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AI Image Generation – Kirkify.live

https://kirkify.live
1•Nancy1230•1m ago•0 comments

Best way to collect LinkedIn post URLs by keyword and comment later

1•stephanemillet•1m ago•0 comments

Amazon Nova

https://aws.amazon.com/nova/
1•jonbaer•3m ago•0 comments

LanguageTool requires premium subscription for browser extension

https://languagetool.org/webextension/premium-announcement
1•unixfox•4m ago•0 comments

Building optimistic UI in Rails (and learn custom elements)

https://railsdesigner.com/custom-elements/
1•amalinovic•6m ago•0 comments

Crashing an AI Promo Event: What to Ask Before Buying into an AI Agent Platform

https://ossa-ma.github.io/blog/crashing-ai-promo
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All About Diffraction Gratings

https://www.edmundoptics.com/knowledge-center/application-notes/optics/all-about-diffraction-grat...
1•thunderbong•6m ago•0 comments

An Interview with Atlassian CEO Mike Cannon-Brookes About Atlassian and AI

https://stratechery.com/2025/an-interview-with-atlassian-ceo-mike-cannon-brookes-about-atlassian-...
1•feross•7m ago•0 comments

Training LLMs for Honesty via Confessions [pdf]

https://cdn.openai.com/pdf/6216f8bc-187b-4bbb-8932-ba7c40c5553d/confessions_paper.pdf
1•goplayoutside•7m ago•0 comments

How confessions can keep language models honest

https://openai.com/index/how-confessions-can-keep-language-models-honest/
1•goplayoutside•9m ago•0 comments

Google's Year in Search: 2025

https://trends.withgoogle.com/year-in-search/2025/
1•ravenical•11m ago•0 comments

OpenAI to Acquire Neptune

https://openai.com/index/openai-to-acquire-neptune/
3•resiros•14m ago•0 comments

PGlite – Embeddable Postgres

https://pglite.dev/
6•dsego•16m ago•0 comments

Haskell Weekly – Issue 501

https://haskellweekly.news/issue/501.html
1•unripe_syntax•18m ago•0 comments

WordPress Playground: 2025 Year in Review

https://make.wordpress.org/playground/2025/12/03/wordpress-playground-2025-year-in-review/
2•program•20m ago•0 comments

Flock cameras are also computers – and perfectly hackable

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2•ThomasNeu•22m ago•0 comments

Porn company fined £1M over inadequate age checks (UK)

https://www.bbc.co.uk/news/articles/c93nll07z3go
7•ndsipa_pomu•22m ago•0 comments

How to Think Like a World-Class Marketer – Rory Sutherland

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1•feross•22m ago•0 comments

We created API-Bench to test how well LLMs execute against APIs

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2•adinagoerres•24m ago•1 comments

Khwand AI – personalized AI tutor (launch)

https://khwand.webflow.io
1•FahadHafeezOff•24m ago•1 comments

The Eternal Canvas – 10yr observation and 2yr full-time documentation (85 docs)

https://publish.obsidian.md/thecanvas
1•DVoidCreationz•24m ago•1 comments

NRC Completes Safety Review of TerraPower Natrium [pdf]

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2•mpweiher•27m ago•0 comments

Production Ready Terraform with Testing, Validation and CI/CD

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LED Streetlights Are Disrupting Ecosystems – A Systems Failure

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Tony Tetro

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Can Europe defend its frontier in the Amazon?

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1•skywal_l•32m ago•0 comments

The Human Writes Font

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No Extensions Allowed: The Chrome DevTools MCP Dilemma

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1•coderrx•38m ago•0 comments

Greed is Destroying the World [video]

https://www.youtube.com/watch?v=W8Z3MfNpJpE
4•RicoElectrico•39m ago•0 comments

Show HN: FirstDistro – I lost a $5k/mo customer overnight

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3•Jide_Lambo•39m 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•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.