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Article URL: https://www.youtube.com/watch?v=W5bdM9yIEiY

Comments URL: https://news.ycombinator.com/item?id=47348134

Points: 1

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Article URL: https://www.ias.edu/sites/default/files/library/UsefulnessHarpers.pdf

Comments URL: https://news.ycombinator.com/item?id=47348122

Points: 1

# Comments: 1

Article URL: https://anemato.de/blog/nuca

Comments URL: https://news.ycombinator.com/item?id=47348119

Points: 1

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Article URL: https://developer.chrome.com/blog/focusgroup-rfc

Comments URL: https://news.ycombinator.com/item?id=47348081

Points: 1

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Article URL: https://animationobsessive.substack.com/p/the-film-that-should-be-nominated

Comments URL: https://news.ycombinator.com/item?id=47348079

Points: 1

# Comments: 0

Article URL: https://wiggmap.com/

Comments URL: https://news.ycombinator.com/item?id=47348053

Points: 2

# Comments: 1

Article URL: https://www.tumblr.com/login_required/wonderfullysacredtrap

Comments URL: https://news.ycombinator.com/item?id=47348047

Points: 1

# Comments: 0

Score any Wikipedia page for sensitivity, quality, and risk in seconds.

Wikimetron analyzes Wikipedia pages across multiple dimensions — editorial quality, content sensitivity, and informational risk — by pulling data directly from the Wikimedia API. It evaluates things like source density, edit frequency, controversy signals, and structural completeness to generate composite scores that would take a human reviewer hours to assess manually.

Built for researchers, journalists, and anyone who needs to know how much to trust a Wikipedia page before citing it.

Comments URL: https://news.ycombinator.com/item?id=47348044

Points: 1

# Comments: 0

Article URL: https://webdirections.org/blog/the-structure-of-engineering-revolutions/

Comments URL: https://news.ycombinator.com/item?id=47348020

Points: 1

# Comments: 0

Article URL: https://buttondown.com/blog/email-in-space

Comments URL: https://news.ycombinator.com/item?id=47348015

Points: 1

# Comments: 0

Article URL: https://purplesyringa.moe/blog/an-ode-to-bzip/

Comments URL: https://news.ycombinator.com/item?id=47347984

Points: 2

# Comments: 0

As anyone with an internet connection knows, there’s been a lot of buzz about how AI is going to reshape the workforce for the past 3 years and layoffs due to “AI” have already started, the most severe of which came last week as Block announced they were chopping off 40% of their workforce for what sounded more like the potential that AI could replace workers as opposed to it actually being able to. There has been a (in my opinion) healthy dose of skepticism regarding the claim that AI is going to make us more productive and these productivity gains are going to put people out of work. I think this skepticism has been felt by many of whom work in tech companies where AI is literally being force-fed to us, and I wonder how much of this skepticism would apply to other companies and the workforce in general. OK, so…

1. Many (possibly most) people at major tech companies spend most of their time doing essentially fake work, which may or may not be actually negatively impacting productivity. Think time spent in pre-meeting meetings for the many layers of WBRs and MBRs on the product side and over-engineering workflows to get promoted on the tech side.* There are many reasons that people have speculated for why these kinds of jobs exist but in my experience, this basically just comes down to gaming the optics of productivity. True productivity is nearly impossible to evaluate so proxies are used and proxies are inevitably gamed (lines of code, meeting attendance, how many people report to you, etc.).

2. AI is really good at generating fake work. We’re starting to see some of the repercussions of the tech-side of AI slop as Amazon apparently is formally addressing some of the engineering issues it’s causing. but on the non-tech side, there’s endless amounts of doc slop and increasingly Slack slop, all filled with emoji-bulleted lists and em-dashes. The maddening part of doc slop is that you really have no idea what the person intended to say so you can never be sure you’re truly responding to them or just what they thought looked good. I suspect a good amount of performance “reviews” now are just managers doc-slopping their way through and stumbling through an oration of whatever ChatGPT spit out.

3. Whether a company benefits from AI comes down to whether the enhanced fake work undercuts the enhanced real work. At companies where personal advancement comes through optics, meeting-scheduling, public-Slack-channel posting, “visibility”, etc. the doc slop and Slack slop are going to be absolutely out of control. These companies are likely rent-extractors that face limited competition, are public, and don’t have much innovation left. They’ve probably been absorbing a hefty amount of fake work for years. I don’t think there’s any way AI helps these kinds of companies and it will likely make it hard for anyone doing real work to stand out and get rewarded. AI is never going to enhance productivity at these places, because people were never really trying to be productive to begin with. On the other hand, companies where visibility/optics/fake work isn’t rewarded but boosting hard metrics like revenue or signing new clients is, AI could help and probably actually replace people. I can’t deny that AI has some real productivity-enhancing abilities IF you are actually trying to enhance productivity, I’ve seen this firsthand.

The logical implication of this is that AI’s overall impact on the workforce is really going to come down to the composition of fake work vs. real work that already existed. In my mind, the economy was never set up to benefit from anything truly productivity enhancing because the amount of fake work so drastically outweighed the amount of real work to begin with.

* The latter ironically leads to real work, which is fixing the over-engineered workflows that fail constantly because the engineer that over-engineered them left after he got promoted for over-engineering.

Comments URL: https://news.ycombinator.com/item?id=47347983

Points: 2

# Comments: 0

Article URL: https://hackaday.io/project/205190-open-source-plfm-radar-up-to-20km-range

Comments URL: https://news.ycombinator.com/item?id=47347980

Points: 2

# Comments: 0

Article URL: https://medium.com/@brightcarvings/celebrating-flickr-technology-3c93c8ddecc2

Comments URL: https://news.ycombinator.com/item?id=47347977

Points: 3

# Comments: 1

Article URL: https://itp.uni-frankfurt.de/~gros/StudentProjects/Projects_2020/projekt_fischer_mrozinski/#heading2

Comments URL: https://news.ycombinator.com/item?id=47347976

Points: 1

# Comments: 0

Article URL: https://www.wsj.com/world/middle-east/iran-sea-mines-strait-of-hormuz-85e623b7

Comments URL: https://news.ycombinator.com/item?id=47347717

Points: 1

# Comments: 0

Article URL: https://www.facebook.com/LipoJaro.Fat.Burn

Comments URL: https://news.ycombinator.com/item?id=47347712

Points: 1

# Comments: 1

Article URL: https://www.scmp.com/week-asia/lifestyle-culture/article/3345751/iran-war-oil-shock-accelerates-southeast-asias-ev-revolution

Comments URL: https://news.ycombinator.com/item?id=47347710

Points: 1

# Comments: 0

Meowth GBA Translator is an open-source, AI-powered tool that automates translation of Pokémon GBA ROMs (including binary hacks like FireRed, Emerald, Ruby/Sapphire, and Mystery Dungeon). Powered by LLMs (supports OpenAI, DeepSeek, Gemini, Claude, Groq, and 10+ others), it extracts text, translates intelligently while preserving codes and context, then rebuilds the ROM — all in one click via a friendly GUI or simple CLI command. Supports 6+ languages (Chinese, English, French, German, Italian, Spanish) with optimized prompts and smart font patching. Focus on gameplay mods, let AI handle the words. Free, MIT-licensed, cross-platform.

Comments URL: https://news.ycombinator.com/item?id=47347680

Points: 2

# Comments: 0