Tag: WebDev

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 the Codex harness, secured the absolute top spot on the new ALE Leaderboard with a 24.0% pass rate, beating Anthropic's highly anticipated, brand new

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 last year.HRM decouples computation into slow-evolving strategic and fast-evolving execution layers. Instead of brute-force autoregressive prediction on raw text, HRM-Text trains exclusively on instruction-response pairs. This is close to real-world enterprise settings, where users usually expect a targeted answer to a specific task.The researchers were able to train a 1B-paramet

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 to maintain public safety as AI capabilities and potential misuses grow.Alongside the essay, Anthropic released two comprehensive policy roadmaps: an Advanced AI Framework targeting catastrophic model risks, and an Economic Policy Framework addressing AI-driven labor displacement backed by $350 million in new funding.The timing couldn't be more important: yesterday, An

Enterprise AI teams face a dilemma: The best models today might not be the best models a year from now. MassMutual's answer is to stop making long-term bets — and build infrastructure that can swap models as the market shifts.“The world of AI today is extremely dynamic,” Sears Merritt, MassMutual CIO, explained in a new VB Beyond the Pilot podcast. “We wanted to make sure we were positioned to ride that wave of dynamism.”The strategy appears to be paying off in a big way. MassMutual has measured a roughly 30% increase in developer productivity, while AI-powered contact center workflows have reduced resolution times from 10 minutes to one and cut costs from dollars to cents. But the broader lesson for IT leaders may be less about the results and more about how the company is thoughtfully building its AI infrastructure and keeping users at the center. Maintaining optiona

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, iPad, Mac, Apple Watch and Vision Pro, as revealed in the WWDC26 Apple Intelligence developer guide.In other words, if your company offers an application on Apple devices, whether it's served on iOS mobile device or Mac, the new Siri AI may force you to change how that application is discovered, served, and its contents and workflows made available to end users. Enterprise developers can expose app content throu

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 production workloads.The new open-source model is a 30 billion parameter mixture-of-experts (MoE) model with 3 billion parameters active per token, built for agentic software engineering including sub-agent orchestration, architecture mapping, code review and terminal work. The model supports a 256,000 token context window with a 64,000 token maximum generation length, and is available on Hugging

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 the weight set off DRAM entirely.The AFM 3 family was developed in collaboration with Google and spans five models: two on-device and three server-based, all running within Apple's Private Cloud Compute boundary. The server-side models, including AFM 3 Cloud Pro for agentic tool use and complex reasoning, run on Nvidia GPUs in Google Cloud. The on-device architecture is Apple's own. AFM 3 Core Advanced is a 20-billion-parameter model that stores weights in NAND flash rather than DRAM."Instead of forcing the entire model into DRAM, the ful