Tag: WebDev

Agent skills have become an important part of real-world AI applications, providing a mechanism — a set of instructions saved in a folder of text-based markdown (.md) files, usually — for models to adapt to specific enterprise use cases and complex workflows. However, optimizing these skills is a slow process and faulty process, as they cannot be trained in the same way as the parameters of the underlying AI model. Instead, users typically must update them manually by retyping the instructions in each file, playing a "guessing game" as to what changes might improve agentic AI performance and reduce errors. SkillOpt, a new, open source (MIT Licensed) framework developed by Microsoft, does one better: it introduces an optimizer designed for agent skills, turning the agent's skill .md document as a trainable o

Xiaomi's MiMo AI team has open-sourced MiMo Code V0.1.0, a terminal-native AI coding assistant that the Chinese electronics giant says outperforms Anthropic's Claude Code on key agentic coding benchmarks, especially on long-horizon, multi-step tasks (200+ steps) — at least, according to its own internal beta release and survey of 576 developers. It's also bundling limited-time free access to MiMo-V2.5, its multimodal flagship model with a million-token context window, requiring no registration to get started.The release was announced June 10, 2026 in a post on the social network X from the official @XiaomiMiMo account, which described the tool as "more than an AI coding assistant in your terminal — it's the smartest coding partner you'll ever work with."MiMo Code is available now on

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 don't translate into real speedups in standard serving infrastructure.A research team from NYU, Columbia, Princeton, University of Maryland, Harvard and Lawrence Livermore National Laboratory published a paper this week that proposes a novel fix. The researchers introduce the concept of  Latent Context Language Models, or LCLMs, a family of encoder-decoder compression models that compress input context before it reaches the decoder. The models are open-sourced on HuggingFace.Unlike KV cache compression methods — the dominant approach i

Presented by F5Enterprise AI teams have spent years solving for compute, securing GPU allocations, negotiating cloud capacity, and benchmarking training throughput. The assumption embedded in that work is that the path between storage and compute will keep up. In production, that assumption increasingly does not hold. Real traffic introduces latency spikes, network jitter, and node degradation that controlled benchmarks fail to capture, resulting in pipelines that perform well in the lab but stall in deployment. A growing response is AI data delivery, deploying an application delivery controller (ADC) or application delivery and security platform (ADSP) in front of storage as a resilient and secure control point."Provisioning solves for capacity but not for delivery, and that is where the constraint now hides," says Hunter Smit, senior manager of product marketing at F5. "

GenAI image generators like Stable Diffusion do not draw a picture pixel by pixel from left to right. They start with noise and iteratively refine the entire image in parallel until it converges, in a process known as diffusion. For years, applying that same principle to text generation had remained out of reach at scale.Standard language models work like a typewriter: one token at a time, left to right, with no ability to revise a committed output. That pattern works in the cloud, where batch sizes keep GPUs saturated. For local inference or low-concurrency deployments, the GPU is idle most of the time.Google's DiffusionGemma, released this week, is an open source experimental model that applies diffusion to text generation at production scale. Built on the Gemma 4 backbone and released under the A

Presented by Capital One Enterprises aren’t struggling to experiment with AI; they’re struggling to make it work in the real world. Moving from promising prototypes to reliable, production-scale systems is where most efforts stall.In my role within Capital One’s AI Foundations organization, I’ve seen firsthand that successful AI implementation isn’t just about adopting the latest models or tools. It requires a disciplined R&D approach that connects foundational research to real-world systems, and holds ideas accountable as they move from concept to production.That’s harder than it sounds. AI capabilities are evolving quickly, but enterprise environments can be complex, fragmented, and risk-minded. The question isn’t just what’s possible, but what actually works — for a specific workflow, user, or decision — with today’s technology and constraints.What follows reflects how organizations can turn AI ambition into produ

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