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ruvector 2026: Adaptive Filtered-ANN — Selectivity-Driven Strategy Switching for High-Performance Predicate Vector Search in Rust

150-char summary: ruvector adds a selectivity-aware filtered ANN planner in Rust: 94× QPS at 1% selectivity, recall@10=1.0, O(1) routing via frequency histogram.

Introduction

Filtered vector search — "find the 10 nearest vectors to this query where category == sports" — is the single most common production workload in vector databases. It's also the hardest to get right.

The problem: the optimal execution strategy is highly sensitive to predicate selectivity (the fraction of corpus vectors that satisfy the filter). Scan everything and filter at the end (post-filter) is fast at high selectivity but collapses at 1%. Pre-filter the candidates and do exact search is fast at 1% but wastes compute at 50%. No single strategy wins.

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ruvector 2026: RaBitQ + IVF — High-Performance 1-Bit Vector Quantization in Rust

150-char summary: ruvector adds RaBitQ (SIGMOD 2024) 1-bit quantization: 18× memory compression, 17× faster queries via IVF partitioning. Pure Rust, zero dependencies beyond rand.

Introduction

Modern AI applications embed documents, images, and code into high-dimensional float vectors. A 128-dimension f32 vector costs 512 bytes. Scale to 100 million documents and you need 50 GB RAM just for raw vectors — before any search index overhead.

ruvector solves this with RaBitQ: a theoretically-grounded 1-bit quantization algorithm (SIGMOD 2024, doi:10.1145/3654970) that compresses each f32 dimension to a single bit while preserving approximate nearest-neighbour search quality through a probabilistic error bound. Combined with an IVF (Inverted File) partition index, the result is a Rust vector search engine that runs 17× faster at 18× lower memory than the f32 baseline.

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#!/usr/bin/env python3
"""
Claude Code token usage analyzer.
Analyzes ~/.claude/projects/ JSONL files for token usage patterns.
"""

import json
import os
import sys
from pathlib import Path

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Public Data Sources

Streaming Metadata

Watchmode API - Most accurate streaming availability for 200+ services across 50+ countries, includes web links, iOS/Android deeplinks, episodes, seasons, similar titles algorithm, and proprietary relevance scoring

Flix Patrol https://flixpatrol.com/about/api/

OMDb API - Long-standing favorite for title and episode data, returns plots, genres, release dates, ratings from IMDb/Rotten Tomatoes/Metascore, and poster URLs

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Claude Memory Template

Copy-Paste Instructions for Optimal AI Interaction

⸻

1. Core Identity and Objective

I am [Your Name/Role], focused on:

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Claude Code Configuration - SPARC Development Environment (Batchtools Optimized)

🚨 CRITICAL: CONCURRENT EXECUTION FOR ALL ACTIONS

ABSOLUTE RULE: ALL operations MUST be concurrent/parallel in a single message:

🔴 MANDATORY CONCURRENT PATTERNS:

1. TodoWrite: ALWAYS batch ALL todos in ONE call (5-10+ todos minimum)
2. Task tool: ALWAYS spawn ALL agents in ONE message with full instructions
3. File operations: ALWAYS batch ALL reads/writes/edits in ONE message

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Performance Analysis Report

NeuralForecast NHITS Integration Performance Validation

Date: June 2025
Analysis Period: Complete Integration Lifecycle
Report Type: Comprehensive Performance Validation

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🎯 Key Features Documented