HackerURLs

一次又一次被重新发现的经典。

同一个链接,相隔一年以上,一次又一次被人重新发现、再次冲上 Hacker News 首页。能经受住这种时间检验的,大多是值得一读再读的文章。每篇附中文标题,讨论最热的那些再加上中文导读和高赞评论翻译。

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  • 4,700+篇经过时间检验的经典

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上首页次数最多、而且有中文导读的 30 篇。时间轴上每个黑点是一次上首页,空心点是一次没冲上去的投稿。

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按第一次上首页的年份分布,点柱子筛选。

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    这些经典是怎么挑出来的

    1. 01

      合并同一个链接

      读取 Hacker News 自 2006 年以来的全部链接投稿,把 http/https、www、跟踪参数等写法不同的同一篇文章合并成一条。

    2. 02

      只留经得起时间的

      同一链接至少两次拿到 50 分以上(约等于上首页),而且两次相隔一年以上;再由 AI 剔除状态页、产品首页和新闻事件。

    3. 03

      写导读、译评论

      上首页次数最多的 2,000 篇配上中文导读、"为什么经久不衰",并精选翻译 HN 高赞评论。导读由 AI 生成,可能有误。

    设计与产品notboring.co

    Excel 永生不死

    Excel Never Dies

    2次上首页
    1年跨度
    646累计得分
    576条评论

    2021 — 2022,它一次次回到首页

    黑点是上首页(≥ 50 分)的那次投稿,空心点是没冲上去的投稿,点越大分数越高。

    导读

    这篇来自 Not Boring 的文章探讨了 Excel 为何历经数十年依然不可替代:电子表格是普通人触达通用计算能力的最成功工具,从财务建模到小型数据库,几乎无处不在。文章分析了 Excel 的灵活性与可塑性如何让它持续统治办公世界。

    为什么经久不衰:它解释了一个反直觉的现象:在无数新工具冲击下,Excel 反而越来越强大,理解这一点对任何做产品或工具的人都有启发。

    关键词 Excel电子表格生产力工具编程语言

    HN 评论精选

    Excel/Sheets 是快速搭建有状态协作小应用的利器,远超数据处理范畴

    大家都在说 Excel 适合快速处理数据集,但它的用途远不止于此——任何需要小型数据库的场景都能用,想象力是唯一的限制。比如我最近用 Google Sheets 给朋友们做了 FATE 桌游的按钮式角色卡:用条件格式标出违规状态,用脚本编辑器加骰子按钮,计算过程完全透明还能帮新手理解规则。没有电子表格的话,搭建一个有状态、可协作、用户能随时自定义的 GUI 应用会是很严肃的工程,而用它只是一个有趣的下午。

    ianhorn查看英文原文

    People are all talking about how it's useful for quick stuff with datasets, but it goes far beyond that. It's useful for quick anything you might want a small database for. Imagination is the limit. For example, if any of you play D&D online, you might be familiar with dndbeyond's character sheets. They're a fantastic way to onboard new players who might not have the inclination to spend hours with the rule books before they even start playing. It does all the calculations for you and gives you some buttons like "roll athletics" and doesn't let you add more spells than your character can have with their stats. I recently persuaded some friends to give FATE a try and built analogous push-button character sheets with google sheets [0]. It was quick and simple. With conditional formatting, you highlight bad states (rules say you can't have more of X than Y!). With the script editor, you can add full on buttons for dice rolls and other state changes with whatever logic you want (anything you can code up!). Checkboxes are obvious but super useful. And the transparency of the calculations is helpful for teaching people the system (this stat is "min(A4, B1+C5)"). Without google sheets, it would be a serious endeavor to build a stateful, database backed, live collaborative GUI that can be added to and customized on the fly by my users. With google sheets, it was a quick fun afternoon hack. Excel/google sheets is an amazing piece of technology. [0] Screenshot of the "app": https://raw.githubusercontent.com/imh/public_images/main/Screen%20Shot%202021-03-08%20at%209.37.17%20AM.png

    Excel 胜在直观,Python 胜在数据规模和可审计性,各有不可替代之处

    Excel,或者说电子表格,是建立数据直觉的最佳方式,没有之一。Python 和 Jupyter 里 pandas 的默认输出糟糕透顶。但 Excel 的短板在于数据量(百万行是上限,十几万行就很难受)和可审计性——把工作簿交给同事意味着对方要花几小时追踪单元格依赖,而这恰恰是 Python 数据分析的强项。

    aarondia查看英文原文

    Excel, and more importantly, the spreadsheet is the best way to build intuition for a dataset, hands down. The alternatives, especially when it comes to Python and the default pandas output in a Jupyter Notebook are horrendous. Where Excel falls short, is data size limitations + auditability. Putting more than 1M rows of data into Excel is not possible, and once you get into the low 100K's, it becomes almost unbearable. And handing off an Excel workbooks to a colleague is handing them hours of cell dependency tracing. On the other hand, data size + auditability are the super powers of Python data analysis. I've been building a Python package, Mito (https://trymito.io/), its an interactive spreadsheet that automatically converts your spreadsheet analysis to the equivalent pandas code. You can write spreadsheet formulas, merge datasets, create pivot tables, etc. And because its implemented in Python, you can manipulate datasets with 10M rows of data with no problem. Our goal is to bring the intuitiveness of Excel data manipulation to Pandas.

    现实世界中真正的业务运转靠的是 Excel,而非企业级系统

    Excel 在统治世界。每个自称 ERP 原生的大型公司都在说谎——事情都是在 Excel 里干完的,然后(如果记得的话)再导回 ERP。那些看起来不该用 Excel 干的事照样用 Excel 干,某财富 500 强的年度财报最终就是 financialresults_v2_nowaitonemorechange_final_FINAL_withcomments_official.xlsx。我女朋友在一家名字以 S 开头以 AP 结尾的 ERP 巨头工作,他们内部也在用自己的 ERP,但员工更爱 Excel。这就像重力,别挣扎了。

    fleddr查看英文原文

    Excel runs the world. Every large company claiming to be ERP-native is telling lies. Shit gets done in Excel, and is then exported back into ERP, if at all. Things you would consider quite irresponsible to be done in Excel, is done in Excel regardless. Indeed, that annual financial report of a Fortune 500 boils down to financialresults_v2_nowaitonemorechange_final_FINAL_withcomments_official.xlsx" My g/f works for one of the leading ERP providers. I won't tell you which one but it starts with an S and ends with AP. They're dogfooding their own ERP but employees prefer Excel. It's like gravity, just stop resisting.

    电子表格的范式要求预先知道数据结构,复杂场景下未必是最优工具

    我最近尝试在电子表格里做一个超出简单记账的东西:输入一个 ID,按个按钮,就把多张表中相关的数据筛选并拼接展示出来。结果令人震惊——即便有脚本能力,整个范式都建立在预先知道数据形状的前提上。要写出'取出另一张表中所有匹配行'的公式非常不直观,返回行数不定时后续操作也很痛苦。我意识到,以我的水平,写一个 CRUD 网页应用可能比在表格里实现更快。我不禁怀疑:Excel 的存在究竟是因为人们已经学会了它,还是它真的是非程序员解决问题的最佳方式?

    jrm4查看英文原文

    As someone with a perhaps well deserved reputation as a Microsoft hater (ha, check my history..) Microsoft Excel is perhaps the greatest program-slash-programming language ever invented. Nothing has much come close in terms of giving regular folks the power of general-purpose computing (sadly, further and further removed from what we're doing today.)

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