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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If you're on LinkedIn and are thinking about verifying your account with them, maybe read this first. It walks through LinkedIn's privacy disclosure to identify 17 companies that may receive and process the data you submit, including name, passport photo, selfie, facial geometry, NFC data chip, national ID #, DoB, email, phone number, address, IP address, device type, MAC address, language, geolocation etc. Unsurprisingly, it seems the biggest recipients are US-based AI companies.

thelocalstack.eu/posts/linkedi

A screenshot from thelocalstack.eu 

// THE 17 COMPANIES THAT TOUCH YOUR FACE

Persona maintains a public list of subprocessors — third-party companies that process your personal data on their

behalf. Here's the ful list:
COMPANY WHAT THEY DO WITH YOUR DATA LocATION
Anthropic Data Extraction and Analysis San Francisco, USA
Openal Data Extraction and Analysis San Francisco, USA
Grogeloud Data Extraction and Analysis San Jose, USA
AWS Infrastructure, Image Processing Houston, USA
Google Cloud Platform Infrastructure as a Service Mountain View, USA
Resistant Al Document Analysis New York, USA
FingerprintJs Device Analysis Chicago, USA
MongoDB Database Services New York, USA
Snowflake Database Services Bozeman, USA
Elasticsearch Search and Analytics Engine Mountain View, USA
Confluent ETL Services Mountain View, USA
DBT ETL Services Philadelphia, USA
Sigma Computing Data Analytics. San Francisco, USA
Tableau Data Analytics. Seattle, USA
Stripe Gredit Card Processing South San Francisco, USA
Twilio Communication APs (Phone, SMS) Denver, USA
Persona Identities Canada Gustomer Support & Development Toronto, Canada
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If you're on LinkedIn and are thinking about verifying your account with them, maybe read this first. It walks through LinkedIn's privacy disclosure to identify 17 companies that may receive and process the data you submit, including name, passport photo, selfie, facial geometry, NFC data chip, national ID #, DoB, email, phone number, address, IP address, device type, MAC address, language, geolocation etc. Unsurprisingly, it seems the biggest recipients are US-based AI companies.

thelocalstack.eu/posts/linkedi

A screenshot from thelocalstack.eu 

// THE 17 COMPANIES THAT TOUCH YOUR FACE

Persona maintains a public list of subprocessors — third-party companies that process your personal data on their

behalf. Here's the ful list:
COMPANY WHAT THEY DO WITH YOUR DATA LocATION
Anthropic Data Extraction and Analysis San Francisco, USA
Openal Data Extraction and Analysis San Francisco, USA
Grogeloud Data Extraction and Analysis San Jose, USA
AWS Infrastructure, Image Processing Houston, USA
Google Cloud Platform Infrastructure as a Service Mountain View, USA
Resistant Al Document Analysis New York, USA
FingerprintJs Device Analysis Chicago, USA
MongoDB Database Services New York, USA
Snowflake Database Services Bozeman, USA
Elasticsearch Search and Analytics Engine Mountain View, USA
Confluent ETL Services Mountain View, USA
DBT ETL Services Philadelphia, USA
Sigma Computing Data Analytics. San Francisco, USA
Tableau Data Analytics. Seattle, USA
Stripe Gredit Card Processing South San Francisco, USA
Twilio Communication APs (Phone, SMS) Denver, USA
Persona Identities Canada Gustomer Support & Development Toronto, Canada
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RE: fosstodon.org/@paulox/11610288

A++ content. I was in a call with @mkennedyMichael Kennedy and @pythonbynightMario Munoz this week, where this topic came up again.

We looked, and I thought Django's package size was 200M bigger than it actually is. Django is only 10.9 MB compressed, which is within ~1 MB of SQLAlchemy, which is kind of impressive to think about.

So running the Django ORM doesn't quite have the same mental tax as I have assumed all of these years.

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緊急快訊:美國最高法院裁定,川普大部份關稅非法無效,認為憲法不授予總統及行政機關權力自行加徵關稅

判決針對動用《國際緊急經濟權力法》IEEPA而開徵的關稅,包括全部「對等關稅」,以及向中加墨三國徵收的「芬太尼關稅」

9名法官以6比3同意這份判辭

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If you're on LinkedIn and are thinking about verifying your account with them, maybe read this first. It walks through LinkedIn's privacy disclosure to identify 17 companies that may receive and process the data you submit, including name, passport photo, selfie, facial geometry, NFC data chip, national ID #, DoB, email, phone number, address, IP address, device type, MAC address, language, geolocation etc. Unsurprisingly, it seems the biggest recipients are US-based AI companies.

thelocalstack.eu/posts/linkedi

A screenshot from thelocalstack.eu 

// THE 17 COMPANIES THAT TOUCH YOUR FACE

Persona maintains a public list of subprocessors — third-party companies that process your personal data on their

behalf. Here's the ful list:
COMPANY WHAT THEY DO WITH YOUR DATA LocATION
Anthropic Data Extraction and Analysis San Francisco, USA
Openal Data Extraction and Analysis San Francisco, USA
Grogeloud Data Extraction and Analysis San Jose, USA
AWS Infrastructure, Image Processing Houston, USA
Google Cloud Platform Infrastructure as a Service Mountain View, USA
Resistant Al Document Analysis New York, USA
FingerprintJs Device Analysis Chicago, USA
MongoDB Database Services New York, USA
Snowflake Database Services Bozeman, USA
Elasticsearch Search and Analytics Engine Mountain View, USA
Confluent ETL Services Mountain View, USA
DBT ETL Services Philadelphia, USA
Sigma Computing Data Analytics. San Francisco, USA
Tableau Data Analytics. Seattle, USA
Stripe Gredit Card Processing South San Francisco, USA
Twilio Communication APs (Phone, SMS) Denver, USA
Persona Identities Canada Gustomer Support & Development Toronto, Canada
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I've started my exploration of using @timbray's Quamina project for saving some compute time in the filters module of

Currently the GoAP storage backends iterate over resources (usually stored as raw JSON bytes), unmarshal them into GoActivityPub object structs, and *only* then apply the custom filtering logic on those objects. Since the majority of the objects generally fail the filtering logic, all that JSON decoding is wasted compute time and makes things slower.

Ideally quamina will allow me to check the raw JSON payloads directly against the filters, streamlining the execution and speeding things up. :goose_hacker:

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미니애폴리스 이후, 노동조합과 흑인 조직이 나서기 시작했다 www.sisain.co.kr/news/article... "인구 40명만의 도시 미니애폴리스에서 최대 10만명이 모이는 트럼프 이민정책 반대 시위가 매주 벌어진다. 그리고 이민정책 반대를 넘어 무도한 공권력에 저항하는 반(反)트럼프 시위로 확대 중이다. 학생들도 학교에 가지 않은 채 시위에 참여하고, 이전까지 트럼프의 이민정책을 강하게 반대하지 않던 노동조합들도 적극 나섰다."

미니애폴리스 이후, 노동조합과 흑인 조직이 나서기 시작...

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Shout out to everyone reading this paper, not understanding it, and drawing the wrong conclusions.🤷🏿‍♂️

nature.com/articles/s41586-026

Switching to Twitter's algorithmic feed from chronological, only shifts political attitudes to the right *for people susceptible to far-right nonsense.*

Because they start following far-right dudes🤡

Switching back to chronological doesn't undo the far-right shift...

Because the people affected don't unfollow the far-right dudes. And they don't start following more normal people🤡

Again, Black folk use Twitter the *most*. Black people get exposed to far-right dudes but are the *least* far-right

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@thisismissemEmelia 👸🏻 I have just implemented that for the GoActivityPub servers and it's easier than it sounds.

The only important step required is to convert the client authorization token (presumably an OAuth2 bearer token) to a valid actor and then further to a valid Private Key with which to sign the remote request. After that the only thing remaining is to pipe verbatim the received response to the client...

@steveSteve Bate @smallcircles🫧 socialcoding.. @evanEvan Prodromou

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We STILL need more snowflake proxies!!!

❄️ Snowflake has been a crucial circumvention tool for censored users in Russia, China, Iran, Turkmenistan and many others regions!

You can help keep the internet free and open. Run a Snowflake proxy!

It's simple as opening a tab on your browser! Copy and paste: embed-snowflake.torproject.org/ (yes, you can open on multiple tabs!)

Or check other options here: snowflake.torproject.org/

This machine fights internet censorship. Tor Snowflake purple logo.
website: https://snowflake.torproject.org
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We STILL need more snowflake proxies!!!

❄️ Snowflake has been a crucial circumvention tool for censored users in Russia, China, Iran, Turkmenistan and many others regions!

You can help keep the internet free and open. Run a Snowflake proxy!

It's simple as opening a tab on your browser! Copy and paste: embed-snowflake.torproject.org/ (yes, you can open on multiple tabs!)

Or check other options here: snowflake.torproject.org/

This machine fights internet censorship. Tor Snowflake purple logo.
website: https://snowflake.torproject.org
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緊急快訊:美國最高法院裁定,川普大部份關稅非法無效,認為憲法不授予總統及行政機關權力自行加徵關稅

判決針對動用《國際緊急經濟權力法》IEEPA而開徵的關稅,包括全部「對等關稅」,以及向中加墨三國徵收的「芬太尼關稅」

9名法官以6比3同意這份判辭

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RE: fosstodon.org/@paulox/11610288

A++ content. I was in a call with @mkennedyMichael Kennedy and @pythonbynightMario Munoz this week, where this topic came up again.

We looked, and I thought Django's package size was 200M bigger than it actually is. Django is only 10.9 MB compressed, which is within ~1 MB of SQLAlchemy, which is kind of impressive to think about.

So running the Django ORM doesn't quite have the same mental tax as I have assumed all of these years.

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@muninFi 🏳️‍⚧️ Every time I've entered a federal or county courthouse—sometimes to serve jury duty, to support a relative before a judge, or to just do some business with the county—I've had to put my phone into a locker. Only lawyers could bring their phones into the building. And I would be checked by guards to ensure that I didn't break the rules.

I can’t imagine that Zuckerberg & co. didn’t know that they were violating the rules of the court.

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휴, 이재명 대통령의 시간 끌기가 성공했구만. 미 연방대법원이 빠르게 결정해줘서 살았네. ㅋㅋ;

일본은 어쩔려나. 일단 첫 건은 투자 하는 거 보여줄려나.

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Klarnamenpflicht und Altersgrenzen sind das Gegenteil von evidenzbasierter Politik. Wofür genau soll das gelten? Fünf Big-Tech-Apps? Alle sozialen Medien? Smartphones? Internet-Zugang? Völlig unklar. Es geht nicht um Fakten, nur um Gefühle. "Die Politik muss was tun, für die Kinder/Demokratie."

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오래 기다리셨습니다!!!

BlueBase: Python으로 밑바닥부터 직접 만들어보는 DBMS

https://theeluwin.github.io/BlueBase/

결국 완성은 못했지만, 일단 공개할 수 있는 부분이라도 공개합니다.

RedBase DBMS을 구성하는 PF, RM, IX, SM, QL 중 PF와 RM을 여러분들이 직접 구현 할 수 있게, 과제의 형태로 제공합니다.

PF는 paged file의 약자로, file을 page 단위로 관리하는 컴포넌트입니다. 대충 4096 바이트 단위로 관리하는데요, file에 바로바로 read하거나 write하지 않고, 자주 사용되는 page는 가능한 memory에 있도록 중간에 buffer manager를 둡니다. 그렇다면 buffer에 공간이 모자라면? buffer에 있는 page 중 누군가를 evict 할 수밖에 없습니다. 그럼 뭘 기준으로 하면 좋을까요? 이 부분을 잘 생각해서 구현해보고, 성능을 비교해보기 바랍니다. 제가 cache hit/miss 시뮬레이션 구현해둔게 있으니, 제 custom 보다 높은 성능을 달성해주세요!

이후 RM은 record management의 약자인데, PF를 사용해서 record들을 가져오거나, 새로 넣거나 등을 하게 해줍니다. 그렇다면 전체 record를 순회하는 scan 연산이 중요하겠죠. 이 부분을 구현하는 것이 핵심입니다. record는 page 앞 부분에 bitmap을 둬서 slot이 비어있는지 아닌지를 확인하는데, 만약 record 삭제 명령이 마지막 slot을 비우게 된다면 해당 page는 더이상 필요 없겠죠. 그렇지만 이를 바로 free로 만드는건 조금 비싼 연산이 필요합니다. free page list를 다시 계산해야하거든요. 그래서 보통 DBMS에서는 이러한 작업들을 vacuum 연산으로 해결합니다. 추가로, 지금은 고정 길이 record만 다룰 수 있습니다만, 가변 길이를 허용하려면 어떻게 해야할까요? 이 부분들은 자유롭게 구현해보시면 좋겠습니다.

문서와 테스트는 모두 공개되어있습니다. 기여해주시면 감사하겠습니다! 다만, 정답 코드와 핵심 로직은 마지막까지 저 혼자 해보고 싶습니다 (도전).

https://github.com/theeluwin/BlueBase

밑바닥부터 직접 만들어보는 DBMS에서 page cache policy에 따른 성능 비교.
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