Mirai Labs is an advanced development platform designed for engineers, researchers, and creators looking to build and deploy artificial intelligence directly on local hardware. By providing custom models, an optimized runtime environment, and robust infrastructure, the platform enables developers to create interactive, ambient, and continuous AI experiences that run locally on user devices. This shift away from cloud-dependent architectures helps reduce latency, enhance user privacy, and ensure uninterrupted functionality even without an active internet connection. Mirai Labs simplifies the complexities of edge computing, offering the tools necessary to integrate responsive AI capabilities into mobile apps, smart devices, and other hardware. It serves as a foundational resource for developers who want to push the boundaries of localized machine learning and build next-generation applications that are both faster and more secure.
Problem: Cloud-based AI assistants introduce noticeable latency and require constant internet connectivity to process multi-step device automations like scheduling and messaging.
Solution: Mirai Labs provides an optimized local runtime and model stack that executes tool calls and workflows offline directly on Apple Silicon.
Example: A developer runs a local CLI command to parse files, create a calendar invite, and send a Slack notification instantly without sending data to an external API.
Problem: Handling sensitive personal data like financial records, private messages, and personal files on cloud-hosted LLMs poses security and compliance risks.
Solution: The platform's on-device architecture ensures all data processing, model inference, and reasoning happen entirely on the local hardware.
Example: An application processes local bank statements and personal documents to generate a financial summary without any data leaving the user's Mac.
Problem: Standard LLM APIs are too slow for interactive, fluid interfaces, often keeping users waiting for streaming responses.
Solution: Mirai's hardware-optimized inference engine achieves high token-per-second speeds, allowing developers to build interfaces that render instantly.
Example: A developer builds an auto-completing, continuous-input coding assistant that runs locally at over 1,000 tokens per second.
Target audience: Best for: macOS and iOS developers, AI engineers building local-first apps, privacy-focused software teams, and hardware-aware system architects.
Pricing: Open Source · Categories: Developer Tools
Tags: AI, API, developer tools, Generative AI