1 points | by Zakaria_Gamal 8 hours ago ago
1 comments
Hi HN,
I built StudyWithMiku, an open-source AI study assistant that:
Ingests PDFs automatically (drop into a folder)
Embeds them into ChromaDB
Uses RAG for grounded answers
Maintains conversational memory with LangGraph
Outputs synthesized character voice using Coqui TTS + DiffSinger
Runs locally (Ollama supported) or with cloud LLMs
The goal was to combine:
• Retrieval-augmented generation • Stateful agent orchestration • Tool execution (web/system tools) • Persistent memory • Character-specific TTS
into one cohesive system.
Engineering challenges included:
Chunking strategy and retrieval accuracy
Avoiding recursive agent tool loops
Managing CUDA/PyTorch/protobuf conflicts
Keeping latency reasonable with local models + TTS
It’s Linux-first (Ubuntu recommended) and GPU helps, but CPU works.
I’m particularly interested in feedback on:
RAG optimization approaches
LangGraph/agent architecture patterns
TTS expressiveness tuning
Local LLM deployment tradeoffs
Happy to answer technical questions.
Hi HN,
I built StudyWithMiku, an open-source AI study assistant that:
Ingests PDFs automatically (drop into a folder)
Embeds them into ChromaDB
Uses RAG for grounded answers
Maintains conversational memory with LangGraph
Outputs synthesized character voice using Coqui TTS + DiffSinger
Runs locally (Ollama supported) or with cloud LLMs
The goal was to combine:
• Retrieval-augmented generation • Stateful agent orchestration • Tool execution (web/system tools) • Persistent memory • Character-specific TTS
into one cohesive system.
Engineering challenges included:
Chunking strategy and retrieval accuracy
Avoiding recursive agent tool loops
Managing CUDA/PyTorch/protobuf conflicts
Keeping latency reasonable with local models + TTS
It’s Linux-first (Ubuntu recommended) and GPU helps, but CPU works.
I’m particularly interested in feedback on:
RAG optimization approaches
LangGraph/agent architecture patterns
TTS expressiveness tuning
Local LLM deployment tradeoffs
Happy to answer technical questions.