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Local, reproducible benchmarking of open LLMs on project management tasks

Deterministic freshness and drift linter for RAG knowledge bases: catch stale chunks, missing/future timestamps, age-threshold breaches, temporal clustering, and embed-model age before they degrade your retrieval.

Static prompt-injection scanner for RAG corpora: catches jailbreak signatures, encoded payloads, hidden instructions, and role-play inducements before they reach the LLM.

Deterministic answer-grounding check for LLM/RAG outputs: catch hallucinated numbers, dates, and quotes without spending a cent on LLM-as-judge.

Lint for RAG corpora and retrievers: catch silent bugs (duplicate chunks, embed-model truncation, eval-set leakage, dim mismatch) before you ship.

Defensive, local-first secret-leak scanner for AI/LLM API keys — finds your own leaks; never validates, pools, or stores a key.

2-bit quantization with fused Metal dequant kernels for Apple Silicon — up to 8× faster local LLM inference

Boruna: a deterministic, capability-safe programming language and framework for LLM-native applications