ML condenses billions of logs into a tiny snapshot your LLM can debug
ML log clustering that sits beside Datadog and Loki without replacing your stack.

LLM logging dashboard, but Langsmith and Llamaindex already do this.
Backend developers building AI applications who need to monitor and debug LLM behavior.
LangSmith · Llamaindex Observability · Arize
ML log clustering that sits beside Datadog and Loki without replacing your stack.
Single Docker container with SQLite beats LangSmith's heavy Postgres dependency.
LLM-specific threat detection (prompt injection, jailbreaks, exfiltration) that WAFs completely miss.
Turns chat history into structured 'belief' and 'cognitive pattern' blocks you can inject into prompts, with simple APIs like run_reflection and run_synthesis that read like a research prototype. It's smart about separating V1 (domain beliefs) from V2 (transferable cognitive patterns), but it's clearly early-stage — tiny repo, Ollama-only workflow, and few commits mean you should treat it as an experimental MVP rather than a drop-in production memory system.
Multilingual tokenization comparison across Arabic, Chinese, French that LangSmith ignores.
Prompt injection detection at 100% precision — but only catches 43% of actual injections.