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Hey we’re Faiz and Saheed and we built Minicor so AI companies who need to integrate to desktop systems with no API can quickly build scalable desktop RPAs. Demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MD0GHZIJ1cw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MD0GHZIJ1cw</a><p>Edit: RPA is an acronym for Robotic Process Automation - things like triggering mouse clicks and key strokes to perform tasks programmatically. Sorry if this wasn&#x27;t clear!<p>We were working on non-RPA integrations when a customer promised to sign a deal in 2 days if we could unblock a sale of theirs that involved integrating with a clinic’s Windows based medical record system. We didn’t know it at the time but it turns out that building desktop RPAs at scale is extremely difficult because scripting is hard (learning the system, defining the automation, UIs changing constantly), orchestration is hard (is the VM up? queuing, parallelizing) and debugging is hard (zero observability, false positives, cascading failures). 30%+ failure rates are not uncommon. At scale we’ve seen cases of failed RPAs leading to thousands of support tickets a month.<p>To solve the problems we were facing, we built an MCP that Claude Code&#x2F;Codex can use to navigate a virtual machine running desktop software with Python to create RPA workflows. The RPA workflows run as Python scripts for speed, cost, and determinism. These workflows can be triggered by API following any input&#x2F;output schema specified, with video replays and logs stored with each run. The MCP can debug RPAs and make changes to the underlying code, all of which are version controlled. We also built tools for cloning VMs for parallelizing RPAs, and handling 2FA&#x2F;OTP challenges. Plus since workflows are code based: we were also able to add triggers for Slack notifications, human-in-the-loop steps, or call an LLM to verify the state of a VM by passing a screenshot.<p>Would love to hear your feedback and if you have any RPA horror stories! (:

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