El backend Pando de OpenAI reduce la latencia mediana de push de Git en un 80 % mientras gestiona casi un petabyte de datos diarios, acelerando significativamente los flujos de trabajo de desarrollo para ingenieros y agentes de IA.
Resumen generado con IA. Consulta el original para comprobar el contexto y la precisión.
Este es el extracto disponible del feed. Puede estar abreviado o en el idioma original.
OpenAI has introduced Pando, a new Git backend that now serves all development in its monorepo. The OpenAI Pando Git backend keeps the standard Git client and protocol. Engineers and Codex agents still run git push and git fetch as before. Only the server changed. OpenAI team reports median push latency fell nearly 80%. Pando now delivers nearly a petabyte of Git data per day. TL;DR Scale: Nearly 1 PB of Git data per day; peak above 57 TB in 1 hour. Runs on: Commodity hardware; standard cloud VMs saturate 10 Gbps links on clone and fetch. Performance: Thousands of concurrent pushers without degradation, per OpenAI’s own testing. Best: Median push latency down nearly 80% (about 5x). Worst: Not open source; repeat branch pushes resend non-trunk history (p99 pack under 3 MiB). What is OpenAI Pando? Pando is a Git-compatible server backend from OpenAI that scales concurrent pushes and fetches for a monorepo. It was written in Rust and reached production in 10 weeks. One engineer designed the core and teams of Codex agents implemented it. It has served production traffic since April 2026. Why did OpenAI need a new Git backend? Codex multiplied parallel work. OpenAI’s commit chart shows commits landed on master up 1,378% in Q3 2026 versus Q1 2024. Its Q4 2026 estimate is +2,442%. Each push now triggers many CI fetches. By late 2025, fetches and pushes slowed, merges failed, and reliability at times fell below 90%. OpenAI concluded the bottleneck was how it served Git, not Git itself. How does Pando work? Pando drops 2 assumptions that most Git servers treat as requirements: 1. Repository-wide consistency: Conventional servers order every ref update repo-wide, so unrelated pushes wait. Pando uses causal consistency instead. Each branch is serialized on its own, and clients only conflict when pushing the same ref. The tradeoff: a reader viewing several branches may see them at different points in time. 2. A unique packfile per client: Standard servers build a
Una ayuda automática para ordenar, basada en fuentes, palabras clave y entidades, en una escala de 1–12. Alta: 9–12; media: 6–8; baja: 1–5. No mide urgencia, verifica noticias ni representa respaldo editorial.
El nuevo método de atención de Zyphra acelera el procesamiento de IA mediante el uso de una estructura de árbol para omitir texto irrelevante, manteniendo un alto rendimiento en tareas complejas.
Letterpress-1 automatiza las mejoras de sitios web analizando mercados y generando cambios de código, ofreciendo una solución alojada que elimina la necesidad de hardware local.
Estos contratos definen los estándares técnicos y operativos para la integración de modelos de IA transformers, garantizando una implementación fluida, segura y eficiente en diversos sistemas.