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Saturday Mac riddles 315

Here are this weekend’s Mac riddles to entertain you through family time, shopping and recreation.

1: It came with a tumbler from Camelot in 1993, then opened in 2008.

2: Replacement for 3 to avoid royalties with transparency has just turned three.

3: CompuServe animated its palette with 256 colours but we still can’t agree how to say it.

To help you cross-check your solutions, or confuse you further, there’s a common factor between them.

I’ll post my solutions first thing on Monday morning.

Please don’t post your solutions as comments here: it spoils it for others.

A brief history of Internet search

Searching the Internet, more recently its web servers, has proceeded in four main phases. Initially, humans built structured directories of sites they considered worth visiting. When those couldn’t keep pace with the Internet’s growth, commercial search engines were developed, and their search results were ranked. Around 2000, Google’s PageRank algorithm became dominant for ranking pages by their popularity. Then from late 2024 that is being progressively replaced with AI-generated summaries. Each of these has been reflected in the tools provided by Mac OS.

Directories

In the earliest years of the Internet, when the first web servers started to appear, and files were downloaded using anonymous FTP, users compiled their own lists by hand. Some curated directories were made public, including one maintained by Tim Berners-Lee at CERN, and another at NCSA. Individuals started using Gopher, a client to discover the contents of servers using the service of the same name. The next step was the development of tools to catalogue Gopher and other servers, such as Veronica and Jughead, but it wasn’t until 1993 that the first search engine, W3Catalog, and a bot, the World Wide Web Wanderer, started to transform Internet search.

Berners-Lee’s directory grew into the World Wide Web Virtual Library, and still exists, although it was last updated several years ago, most is now hosted elsewhere, and some is broken. The most famous directory was originally launched in 1994 and was then known as Jerry and David’s Guide to the World Wide Web, later becoming Yahoo! Directory. This offered paid submission and entry subscriptions, and was closed down at the end of 2014.

The favourite of many (including me) was launched as GnuHoo in 1998, and later that year, when it been acquired by Netscape, became the Open Directory Project, then DMOZ, seen here in the Camino browser in 2004. Although owned by AOL, it was maintained by a volunteer community that grew rapidly to hold around 100,000 links maintained by about 4,500 volunteers, and exceeded a million links by the new millennium. DMOZ closed in 2017 when AOL lost interest, but went on as Curlie using the same hierarchy.

Sherlock was first released in Mac OS 8.5 in 1998. As access to the web grew, this came to encompass remote search through plug-ins that worked with new web search engines.

Those were expanded in Sherlock 2, part of Mac OS 9.0 from 1999 and shown above, and version 3 that came in Mac OS X 10.2 Jaguar in 2002.

Indexing and ranking

Human editors couldn’t keep pace with the growth of the web, and demand grew for searching of indexes. This posed the problem of how to rank pages, and development of a series of ranking algorithms, some of which were patented. The first to use links (‘hyperlinks’) was Robin Li’s RankDex, patented in 1996, two years before Sergey Brin and Larry Page’s PageRank that brought their success in Google.

Ranking search results wasn’t new. In the late twentieth century, sciences started measuring the ‘impact’ of published papers by counting their citations in other papers, and university departments and scientific journals laid claim to their greatness by quoting citation and impact indexes. Early search ranking used features such as the frequency of occurrence of the words in the search term, which proved too crude and was manipulated by those trying to promote pages for gain. The obvious replacement was incoming links from other sites, which also quickly became abused and misused.

Research into networks was limited before 1998, when Jon Kleinberg and the two founders of Google entered the field. As with citation indexes before, they envisaged link-based ranking as a measure of popularity, and popularity as a good way of determining the order in which search results should be presented. They also recognised some of the dangers, and the need to weight incoming links to a page according to the total number of such links made by each linking site. Oddly, Kleinberg’s prior work wasn’t incorporated into a search engine until 2001, by which time Brin and Page were powering Google to dominance, and in June 2000 provided the default search engine for Yahoo!

This is Yahoo! Search seen in Firefox in 2007, by which time it was using its own indexing and search engine.

PageRank and algorithms

Google grew prodigiously, and became rich because of its sales of advertising across the web, a business dependent on promotion of its clients, something that could be achieved by adjusting its PageRank algorithm.

Although it’s hard to find now, at one time Google’s Advanced Search was widely used, as it gives more extensive control. Here it’s seen in Safari of 2011.

Google Scholar gives access to published research in a wide range of fields, and was introduced in late 2004. Here it’s seen in use in 2011, listing work that’s recently become topical again. Scholar doesn’t use the same PageRank-based algorithm for ranking its results, but does give substantial weight to citation counts.

When Apple replaced Sherlock with Spotlight in Mac OS X 10.4 Tiger in April 2005, web search defaulted to newly-arrived Safari and Google’s search engine. Its major redesign, in OS X 10.10 Yosemite in 2014, merged web and local search into Global Spotlight, the search window that opens from the Spotlight icon at the right end of the menu bar. That in turn brought Spotlight Suggestions, which became Siri Suggestions in macOS Sierra.

spotlighticloud

This shows a search in Global Spotlight in macOS 10.12 Sierra, in 2017.

Apple has never explained how Siri Suggestions works, although it appears to use machine learning and includes partial results from web search probably using Google. It offers a taste of what is to come in the future of Internet search.

Summarising

Google started the transition to using Artificial Intelligence in 2024, and that September introduced Audio Overview to provide spoken summaries of documents. This year has brought full AI overviews, in which multiple pages are summarised succinctly, and presented alongside links to the pages used to produce them. Although some can be useful, many are vague and waffly, and some blatantly spurious.

We’ve come a long way from Tim Berners-Lee’s curated directories, and PageRank in particular has transformed the web and more besides.

References

Wikipedia:
Gopher
Web directory
Search engine
Google Scholar

Amy N Langville and Carl D Meyer (2006) Google’s PageRank and Beyond: the Science of Search Engine Rankings, Princeton UP. ISBN 978 0 691 12202 1.

四川还有方法获得 IPV4 吗

zyt5876:

最近出差要频繁连回家里,IPV6 公司网和酒店都没有。tailscale 貌似因为走 UDP 被 QOS 了?稍微有一些带宽需求都满足不了。

四川成都还有比较低廉的 IPV4 获取方法吗,副宽带啥的都可以

🧰 基于个人面试情况撸了个自用的简历优化小工具,分享一下思路

viewer003:

工具地址https://resume.123114.xyz/

功能不多但流程还算完整,主要是想解决两个问题:

  1. 简历内容太泛,不聚焦目标岗位
  2. 面试准备随缘,效率低 & 针对性差

整体 workflow 如下:

  1. 支持导入 PDF 简历(也可以从零新写)
  2. 🤖 使用 Gemini 官方 API 做结构化解析(所以需要代理)
  3. ✍️ 输出针对性的优化建议(结构、关键词、可读性等)
  4. 📌 粘贴 JD ,自动生成定制版简历(会有点夸大,需仔细审查/修改)
  5. 🧠 基于简历 + JD 自动生成面试准备要点
    • 包括基础知识准备、潜在提问、优/劣势分析等

UI 很简陋,主要目的是验证一个思路:

在现在这个卷到极致的市场里,高质量的定制简历 + 针对性准备,已经是最基本的入场门槛。 靠一份“通用简历 + 碰运气”已经很难跑通了,想提升命中率、准备得更主动一点,就得把这套流程工程化起来。


当然,你完全可以使用其他任意一个 AI 产品/工具完成这套流程,主要就是提供一个经过个人验证的简历+面试的准备思路。


希望这套流程对正在准备跳槽、正在 gap 、或刚开始投简历的朋友有点帮助 🙌
祝大家都能顺利 landing ,一发即中 🛬


最后补充

  1. 由于使用的是官方 Gemini API ,请使用 Gemini 支持的代理访问,解析/输出可能较慢,请多担待
  2. 纯 react 前端应用+Gemini API ,无数据存储

昨天温度 40 度,我们小区停电了,谈谈夏季用电与新能源车集中充电的影响。如何保障用电安全?

qxmqh: 坐标山东,我们小区 2016 年开始入住的。记忆中几乎没有停电过。
但是昨天停电了,而且不光是我们小区,周边小区都停电了。
40 度的高温,突然停电停水,而且电梯也停了,地下停车场也是一片漆黑。

我们楼下邻居就被困在电梯里面了。热的差点中暑。
电网的,119 的都来我们小区救援了,十几部电梯。

后来物业在群里说是因为夏季用电太多,负荷太大 造成主供电线路异常了。

由此我想说,现在新能源车占有率还不算太高,如果以后真的说能占到 70%以上。
晚上大家都在小区集中充电,完了又是夏天 集中用电高峰期。

用电安全如何保障?

我看了一些文章说是电网也在努力 云云。但是我总感觉这绝对是一个大隐患。

充电 发热 自燃 爆炸 。

艹 想想就可怕。

亲戚遭遇电信诈骗,求解释骗子的操作…

zhangsimon: 起因是亲戚遭遇了电信诈骗
有人打电话说什么他抖音绑定一个自动扣款
联系上我时,他说给骗子透露了 A 银行卡和 B 银行卡,以及密码

我赶紧让他先把两张银行卡挂失
然后登录 A 银行和 B 银行的 App ,查看财产损失
看完银行 App 的转账记录
发现 A 银行里有一笔 18000 转出到了微信
但 B 银行里有一笔 20000 来自微信的资金转入

我让他登录自己微信看下转账记录
这时才发现他手机里的微信都莫名其妙没了

把微信下载回来用短信登录
顺便修改微信登录密码,未成功
(微信提示新设备 3 天内不能修改密码,所以微信卸载再安装就是按新设备算的吗?)

查看他的微信转账记录
发现是转账过程是把 A 银行的 18000 余额转入到了微信
然后又把他微信余额 2000+18000 一起转入到了 B 银行
最后这 20000 元,还在他的 B 银行卡里,还好财产没有损失

安全起见,我让他把微信绑定的这两张银行卡也解绑了
解绑过程需要输入支付密码,发现支付密码也被骗子篡改了

不过好在资金都在银行卡里
我让他们到银行柜台直接取出来

复盘这个骗局,我有几个疑问

1.骗子是怎么把他微信删除的?(安卓手机,不排除亲戚玩不转手机,被引导操作删除的)
2.骗子既然已经能修改他支付密码了,为啥把钱转到亲戚的卡上,而不是直接转走?
3.这个微信账号现在改不了密码,骗子是不是随时还能登录?怎么办

银行信贷业务材料合规性检测算法开发工程师(远程兼职)

Star3687:

项目概述

我们正在寻找经验丰富的算法工程师,参与开发先进的银行信贷业务材料合规性检测模型及生产工具。主要用于地方商业银行的信贷业务风控、合规检查。

核心职责

图像篡改检测算法开发

1)开发银行流水单、票据、证件等信贷业务佐证材料的图像篡改检测算法

2)实现基于深度学习的图像篡改识别模型

3)构建多模态融合的图片篡改验证系统

文本文件格式合规检测算法开发

1)设计多格式文档结构解析和验证算法

2)开发业务规则引擎和数据格式校验系统

3)实现智能化的格式异常识别系统

垂直领域大模型开发

1)基于金融领域数据训练专用 NLP 大模型

2)开发文档逻辑一致性检验算法

3)实现智能化的异常模式识别

系统集成与部署

1)设计微服务架构的检测系统

2)完成 Linux 服务器环境的部署适配

3)实现高性能、高可用的生产环境部署

技术栈要求

机器学习与深度学习

a.深度学习框架: PyTorch, TensorFlow 2.x

b.计算机视觉: OpenCV, PIL, scikit-image

c.图像处理: 图像增强、特征提取、对抗样本检测

d.模型架构: CNN, ResNet, EfficientNet, Vision Transformer

自然语言处理

a.大模型技术: Transformers, BERT, GPT 系列, LLaMA

b.NLP 框架: Hugging Face, spaCy, NLTK

c.向量化技术: Word2Vec, FastText, Sentence-BERT

d.文本分析: 语义分析、情感分析、异常检测

后端开发技术

a.编程语言: Python 3.8+, Go/Java

b.Web 框架: FastAPI, Flask, Django

c.数据库: PostgreSQL, MySQL, MongoDB, Redis

d.消息队列: RabbitMQ, Apache Kafka

云计算与部署

a.容器化: Docker, Kubernetes

b.Linux 系统: Ubuntu, CentOS, 熟悉 Shell 脚本

c.CI/CD: Jenkins, GitLab CI, GitHub Actions

d.监控运维: Prometheus, Grafana, ELK Stack

数据处理与存储

a.大数据技术: Apache Spark, Hadoop (加分项)

b.数据处理: Pandas, NumPy, Dask

c.特征工程: scikit-learn, Feature-engine

d.模型服务: MLflow, TensorFlow Serving, TorchServe

文档格式处理

a.文档解析: PyPDF2, python-docx, openpyxl, lxml

b.格式验证: JSON Schema, XML Schema, Cerberus

c.正则表达式: re, regex, 复杂模式匹配

d.业务规则引擎: PyKE, Experta, 自定义规则解析器

Linux 部署环境要求

a.操作系统: Ubuntu 20.04 LTS / CentOS 8+

b.Python 环境: Python 3.8+ with conda/virtualenv

c.GPU 支持: CUDA 11.x + cuDNN 8.x

d.内存要求: 32GB+ RAM (模型推理需求)

e.存储空间: 500GB+ SSD (模型和数据存储)

岗位要求

技术能力

1)5 年以上机器学习/深度学习项目经验

2)熟悉计算机视觉和自然语言处理算法

3)具备大模型训练和微调经验

4)精通 Python 及相关机器学习库

5)熟悉 Linux 系统管理和 Docker 部署

领域经验(优先)

1)金融科技或风控系统开发经验

2)文档图像处理和 OCR 技术经验

3)文档格式标准化和合规检测系统经验

4)反欺诈或异常检测系统开发经验

5)大规模机器学习系统部署经验

软技能

1)强烈的技术责任心和安全意识

2)良好的代码规范和文档习惯

3)具备独立解决复杂技术问题的能力

4)优秀的沟通协调能力

看这里:

vx:baixiangguo99999999 请备注 银行材料检测算法

ai-cli-log 记录你的终端 ai 工具会话记录到 markdown 文档

kkeep: 当然现在还是期货中 https://github.com/alingse/ai-cli-log

记录会话为 markdown 文档

好处

1. 可以管理,可以跟随代码一起提交,这样知识和沟通都是有记录的。
2. 便于协作共享,其他人可以看,可以了解你是怎么用的
3. 可以复盘,可以让 AI 下次先读上一次的会话来看。


当然你可以说这里面的的都可以用规则文档,让 ai 在结束会话前做总结,但是我更想用自动化的工具。


另外,build-in-public 我打算,整个工具都用 gmini 实现(前面几个会话手动粘贴记录),这样最后能做到自举。

欢迎大家批判。

https://github.com/alingse/ai-cli-log/blob/main/.ai-cli-logs/0001.md

------------------------------------

Tips for getting started:

Ask questions, edit files, or run commands.
Be specific for the best results.
Create GEMINI.md files to customize your interactions with Gemini.
/help for more information.
我有另外一个想法,和本项目无关。我想创建一个 gemini 或者 claude code 的对话记录的终端工具。它可以启动对应工具后,在退出时,将整个会话保存为 md 文档,不要求特别的格式。请你考虑这个工具的名称。最好是通用的,一望而知的。而且流行的。我需要用来创建 github 仓库。

✦ 这是一个很棒的主意!创建一个这样的工具非常有价值。

考虑到你的要求:通用、一望而知、流行,这里有几个不同风格的名称建议,你可以选择最喜欢的一个来创建 GitHub 仓库。

------------------------------------

胃疼,我吃过最快效有效的“良药”,竟是啤酒

yiroonli:

多年前,有一次有姐夫家吃火锅,大家都在喝啤酒,我强忍着胃疼,吃着菜,以胃疼为由拒绝了喝啤酒,但最后,我觉得太腻太渴了,还是喝上了几小口,没想到,才几分钟,胃疼居然消失了。当时没觉得是啤酒的功劳,以为是吃饭了,胃舒服了。 后来,有一次也是胃疼的情况下喝了点啤酒,才发现,啤酒止胃疼的效果居然这么神! 其实,我也不是经常胃疼,一年可能会发生几次吧,但每次疼起来,像被刀刺一样,疼到趴下的程度,试过多种胃药,也是一样没效,忍受一整天的。 这么多年来,都是啤酒解决了我胃疼难受的煎熬,也不懂是什么原理,也不知道有没副作用,但它真的能立即止痛,比药好,谁又能知道,吃药的副作用会不会比喝啤酒大呢? 我的方法分享给大家,有没用,可能因人而异,至少对我真的有用!

iphone14 pro 进水后,换了 oppo x8 ultra 512G 6399 元,用了两周,总结起来: AI 场景丰富实用、功能全面但基础体验差。

albin504:

先说相比 ios 基础体验的差距:

  • 看微信公众号文章,下拉翻页,总是误触。只要下滑过程中遇到图片,大概率会误触图片,导致打开图片。这个体验极差。
  • 屏幕素质很差,看朋友圈图片总是感觉有颗粒,没有 iphone 细腻。
  • 黑暗环境下人脸识别不了(大概率是的),iphone14 pro 在黑暗环境下各种角度(躺着)都能识别
  • 外放声音比较小,走在路上外放听书,听不到声音。
  • 每次拍完照,习惯性左滑查看相册,界面抖动一下发出一点点声音,UI 交互体验不好。实际上是右滑查看相册( iphone 是左滑习惯了)
  • 全面屏手势,屏幕左侧右划返回上一页,经常遇到不知道返回到哪里去了,总之就是返回到一个不想要的页面。

重点说说 AI 场景:

  • 自助接听电话。看到推销电话,我让 Ai 接听,Ai 会自动接听电话,然后说“请问有什么事”,对方一听到是 Ai 接听,直接挂掉了。这个功能很实用。
  • 微信语音、电话,自动转文字。这个功能很实用,尤其是语音转文字,准确率很高。
  • 唤起小布,和 AI 对话,语音识别准确率非常高。 国内 siri 很弱智,想必国外的 siri 现在也很智能了。
  • AI 识屏、AI 翻译,AI 相册修图。

整体是非常不错的,deepseek 无处不在。用了这部手机之后,对 deepseek 对中国 AI 产业的巨大推动作用有了更深刻的认识。

另外,这部手机买的时候,网上都没有优惠(原价,送一些小东西),我在线下专卖店购买优惠了 600 元。 同时,也去隔壁 vivo 店看了 x200 ultra ,第一感觉摄像头太突出(影响美观、影响手感),然后问了销售一分钱不优惠,立马决定买 oppo x8 ultra 。

其他的,如 iphone 电池极不耐用,也是我想换个安卓的原因。

系统是真流畅,高通这个 cpu 应该是很强悍的。

总结起来,安卓旗舰是不错,相比目前国内不支持 IA 的 iphone ,整体体验是非常不错的。 但是,基础体验差距太大,这个是每家厂商包括测评机构都不会讲的。

安卓、ios 各有千秋,大家可以根据自己的需求选择,不存在谁吊打谁的问题。

另外,新手机买回来后,iphone 静止了两天又正常了,其实就是给自己找个理由又买了一部手机。 我他妈的,跟个神经病一样,最近 1 个月一直想买个安卓机(弄俩手机),拼多多百亿补贴下了六七个单又退了。 真的是跟神经了一样,天天就想着这个事儿,从心理学上如何解释这种想买电子产品的冲动呢?

使用 doocs 微信公众号图床教程

lusyoe:

今天想给大家分享的是如何使用 doocs 微信公众号图床教程。

doocs/md 是一款高度简洁的微信 Markdown 编辑器:支持 Markdown 语法、自定义主题样式、内容管理、多图床、AI 助手等特性。
但是它的微信公众号图床使用较为复杂,如果你自建了 doocs/md 服务,并希望使用微信图床来托管图片内容。

本教程将手把手教你如何配置微信公众号图床代理,包括申请 AppID 、配置图床代理域名及在 doocs 中启用图床功能,解决文档图片无法外链的问题。

更多介绍请参看博客文章: https://blog.lusyoe.com/article/doocs-wechat-image

Wireguard 端点地址如何避免自动切换?

dream0689: 服务器是多 ip(A B C ,多线路)入口,但只允许单 ip 出口(B ,固定某一线路),Wireguard 客户端端点地址如果设置成 A 或者 C ,使用一段时间后会自动变成 B ,有办法不自动切换吗?
补充:配置里看到的设置的 A 或者 C ,但是状态显示的是 B ,一旦改变连接也不通了。
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