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The Temperamental Nature of Alligator Gender-Reveal Parties

https://nautil.us/the-temperamental-nature-of-alligator-gender-reveal-parties-1286547
1•rbanffy•2m ago•0 comments

SwiftNIO: Why ByteBuffer Has Two Cursors

https://ivantokar.com/posts/swiftnio-why-bytebuffer-has-two-cursors/
1•frizlab•4m ago•0 comments

Show HN: Armin – Modern Anki alternative with prerrequisite graphs

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1•ansanabria•4m ago•0 comments

Amazon's Ring debuts security robots, including a drone

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1•gmays•5m ago•0 comments

Nera – Erlang that ships like Go

https://www.process-one.net/blog/nera-erlang-that-ships-like-go/
1•mremond•5m ago•0 comments

Who Is Colette Kress? Meet Nvidia's New Interim CEO

https://www.wsj.com/tech/colette-kress-nvidia-interim-ceo-ff26ef3a
1•pretext•6m ago•0 comments

Show HN: Lumin – Logs, metrics and uptime checks for one-person SaaS

https://getlumin.dev/
1•frederikmartin•6m ago•0 comments

Remote Full Stack Developer Jobs

https://nokku.payanai.com/jobs/remote-full-stack-developer-jobs
1•payanai•7m ago•1 comments

Biomimetic engineering of a fortified ice composite with enhanced properties

https://www.sciencedirect.com/science/article/pii/S0927776526006259?via%3Dihub#sec0005
1•wslh•7m ago•0 comments

Show HN: AppPage – Embeddable community, roadmaps, and support hubs for apps

https://apppage.dev
2•edmundgentle•10m ago•0 comments

HubSpot Announces Leadership Transition: Brian Halligan Returns as CEO

https://ir.hubspot.com/news-releases/news-release-details/hubspot-announces-leadership-transition...
1•jbredeche•10m ago•0 comments

Jensen Huang's China Spy Controversy

https://oplexa.com/blogs/jensen-huang-china-spy-nvidia-investigation-2026/
1•oplexa•10m ago•0 comments

Students at Architecture schools are using AI for everything – what's the fix?

https://www.reddit.com/r/Architects/comments/1xp9yfs/students_at_architecture_schools_are_using_a...
2•jbl0ndie•11m ago•0 comments

Schema.org: a collaborative activity to create structured data on the Internet

https://schema.org/
1•thunderbong•12m ago•0 comments

The Gratitude of Albert Camus

https://www.themarginalian.org/2014/11/19/albert-camus-letter-teacher/
1•rookienumbers30•13m ago•0 comments

Strategy to Be Dropped from MSCI Indexes After New Eligibility Rules

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1•tosh•14m ago•0 comments

Prostate Cancer Screening Now Broadly Recommended, Reversing Past Guidance

https://www.nytimes.com/2026/11/12/well/prostate-cancer-screening-guideline.html
2•julienchastang•14m ago•0 comments

Did Jensen F*** Again?

https://didjensenfuckagain.tech/
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Grid: An open database of income inequality and dynamics across 26 countries

https://www.grid-database.org/
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Open benchmark of AI decision APIs vs. XGBoost for fraud triage

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Talk for Stanford's EE 292J (Designing for Authenticity)

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3•WebSpyder•20m ago•0 comments

What happens if you write a TCP stack in Python? (2014)

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1•zenincognito•20m ago•0 comments

My running data, on my own Mac

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5•fka•21m ago•0 comments

A book of condolences for Jensen Huang's resignation

https://horny.ceo/farewell
2•notelon•21m ago•0 comments

Relate by Neo4j – embedded, agent-native graph database

https://github.com/relatedb/relate
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Nvidia Pair Can't Cluster

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1•nwnht•25m 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.