Ai

Context windows are becoming a computational bottleneck. The longer an agent runs, the more tokens accumulate from retrieved documents, reasoning traces and conversation history, and the more memory and compute that growing context demands. Most existing solutions either degrade model accuracy, require the full context to load before compression begins, or produce memory savings that

Researchers from the University of California, Berkeley’s Center for Responsible, Decentralized Intelligence (RDI), alongside an advisory committee of over 300 domain experts, have launched Agents’ Last Exam (ALE)—a grueling new benchmark built to measure whether artificial intelligence can actually execute economically valuable, long-horizon professional workflows. In a shocking upset, OpenAI’s GPT-5.5 from April, operating through

Training a foundation LLM from scratch costs millions and requires internet-scale data — which is why most enterprises don’t bother. Sapient thinks it has a cheaper path. To overcome this brute-force scaling dogma, researchers at Sapient developed HRM-Text, which replaces standard Transformers with a highly sample-efficient Hierarchical Recurrent Model (HRM), an architecture they first introduced

In a sweeping new essay titled “Policy on the AI Exponential,” Anthropic co-founder and CEO Dario Amodei publicly calls for new government regulations governing the release of powerful AI models — specifically comparing AI industry to commercial aviation, which follows regulations enforced by the U.S. Federal Aviation Administration (FAA) — arguing that this is necessary

Google AI team including the Google DeepMind researchers have just released DiffusionGemma, an experimental open model for text generation. It uses text diffusion instead of standard autoregressive decoding. The model ships under a permissive Apache 2.0 license. Google positions it for devs and researchers exploring speed-critical, interactive local workflows. Examples include in-line editing, rapid iteration,

Apple’s new Siri AI, unveiled yesterday at Apple’s annual Worldwide Developers Conference (WWDC 2026), may look like a consumer product story on the surface. But for enterprise developers and IT leaders, the bigger news from WWDC26 is that Apple is turning Siri into a systemwide AI interface for apps, data and workplace actions across iPhone,

Engineering teams building agentic coding pipelines now have a concrete open-source alternative to managed models like Claude Fable 5 — one that runs on a single H100. The tradeoff: Cohere’s North Mini Code, which launched Tuesday, generated three times the output tokens of comparable models in independent testing, a verbosity cost that compounds in high-volume

On-device AI models have stayed small because the entire weight set has to live in DRAM, capping practical parameter counts well below what server-side deployments use. Enterprise architects evaluating agentic workloads have had to choose between capable cloud-dependent models and limited on-device ones. Apple’s third-generation foundation models, announced at WWDC26, break that constraint by moving