cosmojg

Full walkthrough video: https://youtu.be/mihs6K2ynCs

You've got it backwards. Over a standard business cycle, a healthy, growing economy is generally correlated with higher (or rising) interest rates, while a weak or contracting economy is correlated with lower (or falling) interest rates. Bond investors demand higher interest rates when they have more confidence in the economy as they would otherwise prefer to invest in equities, and investors settle for lower rates when they have less confidence in the economy as they seek to exit equities in favor of low-risk alternatives.

Centralized stablecoins like USDT and USDC aren't layer-one cryptocurrencies like Bitcoin or Ethereum. They're more like applications running on top of other blockchains, usually as smart contracts, and their issuers hold admin keys which can freeze, mint, burn, and otherwise redistribute funds at will. As far as timing goes, stablecoin issuers can't actually cancel or reverse a transaction that's already in a block that's been mined, but they can freeze the receiving address afterward and burn the funds there, then reissue them elsewhere, which has much the same effect.

Looking at HTTP Archive's September 2026 crawl[1], PHP shows up on 4.22M of 8.74M mobile origins, which is 48.3%.

My guess is the 70% figure comes from W3Techs[2], which currently puts PHP at 69.2%, but only of sites "whose server-side programming language we know." Given that PHP is uniquely trivial to fingerprint (via WordPress markup, .php paths, PHPSESSID cookies, etc), it is very likely that it is overrepresented in their dataset[3].

[1] https://httparchive.org/

[2] https://w3techs.com/technologies/overview/programming_langua...

[3] https://en.wikipedia.org/wiki/Streetlight_effect

Is there even a way to prove that something (or someone) is not a stochastic parrot?

1. Buy a BYD in Mexico.

2. Buy a scrapped Tesla in the US.

3. Rebadge the BYD with the Tesla's logos.

4. Bury the Tesla in your yard.

5. Register the rebadged BYD with the Tesla's VIN, slap some plates on it, and drive.

I'm sorry, but I only listen to silence if it's stored in 24-bit hi-res lossless FLAC.

The announcement page is very extra: https://www.kimi.ai/blog/kimi-k3-1-pro

I think the fact that it hasn't yet become an OpenAI repeat says a lot about Anthropic.

Oh my, I'm surprised by the extent to which this spooked them, enough to issue that statement, as well as what the statement itself says about the character of their intended customers.

If you want a little bit more of a batteries-included experience, Zellij[1] is to tmux what Fish is to Bash and what Helix/Neovim is to Vim. If you like any of those, I highly recommend giving Zellij a try.

[1] https://zellij.dev/

Why the /s? That would be an unironically delightful outcome of widespread belief in astrology. Particle astronomy could use the extra funding!

The anthropomorphization of artificial neural networks has been a problem for as long as they've been called "neural" networks. Not that I can complain about it, though. I got my first job as a machine learning engineer coming from academic neuroscience by putting "biological neural networks" on my resume.

Also, IANAL, but 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, similar to how one might treat a ToS agreement. 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-...

Sounds like a great name for a post-rock digicore/hyperpop midwest emo band.

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