ckarani

6 ms·

1.
by ckarani 7mo ago
working on it
2.
by ckarani 7mo ago
Going to get a cli app in the coming hours stay tuned!
3.
by ckarani 7mo ago
Putting out a cli interface in the coming days, linux support is coming for WaxCore by Next week works on macOS, iOS, vision os, watch os In addition were working on a port to python and kotlin
4.
by ckarani 7mo ago
WAL/checkpointing is about control over durability and crash behavior, not “better vectors.” sqlite-vec and Qdrant are storage engines first; their durability is mostly “under the hood.” If your goal is a clean local RAG system,
5.
by ckarani 7mo ago
Excellent Idea, already working on it, thanks for the feedback
6.
by ckarani 7mo ago
It is Not a layer over sqlite_vec
7.
by ckarani 7mo ago
sqlite-vec is a great vector index — Wax actually uses SQLite under the hood too. The difference is the layer. sqlite-vec gives you vec_distance_cosine() in SQL. Wax gives you: hand it a .mov file, get back token-budgeted, LLM-ready context
8.
by ckarani 7mo ago
Yes—Wax can absolutely be used as a general hybrid search layer, not just an “agent memory” feature. It already combines text + vector retrieval and reranking, so you can treat remember(...) as ingestion and recall(query:) as search
9.
Sub-Millisecond RAG on Apple Silicon. No Server. No API. One File(github.com)
130 points by ckarani 7mo ago|39 comments
10.
by ckarani 7mo ago
I built Wax because every RAG solution required either Pinecone/Weaviate in the cloud or ChromaDB/Qdrant running locally. I wanted the SQLite of RAG -- import a library, open a file, query. Except for multimodal content at GPU spe