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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ๅŽŸไพ†้€™ๆฌกๅ‡บไบ‹็š„ๆ˜ฏๅฐๅบฆ่ˆช็ฉบ

็•ถๅนดๅ้Žไป–ๅ€‘็š„้ฆ™ๆธฏๅคง้˜ช็ทš๏ผŒๅป‰ๅƒน่ˆช็ฉบ็š„ๅƒน้Œข๏ผŒๆ™ฎ้€š่ˆช็ฉบ็š„ๆœๅ‹™
ๅ›žๆธฏ้‚ฃไธ€็จฎๅปถ่ชคไบ†ไธ€ๅ€‹ๅคšๅฐๆ™‚้‚„้€ไบŒๅƒๆ—ฅๅœ“็š„้—œ่ฅฟๆฉŸๅ ด้ฃฒ้ฃŸๅˆธ๏ผŒ่ฎ“ๆˆ‘ๅœจๆฉŸๅ ดๅƒไธ€ๅ€‹่•Ž้บฅ้บตๅฅ—้คๆ‰ไธŠๆฉŸ๐Ÿฅบ

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็ง็ ”็ฉถใฎ่ฉฑใ—ใฆใ‚‹ใฎใซใŠใพใˆใŸใถใ‚“ๅคฑๆ‹ใ—ใŸ็›ธๆ‰‹ใ‚’ๅฟ˜ใ‚Œใ‚‰ใ‚Œใชใ„่€…ใธใฎใƒ†ใƒณใƒ—ใƒฌไฝฟใฃใฆๅ›ž็ญ”็”Ÿๆˆใ—ใฆใ‚‹ใ‚ˆใญ๏ผŸ๏ผŸ๏ผ๏ผŸ๏ผŸ๏ผˆChatGPTไบบ็”Ÿ็›ธ่ซ‡๏ผ‰

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The Python Language Summit is a yearly event that takes place before , where core developers, triagers, and other guests discuss the future direction of .

You can read the summaries of each session on the PSF blog: pyfound.blogspot.com/2025/06/p

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Just thinking about better ways to penalize corporations that break the law.

Fines are trivial and never keep pace with reality. Instead, what if companies had to give back a chunk of shares to the government? Issuing new shares that dilute existing shareholder value, and making the govt part owners. Effectively, nationalize the company in degrees with every breach of trust.
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Taking a look at the Wordpress ActivityPub Roadmap, at the very end I see this:

> Client-to-Server API (exploration)
> At this stage, weโ€™re exploring and evaluating this based on community interest and potential use cases.

Client to Server API for ActivityPub is not widely utilized (or at all?) so this would be of great benefit to all fediverse platforms.

SHOW YOUR COMMUNITY INTEREST!!! @activitypub.blogActivityPub for WordPress

activitypub.blog/2025/06/11/ou

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ๆญ็ฝต็ฝตๆ˜ฏไบ‚่ท‘้€ฒๅฎถ่ฃก่ขซๆŠ“ๅŽป็ต็ดฎ็š„่ฒ“
ไป–็œ‹่ตทไพ†ไธ็ˆฝๅฏไปฅ็†่งฃ
ไฝ†ๆ˜ฏ
็™ผ็พๅ“ˆๆฐฃ็„กๆ•ˆ
ๆŠŠ้ ญๆŠฌ้ซ˜่ฆบๅพ—็œ‹ไธๅˆฐไบบๅฐฑไธๆœƒ่ขซ็œ‹ๅˆฐ๏ผŸ
ๆญ็ฝต็ฝตไฝ ้€™ๆจฃโ‹ฏ



ๆŠฌ่ตท้ ญ๏ผŒๆˆ‘ๆฒ’็œ‹ๅˆฐไฝ ไฝ ไนŸๅฐฑๆฒ’็œ‹ๅˆฐๆˆ‘๏ผ่ขซๆŠ“ๅŽป็ต็ดฎๅœจไธ็ˆฝ็š„่ฒ“
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๋งˆ๋ผ์ƒน๊ถˆ๋Š” ๋งˆ๋ผ ๋“ค์–ด๊ฐ€์„œ ์‹ซ์€๋ฐ ์•ˆ์— ๋“ค์–ด๊ฐ„ ๋‚ด์šฉ๋ฌผ์€ ๋ง›์žˆ๊ฒŒ ๋ณด์—ฌ์„œ ๋‚ด๊ฐ€ ๋งˆ๋ผ๋ฅผ ๊ฐ๋‹นํ•˜๋Š” ์—ฐ์Šต์„ ํ•ด์•ผ ํ•˜๋‚˜ ๊ฐ€๋” ์ƒ๊ฐํ•จ ๋ฐฉ๊ธˆ ํ•œ ๋ฒˆ ์ƒ๊ฐํ•จ

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If you speak Ruby, JavaScript and SQL and are looking for a gig (~4 days / week), let's talk.

Bonus points if you aren't afraid of Python, maybe even have some Machine Learning experience, don't get lost in AWS.

Super bonus point: You can speak and write German.

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็ง็ ”็ฉถใฎ่ฉฑใ—ใฆใ‚‹ใฎใซใŠใพใˆใŸใถใ‚“ๅคฑๆ‹ใ—ใŸ็›ธๆ‰‹ใ‚’ๅฟ˜ใ‚Œใ‚‰ใ‚Œใชใ„่€…ใธใฎใƒ†ใƒณใƒ—ใƒฌไฝฟใฃใฆๅ›ž็ญ”็”Ÿๆˆใ—ใฆใ‚‹ใ‚ˆใญ๏ผŸ๏ผŸ๏ผ๏ผŸ๏ผŸ๏ผˆChatGPTไบบ็”Ÿ็›ธ่ซ‡๏ผ‰

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Dusk
Well, this Scene was on my mind from few days. And Enjoying it at Dusk is obviously a breathtaking moment of life.
Created in Blender 3.1.2, Rendered in Cycles Rendering Engine.
Post Work done in Krita.

Artstation: artstation.com/artwork/B3J9EA

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Maximizing Battery Storage Profits via High-Frequency Intraday Trading

Link: arxiv.org/abs/2504.06932
Discussion: news.ycombinator.com/item?id=4

arXiv logo

Maximizing Battery Storage Profits via High-Frequency Intraday Trading

Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper introduces and evaluates an automated high-frequency trading strategy for battery energy storage systems trading on the intraday market for power while explicitly considering the dynamics of the limit order book, market rules, and technical parameters. The standard rolling intrinsic strategy is adapted for continuous intraday electricity markets and solved using a dynamic programming approximation that is two to three orders of magnitude faster than an exact mixed-integer linear programming solution. A detailed backtest over a full year of German order book data demonstrates that the proposed dynamic programming formulation does not reduce trading profits and enables the policy to react to every relevant order book update, enabling realistic rapid backtesting. Our results show the significant revenue potential of high-frequency trading: our policy earns 58% more than when re-optimizing only once every hour and 14% more than when re-optimizing once per minute, highlighting that profits critically depend on trading speed. Furthermore, we leverage the speed of our algorithm to train a parametric extension of the rolling intrinsic, increasing yearly revenue by 8.4% out of sample.

arxiv.org ยท arXiv.org

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