SigMap – shrink AI coding context 97% with auto-scaling token budget
Signature mapping cuts AI context tokens 97% when Cursor and Continue bloat your prompts.

First LLM with per-token interpretability tracing input, concepts, and training provenance.
ML researchers, AI safety engineers, interpretability practitioners
Signature mapping cuts AI context tokens 97% when Cursor and Continue bloat your prompts.
Anki meets Netflix, pulling SRS cards directly from movie subtitles.
Parallel token decoding beats autoregressive LLMs on throughput, if the math holds up.
Runs with one npx command and immediately surfaces a helpful timeline view with token counts, tool I/O panes and subagent nesting — exactly the sort of visibility you want when an agent goes off the rails. Cleverly reads the local ~/.claude/projects traces so setup is trivial, but its usefulness is limited by being Claude-only and local; add search/aggregation or a team-sharing mode and this jumps up a tier.
Language purpose-built for token costs: 55 tokens vs 120 in JavaScript. Real compiler, 1291 tests.
Prefix notation language that cuts LLM token usage by 70% compared to Python or C.