Safe Superintelligence

OpenRouter operates a unified API gateway that aggregates 300+ large language models from 60+ providers into a single interface, processing over 100 trillion tokens annually for more than 5 million developers. Founded in 2023 by Alex Atallah and backed by $40M Series A funding from Andreessen Horowitz, Menlo Ventures, and Sequoia Capital, the platform addresses multi-provider infrastructure complexity through intelligent routing, automatic failover, and consolidated billing across models from Anthropic, OpenAI, Google, Meta, and dozens of other providers. The technical architecture prioritizes reliability and operational flexibility through automatic fallbacks between providers, response healing for malformed JSON outputs, and customizable data policies. The platform standardizes access across heterogeneous model APIs while maintaining transparent per-token pricing without subscription tiers. Public usage rankings provide visibility into model performance patterns across the user base. OpenRouter's infrastructure handles workloads ranging from individual developer projects to enterprise-scale deployments, with completion insurance and routing logic designed to mitigate single-provider outages and rate limiting. The platform's tech stack includes React, Next.js, TypeScript, and Cloudflare Workers for edge deployment. Core operational focus centers on eliminating vendor lock-in while maintaining production-grade uptime across a rapidly expanding model catalog.

Slingshot AI operates as a mental health research lab building a foundation model for psychology and accompanying therapy chatbot. The technical stack spans model development (PyTorch, TensorFlow, JAX) and production infrastructure (GCP, Kubernetes, Cloud Run, gRPC) with client applications in Flutter and Next.js/React. The team combines machine learning engineering, product development, and clinical research expertise, working with therapists and clinicians to align model behavior with therapeutic practices. The core technical challenge is training a domain-specific foundation model that supports user agency in mental health contexts - framing the product as a tool that helps users recognize their own capacity for change rather than an answer-dispensing assistant. This architectural constraint requires careful training objective design and evaluation frameworks that measure therapeutic alignment, not just task completion. The system operates at global scale through partnerships with mental health organizations, though specific throughput or latency metrics are not disclosed. Development follows rapid iteration cycles with emphasis on shipping velocity. The engineering stack reflects production priorities: Rust for performance-critical paths, typed languages (TypeScript, Kotlin) for application logic, and container orchestration for deployment. The team works within the constraint of adapting general-purpose ML infrastructure to specialized clinical requirements while maintaining operational reliability for users seeking mental health support.