By Renata Teixeira, Zhi Li, Reenal Mahajan, and Wei WeiOn January 26, 2026, we flipped an important switch for Live at Netflix: all Live events are now encoded using VBR (Variable Bitrate) instead...

As a follow-up to our previous blog post, “Where can you find MySQL during January to April 2026” (published at the end of January 2026), we would like to share an update. With the dynamic nature...

Binder logoIn February, the mybinder.org service will start tracking the “origin” of requests that launch a Binder session. The website that created a request to mybinder.org will be visible in...

In 2025, MySQL celebrated a significant milestone: the 30th anniversary of its creation. This was not only an occasion for reflection but also for connection and collaboration within the broader...

In 2025, MySQL and MySQL HeatWave embarked on an ambitious journey across various global AI events, highlighting their dedication to advancing AI and cloud solutions in the ever-evolving tech...

流式传输（SSE）是现代Web开发中实时数据推送的重要技术，基于HTTP，支持单向通信，适合文本数据，具有轻量级和自动重连的优点。与WebSocket相比，SSE在简单性和HTTP兼容性上更具优势，但不支持二进制数据。选择技术时需考虑业务需求和数据类型。

In 2025, MySQL proudly commemorated its 30th anniversary, marking three decades of innovation and community engagement in the realm of database technology. Throughout the year, the MySQL Community...

I'm using SDL2 in my game engine and have created custom event types that use the data from SDL_Event. This works well within my own code, but it becomes problematic with ImGui (and other...

By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason TuThis is part 3 in a series called “Behind the Streams”. Check out part 1 and part 2 to learn more.Picture this: It’s...

meetups&events with 30Y ann.celebration

该研究探讨了群体枪击事件中关键信息提取的不足，并提出利用命名实体识别技术获取知识的数据集。研究表明，GPT-4o模型在提取关键实体方面表现最佳，对法律和调查工作具有重要影响。

本研究探讨了手持吸入器使用的低依从性问题，利用wav2vec 2.0模型对吸入器声音进行分类，结果显示模型在数据集上的准确率达到98%。研究首次展示了智能手表在监测吸入器使用依从性方面的潜力。

本研究提出SurvSurf模型，解决了现有生存模型在序列事件首次到达时间预测中的单调性问题。该模型确保累积发生函数的单调性，并允许预测因子的非线性影响。研究结果表明，SurvSurf在多个数据集上优于传统模型，为事件发生时间的精确预测提供了新思路。

本研究提出了一种基于联邦学习的框架，联合建模非线性退化信号与故障事件，以提高剩余使用寿命预测的准确性。研究结果表明，该方法在涡扇发动机数据上的表现优于其他方法，具有显著的应用潜力。