LocalLLaMA users compare local AI stacks

This is a useful reality check: local AI is good enough to be productive, but the “stack” is still mostly glue and tradeoffs.

• –Voice is still the roughest layer; people are chaining transcription, TTS, and model inference instead of relying on one polished default.
• –Code generation still lacks a consensus winner, and tool-calling reliability is emerging as the real differentiator.
• –RAG looks more mature than the rest, which is why small models like Qwen3:4B show up so quickly in local workflows.
• –Memory and web search are still add-ons, not solved defaults, because most users are prioritizing the core assistant loop first.
• –The OP’s example stack, Faster-Whisper, LLM, Kokoro, and LiveKit, captures the current state perfectly: powerful, private, and still DIY.