Dev Tools

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Kimi WebBridge: AI-Powered Browser Automation for Tedious Web Tasks
A browser extension that lets AI agents click, fill forms, navigate, and extract data directly in your Chrome or Edge browser.

Kimi WebBridge: AI-Powered Browser Automation for Tedious Web Tasks

Kimi WebBridge is a browser extension that enables AI agents to automate web browsing tasks like clicking, form filling, and data extraction. It runs locally via Chrome DevTools Protocol, ensuring login sessions and page content never leave your device. Compatible with Kimi Code, Claude Code, Cursor, and more.

xAI Launches Grok Build Beta: CLI with Multi-Agent Coordination
Early beta for SuperGrok Heavy subscribers offers fast, flicker-free CLI with skills, plan viewer, and parallel subagents.

xAI Launches Grok Build Beta: CLI with Multi-Agent Coordination

xAI releases Grok Build Beta, a command-line interface for SuperGrok Heavy subscribers. Features include multi-agent coordination, skills adaptation, plan viewer, marketplaces, and design polish commands. Try now via curl install.

2026 Agentic Coding Trends: The Era of AI Collaboration
From assistance to collaboration: How AI agents are reshaping engineering roles, workflows, and project timelines

2026 Agentic Coding Trends: The Era of AI Collaboration

The 2026 Agentic Coding Trends Report reveals how AI coding agents evolve from experimental tools to production systems, enabling multi-agent teams, long-running autonomous builds, and intelligent human oversight. Key trends include collapsed SDLC cycles, orchestration of specialized agents, and the transformation of engineers into strategic collaborators.

Fast Byte Latent Transformer: Efficient Byte-Level Generation via Diffusion and Speculation
Combining hierarchical latent tokenization with block-wise discrete diffusion and self-speculation for faster byte-level language models

Fast Byte Latent Transformer: Efficient Byte-Level Generation via Diffusion and Speculation

This paper introduces BLT Diffusion (BLT-D), BLT Self-speculation (BLT-S), and BLT Diffusion+Verification (BLT-DV) to accelerate byte-level language models. By replacing autoregressive decoding with block-wise diffusion and verification, the methods achieve over 50% memory-bandwidth reduction and up to 92% with larger blocks, while maintaining competitive performance on translation and code generation tasks.