La IA se AUTOMEJORA (¡Increíble!) 🤯 Nuevo MiniMax 2.7
Minimax engineers developed M2.7, an AI model that can self-improve by analyzing its own code, tests, and performance metrics. The model autonomously improved its performance by 30% over 100 iterations and won 9 gold medals in machine learning competitions while costing 20 times less than competitors like Claude Opus.
Summary
The video discusses Minimax M2.7, a groundbreaking AI model that represents a significant advancement in artificial intelligence self-improvement. The engineers gave the model access to its entire development environment, including code, tests, performance metrics, and error logs, allowing it to autonomously identify failures, propose corrections, apply changes, and measure improvements. Through 100 iterations of this self-improvement process, the model enhanced its performance by 30%. The model was then tested in the MLE BenchLite, which includes 22 machine learning competitions and is the standard OpenAI uses to measure autonomous research capacity, where it achieved 9 gold medals. The M2.7 scored impressively across various benchmarks: 56% in SWE Bench Pro (software development problem resolution), 55.6% in Byte Pro (complete project delivery), and 57% in Terminal Bench 2 (complex system comprehension and agendic environments). What makes this model particularly compelling is its cost-effectiveness and practical applications. At $1.20 per million tokens, it costs significantly less than competitors like Claude Opus ($25), GPT-4 ($15), and Gemini 3 Pro ($12). The model is designed for complex agendic work environments, capable of managing over 50 different tools simultaneously without losing stability. It excels in software engineering tasks beyond just writing code, including log analysis, bug detection, and project refactoring. The presenter demonstrates the model's capabilities through three practical examples using OpenCode: creating a complete multi-page website for an AI agent company, developing an algorithm visualization simulation, and building a comprehensive real estate data analysis dashboard from CSV files - all accomplished with minimal instructions.
About this episode
MiniMax Token Plan 12% OFF: https://platform.minimax.io/subscribe/coding-plan?code=DaqRGAJfwH&source=link MiniMax Platform: https://platform.minimax.io API Documentation: https://platform.minimax.io/docs/guides/text-generation MiniMax acaba de lanzar M2.7, un modelo que está a punto de ser open weight y que se automejora analizando su propio código. Consiguió 9 medallas de oro en competiciones de machine learning de forma autónoma, planta cara a Claude Opus en ingeniería de software y cuesta 20 veces menos. En este vídeo lo pongo a prueba con casos reales en OpenCode y te enseño a instalarlo y configurarlo paso a paso. --- MARCAS DE TIEMPO 00:00 Intro 01:08 Nuevo Minimax 04:29 Ejemplos 13:20 Tutorial instalación --- ✅ SUCRÍBETE A MI CANAL: http://bit.ly/suscribirse_XM --- Instagram: https://www.instagram.com/xavier_mitjana/ Twitter: https://twitter.com/xavier_mitjana Contacto comercial: [email protected] *Al realizar una compra a través de cualquiera de estos enlaces de afiliado, recibimos una comisión muy pequeña sin costo adicional para ti. Esto me ayuda a hacer el canal sostenible y seguir ofreciendo contenido de calidad. #inteligenciaartificial
Key Insights
- Minimax engineers created the first AI model that can autonomously improve itself by giving it access to its entire development environment, allowing it to identify failures, propose corrections, and measure improvements over 100 iterations
- The M2.7 model achieved 9 gold medals out of 22 possible in machine learning competitions using the MLE BenchLite standard, demonstrating autonomous research capabilities that rival leading AI systems
- Minimax claims that the M2.7 model has become the most productive member of their development and engineering team, surpassing human developers in output
- The model costs only $1.20 per million tokens compared to Claude Opus at $25, GPT-4 at $15, and Gemini 3 Pro at $12, making it 20 times more cost-effective than its main competitor
- The M2.7 can simultaneously manage over 50 different tools without losing stability and is designed specifically for complex agendic workflows rather than simple task assignment, positioning it as a comprehensive automation solution
Topics
Transcript
Today we are one step closer to the future that science fiction movies raised. Have you ever wondered what would happen if you gave an artificial intelligence access to its own code and said, find your mistakes and get stronger? Well, you don't have to speculate anymore, because today I bring you the answer. A team of Minimax engineers did exactly that and the result is surprising. They gave their model access to its tests, its results and its own code and put it to work. The model analyzed where it failed, changed its structure, evaluated if it had improved and started again. It did it a hundred times. The engineers only supervised the process. When it finished, it had…
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