One SDK, 12 Modalities: AI Inference Shouldn't Be This Fragmented

GitHub: github.com/nimiplatform/nimi | Apache-2.0 / MIT Local inference is becoming the default. But fragmentation is the real problem. Models are getting stronger and smaller. Local inference is no longer a hobbyist pursuit — it's becoming a standard part of how AI apps are built. IDC predicts that by 2027, 80% of AI inference will run locally or at the edge. The 2025 Stack Overflow Developer Survey found that 59% of developers use three or more AI tools simultaneously. But open any AI project we're working on today. Take an AI character app as an example: it needs speech recognition (STT), text reasoning (LLM), voice synthesis (TTS), scene image generation, and maybe background music. Five modalities, five different capabilities. With today's toolchain, we need: Local text inference: Ollama or llama.cpp Local image generation: ComfyUI or AUTOMATIC1111 Local voice synthesis: Piper or GPT-SoVITS Cloud video generation: Runway API Cloud music generation: Suno API Five tools. Five proces