Dare Divil Collective

FARA-7B, a compact computer-use model that’s powerful enough to run locally without burning your machine to the ground. This thing isn’t another bloated agent chained to the cloud. It’s built to run on regular hardware and still handle real tasks. 

What makes it stand out is its simplicity. Most agents use a giant stack of subsystems that click, scroll, guess at the screen, and call multiple helper models behind the scenes. FARA-7B does the opposite. It looks directly at a screenshot and decides what to do next. No scaffolding. No accessibility-tree parsing. No five-model circus happening backstage. Just one model handling everything. 

The magic comes from Microsoft’s synthetic data engine, Faragen. Instead of harvesting human browsing logs, they trained the model with AIs performing tasks across more than 70,000 websites. These weren’t perfect robotic demos either—they included mistakes, retries, scrolling, searching, and all the messy behavior humans actually do. After that, three separate AI judges verified every session to make sure the actions matched the on-screen reality. 

All of that added up to over a million individual actions used for training, giving the model extremely grounded behavior. The final result is an agent that doesn’t hallucinate clicks and doesn’t go rogue because it learned from full sequences of real web interactions. Most importantly, it runs locally, so latency drops and privacy shoots way up. 

Performance-wise, the numbers are wild for a 7B model. On benchmarks like WebVoyager, WebtailBench, and DeepShop, FARA-7B matches or beats much larger agents while using a fraction of the tokens. A full task costs about two and a half cents compared to thirty cents for big GPT-powered agents. That’s a massive difference in both speed and affordability. 

This is exactly what people hoped AI agents would eventually become—small, private, cheap, and accurate. FARA-7B is one of the first real signs that computer-use models are moving away from cloud-