cosmojg

Yeah, for better or worse, writing style is practically uncorrelated with agentic performance, which is all the rage right now and the thing that most popular benchmarks currently prioritize.

And, in this case, the dollar-amount increase in GDP serves as a virtual quantitative proxy for the increase in mowed lawns (and the value thereof). In other words, the participants in this economy are collectively ~$200 richer with their mowed lawns than they were without them.

Why would anyone assume this uses an LLM? It classifies bird sounds, not human language. I don't mean this as an attack, I'm genuinely curious! This seemed obvious to me, and I want to know what line of thinking might lead one to believe that an LLM is the better (or more likely) tool for this job over a purpose-built classifier.

I still sometimes wear my decade-old Nike Free RNs held together with little more than spoiled hopes, dreams, and shoe goo.

How does this compare to ripgrep and fzf?

If your TV is rootable, you should root it! See: https://cani.rootmy.tv/

Haha, I know that stage by heart, and I've never been to San Francisco. I had no idea that's what it was modeled after!

Newer models[1] are being trained in ways that prioritize coding and agentic performance over raw knowledge[2] such that they increasingly rely on external tools for accessing hard data and information.

[1] https://artificialanalysis.ai/evaluations/omniscience?models...

[2] https://old.reddit.com/r/LocalLLaMA/comments/1vt7l3e/qwen382...

> A dashboard like this one is designed to answer a bounded set of analytical questions ~ requests per day, requests per day for one agency, all-time totals by borough. Each question can be answered by GROUP BY queries, so we can precompute them all ahead of time and save each result as its own small table, called a grouping set. Stack all of the grouping sets in one Parquet file, one section per set, and you have a data cube. A grouping set is only useful if it either enables a question to be answered, or reduces the latency of pulling the data.

What's the benefit of "data cubes" over caching?

Is that a Deepwater Horizon reference?

IANAL, but probably not, at least not in the United States. Under U.S. copyright law, the weights of machine learning models are excluded from copyright as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].

If you want to assess the position of the U.S. Copyright Office for yourself, the relevant text can be found in the Compendium of U.S. Copyright Office Practices § 313.2, "Works That Lack Human Authorship"[2], which states:

> […] the Copyright Act protects “original works of authorship.” 17 U.S.C. § 102(a) (emphasis added). To qualify as a work of “authorship” a work must be created by a human being. See Burrow-Giles Lithographic Co., 111 U.S. at 58. Works that do not satisfy this requirement are not copyrightable.

> […] the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author. The crucial question is “whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.” U.S. COPYRIGHT OFFICE, REPORT TO THE LIBRARIAN OF CONGRESS BY THE REGISTER OF COPYRIGHTS 5 (1965).

Oh, and there's also a bit in the following Section 313.3, "Works That Do Not Constitute Copyrightable Subject Matter"[2], which explicitly excludes mathematical principles, formulas, algorithms, and equations, along with DNA sequences and other genetic or chemical compounds, regardless of whether they are produced by humans or by nature. If one takes the perspective that machine learning models are algorithms, the conclusions on copyrightability are pretty clear.

[1] https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-523...

[2] https://www.copyright.gov/comp3/chap300/ch300-copyrightable-...

These are the right questions. IANAL, but I am of the opinion that model weights are not copyrightable. See my sibling comment: https://news.ycombinator.com/item?id=49073127

It's worth noting that the weights of machine learning models are not subject to copyright in the United States as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].

If you want to assess the position of the U.S. Copyright Office for yourself, the relevant text can be found in the Compendium of U.S. Copyright Office Practices § 313.2, "Works That Lack Human Authorship"[2], which states:

> […] the Copyright Act protects “original works of authorship.” 17 U.S.C. § 102(a) (emphasis added). To qualify as a work of “authorship” a work must be created by a human being. See Burrow-Giles Lithographic Co., 111 U.S. at 58. Works that do not satisfy this requirement are not copyrightable.

> […] the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author. The crucial question is “whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.” U.S. COPYRIGHT OFFICE, REPORT TO THE LIBRARIAN OF CONGRESS BY THE REGISTER OF COPYRIGHTS 5 (1965).

Oh, and there's also a bit in the following Section 313.3, "Works That Do Not Constitute Copyrightable Subject Matter"[2], which explicitly excludes mathematical principles, formulas, algorithms, and equations, along with DNA sequences and other genetic or chemical compounds, regardless of whether they are produced by humans or by nature. If one takes the perspective that machine learning models are algorithms, the conclusions on copyrightability are pretty clear.

[1] https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-523...

[2] https://www.copyright.gov/comp3/chap300/ch300-copyrightable-...

I'm pretty sure that's the same project.

A bar in my Italian family's hometown sells beers for one or two Euros a pop. The gelateria down the street charges a bit more for ice cream, though.

Ooh, this is great! Reminds me of one of my all-time favorite open-source Android apps: Sky Map! https://github.com/sky-map-team/stardroid

I think it's more likely that the big studios will start rolling out trivial offline modes (less risky) rather than overhaul their revenue models (more risky).

This is true of all markets.

They still come in real handy when you're stuck hacking away in a windowless SCIF.

How are you running the base model?

Look up whatever regional burns[1] exist in your country or neighboring countries, and attend one of those. Ideally, join whatever online community exists around it first, and then reach out to some of the camps that interest you about joining and helping with whatever it is they like to do. I love Burning Man, but I can honestly say that I've had a lot more fun at my local regional burns than I've had at the big burn.

[1] https://burnerguides.com/europe-burner-events

That's not a bad idea. It actually sounds like it could be a very useful hedge/insurance play.

This is certainly bad news, but at least an escape hatch exists (the "advanced flow") and it appears to be a one-time pain in the ass. If that changes, I hope GrapheneOS and friends[1] can get Google Pay or some alternative working so I can comfortably jump ship, as I rely pretty heavily on the ability to pay with my phone.

[1] https://eylenburg.github.io/android_comparison.htm

Oh wow, yeah, this is clearly a parody of that IPv8 draft. Just look at the ASCII tables! I suppose it's a commentary on how low the bar is for submitting drafts to the IETF.

Also, isn't insider trading the whole point of prediction markets? They were originally conceptualized as information aggregators.

This is mine: https://cosmo.red

Inspired by: http://bettermotherfuckingwebsite.com and https://github.com/morhetz/gruvbox

How comfortable is it? Do you have to wear it particularly tightly around your wrist or can you let it hang a little loose like a watch?

No, the Internet Archive is an organization that runs a web archive at archive.org while archive.is is an alternative domain for archive.today, a competing web archive run by an individual.

I must admit, one of my favorite recent-ish Android[1] features is that all text is made selectable in the app switcher using on-device OCR. Regardless of the app[2], you can just swipe up and start selecting text.

[1] ...at least on the Google Pixel.

[2] ...unless it's a banking app and it blocks permissions for screenshots and similar things.

Have you tried Google's Gemma-3n-E4B-IT in their AI Edge Gallery app? It's the first local model that's really blown me away with its power-to-speed ratio on a mobile device.

See: https://github.com/google-ai-edge/gallery/releases/tag/1.0.3