Clark
An AI coworker with its own cloud computer
783 followers
An AI coworker with its own cloud computer
783 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.
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Launch Team / Built With
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.
Clark
@yeliz972799 thanks!
A cost and token usage breakdown for every completed task would be really helpful, especially for teams running large research or coding workflow.
Clark
@ill_robyn will add! 💜
Macaly
giving the agent its own cloud computer is the right bet imo 👏 the async close-the-tab flow is nice. hows reliability on longer tasks?
Clark
@petrkovacik it is quite good! and you can always try
https://www.clarkchat.com/clark-code Clark Code and its "/goal describe your problem" to accomplish full day type of tasks
Tried Clark on a task to gather information on investors and VCs. It came back with a clean, well-structured spreadsheet, relevant investor profiles, and source links attached for every data point. Made the initial research process much faster and easier to verify. Good Luck
Clark
@giorgi_daraselia thank you! try clark code IDE as well!
Love that Clark can fan work out to parallel specialists and hand back artifacts with sources. One thing I'd love is a simple "trust but verify" mode where I can quickly diff what Clark did against my own expectations before it writes to a repo or ships a spreadsheet, so I can catch small errors without babysitting the run.
Clark
@ebru1lid great feature request!
the scheduled monitor use case is the one I'd want most out of this. when it re-runs on a timer, does it diff against the last artifact and surface only what changed, or hand back a fresh full artifact every time and leave the comparison to you?