Launching today
Clark
An AI coworker with its own cloud computer
609 followers
An AI coworker with its own cloud computer
609 followers
Clark is an AI coworker with its own cloud computer - browser, terminal, files, and code. Hand it a real task, close the tab, and come back to finished work: wide, sourced research; websites; spreadsheets; decks; audits; or tested code. It can fan work out to parallel specialists, run on a schedule, and return artifacts with the evidence behind them. Use Clark on web or mobile, work in real repositories with Clark Code, or embed the agent through an OpenAI-compatible API.
Free Options
Launch Team / Built With
Clark
@stanislav_kirdey To be honest , I am actually waiting for something that can own an entire workflow instead of just helping with one prompt at a time. The cloud computer+ parallel specialists approach is a really interesting direction. Excited to give it a try.
@stanislav_kirdey Seriously the ability to walk away from a task and come back to finished work is exactly the kind of experience I've been hoping AI would evolve toward . Excited to try this out.
@stanislav_kirdey What’s one real task you’d hand Clark right now and why; and what would you need to see in the returned files or logs to feel confident you could rely on it again?
The 'own cloud computer' framing is what separates this from a chat wrapper for me — but it lives or dies on state. Between tasks, is each job a fresh ephemeral VM wiped clean every time, or a durable workspace that keeps files and context so a scheduled monitor run actually builds on the last one? And with Clark Code in a real repo, does it work on a clone in its own env and hand back a PR/diff I can inspect, or does it need direct write access to the repo?
I've tried plenty of AI tools, but the ability to return completed research with evidence feels much more practical. I'm curious how well it adapts to different workflows over time.
I've been looking for something that can manage research without constant supervision, and this sounds promising. The idea of parallel specialists is especially interesting. I'd love to know how the system decides which specialist handles each part of a project.
I'm intrigued by the combination of research, coding, spreadsheets, and presentations in one workflow. I've used separate tools for each of those tasks, so bringing everything together sounds efficient. My biggest question is how well it maintains context across longer projects where priorities and requirements change over several days.
For me, the most valuable feature is returning complete deliverables instead of stopping halfway through the process. I've always felt that execution matters more than speed alone. I'd be interested in seeing a real example comparing the workflow with and without Clark on the same research task.
Tried Clark on a market research task and came back to a clean spreadsheet with sources attached, which saved me a real chunk of time. The parallel specialists angle feels like more than a gimmick, the output actually held together.