Mother Computer

Thinking Machines Lab is a 2025-founded AI research and product company led by Mira Murati, former CTO of OpenAI. The organization addresses a concentration problem: training methods for frontier AI systems have remained largely confined to top labs, constraining public understanding and limiting users' ability to customize systems to specific needs. The team - comprising scientists and engineers who previously built ChatGPT, Character.ai, and contributed to PyTorch - focuses on making AI systems more widely understood, customizable, and generally capable through open science publications and code releases. The company's technical work centers on multimodal systems designed to adapt across the full spectrum of human expertise, with an explicit architectural preference for human–AI collaboration over full autonomy. Their stack includes Python, Rust, PyTorch, React/TypeScript, Kubernetes, and Spark. Development priorities span training and analysis of frontier models, multimodal system design, and foundational ML framework work - reflecting the team's prior experience building widely-deployed products and infrastructure. The operational model emphasizes open science: research findings and implementations are released publicly rather than held proprietary. This approach targets both the customizability bottleneck - where users cannot effectively tune systems to domain-specific requirements - and the knowledge distribution problem that limits informed discourse about frontier model development. Product outputs include multimodal systems and published research artifacts alongside the methodological contributions inherent in their open release practice.

Eve builds AI-native infrastructure for plaintiff law firms, operating as an intelligent case assistant platform that manages litigation workflows from intake through resolution. The system processes more than 200,000 legal cases annually, handling case evaluation, medical chronology generation, demand letter drafting, and discovery responses. Developed in collaboration with OpenAI and Anthropic, the platform learns each firm's tone and style to generate documents that match attorney output, with attorneys able to train and teach the system for their specific practice patterns. The platform targets labor and employment practices and personal injury firms. Client firms report 250% year-over-year revenue growth and 2.5X case capacity increases without additional headcount, though these are self-reported outcomes rather than platform-wide guarantees. Eve claims to be the first legal AI to achieve SOC II Type 2 certification while maintaining HIPAA compliance, addressing the compliance and security requirements of handling protected health information and sensitive legal data at scale. The technical challenge set involves natural language processing for document generation, AI workflow development that adapts to individual firm processes, and maintaining enterprise-grade security infrastructure. The platform must handle the operational complexity of legal document generation across varied practice areas while meeting regulatory requirements for data handling in the legal and healthcare domains.

Mirelo AI builds foundation models for generating synchronized audio for video content, targeting the latency and quality bottleneck in audio-for-video workflows. Founded in 2023 in Berlin, the company raised $41 million in seed funding co-led by Index Ventures and Andreessen Horowitz. Their models generate synchronized sound effects in seconds rather than the hours typically required for manual sound design, addressing production throughput constraints across gaming, film, social media, and broader visual content verticals. The technical stack centers on PyTorch with transformer architectures, optimized for H100 and H200 GPUs using Nsight profiling and SLURM for cluster orchestration. The team sources from Google Brain, Amazon, Meta FAIR, Disney, ETH Zürich, and Max Planck Institutes, combining AI research depth with domain expertise from musicians and product specialists. Co-founder and CEO CJ Simon-Gabriel previously worked at AWS Labs, where the founding team originated. The core technical challenge is tight audio-visual synchronization at generation time - a constraint that spans model architecture design, latency optimization, and evaluation methodology. Production systems must handle variable-length video inputs while maintaining temporal coherence across generated audio, requiring careful trade-offs between generation speed, output quality, and computational cost. The company positions its models as infrastructure for visual content pipelines, treating audio generation as a systems problem rather than a standalone creative tool.