The previous post on the Linux 7.0 pgbench regression ended with the same instruction every other Postgres performance post ends with: set huge pages. This post is the long version. If you have...

There is a point in each technology cycle when the workaround turns into the product. We saw it when spreadsheets displaced paper ledgers and then became the planning tool. Emails supplanted...

CIOs are re-building their teams for the agentic AI age. This requires rethinking how to hire, build internal capabilities, and negotiate vendor strategies to speed innovation.

You might be doing well at work. But are you doing what’s right for you?

To integrate agentic AI into credit-related workflows and realize improvements, risk teams must identify critical, scalable use cases and convene cross-functional teams to develop solutions.

We need to do the front-end with spa. The question is whether to use a full-stack framework like next.js, but only the front-end part, without server-side components. If I don't use them, could...

In a competitive and commoditized insurance market, the German insurance provider didn’t just want to regain market share. The goal was to reset the bar completely.

AI agents are poised to make sophisticated money decisions for customers, reshaping banking and affecting billions of dollars in revenue.

Business leaders are using AI and agentic AI to create value despite unprecedented change. A pioneer’s journey offers many lessons.

本研究提出了一种新的自主驾驶框架（iPad），旨在解决现有系统的信息损失和规划效率问题。通过引入锚定注意力机制，该框架显著提升了规划质量和效率，实验结果在多个基准测试中表现优异。

本文探讨了大型音频语言模型的越狱攻击，指出现有文本攻击的不足，并提出了一种新方法AudioJailbreak，具有异步性、普遍性、隐蔽性和抗干扰性，能有效提升模型安全性。

本研究提出了多智能体框架MLZero，旨在解决现有自动机器学习系统在处理多模态数据时需要大量手动配置的问题。MLZero通过大型语言模型实现端到端自动化，显著减少人类干预，并在多个基准测试中表现优异，展示了其在机器学习自动化领域的重要潜力。

本研究提出DynMo，一种自主动态负载平衡解决方案，旨在提高大型语言模型（LLMs）的训练效率。通过自适应负载平衡和智能任务打包，DynMo在多GPU和多节点系统中可将训练速度提升至最高4.52倍。

Replace custom CRUD endpoints, reduce infrastructure complexity, and accelerate product delivery.

TrialMatchAI是一个人工智能驱动的临床试验推荐系统，旨在解决患者招募中的瓶颈。该系统通过处理异构临床数据，提供透明的推荐结果，验证显示92%的肿瘤患者在前20个推荐中找到相关试验，为精准医学提供了重要解决方案。

本研究提出了一种轻量级端到端文本到语音合成模型（LE2E），旨在解决现有模型在低资源设备上无法实时应用的问题。该模型在保持高音质的同时，计算资源消耗减少90%，实时处理速度提升10倍，展示了在低资源环境中实现高质量语音合成的潜力。

本文提出了一种新颖的端到端框架，结合ResNet和视觉变换器，利用可变形卷积等先进技术，显著提升自然图像的文本识别性能。实验结果表明，该框架在多个数据集上表现优异。

本研究提出了SacFL框架，旨在解决终端设备在持续学习中面临的存储资源有限和任务转移检测能力不足的问题。通过编码器-解码器结构和对比学习机制，显著降低了存储需求，并实现了自主的数据转移检测。实验结果验证了该框架在资源受限设备上的有效性。

本研究提出了LightEMMA，一个轻量级的多模态模型，旨在提升视觉语言模型（VLMs）在自动驾驶应用中的表现，指出VLMs在实际应用中仍需改进。