Launch HN: Minicor (YC P26) – Windows desktop automations at scale
46 points - today at 2:57 PM
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: https://www.youtube.com/watch?v=MD0GHZIJ1cw
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.
To solve the problems we were facing, we built an MCP that Claude Code/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/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/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.
Would love to hear your feedback and if you have any RPA horror stories! (:
Congrats on the launch. One complaint: RPA this, non-RPA that, but you never explain what it means. I would write down the acronym fully once at the first mention on the landing page.
throw03172019today at 6:16 PM
Biggest question is how much of this can be stored / processed on our own infra and with our own lifecycle rules? For example, this can touch a lot of PHI. Screenshots, videos, JSON inputs/outputs etc.
throw03172019today at 5:43 PM
Does this only revert back to LLM Vision when it catches an error? I.e once the RPA / workflow is built once, it’s efficient for running multiple times (until it catches an error state)?
dragonsenseiguytoday at 4:45 PM
Small website nitpick: I feel like the "In production with" section's companies logos should be a bit darker, I could barely tell there was something there.
a-dubtoday at 5:28 PM
i'm curious: how does the steady state error rate of a stochastic automated system like this compare with the downtime and errors that come from a (brittle) deterministic bridge that can fail with upgrades? what does the observability look like? (i'm guessing one feature is that the execution log including images/screenshots for each transaction gets saved, which is probably a huge improvement.)
ilundintoday at 6:06 PM
Is the cloud LLM the judge based on screenshots with patient/customer data included ? That seems like a no-go for many countries given privacy concerns ?
throw03172019today at 6:03 PM
How does this compare with CyberDesk (also YC)?
theaniketmauryatoday at 4:25 PM
Congrats on the launch! Legacy system users are also one of the slowest to adopt AI. How do you navigate that?
throw03172019today at 4:36 PM
So AI companies would install this on their customer (practices) computers?
mingabungatoday at 3:20 PM
Could you use this to test new releases of software for bugs? A bit like TDD but for GUI interactions
snozollitoday at 5:27 PM
Computer use agents that run on Windows VMs or in the browser. On-premise, cloud