Less debugging. More discovery.
Seven local tools for AI engineers, researchers, and PhD students. Check data, compare experiments, and test sampling or decoding hypotheses with explicit assumptions.
Tokenizer · Context budget · Payload inspector →
Prompt diff & token impact
Compare two prompt versions line by line and measure their token and UTF-8 byte changes.
Open tool →JSONL dataset inspector
Find malformed records, repeated samples, long inputs, and exact train/validation overlap.
Open tool →RAG chunking lab
Explore Unicode-safe chunk boundaries, overlap, and exact text-token counts.
Open tool →Paired evaluation comparison
Compare two systems on the same examples with a reproducible bootstrap interval.
Open tool →GPU memory estimator
Estimate weights, KV cache, and full-training states before scheduling a run.
Open tool →Sampling lab: logits → tokens
Test temperature, top-k and nucleus sampling against a reproducible local distribution.
Open tool →Speculative decoding lab
Explore when a draft model helps—or hurts—under explicit acceptance and latency assumptions.
Open tool →