WorldofAI demonstrates how to integrate Kestra with AI agents to create reliable, human-in-the-loop automation workflows. By leveraging YAML-based configurations and native integrations with services like GitHub, users can manage predictable tasks alongside AI-driven reasoning, ensuring critical actions only proceed after manual review and approval.
Earlier this week, the AI startup Liquid, formed in 2023 by former MIT computer scientists, debuted LFM2.5-2.6B, a new open-weight language model designed specifically for agentic workloads.
In release materials and a recent interview with VentureBeat, Liquid's researchers said LFM2.5-2.6B can run entirely on local hardware — from smartphones and laptops down to a Raspberry Pi — without relying on cloud inference or GPUs, unlocking edge AI applications and giving more options to enterprises working in regulated industries or with sensitive information they don't want to send up to the cloud.
Hark, the secretive AI startup founded earlier this year by serial entrepreneur and roboticist Brett Adcock, today announced Handoff, a "computer use agent" (CUA) that it says is among the top-performing in the world at navigating the open web on a user's behalf — ordering dinner on DoorDash, booking flights on United and Delta, or messaging job candidates on LinkedIn — all autonomously, end-to-end.
WorldofAI explores the capabilities of this large-scale model, focusing on its performance in coding, research, and long-horizon reasoning. The review examines its multimodal functionality and utility in generating 3D assets, web applications, and interactive simulations, while demonstrating the test-sprite CLI tool as a mechanism for verifying agent-driven code outputs.
The @cloudflare/computer package provides an agent runtime where the details and mechanics of what code runs in an isolate, a container sandbox, or a web browser are handled by the platform. Each agent gets a computer, the runtime optimizes for efficiency, and scalability.
Boris Churnney explains strategies for optimizing AI agent workflows, emphasizing why clearing outdated setup files and prompts is crucial when new models are released. Learn how to refine project management by implementing strict evaluation checks, utilizing effective coding habits, and balancing automation with manual oversight to ensure better results from intelligent systems.
In this video, Sam Witteveen looks at ThinkingCap, a fine-tune of the Qwen3.6-27B model. BottleCap's goal was intentionally conservative: do not try to make the model smarter or teach it new capabilities. They wanted to preserve knowledge, reasoning ability, answer quality, conversational style, instruction following, and safety behavior.
The only thing they wanted to change was the amount of computation spent reaching an answer. In other words: keeping the same model, and making sure it overthinks less.
