A call for contribution to establish reproducible, practical reinforcement learning environments sourced from real open source development work — with a $1M commitment to support open source maintainers.
Cline has raised $32M in combined Series A and Seed funding, led by Emergence Capital and Pace Capital, with participation from 1984 Ventures
Picture this: Your senior engineers spending most of their time on high-level architecture instead of debugging nested loops. Your new hires becoming productive in days instead of months. Your entire team shipping more code with fewer bugs while actually leaving work feeling energized instead of burnt out. Sound like fantasy? It's not. It's what happens when you put a senior engineer in everyone's back pocket. I've been watching engineering teams using Cline (our autonomous coding agent for VS
Remember when installing new apps meant typing cryptic commands into a terminal, manually resolving dependencies, and praying nothing would break? That's kind of where we are with Model Context Protocol (MCP) servers right now. But not anymore. Today, I'm excited to announce Cline's MCP Marketplace - think of it as the App Store for your AI's capabilities. It's our answer to making AI superpowers accessible to everyone, not just the tech-savvy few. Why This Matters A few months ago, Anthropic
At Cline, we've scaled to 500k+ users and raised significant funding from top-tier VCs. As Head of AI, I recently interviewed a strong ML engineer candidate. Despite their solid background, I voted "no hire." Let me explain why - it reveals a broader pattern about what AI companies actually need right now, and getting this wrong can be a $200k+ mistake. The $200k Mistake: Why Hiring MLEs Too Early Kills AI Startups Here's a pattern I see repeatedly in well-funded AI startups: 1. Raise a sub