Prepare for coding interviews via deliberate practice
Beats static Grind 75 lists with a spaced repetition engine and prerequisite graph.

LeetCode for AI roles, but StrataScratch and InterviewQuery already cover this ground.
AI engineers preparing for technical interviews
StrataScratch · InterviewQuery · LeetCode
Beats static Grind 75 lists with a spaced repetition engine and prerequisite graph.
Timed system-design rounds with canonical prompts beat static LeetCode grind.
Swapping global vector scans for O(k) prefix/deterministic retrieval is a clever pivot that could cut latency and cost for local agent memory. The repo ships a usable Windows binary plus an MIT Python SDK and LangChain-friendly badges — enough to test the claim quickly — but the core engine is proprietary and lacks reproducible benchmarks, so you’ll want evidence before trusting it at scale.
Structured memory layers for agents—but vector search already solves this problem.
Hybrid search in-process — BM25 + vectors + RRF, zero external DB, validated on BEIR benchmarks.
VSA memory uses 32× less RAM than float vectors while beating RAG on recall.