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Insights & Updates
Deep dives into autonomous AI agents, real case studies, engineering decisions, and where the industry is heading.
The Best Computer Use Platform 2026: Why Everyone Else Is Lying
Claude and OpenAI are bragging about low scores while Coasty is quietly dominating OSWorld with 85.6% accuracy. Here's why you should stop believing the benchmarks.
The CIO Case for Moving from RPA to Computer Use Agents
RPA bots break when UIs change and halt on the first exception. Computer use agents see the screen, adapt, and recover, making them the durable choice for modern enterprise automation.
Why Your Email Outreach Automation Is Wasting 47% of Your Time (And How to Fix It)
Manual outreach is dead. But most AI tools are too broken to actually work. Here's what you should do instead.
Low Code RPA vs Prompt Driven AI Agents for the Enterprise
Traditional low code RPA handles predictable volume but breaks when UIs shift or exceptions appear. Prompt driven AI agents see the screen, follow SOPs, and recover from errors, making them the durable path for complex, changing work.
The RPA Maintenance Treadmill and How to Get Off It
Legacy RPA is a rebuild-on-change treadmill that slows down digital transformation. Computer use agents see the screen and adapt, letting you finally offload the long tail of SOP-driven work without constant rework.
Stop Copy-Pasting Invoices (You're Wasting $47K/Year)
Manual invoicing costs your team 12 minutes per invoice. With 500 invoices a month at $35/hour, that adds up to $47,000 in wasted payroll. Here's how to fix it with AI computer use, not RPA hype.
Why RPA Needs a Developer for Every Change and AI Agents Do Not
A single UI update can kill an RPA bot and a developer needs to rebuild it. AI computer use agents see and adapt instead. Learn the difference and how to move forward.
The True Total Cost of Ownership of an Enterprise RPA Program
Most companies only count licensing and dev hours, but the real cost shows up in maintenance backlogs and broken bots. Here’s what the numbers really look like, and why computer use agents are the durable path forward.
Synthetic Data vs Real Data for Training AI Agents
Training AI agents is hard because real interaction data is scarce, expensive, and risky. Synthetic data offers a practical alternative with strong performance gains and zero privacy concerns.
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