If in doubt, delete the uv.lock file
Score
60Why selected
保留在全量动态池，可作为背景信息或后续聚合信号。
AI commentary
「来自 Simon Willison 博客（Atom） 的这条信息指向行业格局和商业判断变化，适合用于判断近期 AI 圈的真实动向。」
Quick overview
I've been getting the occasional confusing Python dependency error since switching over to Python 3.15 and the rule I'm having to remember is: If in doubt, delete the uv.lock file Most of my projects are libraries which…
- I've been getting the occasional confusing Python dependency error since switching over to Python 3.15 and the rule I'm having to remember is: If in doubt, delete the uv.lock file Most of my projects are libraries which…
- Running uv run pytest etc tends to leave uv.lock files lying around, which are listed in .gitignore and so do not get committed to my repositories.
- sometimes that uv.lock pins a version of a dependency that doesn't yet have a Python 3.15 wheel.
Full text
I've been getting the occasional confusing Python dependency error since switching over to Python 3.15 and the rule I'm having to remember is:
If in doubt, delete the uv.lock file
Most of my projects are libraries which don't include a uv.lock file, since it's rude for a library dependency to lock versions for anything else that uses it.
Running uv run pytest etc tends to leave uv.lock files lying around, which are listed in .gitignore and so do not get committed to my repositories.
But... sometimes that uv.lock pins a version of a dependency that doesn't yet have a Python 3.15 wheel. An example from earlier today:
error: Failed to build `watchfiles==1.1.1`
cause: Call to `maturin.build_wheel` failed
error: the configured Python interpreter version (3.15) is newer
than PyO3's maximum supported version (3.14)
Deleting that file and running uv run pytest again usually fixes the problem.
Related items
89
FeaturedOpenAI Research: AI Is Redefining Job Boundaries
OpenAI研究：AI正在重新定义工作边界
If you care about the real impact of AI on work, this research is a must-read. Instead of theory, it uses data to show how ChatGPT users expand their task scope, offering direct insights for HR, managers, and every professional looking to upgrade their skills.
87
FeaturedOpenAI CFO Shares Five Lessons for Building an AI-Native Finance Function
OpenAI CFO 分享构建 AI 原生财务职能的五大经验
As an editor, I find this article uniquely valuable because it comes from a finance chief at an AI leader, not an external consultancy, making it highly practical. I recommend it to finance digitalization leads, CFOs, and anyone interested in AI-driven transformation.
87
FeaturedScientific Computing in the Age of Agentic AI: OpenAI Releases New Field Report
智能体AI时代下的科学计算：OpenAI发布新领域报告
This report is one of the most practical analyses in the current AI agent wave, especially suitable for practitioners in computational biology and drug discovery. It answers the key question 'What can AI agents do besides writing code?' and directly demonstrates productivity gains in complex scientific computing.
87
FeaturedReal World VoiceEQ: Measuring the Human Quality of Voice AI
Real World VoiceEQ：量化语音AI的人类自然度
What's noteworthy is that this benchmark truly focuses on the soft metric of 'human-likeness', not traditional speech recognition rate or MOS scores. For teams working on speech synthesis, voice cloning, or voice interaction, this is a highly practical reference tool that quickly identifies model deficiencies in emotion, prosody, etc. However, language support is currently limited; looking forward to future expansion.
85
FeaturedOpenAI Introduces AI Age Scorecard: A New Way to Measure ROI
OpenAI 推出AI时代记分卡：衡量投资回报的新方法
This blog post hits a sore point in the current AI industry: how to quantify input-output. For enterprise decision-makers deploying or planning large-scale AI, it helps avoid herd mentality. My personal advice: try defining 'useful work' based on your own business scenario, and avoid designing metrics that are merely formality.
85
FeaturedHow to Manage AI Investments in the Agentic Era: Focus on Useful Work per Dollar and High-Value Workflows
代理时代如何管理AI投资：聚焦每美元有用功与高价值工作流
This article is especially important for enterprises scaling AI agents. It reminds us that focusing solely on falling API prices is a trap; what really matters is the return per unit of work. I recommend CTOs and CFOs read it together and establish quantitative evaluation mechanisms internally.
85
FeaturedDesigning Organisations That Can Keep Up With AI: Organisational Latency as the Biggest Barrier
设计能跟上AI的组织：组织延迟成为最大障碍
What makes this article truly worth following is that it raises a neglected bottleneck: no matter how strong the technology is, if the organisation can't keep up, it's useless. For CTOs, CIOs, and transformation leaders, this is a more important framework than tech selection.
84
FeaturedRuff v0.16.0 Released: Default Rules Jump from 59 to 413, CI Pipelines Need Attention
Ruff v0.16.0 发布：默认规则从59条激增至413条，CI工作流需关注
For Python developers, this is an unmissable update signal. The significant increase in Ruff's default rules means we can no longer ignore potential issues in our code, and AI fix tools can lower the upgrade cost. Notably, Astral (OpenAI) is deeply integrating AI coding capabilities with static analysis tools, which may become the norm in future engineering practices.