Lew Zong En@maxlze336

answers tasks across eight capability domains — factual knowledge, math, sentiment, summarization, NER, code debugging, logic, and code generation — while minimizing paid API usage. Every prompt is classified locally with a zero-token regex router, then sent down the cheapest path that can answer it well: easy categories run on a bundled Qwen2.5-3B (4-bit, llama.cpp) model entirely inside the container, costing zero Fireworks tokens, while reasoning- and code-heavy tasks route to the most suitable Fireworks model with terse instructions and tight token caps. The agent is engineered for hostile grading conditions: an 8-token startup calibration measures real CPU speed, and a time-budget scheduler spills local work to the API before the 10-minute limit is ever at risk. Rejected models are disqualified after one call, truncated answers are salvaged, malformed tasks are isolated, and results are always written atomically — accuracy first, tokens near zero.

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