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In this post, we share the motivations, design choices, experiments, and learnings that informed its development, as well as an evaluation of the model’s performance and guidance on how to use it. Our goal is to contribute practical insight to the community on building smaller, efficient multimodal reasoning models and to share an open-weight model that is competitive with models of similar size at general vision-language tasks, excels at computer use, and excels on scientific and mathematical multimodal reasoning.
Scan — Discovers all JSONL session files under ~/.claude/projects/ and parses them in parallel using a thread pool. A fast-reject byte check skips progress and history-snapshot lines (~77% of all lines) before touching the JSON parser.