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GitHub Copilot pasa a AI Credits por tokens: qué revisar antes del 1 de junio de 2026

Mañana cambia el billing de Copilot: las premium requests dan paso a AI Credits calculados por tokens. Esto es lo que debe revisar un equipo técnico. El 1 de junio de 2026 GitHub Copilot empieza a migrar desde el modelo de premium requests hacia billing por uso con GitHub AI Credits. La unidad deja de ser una petición premium más o menos abstracta y pasa a reflejar consumo de tokens: entrada, salida y tokens cacheados, con precios vinculados al modelo usado. Decisión rápida Qué cambia mañana La idea de GitHub es alinear precio con coste real. Una pregunta rápida a un modelo ligero y una sesión larga de agente sobre varios archivos ya no son equivalentes. Para equipos técnicos, eso obliga a tratar Copilot como infraestructura de IA, no como una extensión de editor de coste fijo. Este artículo complementa la guía previa de AI Credits, pero se centra en el cambio operativo inmediato: qué mirar antes de que el modelo entre en vigor mañana. Briefing Qué es un AI Credit GitHub define AI Credits como una unidad de billing donde 1 AI Credit equivale a 0,01 USD. Cada interacción que usa modelos consume tokens. Esos tokens se valoran según el modelo y se convierten a créditos. En planes individuales, Copilot Pro, Pro+ y Max incluyen asignaciones mensuales de AI Credits. En organizaciones y empresas, cada licencia aporta créditos que se agrupan en un pool compartido a nivel de billing entity. La diferencia clave con el sistema anterior es que el consumo puede variar mucho dentro de una misma función. Dos sesiones de chat no cuestan igual si una es una pregunta corta y otra arrastra contexto de repositorio, varias iteraciones y generación de código extensa. Lectura práctica Qué consume créditos y qué no GitHub documenta que consumen AI Credits funciones como Copilot Chat, Copilot CLI, Copilot cloud agent, Copilot Spaces, Spark y agentes de terceros. Las code completions y Next Edit suggestions no se facturan en AI Credits y siguen incluidas en planes de pago. Esta distinción es

2026-06-01 原文 →
AI 资讯

I Rebuilt My Karaoke App So Everyone's Phone Could Be a Remote

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built VKara is a browser-based karaoke room app for singing at home with friends or family. It is not trying to replace YouTube. YouTube is already great at playing videos. It already has almost every karaoke song we need. But YouTube is not really designed to manage a karaoke night where many people want to choose songs together. That is the gap VKara tries to fill. You open VKara on a TV or laptop as the main playback screen. Everyone else joins the same room from their phone using a 4-digit room code or QR code. Then anyone can search for songs, add them to the queue, pause, resume, or skip. The TV only needs to play the video. Everyone's phone becomes their own remote. That is the whole idea. Simple enough to explain in one sentence. Not simple enough to build in one weekend. I learned that part the hard way. Demo Links: Live demo: https://vkara.vercel.app/en GitHub repo: https://github.com/lehuygiang28/vkara Before branch: https://github.com/lehuygiang28/vkara/tree/before Old backend repo: https://github.com/lehuygiang28/vkara-api Small warning: the demo is running on limited resources, so if it is slow, please give it a moment. My wallet is still a student wallet. lol. The flow is: Open VKara on a TV or laptop. Join the room from a phone by code or QR. Search for a karaoke video. Add it to the shared queue. Control playback together. Before: the idea worked, but the product still felt like a video app squeezed into a karaoke use case. After: the mobile flow is now focused on joining, searching, choosing an action, and controlling playback. The Comeback Story I started VKara around early 2025. At that time, my goal was very personal. I wanted a better way to sing karaoke at home with friends. The normal setup was: open YouTube on a TV, search for karaoke videos, and pass control around. It worked, but it was awkward. One person was searching. Another person accidentally played a video immedia

2026-06-01 原文 →
开发者

🌐OS May Recap: Learning to Navigate the Open-Source Galactica

In May, I continued my "One Commit a Day" Challenge and spent more time contributing across different open-source projects. Compared to April, I was able to contribute a bit more and explore a wider variety of repositories. Repositories That Stood Out Some of the projects that left the biggest impression on me were: python-odpt Huggin Face Context Course Human Signal ML ScribeSVG A Stable Checkpoint One milestone I was happy about this month was reaching a stable checkpoint for my Tokyo MCP Server project. It is still a work in progress, but getting to a point where the project feels stable enough to build upon was a satisfying moment. Documentation Matters Another contribution that stood out was helping improve a python-odpt README documentation . It wasn't a large technical contribution, but it reminded me that making a project easier for others to understand can be just as valuable as writing code. Good documentation lowers the barrier for future contributors. Sometimes, a clearer README can help more people than a small code change. Learning Beyond Python One practical lesson I learned this month was that being a Python-focused contributor doesn't mean I can ignore the JavaScript ecosystem . While working with different repositories, I finally installed Node.js and started using npm . Many modern open-source projects rely on TypeScript-based tooling, build systems, or development workflows, and understanding those tools makes contributing much easier. The Biggest Challenge: Finding Information And Communication Matters The biggest challenge I faced wasn't coding. It was documentation. Every repository has its own way of organizing information. There are definitely common patterns, but every project also develops its own style over time. Sometimes the information I need is in the README. Sometimes it's in a wiki. Sometimes it's buried in a docs folder several levels deep. And sometimes it's spread across all three. Open Source Is Also About Navigation As a contri

2026-06-01 原文 →
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