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Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification

https://arxiv.org/abs/2608.18066
1•tcp_handshaker•1m ago•0 comments

Show HN: Building a full agentic harness around a 4B model is hard

https://orvena.app/
1•mehrant•2m ago•0 comments

Chinese humanoid robot maker surges 600% in trading debut

https://www.ft.com/content/14cd8246-7fb1-4f8f-81b4-8de11ced79e5
1•thm•3m ago•0 comments

DataSmith: Automating Data Research

https://www.datologyai.com/blog/datasmith
1•vtemian•5m ago•0 comments

Inside Big Tech’s Frantic Race to Quell the Growing Backlash to AI

https://www.wsj.com/tech/inside-big-techs-frantic-race-to-quell-the-growing-backlash-to-ai-2a717339
2•thm•6m ago•0 comments

Censorship Industry: The European Firms Monetizing the DSA

https://foundationforfreedomonline.com/censorship-industry-the-european-firms-monetizing-the-dsa/
1•cubefox•8m ago•1 comments

The history (and future) of technology form factors

https://blog.jacobstechtavern.com/p/technology-form-factors
1•jakey_bakey•10m ago•0 comments

Why it might be time to rethink the human family tree

https://theconversation.com/why-it-might-be-time-to-rethink-the-human-family-tree-289819
2•georgecmu•11m ago•1 comments

Carbon: Graduating from the experiment – Chandler Carruth – NDC Toronto 2026 [video]

https://www.youtube.com/watch?v=WJl4ftb5Fxg
1•dtoma•14m ago•0 comments

Cixin Liu's Essay: The Ladder of Immortality

https://masterdai.blog/cixin-lius-essay-the-ladder-of-immortality/
1•KitN•17m ago•0 comments

Shape of the System Structure over vigilance: Engineering for bounded cognition

https://shapeofthesystem.com/
1•Bluestein•21m ago•0 comments

LG Display unveils FLiPP, achieving dream next-generation OLED technology

https://news.lgdisplay.com/en/2026/08/lg-display-unveils-flipp-achieving-dream-next-generation-oled/
1•ledoge•26m ago•0 comments

A live browser fingerprinting and bot detection playground

https://fingerprint-scan.com/
2•avastel•29m ago•0 comments

The Psychological Transition from C to Rust

https://strawberry9.github.io/the-wrong-memory/Appendix_04.html
1•VolatileRegiste•30m ago•0 comments

Fan Performance Database

https://www.cybenetics.com/index.php?option=fan-performance-database
1•picture•32m ago•0 comments

Rune – persistent context for AI coding tools

https://github.com/thecolourfoundation/rune
2•malixp•33m ago•0 comments

Pander Score: How much do AI models mirror what users believe?

https://sophronresearch.org/pander/
2•stared•33m ago•0 comments

Eberto Streaming Companion

https://eberto.net/top/us/movies/netflix
2•nkrycek•35m ago•0 comments

Optimizing Base58: up to 67× faster encoding and decoding

https://fluxrpc.com/blog/base58-optimization-solana-rpc
1•cloakd•37m ago•0 comments

Shares in humanoid robot firm Unitree surge 600% on Chinese stock market debut

https://www.theguardian.com/technology/2026/aug/19/unitree-shares-surge-humanoid-robot-firm-chine...
1•martyvis•41m ago•1 comments

Build your first FlaskTrack MCP agent

https://flasktrack.com/build-mcp-agent
1•boredumb•43m ago•0 comments

A multi-tenant SaaS built in a 92-hour AI-augmented engineering sprint

https://fast-and-flow-production.onrender.com/case-study
2•caredeo•43m ago•0 comments

IOmap Improvement for Linux 7.3 Takes EXT4 and XFS Performance Further

https://www.phoronix.com/news/IOmap-Linux-7.3-Faster
2•rbanffy•48m ago•0 comments

Greenhouse gas emissions of all world countries – 2025 report

https://edgar.jrc.ec.europa.eu/report_2025
2•leonidasrup•51m ago•0 comments

Rebuilding Linear's delta sync read path

https://linear.app/now/rebuilding-delta-sync-read-path
1•tosh•53m ago•0 comments

Supersonic, a cross-platform Subsonic client

https://github.com/supersonic-app/supersonic
1•tentacleuno•53m ago•0 comments

NASA images show crater carved by a SpaceX rocket that slammed into the moon

https://apnews.com/article/spacex-rocket-moon-crash-crater-52b76d61289c06b7d32348b082a8e6b8
1•bushwart•54m ago•0 comments

CS229 Lecture Notes [pdf]

https://cs229.stanford.edu/main_notes.pdf
1•tosh•54m ago•0 comments

Cloning takes a weekend, finding the 38% took months

https://dev.profullstack.com/~anthony/blog/021-post.html
2•buffer_overlord•59m ago•1 comments

Cerebras Overclocks WSE-3 Waferscale Engine to Boost Inference in "Nexus" CS-4

https://www.nextplatform.com/compute/2026/08/19/cerebras-overclocks-wse-3-waferscale-engine-to-bo...
2•rbanffy•1h 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.