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The Craziest Stock Market Has Turned into a Fright Ride

https://www.wsj.com/finance/stocks/stock-market-crash-south-korea-ai-ccb00cba
1•thm•51s ago•0 comments

Meta pixel: Measure, optimise and retarget ads on Facebook and Instagram

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1•thunderbong•1m ago•0 comments

Show HN: Npx x402-hosting deploy – hosting NextJS with a single cmd

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Show HN: Square Sketch – A vector graphics editor with a modeless interface

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35 years ago, Linus Torvalds made a Usenet post that introduced Linux to world

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Show HN: Efficient way to learn breadth-first

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What one invoice costs across 33 invoicing plans

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A counterexample to Kusner's conjecture on equilateral sets

https://arxiv.org/abs/2608.14013
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An 2026-07-28 MCP Server from Scratch

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The most censored Chinese model is censored at the host, not the model

https://trustedrouter.com/blog/censored-at-the-host-not-the-model
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A Hitchhiker's Guide to Reinventing a Prolog Machine

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Nearly One Third of P=None DMARC Records Contain Only Two Tags

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Run manual QA test cases in a real browser with an AI agent

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Minicomputers Made by Nvidia Are Powering Moscow's A.I. Drones

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Human judgment doesn't leave the software factory. It relocates

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America's Murder Rate Is at a 70 Year Low

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Analysis: Can we license LLM generated "vibe code" into Free Software?

https://fsfe.org/news/2026/news-20260825-01.en.html
3•kirschner•15m ago•0 comments
Open in hackernews

Show HN: TheorIA – An Open Curated Physics Dataset (Equations,Explanations,JSON)

https://theoria-dataset.github.io/theoria-dataset/
9•ManuelSH•1y ago
We’re building TheorIA— an open, high quality dataset of theoretical physics results: equations, derivations, definitions, and explanations — all in structured, machine- and human-readable JSON.

Why? Physics is rich with beautiful, formal results — but most of them are trapped in PDFs, LaTeX, or lecture notes. That makes it hard to:

- train symbolic/physics-aware ML models,

- build derivation-checking tools,

- or even just teach physics interactively.

THEORIA fills that gap. Each entry includes:

A result name (e.g., Lorentz transformations)

Clean equations (AsciiMath)

Straightforward step-by-step derivation with reasoning

Symbol definitions & assumptions

Programmatic validation using sympy

References, arXiv-style domain tags, and contributor metadata

Everything is in open, self-contained JSON files. No scraping, no PDFs, just clear structured data for physics learners, teachers, and ML devs.

Contributors Wanted: We’re tiny right now and trying to grow. If you’re into physics or symbolic ML:

Add an entry (any result you love)

Review others' derivations

Build tools on top of the dataset

GitHub https://github.com/theoria-dataset/theoria-dataset/

Licensed under CC-BY 4.0, and we welcome educators, students, ML people, or just anyone who thinks physics deserves better data.

Comments

somethingsome•1y ago
There are only 3 entries, am I correct?
ManuelSH•1y ago
Yes, we are at very early stage. Looking for other physics experts to help increasing it.
somethingsome•1y ago
I like the idea of having a dataset for physics, but those entries are very basics, most of the physics happens with very complicated maths and it will be difficult to make an entry for a lot of physics.

For example, imagine the entry for the standard equation, should all the derivation and symbolic implementation done as a unique entry? It will be difficult to separate it in logical entries that reference each others, and many physical ideas are fundamentally different, leading to divergences.

I have the impression that it should be easier to just parse reference books and format each paragraph/section as an entry, and maybe build a graph. (considering the reference book as authoritative on the subject)

ManuelSH•1y ago
I guess you mean the Lagrangian of the Standard Model… which I agree, it will be daunting… although there is no limit in a json…

The idea of automatically parsing books is very nice and possibly faster, but note that:

- there are already various datasets of physics papers and such content - the result will be quite different versus what we intent here, which is to have a high quality dataset of physics results with clear derivations (whenever derivation exist)

Maybe we can still use your idea to achieve the last point in some way… maybe there is a book that is already formatted as the dataset and we could use it as a starting point. But I don’t know any.

BrandiATMuhkuh•1y ago
This is some cools work.

Not sure if it fits but I still have ~20k currated step by step solution for mathematics (pedagogical math) "lying" around from my previous startup. They are all hand currated. And could even be used for fine tuning or so.

Here are some details: The dataset has 20.600 Abstract Exercises which turn into 1.193.958 Concrete Exercises.

An Abstract Exercise looks like this: a + b = c A Concrete Exercise looks like this: 2 + 3 = 5 Tital compiled file size (JSONL): 11.6GB

And here is an explorer to see some of the data https://curriculum.amy.app/ToM

ManuelSH•1y ago
very nice! maybe you can put this dataset in some repository like github, kaggle or hugging face, if you are not doing anything with it. Can be helpful to train models.