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The Hidden Crisis of Desktop Automation: How Minicor Is Solving the “Unsolvable” Problem of Legacy Systems
In the age of cloud-native APIs, microservices, and instant integrations, one stubborn relic refuses to go quietly into the night: the Windows desktop application. From hospitals managing patient records to insurance firms processing claims, critical business workflows still run on decades-old software with no modern interfaces—no REST APIs, no webhooks, not even a simple JSON export. Enter Robotic Process Automation (RPA), the digital duct tape that mimics human actions—clicking buttons, typing text, navigating menus—to bridge the gap between legacy systems and modern data pipelines. But as Faiz and Saheed discovered firsthand, building RPA at scale isn’t just hard—it’s often a disaster waiting to happen.
Their startup, Minicor (YC P26), emerged from a high-stakes customer ultimatum: integrate with a clinic’s Windows-based medical record system in 48 hours, or lose a major deal. What started as a last-ditch effort to save a sale has evolved into a bold reimagining of how AI can tame the chaos of desktop automation. The result? A scalable, code-driven RPA platform that treats desktop workflows like software—version-controlled, debuggable, and observable.
Why Desktop RPA Is the Tech Industry’s Silent Nightmare
Most people think of automation as sleek bots zipping through cloud databases or APIs. But in reality, over 70% of enterprise workflows still rely on desktop applications, according to Gartner. These aren’t just niche tools—they’re core to industries like healthcare, finance, and logistics. Think of hospital billing systems, insurance claim processors, or manufacturing inventory trackers, all running on Windows desktops with UIs frozen in the early 2000s.
The problem? These systems were never designed to be automated. They lack APIs, have inconsistent UI elements, and are prone to silent failures. When an RPA script clicks the wrong button because a dialog box shifted by two pixels, the entire workflow can collapse—without any error message. Worse, these failures often cascade: a missed patient record update can trigger incorrect billing, leading to compliance violations or even denied insurance claims.
Traditional RPA tools like UiPath or Automation Anywhere offer visual drag-and-drop interfaces, but they struggle at scale. Scripts break when UIs change, debugging is nearly impossible, and orchestration across multiple virtual machines (VMs) becomes a logistical nightmare. Failure rates of 30% or higher are common in production environments—meaning nearly one in three automated tasks fails silently.
The Minicor Breakthrough: Treating RPA Like Real Software
Minicor’s founders realized that the root of the problem wasn’t the lack of tools—it was the lack of software engineering discipline in RPA. Instead of relying on fragile visual scripts, they built a system where every automation is a Python script, version-controlled and executable like any other piece of code.
This approach brings several game-changing advantages. First, Python’s speed and determinism make workflows more reliable than interpreted visual scripts. Second, because the automation is code, it can be tested, reviewed, and deployed using standard DevOps practices. Third, every execution is logged with full video replays and detailed logs—turning the black box of RPA into a transparent, auditable process.
But the real innovation lies in how Minicor integrates with AI. The team built a Model Context Protocol (MCP) that allows AI assistants like Claude Code or GitHub Codex to interact directly with a virtual machine running desktop software. The AI can “see” the screen, interpret UI elements, and generate or modify Python scripts on the fly. Need to automate a new form? The AI watches a human do it once, then writes the code to replicate the steps.
Scaling the Unscalable: Orchestration, Debugging, and Observability
One of the biggest challenges in RPA isn’t writing the script—it’s running it reliably across dozens or hundreds of instances. Minicor tackles this with a full orchestration layer that manages VM lifecycles, parallel execution, and resource allocation. Their system can clone VMs on demand, spin up parallel workflows, and handle bottlenecks automatically.
But orchestration is only half the battle. Debugging RPA failures used to be like finding a needle in a haystack—no logs, no screenshots, just a silent failure. Minicor changes that by capturing video replays of every execution, along with detailed logs of mouse movements, keystrokes, and system states. If a script fails, engineers can watch exactly what happened, down to the pixel.
The platform also includes built-in tools for handling common roadblocks like two-factor authentication (2FA) and one-time passwords (OTP). Instead of halting automation, Minicor can pause a workflow, send an alert to a human operator via Slack, and resume once the OTP is entered. It even supports “human-in-the-loop” steps, where an AI verifies a screenshot of the VM state before proceeding.
AI as the Ultimate RPA Co-Pilot
Perhaps the most futuristic aspect of Minicor is its use of AI not just to generate code, but to debug and evolve it. When a workflow fails, the MCP can analyze the logs, compare them to previous successful runs, and suggest code changes. Over time, the system learns which UI elements are stable and which are prone to change, adapting scripts proactively.
This creates a feedback loop where automation improves with use. For example, if a medical software update moves a “Save” button from the top-right to the bottom-left, Minicor’s AI can detect the change, update the script, and deploy the fix—all without human intervention.
Workflows can be triggered via API with custom input/output schemas, enabling seamless integration with modern systems.
Every automation is version-controlled, allowing rollbacks and audit trails.
The platform supports Slack notifications, LLM-based state verification, and human-in-the-loop workflows.
VM cloning enables parallel execution, scaling from 1 to 1,000+ concurrent automations.
This level of intelligence transforms RPA from a brittle workaround into a resilient, adaptive layer of enterprise infrastructure.
Real-World Impact: From Clinics to Call Centers
Minicor’s technology isn’t just theoretical—it’s already solving real problems. In one case, a healthcare provider used Minicor to automate patient intake forms across 50 clinics. Before, staff spent hours manually entering data from paper forms into a legacy Windows system. With Minicor, the process is fully automated, reducing entry time by 90% and eliminating transcription errors.
In another example, an insurance company automated claim processing for a system that hadn’t been updated since 2005. The RPA bot extracts data from scanned documents, inputs it into the desktop app, and verifies completion—all without human touch. The result? Claims processing time dropped from 48 hours to under 20 minutes.
The concept of RPA dates back to the 1990s, when companies first began using “screen scraping” tools to automate data entry. But these early systems were fragile and required constant maintenance—problems Minicor’s code-first approach finally solves.
The Future of Legacy Integration
Minicor represents a paradigm shift: instead of forcing legacy systems into the modern world, we’re bringing modern engineering practices to the legacy world. By treating desktop automation as software—not magic—they’ve created a system that’s scalable, debuggable, and intelligent.
As AI continues to advance, platforms like Minicor will become essential for any organization still reliant on desktop software. The goal isn’t to replace these systems overnight, but to make them interoperable, observable, and resilient.
In the end, the true innovation isn’t the automation itself—it’s the engineering rigor applied to a problem long dismissed as unsolvable. As Faiz and Saheed proved, sometimes the most stubborn tech challenges aren’t about building something new, but about rebuilding how we think about the old.
This article was curated from Launch HN: Minicor (YC P26) – Windows desktop automations at scale via Hacker News (Top)
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