知鸦日报2024-10-12

Astro具有轻量级与高性能、卓越的SEO优化、兼容性与灵活性、简洁的开发体验、出色的社区支持、成本效益高等优势。

电子竞技如此吸引人，不仅仅是因为它的刺激和竞争性，更是因为背后精确的数学匹配机制。

Retrieval-Augmented Generation (RAG) is a powerful process that is designed to integrate direct function calling to answer queries more efficiently by retrieving relevant information from a broad database. In the rapidly evolving business landscape, Data Analysts (DAs) are struggling with the growing number of data queries from stakeholders. The conventional method of manually writing and running similar queries repeatedly is time-consuming and inefficient. This is where RAG-powered Large Language Models (LLMs) step in, offering a transformative solution to streamline the analytics process and empower DAs to focus on higher value tasks.

In this article, we will share how the Integrity Analytics team has built out a data solution using LLMs to help automate tedious analytical tasks like generating regular metric reports and performing fraud investigations.

In today’s fast-paced tech environment, maintaining robust on-call operations is crucial for ensuring seamless service functioning. Modern platform engineering teams face the challenge of efficiently managing on-call schedules, incident response, communication during critical moments, and strong customer support on Slack® channels.

This post describes Genie, an on-call copilot we built that uses generative AI to optimize communication and question-answering with on-call engineers.

We’ve been working to bring components of Quip’s technology into Slack with the canvas feature, while also maintaining the stand-alone Quip product. Quip’s backend, which powers both Quip and canvas, is written in Python. This is the story of a tricky bug we encountered last July and the lessons we learned along the way about being careful with TCP state. We hope that showing you how we tackled our bug helps you avoid — or find — similar bugs in the future!

Offline batch inference involves operating over a large dataset and passing the data in batches to a ML model which will generate a result for each batch. Offline batch inference jobs generally consist of a series of steps: dataloading, preprocessing, inference, post processing, and result writing. These offline batch inference jobs can be both I/O and compute intensive.

开始阐述背景之前，先分享一个实验数据，经过线上灰度版本的验证，前置广告流程可以缩短启动平均耗时约300ms。接下来就展开说说为什么我们需要做这件事了。

启动优化是老生常谈的话题了，Soul App也持续在进行启动相关的优化。常规和"黑科技"方案都有探索并上线。但一直有一个痛点难以跨越，在核心的启动流程中，因为业务特定要求，需要等到Application执行结束后开启广告加载的流程，这样串行执行的过程，其实非常影响启动体验。

聚焦草稿模型训练与解码间差异，强化两者在目标和上下文上对齐。

服务器成本降低95%，新数据源接入效率提升90%。

360内部对于智算中心的核心诉求是性能和稳定性，本文将深入探讨360智算中心在万卡GPU集群中的落地实践过程，包括算力基础设施搭建、集群优化、AI开发平台建设、以及训练和推理加速的实现。

在企业的经营管理中，复盘是战略落地的重要一环，运用得好，对组织、对个体都有巨大的助力。