What · Why · How
把复杂的 AI 系统做得可靠、可解释、可复现。
Building trustworthy AI systems, from inference runtimes to evidence-first agent workflows.
What: LLM serving, inference engines, and agent workflows.
Why: Complex AI systems should be trustworthy, inspectable, and reproducible.
How: Rust, Python, CUDA, pinned dependencies, explicit contracts, and evidence-backed verification.
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Serves DeepSeek-V4.1-Flash on 8×A100 with a pinned vLLM image and an adaptation layer, plus tools for weight verification, image builds, guarded launch, and functional and performance testing.
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Rust LLM inference engine built on Candle.
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Coordinates approved Ticket dependency graphs.
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Routes A-share research questions to a deterministic Python CLI whose results record the security, as-of date, sources, and calculation lineage.
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Personal agent rules, Skill sources, and MCP settings kept in one repository and shared across Codex, Claude, DSH, and Cursor.
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What matters. Why it matters. How to solve it reliably.

