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README.md
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The application is built with a buildless Preact frontend served by a Gradio Server (`gr.Server`), which also exposes the benchmark results through typed API endpoints. The headline edge agent is `openbmb/MiniCPM5-1B` distilled into a project-controls forecasting agent ([`slipstream-minicpm5-1b-evm`](https://huggingface.co/build-small-hackathon/slipstream-minicpm5-1b-evm)). Off the shelf, the 1B base model returns a usable forecast less than 2% of the time; after distillation, it reaches ~99% validity with a median cost error of roughly 2.7%, matching the traditional Earned Schedule baseline. It serves as the default model in the live demo. We also distilled `nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16`, a hybrid Mamba-2 + attention model, into [`slipstream-nemotron3-nano-4b-evm`](https://huggingface.co/build-small-hackathon/slipstream-nemotron3-nano-4b-evm) - our strongest student model, achieving about 2.37% cost error and a 0.61-period finish error. It was trained on its dedicated CUDA stack on Modal and is selectable in the live demo.
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## Try it live
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The final slide runs a real held-out project through the agentic layer live on Modal, next to Earned
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The application is built with a buildless Preact frontend served by a Gradio Server (`gr.Server`), which also exposes the benchmark results through typed API endpoints. The headline edge agent is `openbmb/MiniCPM5-1B` distilled into a project-controls forecasting agent ([`slipstream-minicpm5-1b-evm`](https://huggingface.co/build-small-hackathon/slipstream-minicpm5-1b-evm)). Off the shelf, the 1B base model returns a usable forecast less than 2% of the time; after distillation, it reaches ~99% validity with a median cost error of roughly 2.7%, matching the traditional Earned Schedule baseline. It serves as the default model in the live demo. We also distilled `nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16`, a hybrid Mamba-2 + attention model, into [`slipstream-nemotron3-nano-4b-evm`](https://huggingface.co/build-small-hackathon/slipstream-nemotron3-nano-4b-evm) - our strongest student model, achieving about 2.37% cost error and a 0.61-period finish error. It was trained on its dedicated CUDA stack on Modal and is selectable in the live demo.
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DEMO: https://youtu.be/v-k7lxXskTA
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## Try it live
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The final slide runs a real held-out project through the agentic layer live on Modal, next to Earned
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