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AI 资讯

Show HN: Yamanote.fun – A complete soundscape for Tokyo's Yamanote line

After visiting Japan for the first time a decade ago I became completely enamoured with Tokyo's Yamanote Line railway loop. Particularly the sonic experience of it. Like so many others I fell in love with the charming departure melodies and enjoyed discovering experiences like Yamanot.es ( https://news.ycombinator.com/item?id=45045307 ) here on Hacker News when I returned home. But it wasn't until my second trip to Tokyo that I truly appreciated how much the door chimes, on-board announcements a

2026-07-07 原文 →
AI 资讯

Can ChatGPT Really Predict the Stock Market? I Took Apart How It Actually Thinks to Find Out

Nephew saw a YouTube ad. Someone was selling a "secret prompt" for ₹199, claiming ChatGPT and Claude can analyze the stock market and place trades with 90% accuracy — no technical analysis, no fundamentals, just paste this prompt. He brings it straight to Uncle. The Ad 👦 Nephew: Uncle, I saw an ad on YouTube. Some guy was saying, "Use ChatGPT and Claude AI for stock market analysis, take trades with 90% accuracy. You don't even need to know technical analysis or fundamentals — just use this prompt and you'll get all the results." Is that actually possible? 👨‍🦳 Uncle: (laughs) Ah, here we go. This is exactly how a lot of scams happen — and honestly, it's rarely because of some clever new invention. It's because of a lack of understanding, and people treating these models as a magic black box. 👦 Nephew: So are they scamming us? Or genuinely fooling themselves too? 👨‍🦳 Uncle: Not exactly a straightforward scam, and not exactly genuine either. Here's the honest split: they're maybe 30% correct, and 70% wrong. 👦 Nephew: What does that even mean? 👨‍🦳 Uncle: I'll accept this much — there genuinely are AI models out there that can do a solid job predicting stock trends or running fundamental analysis, because that kind of prediction is heavily mathematical, numerical work. But — and this is the important part — ChatGPT, Claude, and Gemini are not that kind of model. 👦 Nephew: Why not? It can literally write code. It can do math inside code. Why can't it just... do math for stock prediction too? I genuinely don't get it. 👨‍🦳 Uncle: Come, sit. This needs a proper, from-scratch conversation. We're going to dig all the way down to what these models actually are , and by the end, you'll understand exactly why ChatGPT, Claude, and Gemini are the wrong tool for this specific job — not a scam exactly, but sold by people who never actually opened the box themselves. Part 1: What Is an LLM, Really — In One Honest Sentence 👨‍🦳 Uncle: Before anything else, one sentence, and hold onto i

2026-07-07 原文 →
开发者

Show HN: PostgreSQL performance and cost across 23 EC2 instance types

Hey! I'm Andrei. I got frustrated by how people tend to build overcomplicated backend systems, being "motivated" by big tech case studies and popular books. So, I started exploring lean architecture, and building my digital garden of ideas, approaches and data that align with this direction. Here I want to present one of the tools – Sizing tool for PostgreSQL. I've benchmarked PostgreSQL on different EC2 instances and disks, with different initial data sets to see performance that these instance

2026-07-07 原文 →
AI 资讯

Tell HN: Who wants to be hired" posts outpace "Who's hiring" 2 to 1

Lately I've noticed that "Who wants to be hired" posts are getting more comments than the "Who's hiring" ones. I pulled the data using HN API and confirmed this "reversal". "Who wants to be hired" posts are getting 2x more comments than "Who's hiring" posts (and seems to be accelerating). For reference, in 2022 it used to be 0.25. I'm old, I'm from the times where demand for software devs greatly outpaced supply, but maybe we're seeing a reversal of it? I don't want to draw any conclusions yet,

2026-07-07 原文 →
AI 资讯

Show HN: Frugon – Find which LLM calls a cheaper model could handle (local, MIT)

I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week. I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks. I then decided to t

2026-07-07 原文 →