I built a chat client that uses embeddings to cluster messages by topic
Cosine similarity scoring against the last three messages keeps context tight.
EmbedAudit CLI for auditing embedding spaces. Runs neighborhood consistency checks, drift detection, intrinsic dimensionality, and outlier analysis across sentence-transformers and OpenAI embeddings with HTML reports.
Embedding auditor with 5 checks and pretty plots, but crowded niche with unclear novelty.
ML engineers, NLP researchers, anyone validating embedding quality before production
Nucleus Semantic Search · Embedding validators in Weights & Biases · Custom embedding audit scripts in pandas/scikit-learn
Cosine similarity scoring against the last three messages keeps context tight.
Human-interpretable embeddings replace arbitrary latent dimensions.
Isolation Forest + K-means clustering detects log anomalies visually, but Datadog and Splunk already ship this.
Three-tier memory architecture solves context bloat for Claude Code and Copilot CLI users.
Reverse-engineers RBAC from audit logs; solves the 403 cluster-admin doom spiral automatically.
Vector search inside images beats caption/title matching for finding obscure public domain art.