python-weekly
A free weekly newsletter featuring noteworthy articles, tutorials, open-source projects, podcasts, videos, trending topics, and more.Python 潮流周刊,分享文章、教程、开源项目、软件工具、播客和视频、热门话题等内容。
Stars: 1714
Python Trending Weekly is a curated newsletter by Python猫 that selects the most valuable articles, tutorials, open-source projects, software tools, podcasts, videos, and hot topics from over 250 English and Chinese sources. The newsletter aims to help readers improve their Python skills and increase their income from both professional and side projects. It offers paid subscription options and is available on various platforms like GitHub, WeChat, blogs, email, Telegram, and Twitter. Each issue shares a collection of articles, open-source projects, videos, and books related to Python and technology.
README:
在这个信息过载的时代,人们获取信息的条件非常便利,但是,筛选优质信息的成本却大大增加。
读你想读、读你所需、读能使人成长的内容,这些并不是每个人都能做到。
Python 潮流周刊由 Python猫 出品,精心筛选中英文的 250+ 信息源,为你挑选最值得分享的文章、教程、开源项目、软件工具、播客和视频、热门话题等内容。
周刊愿景:帮助所有读者精进 Python 技术,并增长职业和副业的收入。
欢迎投稿,推荐或自荐文章/项目/资源/信息源,请 提交 issue。
本周刊从第 47 期开始转为付费模式,如果你想了解付费专栏,想知道为什么我们会转为付费专栏,欢迎阅读这篇博文 。
付费订阅入口,目前只支持三种方式:
以后周刊更新期数越来越多,我们会逐步免费开放早期的内容(第 n+50 期免费),欢迎保持关注。
为了方便读者及时获取最新内容,我会在多个平台上发布本周刊,欢迎订阅关注!
- Github:你可以获取本周刊的 Markdown 源文件,做任何想做的事!
- 微信公众号:除更新周刊外,还发布其它原创作品,并转载一些优质文章。(可加好友,可加读者交流群)
- 博客 及 RSS:我的独立博客,上面有历年原创/翻译的技术文章,以及从 2009 年以来的一些随笔。
- 邮件 及 RSS:在 Substack 上开通的频道,满足你通过邮件阅读时事通讯的诉求。
- Telegram:除了发布周刊的通知外,我将它视为一个“副刊”,补充发布更加丰富的资讯。
- Twitter:发布一些即时内容,欢迎与我建立社交联系。我的关注列表里有大量 Python 相关的开发者与组织的账号。
- 第 72 期:Python 3.13.0 最终版已发布!
- 分享了 14 篇文章,12 个开源项目,4 则音视频
- 第 71 期:PyPI 应该摆脱掉它的赞助依赖
- 分享了 12 篇文章,12 个开源项目,1 则音视频
- 第 70 期:微软 Excel 中的 Python 正式发布!
- 分享了 12 篇文章,12 个开源项目,2 则音视频
- 第 69 期:是时候停止使用 Python 3.8了
- 分享了 12 篇文章,12 个开源项目
- 第 68 期:2023 年 Python 开发者调查结果
- 分享了 12 篇文章,12 个开源项目,2 则热门讨论
- 第 67 期:uv 的重磅更新
- 分享了 12 篇文章,12 个开源项目
- 第 66 期:Python 的预处理器
- 分享了 12 篇文章,12 个开源项目,1 则音视频
- 第 65 期:CSV 有点糟糕
- 分享了 12 篇文章,12 个开源项目
- 第 64 期:Python 的函数调用还很慢么?
- 分享了 11 篇文章,13 个开源项目,1 则音视频
- 第 63 期:开发 Python Web 项目
- 分享了 10 篇文章,13 个开源项目,2 则热门话题
- 第 62 期:试用自由线程 Python
- 分享了 12 篇文章,12 个开源项目
- 第 61 期:PyPI 管理员密钥泄露事件
- 分享了 12 篇文章,12 个开源项目,2 则音视频,2 则热门话题
- 第 60 期:Python 的包管理工具真是多啊
- 分享了 13 篇文章,13 个开源项目
- 第 59 期:Polars 1.0 发布了,PyCon US 2024 演讲视频也发布了
- 分享了 12 篇文章,12 个开源项目,2 则视频,赠书 5 本
- 第 58 期:最快运行原型的语言
- 分享了 12 篇文章,12 个开源项目,赠书 5 本
- 第 57 期:Python 该采用日历版本吗?
- 分享了 12 篇文章,12 个开源项目,赠书 5 本
- 第 56 期:NumPy 2.0 里更快速的字符串函数
- 分享了 12 篇文章,12 个开源项目,赠书 5 本
- 第 55 期:分享 9 个高质量的技术类信息源!
- 特别加更系列,分享几个优质的周刊类信息源
- 第 54 期:ChatTTS 强大的文本生成语音模型
- 分享了 12 篇文章,12 个开源项目,3 则音视频
- 第 53 期:我辈楷模,一个约见诺奖得主,一个成为核心开发者
- 分享了 12 篇文章,12 个开源项目,赠书 5 本《程序是怎样跑起来的(第3版)》
- 第 52 期:Python 处理 Excel 的资源
- 分享了 12 篇文章,12 个开源项目,赠书 5 本《网络是怎样连接的》
- 第 51 期:用 Python 绘制美观的图表
- 分享了 12 篇文章,12 个开源项目,赠书 5 本《图解IT基础设施》
- 第 50 期:我最喜欢的 Python 3.13 新特性!
- 分享了 12 篇文章,11 个开源项目,2 则音视频,赠书 5 本《黑客与画家(10万册纪念版)》
- 第 49 期:谷歌裁员 Python 团队,微软开源 MS-DOS 4.0
- 分享了 12 篇文章,12 个开源项目,2 则视频,赠书 5 本《Hello算法》
- 第 48 期:Python 3.14 的发布计划
- 分享了 12 篇文章,11 个开源项目,赠书 5 本《图解TCP/IP(第6版)》
- 第 47 期:当你的老师希望你去做开源
- 分享了 12 篇文章,12 个开源项目,2 则音视频,赠书 5 本《Python编程:从入门到实践(第3版)》
- 第 46 期:如何用 Python 预测日食的时间和轨迹?
- 分享了 13 篇文章,12 个开源项目,2 则音视频,赠书 7 本《Python基础教程(第3版·修订版)》
- 第 45 期:越来越多的 AI 自动开发框架
- 分享了 13 篇文章,11 个开源项目,2 则音视频,赠书 5 本《Python语言及其应用(第2版)》
- 第 44 期:Mojo 本周开源了;AI 学会生成音乐了
- 分享了 12 篇文章,13 个开源项目,赠书 5 本《明解Python算法与数据结构》
- 第 43 期:在开源与家庭之间,他选择了家庭
- 分享了 12 篇文章,12 个开源项目,2 则音视频,赠书 5 本《Python数据结构与算法分析(第3版)》
- 第 42 期:小公司用 Python 开发,能做到什么程度?
- 分享了 12 篇文章,12 个开源项目。赠书 6 本《流畅的Python》
- 第 41 期:写代码很简单,但写好代码很难
- 分享了 12 篇文章,12 个开源项目。赠书 5 本《Python工匠》
- 第 40 期:白宫建议使用 Python 等内存安全的语言
- 分享了 12 篇文章,11 个开源项目
- 第 39 期:Rust 开发的性能超快的打包工具
- 分享了 13 篇文章,13 个开源项目,2 则播客
- 第 38 期:Django + Next.js 构建全栈项目
- 分享了 12 篇文章,12 个开源项目。赠书 5 本《AI 绘画实战:Midjourney从新手到高手》
- 第 37 期:Python “令人失望”的动态类型超能力
- 分享了 12 篇文章,12 个开源项目
- 第 36 期:Python 打包生态依然不乐观
- 分享了 11 篇文章,12 个开源项目
- 第 35 期:Python JIT 编译器和 Numpy2 即将推出
- 分享了 12 篇文章,12 个开源项目,2 则热门讨论
- 第 34 期:Python 3.13 的 JIT 方案又新又好!
- 分享了 13 篇文章,13 个开源项目,2 则音视频
- 第 33 期:FastAPI 很好,Flask 也没死,它们都有未来
- 分享了 15 篇文章,13 个开源项目,1 则视频
- 第 32 期:打造个人的新闻聚合阅读器
- 分享了 10 篇文章,10 个开源项目,3 则音视频
- 第 31 期:继 iOS 后,新 PEP 提议官方添加 Android 为支持平台
- 分享了 13 篇文章,12 个开源项目,3 则音视频
-
第一季内容合集
- 第 1~30 期周刊的精华内容合集,全文共计 62K 字
- 第 30 期:非洲 Python 社区给 PSF 的一封公开信
- 分享了 12 篇文章,12 个开源项目
- 第 29 期:Rust 会比 Python 慢?!
- 分享了 12 篇文章,12 个开源项目,2 则播客,2 个热门讨论
- 第 28 期:两种线程池、四种优化程序的方法
- 分享了 12 篇文章,12 个开源项目
- 第 27 期:应该如何处理程序的错误?
- 分享了 12 篇文章,12 个开源项目,2 则视频
- 第 26 期:requests3 的现状
- 分享了 12 篇文章,12 个开源项目,3 则音视频
- 第 25 期:性能最快的代码格式化工具 Ruff!
- 分享了 12 篇文章,12 个开源项目
- 第 24 期:no-GIL 提案正式被采纳了!
- 分享了 12 篇文章,12 个开源项目,3 则音视频
- 第 23 期:35 个容易上手的 Python 小项目
- 分享了 12 篇文章,12 个开源项目,2 则音视频
- 第 22 期:Python 3.12.0 发布了!
- 分享了 12 篇文章,12 个开源项目,2 则视频
- 第 21 期:如何提升及测量 Python 代码的性能?
- 分享了 12 篇文章,10 个开源项目,2 则音视频
- 第 20 期:三种基准测试的方法、为什么代码在函数中运行得更快?
- 分享了 14 篇文章,10 个开源项目
- 第 19 期:Mojo 终于提供下载了!
- 分享了 12 篇文章,8 个开源项目
- 第 18 期:Flask、Streamlit、Polars 的学习教程
- 分享了 12 篇文章,10 个开源项目
- 第 17 期:Excel 终于支持 Python 了、Meta 重磅开源新项目、Mojo 新得 1 亿美元融资
- 分享了 16 篇文章,13 个开源项目,3 则视频,2 则热门话题
- 第 16 期:优雅重要么?如何写出 Pythonic 的代码?
- 分享了 16 篇文章,12 个开源项目,2 则视频
- 第 15 期:如何分析异步任务的性能?
- 分享了 15 篇文章,9 个开源项目,4 则播客
- 第 14 期:Lpython 高性能编译器、Python 与 JavaScript 实现互通
- 分享了 15 篇文章,12 个开源项目,1 则播客
- 第 13 期:Jupyter Notebook 7 发布了,无 GIL 提案传来大好消息!
- 分享了 15 篇文章,12 个开源项目,1 则热门话题
- 第 12 期:Python 中如何调试死锁问题?
- 分享了 14 篇文章,10 个开源项目,5 则音视频
- 第 11 期:如何使用 Golang 运行 Python 代码?
- 分享了 15 篇文章,12 个开源项目,2 则播客,2 个热门话题
- 第 10 期:Twitter 的强敌 Threads 是用 Python 开发的!
- 分享了 13 篇文章,12 个开源项目,2 个热门问题
- 第 9 期:如何在本地部署开源大语言模型?
- 分享了 15 篇文章,10 个开源项目
- 第 8 期:Python 3.13 计划将解释器提速 50%!
- 提及了 12 篇文章,8 个开源项目/资源,2 则热门话题
- 第 7 期:我讨厌用 asyncio
- 提及了 15 篇文章/教程,11 个开源项目/资源,3 则音视频内容。赠书 5 本
- 第 6 期:Python 3.12 有我贡献的代码!
- 提及了 15 篇文章/教程,11 个开源项目/资源,3 则音视频内容
- 第 5 期:并发一百万个任务要用多少内存?
- 提及了 12 篇文章/教程,9 个开源项目/资源,6 则音视频内容
- 第 4 期:Python 2023 语言峰会
- 提及了 8 篇文章
- 第 3 期:PyPI 的安全问题
- 提及了 12 篇文章/教程,8 个开源项目/资源,2 则视频,2 则热门讨论
- 第 2 期:Rust 让 Python 再次伟大
- 提及了 10 篇文章/教程,5 个开源项目/资源,4 则音视频,3 则热门讨论
- 第 1 期:如何系统地自学Python?
- 提及了 8 篇文章/教程,4 个开源项目,4 则音视频,3 则热门讨论
如果你喜欢本周刊,请给一个 star 吧!
欢迎分享给其他需要的同学,让更多人可以从中受益~
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awesome-ml-blogs
awesome-ml-blogs is a curated list of machine learning technical blogs covering a wide range of topics from research to deployment. It includes blogs from big corporations, MLOps startups, data labeling platforms, universities, community content, personal blogs, synthetic data providers, and more. The repository aims to help individuals stay updated with the latest research breakthroughs and practical tutorials in the field of machine learning.
Call-for-Reviewers
The `Call-for-Reviewers` repository aims to collect the latest 'call for reviewers' links from various top CS/ML/AI conferences/journals. It provides an opportunity for individuals in the computer/ machine learning/ artificial intelligence fields to gain review experience for applying for NIW/H1B/EB1 or enhancing their CV. The repository helps users stay updated with the latest research trends and engage with the academic community.
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weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.
agentcloud
AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.
oss-fuzz-gen
This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.
LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.