【Hacker News搬运】从三到七的数学
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Title: Math from Three to Seven
从三到七的数学
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Url: https://www.thepsmiths.com/p/review-math-from-three-to-seven-by
很抱歉，我无法直接访问或抓取网页内容，包括您提供的链接。JinaReader 是一个文本分析和信息提取的工具，通常需要通过编程方式访问网页内容，然后进行文本处理和分析。 如果您想使用 JinaReader 或类似的工具来分析网页内容并总结，以下是一个基本的步骤概述： 1. **网页抓取**：首先，您需要使用一种方法来获取网页内容。这可以通过编写一个脚本来实现，使用如 Python 的 `requests` 库来发送 HTTP 请求并获取网页内容。 2. **内容清洗**：获取网页内容后，可能需要对内容进行清洗，去除HTML标签、JavaScript代码、广告、脚注等非文本内容。 3. **语言检测**：在分析内容之前，您可能需要检测内容的语言。如果内容不是中文，您可以使用如 `langdetect` 这样的库来检测语言。 4. **翻译**：如果内容不是中文，您可以使用翻译API（如Google翻译API）将内容翻译成中文。 5. **文本分析**：使用 JinaReader 或其他自然语言处理工具对内容进行分析，提取关键信息、总结段落等。 6. **总结**：基于分析结果，编写一个简短的总结。 以下是一个使用 Python 和 `requests` 库抓取网页内容的简单示例： ```python import requests from bs4 import BeautifulSoup from langdetect import detect from googletrans import Translator # 获取网页内容 url = 'https://www.thepsmiths.com/p/review-math-from-three-to-seven-by' response = requests.get(url) # 使用 BeautifulSoup 清洗内容 soup = BeautifulSoup(response.text, 'html.parser') cleaned_content = soup.get_text() # 检测语言 language = detect(cleaned_content) # 如果不是中文，则翻译 translator = Translator() if language != 'zh-cn': translation = translator.translate(cleaned_content, src=language, dest='zh-cn') cleaned_content = translation.text # 使用 JinaReader 分析内容（此处为示例，JinaReader 实际使用方法可能不同） # analysis = JinaReader.analyze(cleaned_content) # summary = analysis.get_summary() # 输出总结 # print(summary)请注意，上述代码仅为示例，
JinaReader的使用方法可能有所不同，您需要查阅相应的文档来正确使用该工具。同时，由于我无法访问外部网站，无法执行上述代码。## Post by: background ### Comments: **com2kid**: From the book being reviewed:<p>> All joking aside, we fledgling mathematicians understood that the single most important thing was not raw intelligence or knowledge (Americans tend to lag behind in the latter compared to all international students). What mattered was passion. The way to become successful in mathematics, like almost every endeavor, is to care about it, to love it, to obsess over it. And in this, Eastern Europeans had a clear superiority, a cultural advantage. They had been trained, from an early age, to love mathematics more intensely.<p>IMHO this is what drove American superiority in software engineering for several decades. The people who self selected into software engineering really loved the field.<p>I suspect we'll see a continuous slow decrease in all aspects of quality of software as those who have a genuine love and passion for the field are replaced by those in it just for the money. > **com2kid**: 来自正在审阅的书：<p>>；暂且不谈玩笑，我们这些初出茅庐的数学家明白最重要的不是原始的智力或知识（美国人倾向于与所有国际学生相比，后者落后）。什么重要的是激情。在数学上取得成功的方法，比如几乎每一次努力，都是为了关心它，热爱它，痴迷于它这一点，东欧人有着明显的优势，一种文化优势。他们从小就被训练成更热爱数学<p> 依我之见，这就是几十年来美国在软件工程方面的优势所在。那些自主选择进入软件工程领域的人真的很喜欢这个领域<p> 我怀疑我们；我会看到软件质量的各个方面都在持续缓慢下降，因为那些对该领域有真正热爱和热情的人被那些仅仅为了钱而从事该领域的人所取代。 **bee_rider**: I think this must be a very stupid question, but I’ll ask it anyway. I always thought the Soviet Union was smaller than the US population wise, and really did punch above their weight. But Soviet Union census of 1970 lists 241,720,134 people, while the US census of 1970 lists 203,392,031 people.<p><a href="https://en.m.wikipedia.org/wiki/1970_Soviet_census" rel="nofollow">https://en.m.wikipedia.org/wiki/1970_Soviet_census</a><p><a href="https://en.m.wikipedia.org/wiki/1970_United_States_census" rel="nofollow">https://en.m.wikipedia.org/wiki/1970_United_States_census</a><p>Is this statistic somehow not representative?<p>If so, what’s up with that?<p>If not, is the belief that the Soviet Union was smaller than the US population widespread and wrong? If it is widespread and wrong, where’s it come from? (Although, I must admit the possibility that it isn’t widespread, and was just unusually wrong. In which case the answer is just that I’m unusually bad at geopolitics, which would not be surprising at all). > **bee_rider**: 我认为这一定是一个非常愚蠢的问题，但无论如何我都会问。我一直认为苏联的人口比美国少，而且确实超过了他们的体重。但苏联1970年的人口普查列出了241720134人，而美国1970年的普查列出了203392031人<p> <a href=“https://en.m.wikipedia.org:wiki&1970_Soviet_consumer”rel=“nofollow”>https://en.mwikipedia.org/；en.m.wikipedia.org；维基；1970_电视普查</a><p><a href=“https:”en.m.wikipedia.org“wiki:”1970_美国人口普查“rel=”nofollow“>https:”/；en.m.wikipedia.org；维基；1970_美国人口普查</a><p>这个统计数据是否不具有代表性<p> 如果是这样，那是怎么回事<p> 如果不是，认为苏联人口比美国人口少的观点是否普遍和错误？如果它是普遍和错误的，它从哪里来？（尽管如此，我必须承认这种可能性并不普遍，而且只是异常错误。在这种情况下，答案就是我在地缘政治方面异常糟糕，这一点也不奇怪）。 **0xdde**: The author raises an interesting question as to how the Soviets produced so much scientific talent, but his discussion of math circles strikes me as more of a tangent than a convincing answer. Were these math circles really so widespread, and were they a big part of producing mathematical and scientific question? He doesn't address this. However, the book he is reviewing is available online [1] and I see from skimming it that Zvonkin says only one of his students ultimately chose math as a profession. My hunch is that the structure of the formal education system in the USSR played a larger role.<p>[1] <a href="https://sites.icmc.usp.br/sasha_a/zvonkin-e.pdf" rel="nofollow">https://sites.icmc.usp.br/sasha_a/zvonkin-e.pdf</a> > **0xdde**: 作者提出了一个有趣的问题，即苏联是如何产生如此多的科学人才的，但他对数学界的讨论让我觉得更多的是一个切线，而不是一个令人信服的答案。这些数学圈真的如此普遍吗？它们是产生数学和科学问题的重要组成部分吗？他没有；不要解决这个问题。然而，他正在评论的这本书可以在网上找到[1]，我从浏览中看到，Zvonkin说他的学生中只有一个最终选择了数学作为职业。我的预感是，苏联正规教育体系的结构发挥了更大的作用<p> [1]<a href=“https:”sites.icmc.usp.br“sasha_a”zvonkin-e.pdf“rel=”nofollow“>https:”/；sites.icmc.usp.br；sasha_a；zvonkin-e.pdf</a> **otar**: > All joking aside, we fledgling mathematicians understood that the single most important thing was not raw intelligence or knowledge (Americans tend to lag behind in the latter compared to all international students). What mattered was passion. The way to become successful in mathematics, like almost every endeavor, is to care about it, to love it, to obsess over it.<p>This is the most important point from the article. My theory is that if you are not obsessed with something, you won’t be good enough with it, wether it’s a math, coding, business or something else… Thats how most of us got started in tech from the early ages. > **otar**: >；撇开玩笑不谈，我们这些初出茅庐的数学家明白，最重要的不是原始的智力或知识（与所有国际学生相比，美国人在后者方面往往落后）。重要的是激情。在数学上取得成功的方法，就像几乎所有的努力一样，就是关心它，热爱它，痴迷于它。<p>这是这篇文章中最重要的一点。我的理论是，如果你不痴迷于某件事，无论是数学、编码、商业还是其他方面，你都不会做得足够好……这就是我们大多数人从小就开始从事科技工作的方式。 **Illniyar**: Regarding Soviet prowess, I always considered the fact that going to higher education considerably shortened and made easier your military draft term to be a main factor.<p>Everyone who could went to university, because why wouldn't you? This incentive pressure and selection bias we're probably insane. > **Illniyar**: 关于苏联的实力，我一直认为，接受高等教育大大缩短了你的军事征兵期，这是一个主要因素<p> 每个可以上大学的人，因为为什么不去；你呢？这种激励压力和选择偏见；你可能疯了。