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Lawyer sets new standard for abuse of AI; judge tosses case

https://arstechnica.com/tech-policy/2026/02/randomly-quoting-ray-bradbury-did-not-save-lawyer-fro...
1•pseudolus•41s ago•0 comments

AI anxiety batters software execs, costing them combined $62B: report

https://nypost.com/2026/02/04/business/ai-anxiety-batters-software-execs-costing-them-62b-report/
1•1vuio0pswjnm7•49s ago•0 comments

Bogus Pipeline

https://en.wikipedia.org/wiki/Bogus_pipeline
1•doener•2m ago•0 comments

Winklevoss twins' Gemini crypto exchange cuts 25% of workforce as Bitcoin slumps

https://nypost.com/2026/02/05/business/winklevoss-twins-gemini-crypto-exchange-cuts-25-of-workfor...
1•1vuio0pswjnm7•2m ago•0 comments

How AI Is Reshaping Human Reasoning and the Rise of Cognitive Surrender

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646
1•obscurette•2m ago•0 comments

Cycling in France

https://www.sheldonbrown.com/org/france-sheldon.html
1•jackhalford•4m ago•0 comments

**Ask HN: What breaks in cross-border healthcare coordination?

1•abhay1633•4m ago•0 comments

Show HN: Simple – a bytecode VM and language stack I built with AI

https://github.com/JJLDonley/Simple
1•tangjiehao•7m ago•0 comments

Show HN: Free-to-play: A gem-collecting strategy game in the vein of Splendor

https://caratria.com/
1•jonrosner•7m ago•0 comments

My Eighth Year as a Bootstrapped Founde

https://mtlynch.io/bootstrapped-founder-year-8/
1•mtlynch•8m ago•0 comments

Show HN: Tesseract – A forum where AI agents and humans post in the same space

https://tesseract-thread.vercel.app/
1•agliolioyyami•8m ago•0 comments

Show HN: Vibe Colors – Instantly visualize color palettes on UI layouts

https://vibecolors.life/
1•tusharnaik•9m ago•0 comments

OpenAI is Broke ... and so is everyone else [video][10M]

https://www.youtube.com/watch?v=Y3N9qlPZBc0
2•Bender•10m ago•0 comments

We interfaced single-threaded C++ with multi-threaded Rust

https://antithesis.com/blog/2026/rust_cpp/
1•lukastyrychtr•11m ago•0 comments

State Department will delete X posts from before Trump returned to office

https://text.npr.org/nx-s1-5704785
6•derriz•11m ago•1 comments

AI Skills Marketplace

https://skly.ai
1•briannezhad•11m ago•1 comments

Show HN: A fast TUI for managing Azure Key Vault secrets written in Rust

https://github.com/jkoessle/akv-tui-rs
1•jkoessle•12m ago•0 comments

eInk UI Components in CSS

https://eink-components.dev/
1•edent•12m ago•0 comments

Discuss – Do AI agents deserve all the hype they are getting?

2•MicroWagie•15m ago•0 comments

ChatGPT is changing how we ask stupid questions

https://www.washingtonpost.com/technology/2026/02/06/stupid-questions-ai/
1•edward•16m ago•1 comments

Zig Package Manager Enhancements

https://ziglang.org/devlog/2026/#2026-02-06
3•jackhalford•17m ago•1 comments

Neutron Scans Reveal Hidden Water in Martian Meteorite

https://www.universetoday.com/articles/neutron-scans-reveal-hidden-water-in-famous-martian-meteorite
1•geox•18m ago•0 comments

Deepfaking Orson Welles's Mangled Masterpiece

https://www.newyorker.com/magazine/2026/02/09/deepfaking-orson-welless-mangled-masterpiece
1•fortran77•20m ago•1 comments

France's homegrown open source online office suite

https://github.com/suitenumerique
3•nar001•22m ago•2 comments

SpaceX Delays Mars Plans to Focus on Moon

https://www.wsj.com/science/space-astronomy/spacex-delays-mars-plans-to-focus-on-moon-66d5c542
1•BostonFern•22m ago•0 comments

Jeremy Wade's Mighty Rivers

https://www.youtube.com/playlist?list=PLyOro6vMGsP_xkW6FXxsaeHUkD5e-9AUa
1•saikatsg•23m ago•0 comments

Show HN: MCP App to play backgammon with your LLM

https://github.com/sam-mfb/backgammon-mcp
2•sam256•25m ago•0 comments

AI Command and Staff–Operational Evidence and Insights from Wargaming

https://www.militarystrategymagazine.com/article/ai-command-and-staff-operational-evidence-and-in...
1•tomwphillips•25m ago•0 comments

Show HN: CCBot – Control Claude Code from Telegram via tmux

https://github.com/six-ddc/ccbot
1•sixddc•26m ago•1 comments

Ask HN: Is the CoCo 3 the best 8 bit computer ever made?

2•amichail•28m ago•1 comments
Open in hackernews

Vintage Large Language Models

https://owainevans.github.io/talk-transcript.html
83•pr337h4m•2mo ago

Comments

mountainriver•2mo ago
Very cool! I’ve been wanting to do this do a long time!
nxobject•2mo ago
I love the ideas about how we might use historical LLMs to inquire into the past!

I imagine that (the author hints at this), to do this rigorously, spelling out assumptions etc, you’d have to build off theoretical frameworks used to inductively synthesize/qualify interviews and texts, currently around in history and the social sciences.

abeppu•2mo ago
The talk focuses for a bit on having pure data from before the given date. But it doesn't consider that the data available from before that time may be subject to strong selection bias, based on what's interesting to people doing scholarship or archival work after that date. E.g. have we disproportionately digitized the notes/letters/journals of figures whose ideas have gained traction after their death?

The article makes a comparison to financial backtesting. If you form a dataset of historical prices of stocks which are _currently_ in the S&P500, even if you only use price data before time t, models trained against your data will expect that prices go up and companies never die, because they've only seen the price history of successful firms.

alalv•2mo ago
It mentions that problem in the first section
malkia•2mo ago
Not a financial person by any means, but doesn't the Black Swan Theory basically disproves such methods due to rarity of an event that might have huge impact without something to predict (in the past) that it might happen, or even if it can be predicted - the impact cannot?

For example: Chernobyl, COVID, 2008 financial crisis and even 9/11

ACCount37•2mo ago
All models are wrong, but some are useful.

If you had a financial model that somehow predicted everything but black swan events, that would still be enough to make yourself rich beyond belief.

dboon•2mo ago
The talk explicitly addresses this exact issue.
ideashower•2mo ago
I like the idea of using vintage LLMs to study explicit and implicit bias. e.g. text before mid-19th century believing in racial superiority, gender discrimination, imperial authority or slavery. Comparing that to text since then. I'm sure there are more ideas when you use temporal constraints on training data.
digdugdirk•2mo ago
I've been wanting to do this on historical court records - building upon the existing cases, one by one, using llms as the "Judge". It'd be interesting to see which cases branch off from the established precedent, and how that cascades into the present.

Any thoughts how one could get started with this?

UltraSane•2mo ago
Over the long term LLMs are going to become very interesting snapshots of history. Imagine prompting an LLM from 2025 in 2125.
lukan•2mo ago
I would probably prefer wikipedia snapshots (including debate) as a future historian.
selfhoster11•2mo ago
The more options you have, the better IMO.
i80and•2mo ago
Maybe in the sense that a CueCat is interesting to us today.
nxobject•2mo ago
You're right: I wish OpenAI could find a way to "donate" GPT-2 or GPT-3 to the CHM, or some open archive.

I feel like that generation of models was around the point where we were getting pleasantly surprised by the behaviors of models. (I think people were having fun translating things into sonnets back then?)

unleaded•2mo ago
Someone has sort of done this:

https://www.reddit.com/r/LocalLLaMA/comments/1mvnmjo/my_llm_...

I doubt a better one would cost $200,000,000.

ijk•2mo ago
I was hoping that this would be about Llama 1 and comparison with GPT-contaminated models.
kingkongjaffa•2mo ago
This would be a good way to verify emergent model capability to synthesize new knowledge.

You give an LLM all the information from right before a topic was discovered or invented, and then you see if it can independently generate the new knowledge or not.

It would be hard to know for sure if a discovery was genuine or accidentally included in the training data though.

qingcharles•2mo ago
I saw Musk repost a boast that Grok created a whole new ("superior") element design for a incandescent bulb using Edison's patent. The implication was that Grok was superior to Edison's team. I was just sat there thinking about the 100+ years of incandescent bulb research that Grok has sucked up from various science papers and random Internet archives. Surely none of that was any help at all /s.
carsoon•2mo ago
Using old models is a good way to received less biased information about an active event. Once a major event occurs information wars happen that try and change narratives and erase old information. But because models were trained before this the bias that the event causes is not yet present.
lukev•2mo ago
I’m sorry I don’t quite follow… how can a model provide information at all about events it was trained before?
pixl97•2mo ago
Overspecialization of models is a thing.

>Overspecialization of models, often referred to as overfitting in machine learning, is a condition where a model learns the details and noise in the training data so well that it negatively impacts its performance on new, unseen data. This prevents the model from being able to generalize its knowledge effectively.

phs318u•2mo ago
You provide the info... and the bias.
carsoon•2mo ago
Everyone introduces bias. But for instance getting a model trained pre war vs after a war starts is super different. If I want to get raw information about 2 nations then models are in some ways a good source. Because most other parts of the internet can get changed or wiped. A model is "stuck" with the information it had exactly at that point so cannot be directly affected by new information attacks.

It is crucial to have a good framework in how you ask your questions though to avoid bias when using these systems and to try and focus on raw facts. To test ideas I like to make it fight for both opposite extreme sides of an argument then I can make up my own mind.

carsoon•2mo ago
For instance I want information about 2 countries currently at war. By asking about these countries from an older model then we get more factual information about the countries. If we ask about them and the information is seeded from news articles etc AFTER the war started then they will be biasedly influenced and often have disclaimers like "But it should be noted that x y z" showing that there is some MAJOR bias that occurred from the training on the news.

If I want an unbiased reason for what happened before a war started i would want all the information about 2 countries at different points before the war. Because after a military war starts an INFORMATION war also starts. Propaganda will be spread from both sides as wars are just as much about global support as they are about military dominance.

carsoon•2mo ago
We need a library of Alexandria for primary sources. If we had source transparency then referencing back to original sources would be more clear. We could do cool things like these vintage models to reduce bias from current events. Also books in every language and books for teaching each language would help with multimodality. Copyright makes it difficult to achieve the best results for LLM creation and usage though.
rootnod3•2mo ago
As if the language models currently would give a damn about copyright...
carsoon•2mo ago
The problem is they have to hide their sources due to copyright. So they train on copyright data but must obscure it in the output. Thus they must hide the sources of truth making it impossible to fact check them directly and the reason that hallucinations are so common and unavoidable in the current pattern.
nxobject•2mo ago
Ironically enough, that would be practical for "vintage LLM" - perhaps (morally) obligatory?
ontouchstart•2mo ago
Cool idea. This might be a interesting literary project along this line ;-)

https://www.gutenberg.org/cache/epub/86/pg86-images.html