I wrote this survey for work. The goal wasn’t to win a tool-bake-off or prove a pet stack was superior — it was to answer one honest question: how do the people I build with actually spend their days with AI?
Every section maps to a layer of the modern dev toolchain. If you’re assessing a team’s workflow, onboarding new people, or just want to see where you sit relative to your peers, steal it. It’s licensed by the human copyright office of “you can just use this.”
Your AI can write code, summarize meetings, and research anything. But can it manage your to-do list? With TickTick MCP, it can — your AI assistant becomes a task manager you talk to instead of a UI you click.
Every task manager has a UI, and every AI has a chat box. The friction is the gap between them: you think of something, you have to switch to the app, click through to create a task, set the date, pick the list, tag it. That’s exactly the kind of repetitive friction that AI should be eating.
The big gap in AI agents isn’t intelligence — it’s hands. An LLM can write a perfect playbook, but if it can’t hold a persistent interactive session, follow a live log, drive the SSH client through a password prompt, or watch the browser as it runs, it’s still stuck translating thought into brittle one-shot tool calls.
I went looking at what’s actually shipping right now to give agents a real computer. Six repos, three distinct layers: full sandboxes, interactive PTY servers, and browser control. Here’s how they stack up.
