What is Hackers' Pub?

Hackers' Pub is a place for software engineers to share their knowledge and experience with each other. It's also an ActivityPub-enabled social network, so you can follow your favorite hackers in the fediverse and get their latest posts in your feed.

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Please stop making software that writes unnecessarily to disk. I'm especially angry at web browsers. I don't want the web browsers to be writing 1~3mbps to disk the entire time they're open and visiting sites. SSDs have become unaffordable, and they have limited write lifetime. Please stop making software that does this. Thanks.

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@Jamessocoljames @b0rkJulia Evans We put a huge emphasis on docs from day 1. Our policy has always been that new code can’t be merged unless it includes docs — we’ve held up major features because the docs weren’t done/good. That’s a virtuous cycle: good docs attract contributors who value good docs. So yeah it’s super intentional, and always nice to hear from someone that it seems to be working :)

@jacobjacobian @Jamessocoljames @b0rkJulia Evans I can, without reservation, say that the reason I started using Django 20 years ago, eventually joined the core team, and everything that flows from that, is significantly due to the fact that Django’s documentation, even in the 0.91 days, was *excellent*.

I have a PhD in CS - so I theoretically knew stuff, but couldn’t actually do things like “build a web site”. I bounced off PHP because the docs at the time were incoherent. I bounced off Rails and CherryPy because while the tutorial got a TODO app going, the next steps were unclear.

I don’t think you can understate the influence you and @adrianAdrian Holovaty’s focus on good docs in Django had on the Python ecosystem.

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「検索してもノイズにまみれていい結果に出会うの苦労しそうだからまずはAIに聞くか……」
ってなることあって、こうやってAIの優位性を体験していくのかなあ、なんて思ってたけど、
いやいや、それ、検索エンジンがクソってだけだからな、騙されるところだった。

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근데 많은 경우 정부가 이런 생산과 분배 문제를 적극적으로 해결해야하는데 오히려 반대로 악용하고 사람들 사이를 더 구조적으로 벌어지게 만드 상당히 괘씸한듯 특히 미국 정부가 요새 그러는데 음... 이쯤되면 정부는 왜 있나 싶은 생각도 자주 들고...

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An absolutely brilliant long form article by Richard David Hames. It’s so, so good.

Some curated quotes:

Donald Trump is not an interruption of the American story; he is one of its clearest chapters. He condenses habits that long predate him—mythmaking, extraction, spectacle, racial hierarchy, masculine anxiety—into a single, gaudy figure. To look at him carefully is to see the civilisation that made him.

The United States was founded on a dazzling contradiction: “all men are created equal,” written by men who owned other humans. Its history since has been a choreography of grand ideals and systematic betrayal. One response to this tension has been hypocrisy—keeping the language of virtue while quietly doing the opposite. Another response, Trump’s response, is to drop the mask.

richarddavidhames.substack.com

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It's surprising in a positive way that the cheap little kit antennas of the RTL-SDR allow me to receive some ADS-B data from within my apartment with no clear view to the sky or horizon in any direction :)

Also receiving temperature and humidity sensor data from cars and weather stations is crazy. No need for your own weather station :D

I definitely feel the appeal of this particular nerd rabbit hole. Caught myself reading about DIY antenna builds already :D

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AI for mathematics: Progress, challenges, and prospects. ~ Haocheng Ju, Bin Dong. arxiv.org/abs/2601.13209v2

arXiv logo

AI for Mathematics: Progress, Challenges, and Prospects

AI for Mathematics (AI4Math) has emerged as a distinct field that leverages machine learning to navigate mathematical landscapes historically intractable for early symbolic systems. While mid-20th-century symbolic approaches successfully automated formal logic, they faced severe scalability limitations due to the combinatorial explosion of the search space. The recent integration of data-driven approaches has revitalized this pursuit. In this review, we provide a systematic overview of AI4Math, highlighting its primary focus on developing AI models to support mathematical research. Crucially, we emphasize that this is not merely the application of AI to mathematical activities; it also encompasses the development of stronger AI systems where the rigorous nature of mathematics serves as a premier testbed for advancing general reasoning capabilities. We categorize existing research into two complementary directions: problem-specific modeling, involving the design of specialized architectures for distinct mathematical tasks, and general-purpose modeling, focusing on foundation models capable of broader reasoning, retrieval, and exploratory workflows. We conclude by discussing key challenges and prospects, advocating for AI systems that go beyond facilitating formal correctness to enabling the discovery of meaningful results and unified theories, recognizing that the true value of a proof lies in the insights and tools it offers to the broader mathematical landscape.

arxiv.org · arXiv.org

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