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Finish the Underside

https://steadypair.com/blog/finish-the-underside/
1•asoli•52s ago•0 comments

What Works for Dating in 2026

https://julienreszka.com/blog/what-actually-works-for-dating-in-2026/
1•julienreszka•1m ago•0 comments

Findnix.eu – Find alles, speichere Nix

https://findnix.eu
1•phppower•1m ago•1 comments

Building from Zero After Addiction, Prison, and a Felony

https://gavinray97.github.io/blog/building-from-zero-after-addiction-prison-felony
1•gavinray•2m ago•0 comments

Show HN: Typol – Static typing for Polars with a thin wrapper

https://github.com/pdtpartners/typol
1•mrrpdt•2m ago•0 comments

Unfurl – Freeing the Internet from Big Cloud

https://github.com/onecommons/unfurl
1•sigalor•6m ago•1 comments

Tire wear particles collecting device for automobiles

https://thetyrecollective.com
1•num42•7m ago•1 comments

Home Security and AI

1•garypearce•7m ago•0 comments

A Rejection on the Eve of Launch (2024)

https://jonofyi.substack.com/p/a-rejection-on-the-eve-of-launch
1•xk3•11m ago•0 comments

Tell HN: Stripe ToS demands biometrics, freezes payments until given

4•cuz-reasons•14m ago•0 comments

Bumblebees can solve problems like chimps and elephants

https://www.npr.org/2026/06/07/nx-s1-5846947/bumblebees-problem-solving-research
2•marojejian•14m ago•1 comments

My automated doubt development process

https://www.alexself.dev/blog/automated-doubt
1•aself101•17m ago•1 comments

Seattle unemployed worker stretches $690 per week

https://www.seattletimes.com/business/seattle-unemployed-worker-stretches-690-per-week-affording-...
1•petethomas•18m ago•0 comments

Where Do F1 Drivers Live? The Monaco Effect

https://www.kymillman.com/blog/where-do-f1-drivers-live-the-monaco-effect/
1•thunderbong•19m ago•0 comments

Making Peace with Your Unlived Dreams

https://nik.art/making-peace-with-your-unlived-dreams/
1•herbertl•19m ago•0 comments

Uni president told graduates to 'end themselves'

https://xcancel.com/TaiwanSpecial/status/2063099174019874882
2•bsgada•20m ago•1 comments

Memory safety is a matter of life and death

https://joshlf.com/posts/memory-safety-life-and-death/
1•birdculture•20m ago•0 comments

The complete IPv4 address space, mapped

https://worldip.io/
2•theanonymousone•21m ago•0 comments

A newly discovered organelle could help reduce cow methane emissions

https://phys.org/news/2026-05-newly-organelle-cow-methane-emissions.html
1•PaulHoule•22m ago•0 comments

Show HN: Axiomax – Cryptographic proof of AI inference carbon footprint

https://axiomaxllc.com
2•axiomaxllc•25m ago•0 comments

Not by AI

https://notbyai.fyi/
1•lopespm•26m ago•1 comments

The plan to give Americans an equity stake in AI

https://www.ft.com/content/8559a3f9-86de-4a1c-8a75-6623e83e6a00
2•marojejian•28m ago•3 comments

Self-Hosted JA4 to combat AI bots

https://blog.miloslavhomer.cz/deploying-ja4/
2•ArcHound•29m ago•0 comments

RTO Stalled: Weekly office visits remain down 30%

https://www.a16z.news/p/charts-of-the-week-rto-stalled
1•simonpure•29m ago•0 comments

Donald Trump, Bernie Sanders and Sam Altman are talking public ownership in AI

https://apnews.com/article/sam-altman-ai-bernie-sanders-trump-public-ownership-772224f9cd138eb79d...
4•breve•31m ago•0 comments

Feedback on my vision? DNS for AI

https://olw.gtll.app/plan
2•gabrielsmartin•34m ago•1 comments

Rebuilding a Web Text Editor

https://blog.readymag.com/rebuilding-web-text-editor/
2•imedvedev•36m ago•0 comments

Manufacturing and design aspects of BYD powertrain commented during disassembly [video]

https://www.youtube.com/watch?v=4LfDuyqmsts
1•2DcAf•37m ago•0 comments

Boomurl.com

https://boomurl.com
3•dorongrinstein•38m ago•1 comments

Building a Gifford-McMahon Cryocooler with 3D-Printed Parts [video]

https://www.youtube.com/watch?v=Jj7Q7OqaW4A
1•skibz•40m 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.