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Show HN: I'm 75, building an OSS Virtual Protest Protocol for digital activism

https://github.com/voice-of-japan/Virtual-Protest-Protocol/blob/main/README.md
1•sakanakana00•43s ago•0 comments

Show HN: I built Divvy to split restaurant bills from a photo

https://divvyai.app/
1•pieterdy•3m ago•0 comments

Hot Reloading in Rust? Subsecond and Dioxus to the Rescue

https://codethoughts.io/posts/2026-02-07-rust-hot-reloading/
2•Tehnix•3m ago•1 comments

Skim – vibe review your PRs

https://github.com/Haizzz/skim
1•haizzz•5m ago•1 comments

Show HN: Open-source AI assistant for interview reasoning

https://github.com/evinjohnn/natively-cluely-ai-assistant
2•Nive11•5m ago•2 comments

Tech Edge: A Living Playbook for America's Technology Long Game

https://csis-website-prod.s3.amazonaws.com/s3fs-public/2026-01/260120_EST_Tech_Edge_0.pdf?Version...
1•hunglee2•9m ago•0 comments

Golden Cross vs. Death Cross: Crypto Trading Guide

https://chartscout.io/golden-cross-vs-death-cross-crypto-trading-guide
1•chartscout•11m ago•0 comments

Hoot: Scheme on WebAssembly

https://www.spritely.institute/hoot/
2•AlexeyBrin•14m ago•0 comments

What the longevity experts don't tell you

https://machielreyneke.com/blog/longevity-lessons/
1•machielrey•15m ago•1 comments

Monzo wrongly denied refunds to fraud and scam victims

https://www.theguardian.com/money/2026/feb/07/monzo-natwest-hsbc-refunds-fraud-scam-fos-ombudsman
3•tablets•20m ago•0 comments

They were drawn to Korea with dreams of K-pop stardom – but then let down

https://www.bbc.com/news/articles/cvgnq9rwyqno
2•breve•22m ago•0 comments

Show HN: AI-Powered Merchant Intelligence

https://nodee.co
1•jjkirsch•25m ago•0 comments

Bash parallel tasks and error handling

https://github.com/themattrix/bash-concurrent
2•pastage•25m ago•0 comments

Let's compile Quake like it's 1997

https://fabiensanglard.net/compile_like_1997/index.html
2•billiob•26m ago•0 comments

Reverse Engineering Medium.com's Editor: How Copy, Paste, and Images Work

https://app.writtte.com/read/gP0H6W5
2•birdculture•31m ago•0 comments

Go 1.22, SQLite, and Next.js: The "Boring" Back End

https://mohammedeabdelaziz.github.io/articles/go-next-pt-2
1•mohammede•37m ago•0 comments

Laibach the Whistleblowers [video]

https://www.youtube.com/watch?v=c6Mx2mxpaCY
1•KnuthIsGod•38m ago•1 comments

Slop News - HN front page right now as AI slop

https://slop-news.pages.dev/slop-news
1•keepamovin•43m ago•1 comments

Economists vs. Technologists on AI

https://ideasindevelopment.substack.com/p/economists-vs-technologists-on-ai
1•econlmics•45m ago•0 comments

Life at the Edge

https://asadk.com/p/edge
3•tosh•51m ago•0 comments

RISC-V Vector Primer

https://github.com/simplex-micro/riscv-vector-primer/blob/main/index.md
4•oxxoxoxooo•54m ago•1 comments

Show HN: Invoxo – Invoicing with automatic EU VAT for cross-border services

2•InvoxoEU•55m ago•0 comments

A Tale of Two Standards, POSIX and Win32 (2005)

https://www.samba.org/samba/news/articles/low_point/tale_two_stds_os2.html
3•goranmoomin•58m ago•0 comments

Ask HN: Is the Downfall of SaaS Started?

3•throwaw12•1h ago•0 comments

Flirt: The Native Backend

https://blog.buenzli.dev/flirt-native-backend/
2•senekor•1h ago•0 comments

OpenAI's Latest Platform Targets Enterprise Customers

https://aibusiness.com/agentic-ai/openai-s-latest-platform-targets-enterprise-customers
1•myk-e•1h ago•0 comments

Goldman Sachs taps Anthropic's Claude to automate accounting, compliance roles

https://www.cnbc.com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html
4•myk-e•1h ago•5 comments

Ai.com bought by Crypto.com founder for $70M in biggest-ever website name deal

https://www.ft.com/content/83488628-8dfd-4060-a7b0-71b1bb012785
1•1vuio0pswjnm7•1h ago•1 comments

Big Tech's AI Push Is Costing More Than the Moon Landing

https://www.wsj.com/tech/ai/ai-spending-tech-companies-compared-02b90046
5•1vuio0pswjnm7•1h ago•0 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
4•1vuio0pswjnm7•1h ago•0 comments
Open in hackernews

Humans Learn to Read/Write from a Few Books but LLMs Require Thousands: why?

5•giardini•7mo ago

Comments

tolerance•7mo ago
This is a great question.

My remedial guess is that the human mind is more efficient at the pattern recognition that LLMs excel at in their own right.

We can do a lot more with less data, exert less effort and come to a reasonably accurate conclusion.

LLMs can artificially reason, but it requires intricate software that took decades to develop to the standard that it's reached now, and computers that suck the earth of its resources at a hair-raising scale, and like you've mentioned a lot of data. A lot of data. Apparently the entire internet and then some on a carousel.

Intelligence is an innate faculty of man and man's measure of intelligence generally doesn't require that much, depending on what's expected of the man throughout the course of his life.

Because AI is a technology the expectations we place on it are way higher.

A manuscript with a few errors, blotches, misspellings, omissions, what have you, is excused. If your printer does the same thing for every four or five jobs, it's defective.

fasthands9•7mo ago
I think this is mostly right, but also I'm not sure I agree completely with the premise. Humans have years of conversations they've heard before they attempt to read or write. They already have a concept of what a 'dog' is before they see the word, and know what it is likely to do. Not the same with something that only sees text.
tolerance•7mo ago
I agree with you 100% and I'm not sure if it contradicts my point that humans have a natural advantage over LLMs in the way I tried to illustrate.

My initial comment was going to make an abstract reference to how human beings are pretty much wired for reasoning from the time that they're being breastfed, or at least reared in the clutch of their mother. It has something to do with the impression I've picked up of how the inheritance of a language, and subsequently literacy, starts with your mom—in ideal cases.

I don't know if this is a strike against humans in the whole argument for efficiency. But I don't think it does.

Computers don't have Moms. Go Moms.

techpineapple•7mo ago
Yeah one thing I’ve wondered (and maybe they do this) but find ways to cross encode different kinds of data, words yes, but auditory and visual data too. The algorithms to do this might be complicated (or incomprehensible) but for sure lots of creativity say comes from the interrelationship between senses, combine that with emotion as well, and I imagine it partially comes down to, our writing ability isn’t limited to the collection of what we’ve read.

Then maybe the other thing is that rules and relationships must be encoded in a special way. In LLM’s I assume rules are emergent, but maybe we have a specific rules engine that gets trained based on the emotional salience of what we read/hear.

Maybe another reason is what’s encoded in our DNA, which might imagine our brain structure is fundamentally designed for some of this stuff.

NoahZuniga•7mo ago
Humans have tons of "pretraining" encoded in their DNA
JohnFen•7mo ago
My guess is that it's because humans are intelligent. What I mean by that is that humans are actually understanding what they're reading. If you understand what the words you're reading mean, that makes it easier to read the same words in other contexts.