instead of renting AI tools - they're building the system of record for AI work outputs, not just another chat interface.

The DOE just demonstrated an AI safety breakthrough with HARNESS (Human-Agent Risk Navigation and Event Safety System), rolled out yesterday across high-risk facilities. The system combines LLMs with structured work data and expert feedback to predict hazards before they happen. Early results show the AI-human collaboration loop is significantly improving operational safety at nuclear and energy sites where mistakes could be catastrophic. It's like having a safety expert with perfect memory and pattern recognition working 24/7.