Polaris @PolarisFund

we are a crypto investment firm. polaris.fund Joined February 2025

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Polaris @PolarisFund

a week ago

Large language models improve in a predictable way. More text, more parameters, and more compute mostly lead to steady improvements in performance. But RFMs don’t really follow the same pattern. As we discussed in the last report as well, their progress + progression is highly…

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Polaris @PolarisFund

3 weeks ago

With language models, scale is simple: feed them more text, bigger networks, and more compute, and they usually get better. Robotic foundation models (RFMs) are built a bit different. They live and operate in the physical world with hardware, sensors, moving parts, and real…

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Polaris @PolarisFund

a month ago

Robots today come in all forms - walking, rolling, flying, swimming, even squishing through tight spaces. Their diversity isn’t just about how they look: it’s about what they’re built to do. From humanoids that mirror human motion to drones that chart the skies, each type…

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Polaris @PolarisFund

2 months ago

Robotics has historically focused on building better hardware and tightly engineered control systems. But the real long-term advantage is shifting toward something far less visible 'proprietary motion data' - which is generated by fleets of operating robots. Motion logs…

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Polaris @PolarisFund

5 months ago

For decades, robotics was limited to rigid, rule-based machines confined to factory floors. These systems were manually programmed for narrow tasks, operating in structured environments with no ability to perceive, reason, or adapt. They relied on hardcoded scripts, required…

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Polaris @PolarisFund

6 months ago

Today, training and fine-tuning of AI models is dominated by a handful of companies, requiring billions of dollars in compute and relying on proprietary cloud infra. Decentralized training can help mitigate this dependency, enabling an open, verifiable, and community-driven…