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Spotify Loader Benchmark
The Spotify Loader Benchmark is a comprehensive framework for evaluating different data loading strategies on the Spotify dataset, providing insights into the performance of various loading mechanisms. By comparing sequential, vectorized, multithreaded, raw SQL, Celery-based, and Flink-based loaders, this project helps developers optimize their data loading processes. With its robust architecture and flexible design, the Spotify Loader Benchmark is an essential tool for anyone working with large datasets.
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JARVIS
Title: JARVIS: Voice-First Autonomous AI Assistant
Description:
A locally hosted AI assistant that listens, reasons, and acts on my computer with human approval where it matters.
What I built:
Voice interface with a "Hey Jarvis" wake word, speech-to-text and text-to-speech
Autonomous coding agent that locates, patches, tests, and fixes its own code
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13 tools, approval gates, and an audit trail for safety
n8n workflow automation and a REST/WebSocket API
Tools: Python, FastAPI, LangChain, PostgreSQL, Redis, ChromaDB, Ollama, Docker, n8n
Having automatic fallback across multiple LLM providers keeps the assistant running smoothly even when one service drops.
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