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AgentCost – Track, control, and optimize your AI spending (MIT)
One-line wrapping eliminates invisible LLM spend; real cost forecasting and model recommendations.
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agentcostin
314mo ago
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Make a free 3.8B model as reliable as one 7× bigger at parsing data
by pcoz·Jun 1, 2026·4 points·1 comment
AI Analysis
●●SolidBig BrainDark Horse
Deterministic verification loop makes 3.8B models match 7x larger ones for structured extraction.
Strengths
- •Regime gate plus exact graph analysis plus explicit refusal is genuinely novel architecture.
- •Zero runtime dependencies and runs with no model at all is impressive flexibility.
- •Bounded re-extraction loop fills gaps by re-asking with pointed-out missing fields.
Weaknesses
- •No benchmarks shown to verify the 3.8B vs 7x larger claim in the README.
- •Instructor, Pydantic, and guidance already handle structured LLM output.
Category
Target Audience
Developers using local LLMs for structured data extraction
Similar To
Instructor · Pydantic · guidance
Post Description
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