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Why we stopped allowing autonomous fixes in production (even when tests pass)?

1•v_CodeSentinal•29s ago•0 comments

Show HN: Camel OpenAI Integration Patterns

https://github.com/ibek/camel-openai-patterns
1•aivi•37s ago•0 comments

Fish 4.4.0

https://github.com/fish-shell/fish-shell/releases/tag/4.4.0
1•voxadam•1m ago•0 comments

Show HN: Ultra-Dex v3.5 – AI orchestration layer with 17 agents and 61 commands

https://github.com/Srujan0798/Ultra-Dex
1•maya0769•3m ago•0 comments

Show HN: PageSpeed – AI that suggests code-level fixes for specific frameworks

https://pagespeed.deployhq.com
2•deployhq•3m ago•1 comments

Divan – A Modern News Aggregator with AI-Powered Intelligence

3•iedayan03•5m ago•0 comments

Leo satellites won't save us (but good regulation will)

https://blog.apnic.net/2026/02/04/thousands-of-leo-satellites-wont-save-us-but-good-regulation-will/
1•speckx•5m ago•0 comments

Fairphone 6 review: cheaper, repairable and longer-lasting Android

https://www.theguardian.com/technology/2026/feb/04/fairphone-6-review-cheaper-repairable-longer-l...
1•Erikun•6m ago•0 comments

Hell Isn't Meeting the Person You Could Have Been

https://www.mitchschwartz.co/2026/02/hell-isnt-meeting-person-you-could-have.html
1•MitchSchwartz•6m ago•0 comments

Deepdive: Tech companies choose the next generation of dev tools

https://newsletter.pragmaticengineer.com/p/measuring-ai-dev-tools
2•gailaxelrod•6m ago•0 comments

Show HN: Vibefs – A file preview server for remote vibe coding

https://github.com/reorx/vibefs
1•novoreorx•11m ago•0 comments

MotherDuck: Self-Serve Recovery with Point-in-Time Restore

https://motherduck.com/blog/point-in-time-restore/
1•tosh•11m ago•0 comments

Anthropic says 'Claude will remain ad-free,' unlike ChatGPT

https://www.theverge.com/ai-artificial-intelligence/873686/anthropic-claude-ai-ad-free-super-bowl...
1•kstonekuan•14m ago•2 comments

Why do we use async-await in our Code

https://furotmark.github.io/2026/02/04/Why-Do-We-Use-Async-Await-In-Our-Code.html
1•furoTmark•14m ago•0 comments

Starting to Acknowledge Ukraine's Untenable Position and Inevitable Loss

https://www.nakedcapitalism.com/2026/02/collective-west-press-starting-to-acknowledge-ukraines-un...
1•hackandthink•14m ago•0 comments

The More Protein, the Better?

https://www.nytimes.com/2025/04/09/well/eat/protein-fact-check.html
2•hampelm•16m ago•0 comments

Yet another currency converter app, but with cryptos and offline-first support

https://apps.apple.com/us/app/travel-with-currency-converter/id6746667973
1•hieuwu•18m ago•0 comments

System Management Mode: How SMM isolation hardens compute platforms (2020)

https://www.microsoft.com/en-us/security/blog/2020/11/12/system-management-mode-deep-dive-how-smm...
1•transpute•18m ago•0 comments

SHA-3 Explained: Deep Dive into Keccak, the Sponge Construction and TurboSHAKE

https://kerkour.com/sha3
1•unsolved73•18m ago•0 comments

OpenAI's Funding History: Product-Business Strategy Lessons

https://founderleverage.com/2026/02/04/openai-funding-history-analysis/
1•harshalachavan7•18m ago•0 comments

How to make employees learn cybersecurity?

1•dkozyatinskiy•19m ago•0 comments

How Containers Work: Building Container Networking from Scratch

7•amalChandru•21m ago•0 comments

Multifloats.jl: the world's fastest extended-precision floating-point arithmetic

https://github.com/dzhang314/MultiFloats.jl
1•postflopclarity•21m ago•1 comments

Show HN: SlitherPong, a hybrid of the Snake and Pong video games

https://www.slitherpong.com/
3•AmbroseBierce•23m ago•0 comments

Voxtral Transcribe 2

https://mistral.ai/news/voxtral-transcribe-2
3•meetpateltech•23m ago•0 comments

Blind Trust in VS Code Extensions

https://opista.com/posts/blind-trust-in-vs-code-extensions
2•StrangeSound•25m ago•0 comments

Show HN: Nemp – Claude Code memory with zero cloud (just JSON on your machine)

https://github.com/SukinShetty/Nemp-memory
1•sukinai•25m ago•1 comments

Explosion of Black Hole Could Explain (Almost) Everything

https://www.umass.edu/news/article/did-we-just-see-black-hole-explode-physicists-umass-amherst-th...
1•wjSgoWPm5bWAhXB•25m ago•0 comments

Show HN: I Created Full YouTube Suite Skill for OpenClaw

https://clawhub.ai/therohitdas/youtube-full
2•nikhonit•26m ago•0 comments

Long branches in compilers, assemblers, and linkers

https://maskray.me/blog/2026-01-25-long-branches-in-compilers-assemblers-and-linkers
1•PaulHoule•29m 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•8mo 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•8mo ago
There are only 3 entries, am I correct?
ManuelSH•8mo ago
Yes, we are at very early stage. Looking for other physics experts to help increasing it.
somethingsome•8mo 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•8mo 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•8mo 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•8mo 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.