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AI coding agent setups from a 663 Hacker News comments

https://theainewsreport.com/2026-11-06-hn-agentic-setups-counted.html
1•titusblair•33s ago•0 comments

The Nuclear Company Proposes 6.6-GW AP1000 Nuclear Campus in Kentucky

https://www.powermag.com/the-nuclear-company-proposes-6-6-gw-ap1000-nuclear-campus-in-kentucky/
1•mpweiher•1m ago•0 comments

What if 90% of your prompt is content you can't control?

https://www.brex.com/journal/articles/what-if-you-cant-control-your-prompt
1•ltononro•1m ago•0 comments

AI agent can move money in Stripe

https://stripe.dev/blog/your-ai-agent-can-move-money-in-stripe
1•ltononro•3m ago•2 comments

Grimes Joins Cast of Fully AI-Generated Movie 'Gods Don't Give Gifts'

https://gizmodo.com/grimes-shows-off-her-acting-chops-in-new-ai-movie-or-does-she-2000835869
1•bookofjoe•4m ago•1 comments

Chainalysis says breach exposed password hashes and live tokens

https://cointelegraph.com/news/chainalysis-cyberattack-password-hashes-live-tokens-exposed
2•6thbit•4m ago•0 comments

Show HN: A tool for looping and transcribing YouTube Videos

https://looptranscribe.com
1•javier123454321•4m ago•0 comments

Show HN: Infinity – Git-style branching for AI chats

https://infinitychat.app/
1•nkotov•4m ago•0 comments

Show HN: noise'r'us, like placekittens.com but for audio

https://noiserus.greg.technology/
1•gregsadetsky•5m ago•0 comments

The Harness: Deterministic Systems

https://klaviyo.tech/inside-the-harness-deterministic-systems-476413ae913b
1•tremdog•8m ago•0 comments

Addressing Corrupt Uses of Artificial Intelligence (2025) [pdf]

https://files.transparencycdn.org/images/2025_WorkingPaper_Addressing-Corrupt-Uses-of-Artificial-...
1•yubblegum•9m ago•0 comments

Scriptable GeoDNS: Leaving AWS for Bunny

https://vincent.bernat.ch/en/blog/2026-bunny-geodns
1•sshah03•9m ago•0 comments

Promethevs: Full DeepSeek-v4.1-Flash-1017, locally, on 12GB of VRAM

https://github.com/Magnvs7/Promethevs
1•ar-nelson•9m ago•0 comments

WebAssembly Specification Release 3.0 (2026-11-04)

https://webassembly.github.io/spec/core/
2•Tepix•10m ago•0 comments

The United States of Inference

https://subconscious-systems.github.io/united-states-of-inference/
1•Jackobrien•11m ago•1 comments

Six pallets of Nvidia GPUs worth $11.4M stolen in California in Aug 2026

https://www.tomshardware.com/pc-components/gpus/six-pallets-of-nvidia-gpus-worth-usd11-4-million-...
2•paaloeye•12m ago•0 comments

Devin Review 2.0

https://devin.ai/blog/devin-review-2
1•gmays•13m ago•0 comments

I tried to connect every GPU on the planet together (kinda)

2•lukefrisker•13m ago•0 comments

Democrats have the votes to make the Electoral College obsolete

https://therealnews.com/democrats-finally-have-the-votes-to-make-the-electoral-college-obsolete
4•vrganj•13m ago•0 comments

The Formula for Lovable Cities

https://worksinprogress.co/issue/the-forgotten-formula-for-lovable-cities/
1•rafaelc•14m ago•0 comments

AI models built outside the US and China are nearly a year behind

https://village.dev/blog/outside-the-us-and-china-the-world-is-building-last-years-ai/
2•kevin_hu•14m ago•0 comments

LeanPRM

https://twitter.com/rodney_lafuente/status/2118736694937629167
2•andes314•14m ago•0 comments

The Comprehensive Guide to the Bank of Nvidia

https://hntrbrk.com/investigations/nvidia
1•mxschumacher•14m ago•0 comments

China's Connected Industrial Ecosystem

https://michaelhillaert1.substack.com/p/chinas-connected-industrial-ecosystem
1•michaelhillaert•15m ago•0 comments

$11.4M of Nvidia Chips Vanished in Oakland

https://tech-insider.org/nvidia-chips-stolen-truck-oakland-11-4-million-2026/
2•paaloeye•17m ago•0 comments

How Accurate Were Prediction Markets at Picking Midterm Winners?

https://www.nytimes.com/interactive/2026/11/06/business/polymarket-kalshi-prediction-markets-midt...
1•duxup•17m ago•1 comments

Show HN: NASA's Swift telescope's 22 years in orbit, rebuilt from its archive

https://swift.rory.codes/
1•mrroryflint•17m ago•0 comments

ING reportedly seeks buyers for $500M of data center loans

https://www.cnbc.com/2026/11/06/ing-ai-data-center-loans-debt.html
3•Betelbuddy•17m ago•1 comments

Using decision models to speed up design rule violation repair in chip design

https://engineering.flexcompute.com/articles/decision-models-drc-repair/
1•twhughes•17m ago•0 comments

How Bob Dylan Met the Pope

https://www.flaggingdown.com/p/how-bob-dylan-met-the-pope
1•NaOH•17m 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.