# AdaptSim

- **Event:** [National Security Hackathon (by Army xTech)](https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon)
- **When:** May 2 at 9:00 AM – May 3 at 4:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Nick Parkes](https://cerebralvalley.ai/u/nick_parkes), [Mackenzie Lloyd](https://cerebralvalley.ai/u/Mackenzie-Lloyd)
- **GitHub:** https://github.com/nickparkes7/adaptsim-hackathon
- **Demo video:** https://www.loom.com/share/277f5654db6f42ed8a00a99a47fa1dff
- **Gallery:** https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery/61

Military and industrial training is still constrained by two brittle modes.

Physical simulation is expensive, fixed in place, and hard to reset. Live-fire ranges, wet trainers, mockups, and rehearsals can be valuable, but they usually produce limited repetitions against a small number of preplanned situations.

Digital simulation has the opposite problem. It is easier to repeat, but useful scenarios still require teams of designers, engineers, and subject matter experts to author specific environments, assets, decision trees, and failure modes in advance. The long tail of real-world situations is rarely represented.

The result is a gap between the schoolhouse and the field. Training often certifies that a team can pass a known standard, but it does not always expose how individuals and teams adapt under ambiguous, changing, high-pressure conditions.

AdaptSim is aimed at that gap: quickly turn a real environment into a reusable training space, then generate doctrine-informed adversarial scenarios that vary across repetitions while remaining physically grounded in the scanned space.

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Markdown version of https://cerebralvalley.ai/e/3rd-annual-natsec-hackathon/hackathon/gallery/61. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt