I build open-source tools for token economics: measuring LLM cost accurately, reducing prompt waste deterministically, and routing work to the cheapest model that is still good enough.
The through-line is simple: model choice should be an engineering decision with evidence, not a default dropdown.
| Project | What it does | Role in the stack |
|---|---|---|
tokenometer |
Multi-provider token counts, USD cost, latency benchmarks, CI cost guardrails, VS Code/Cursor extension, and Claude Code skill. Live at tokenometer.dev. | Measure |
llm-tokens-atlas |
Open benchmark for offline-vs-empirical tokenizer calibration across providers and prompt formats. Dataset on Hugging Face. | Calibrate |
promptc |
Deterministic, LM-free prompt compiler with behavior-preserving cost-reduction passes. | Reduce |
routerlab |
Cost-quality routing for LLM APIs with reproducible Pareto frontiers per task class. | Route |
ast-ai-model-router |
AST-aware Claude/Codex wrapper that picks models from task and code complexity signals. | Apply routing to coding agents |
commerce-api-starter |
TypeScript Express commerce API starter with OpenAPI docs, tests, Docker, and LLM cost-guardrail examples. | Demonstrate |
- Use Tokenometer if you need a practical tool today: CLI, CI guardrail, GitHub Action, VS Code/Cursor extension, MCP server, and web playground.
- Use llm-tokens-atlas if you need reproducible evidence about how far offline tokenizers drift from provider-empirical counts.
- Use PromptC if you want deterministic, LM-free prompt optimization with explicit pass semantics instead of opaque prompt rewriting.
- Use RouterLab if you want to make model choice a cost-quality frontier decision rather than a default model setting.
- Use AST AI Model Router if you want that routing idea applied to local Claude Code / Codex workflows.
- Use Commerce API Starter if you want a compact TypeScript API starter that shows tests, OpenAPI-style docs, Docker, and Tokenometer cost-guardrail examples in a normal application repo.
- Publishing empirical tokenizer calibration results that show where offline counters under-budget real provider cost.
- Turning prompt optimization into a compiler problem: typed IR, deterministic passes, and auditable behavior-preservation checks.
- Building practical model routers where cost, latency, and task quality are first-class inputs.
- Connecting local coding agents to the same economics: use smaller/faster models for simple work, stronger models for architecture and high-risk changes.
- Tokenizer calibration: when proxy tokenizers are accurate, biased, or systematically unsafe for budgeting.
- Prompt compilers: deterministic transformations that reduce cost without asking another model to rewrite the prompt.
- Cost-quality frontiers: reproducible routing policies that choose models rationally per task class.
- Agent model selection: AST and repo signals that predict when a coding task needs stronger reasoning.