Citi forecasts severe memory undersupply through 2031, with HBM demand surging 62% in 2027 and 69% in 2028, while DRAM and NAND supply growth will lag demand, creating deficits of 8-10%. A commentator argues that AI labs underestimated efficiency improvements in model architectures and training techniques, which could significantly reduce semiconductor demand growth projections.
Fusion is a new dual-model architecture for Devin Desktop and CLI that pairs a frontier model for planning and review with a cost-effective model for execution, achieving up to 39% better efficiency on coding benchmarks. The system runs two parallel agents with separate contexts, allowing the lead model to maintain control while the sidekick handles implementation, avoiding the pitfalls of traditional model routing. Devin reports that using more expensive, token-efficient models can reduce overall costs by delegating effectively and maintaining prompt caches.
GLiClass is an open-source zero-shot sequence classification model inspired by GLiNER that achieves comparable performance to cross-encoder models while being 10 times faster through single forward pass classification. It supports hierarchical labels, in-context examples, custom prompts, and long document chunking for improved accuracy and flexibility.
Occamy-1.0 is a 35B parameter language model optimized for multi-step workflows combining information gathering, tool use, and coding. Trained on execution-grounded data, it achieves competitive performance with larger frontier models while maintaining cost efficiency on agentic benchmarks. The model weights and training data are released to support research on practical co-work agents.
TypeSafe AI announced Jev, a new frontier model optimized for structured decision-making and automation rather than text generation. Jev achieves comparable intelligence to existing large language models while being 40-400x cheaper and 20-200x faster, with parallel sampling that eliminates hallucinations and type errors.
Carnot engines achieve 68-72% thermal efficiency compared to conventional engines' 25-35% by eliminating cooling systems and heat losses, while supporting multiple fuel types including hydrogen, diesel, and biofuels. This doubling of efficiency reduces fuel consumption and emissions by approximately 50%, with applications targeting hard-to-abate sectors like marine, heavy-duty vehicles, and off-grid power generation.