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Magic is focused on automating AI research and code generation to create safe AGI. By leveraging frontier-scale pre-training, domain-specific reinforcement learning, ultra-long context, and inference-time compute, they aim to improve models more reliably than human capabilities allow. Their team consists of engineers and researchers dedicated to solving fundamental problems on the path to AGI.
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Key features
- Frontier-scale pre-training
- Domain-specific reinforcement learning
- Ultra-long context
- Inference-time compute
- Focus on AGI
- Collaboration with Google Cloud
Use cases
- 01Software engineering automation
- 02AI research automation
- 03Model improvement
- 04Alignment problem solving
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