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The Case Against LLMs as Rerankers

https://blog.voyageai.com/2025/10/22/the-case-against-llms-as-rerankers/
1•fzliu•1m ago•0 comments

Investigating a Possible Scammer in Journalism's AI Era

https://thelocal.to/investigating-scam-journalism-ai/
1•coloneltcb•3m ago•0 comments

Show HN: Lucen – AI dating coach for over-thinkers (like me)

https://lucen.app
1•omarfarooq360•7m ago•0 comments

The end of progress against extreme poverty?

https://ourworldindata.org/end-progress-extreme-poverty
1•Teever•13m ago•0 comments

A Call for Public Discussion about App Store Oligopoly

https://dkg.fifthhorseman.net/blog/2025-app-store-oligopoly.html
3•pabs3•16m ago•0 comments

Revision 2025

https://canmom.art/adventure/demoscene/revision-2025
1•panic•16m ago•0 comments

AI Diplomacy

https://every.to/diplomacy
1•mooreds•17m ago•0 comments

Memchr: Optimized string search routines for Rust

https://github.com/BurntSushi/memchr
2•klaussilveira•18m ago•0 comments

Stanford AI Club: Jason Wei on 3 Key Ideas in AI in 2025 [video]

https://www.youtube.com/watch?v=b6Doq2fz81U
1•freediver•20m ago•0 comments

DeepSeek Linear-Programming-Based Load Balancer

https://github.com/deepseek-ai/LPLB
3•prydt•23m ago•0 comments

Pairing (Voting Pact)

https://www.parliament.uk/site-information/glossary/pairing/
2•gorgoiler•26m ago•0 comments

Endstorm – A Solo-Dev Automation Engine That Mass-Produces Digital Products

1•EndstormAi•28m ago•0 comments

Lloyds Banking Group to Acquire Curve

https://www.curve.com/blog/curve-is-joining-lloyds-banking-group/
1•dsego•28m ago•0 comments

Using iwd for WiFi in Fedora (2023)

https://nullr0ute.com/2023/01/using-iwd-for-wifi-in-fedora/
1•sipofwater•31m ago•0 comments

Show HN: I made a macOS app to index media and transcribe audio locally

https://meetcosmos.com/resources/cosmos-desktop/
1•correa_brian•33m ago•0 comments

One-line tensor visualization for PyTorch and NumPy

https://github.com/anilzeybek/vizy
1•anilz•35m ago•0 comments

The Endlessly Examined Life: A most chronic depression (2014)

https://thebaffler.com/salvos/endlessly-examined-life
1•bookofjoe•40m ago•0 comments

Low-Cost Biosensor of BDNF in Saliva for Diagnosis of Mental Disorders

https://pubs.acs.org/doi/10.1021/acspolymersau.5c00038
4•PaulHoule•52m ago•2 comments

Building a Hand-Wired Cosmos Dactyl Split Keyboard

https://julianyap.com/posts/2025-11-16-1763340628/
1•todsacerdoti•52m ago•0 comments

Codex Cloud

https://chatgpt.com/codex
1•RyanShook•53m ago•0 comments

A surprise with how ' ' handles its program argument in practice

https://utcc.utoronto.ca/~cks/space/blog/unix/ShebangRelativePathSurprise?showcomments
1•birdculture•53m ago•0 comments

Lix 2.94 "Açaí na tigela"

https://lix.systems/blog/2025-11-18-lix-2.94-release/
2•birdculture•54m ago•0 comments

Ask HN: Why this story has almost the same number of comments and points?

2•carabiner•56m ago•3 comments

Luma AI raises $900M in funding round led by Saudi AI firm Humain

https://www.cnbc.com/2025/11/19/luma-ai-raises-900-million-in-funding-led-by-saudi-ai-firm-humain...
1•lastdong•57m ago•0 comments

Making a Stone Tub (2023) [video]

https://www.youtube.com/watch?v=dFdkO2mlIOM
1•sipofwater•1h ago•0 comments

Disallow code usage with a custom `clippy.toml`

https://www.schneems.com/2025/11/19/find-accidental-code-usage-with-a-custom-clippytoml/
2•todsacerdoti•1h ago•0 comments

The Gut-Brain Connection

https://williamjbarry.substack.com/p/the-gut-brain-connection
3•wjb3•1h ago•0 comments

Axial Flux Motor Powers Supercars to New Heights

https://spectrum.ieee.org/axial-flux-motor-yasa
1•jnord•1h ago•0 comments

Ask HN: Have you directly authored a high-profile bug? How does it feel?

1•ctxc•1h ago•1 comments

World-record MAX-CUT approximation with a physics-inspired optimizer (99.9999%)

https://github.com/Kretski/GravOptAdaptiveE
1•DREDREG•1h ago•1 comments
Open in hackernews

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

https://theoria-dataset.github.io/theoria-dataset/
9•ManuelSH•6mo 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•6mo ago
There are only 3 entries, am I correct?
ManuelSH•6mo ago
Yes, we are at very early stage. Looking for other physics experts to help increasing it.
somethingsome•6mo 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•6mo 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•6mo 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•6mo 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.