## Short Segments

Miso Labs unveils MisoTTS, an 8-billion-parameter text-to-speech model with open weights, promising a new level of expressiveness in AI-generated speech. Today, we're diving into MisoTTS, a groundbreaking text-to-speech model from Miso Labs that claims to deliver human-like emotive speech with unprecedented speed. Later, we'll explore OpenJarvis, a local-first framework for on-device personal AI agents, offering a shift from cloud dependency to enhanced privacy and autonomy. Miso Labs has released MisoTTS, an open-weights 8-billion-parameter text-to-speech model designed to generate expressive speech from both text and audio context. The model employs residual vector quantization to expand its sonic range without increasing parameter count, addressing the vocabulary size problem common in standard transformers. With a latency of just 110 milliseconds, MisoTTS is significantly faster than competitors like ElevenLabs and Sesame. This speed, combined with its ability to condition on both text and prior audio, allows MisoTTS to respond to a speaker's tone, making it a promising tool for developers seeking to create more natural and responsive voice applications. By open-sourcing the model weights, Miso Labs is inviting developers to explore new possibilities in emotive speech generation.

## Feature Story

OpenJarvis, a new framework from Stanford University and Lambda Labs, is redefining personal AI by running entirely on-device, offering a local-first alternative to cloud-dependent systems. Announced on March 12, 2026, OpenJarvis is an open-source framework that allows users to build personal AI agents with tools, memory, and learning capabilities, all while maintaining user privacy and data sovereignty. This shift from cloud-first to edge-first architecture marks a significant change in AI development philosophy. OpenJarvis is not a single model but a framework that integrates any supported model with a configurable agent stack, evaluated across 11 local models from four families. Under the research's benchmark protocol, OpenJarvis models achieve performance within 3.2 percentage points of the best cloud models, at a fraction of the cost and latency. This efficiency is built on the team's earlier research, which demonstrated that local models could handle 88.7% of single-turn chat and reasoning queries at interactive latency, with intelligence efficiency improving 5.3 times from 2023 to 2025. The framework's release on GitHub has already garnered significant attention, with over 5,400 stars and 1,200 forks as of June 2026. OpenJarvis supports multiple programming languages, including Python, Rust, and TypeScript, making it accessible to a wide range of developers. By keeping AI inference and personal data local, OpenJarvis offers a compelling solution for privacy-sensitive users and enterprises looking to reduce reliance on cloud APIs. As AI continues to evolve, the demand for privacy and autonomy in personal AI systems is growing. OpenJarvis addresses these concerns by providing a framework that prioritizes user control over data and operations. This local-first approach not only enhances privacy but also reduces latency and operational costs, making it an attractive option for developers and users alike. Looking ahead, OpenJarvis could pave the way for more decentralized AI systems, challenging the dominance of cloud-based solutions. As more developers adopt this framework, we may see a shift towards AI systems that empower users with greater control and flexibility. For now, OpenJarvis stands as a testament to the potential of local-first AI, offering a glimpse into a future where personal AI agents are both powerful and private.