Notarization is Apple's strict code signing process, which makes it hard for non paying and anonymous developers to distribute third party Mac software. Linux lets you run unsigned software without compromise.

Thanks for the definition.

So far, I've not run into a problem with this....

And with specific regard to my local AI and OpenClaw experiments and tools required for that....haven't run into a problem at all yet.

So, not a barrier of merit.

Especially with AI and ARM64 server development. Having to go through brew, containers and Apple's notarization process makes MacOS not a serious platform compared to bare metal Linux. In a twist of fate it is MacOS that is not ready for the AI desktop. Asahi is stuck on M1 and M2 so can't help you with M6 Macs.

Err...actually, OSX, so far in my experience is quite adapted to local AI.

Brew? What's wrong with that...pretty much the equivalent of of apt-get on some Linux installs, or whatever package manager you use there...

Docker is easy to run on Mac...actually it seems to give a bit more isolation they way it works on Mac...but set up is a breeze and while containerization is not required to run AI or OpenClaw on a Mac, it IS a good idea for security reasons to do so....Mac or Linux!!

Not familiar with any 'notarization" on my Mac set up, please explain?

OH...and running stuff on ollama, you get to take advantage of of the MLX framework for running local AI models....really speeds things along....

So, not quite sure why you are trying to say Macs aren't good for AI work....they actually are quite good for it AND I guess a bit more plug and play to get things set up and running.

And with any platform the more secure you make it, well that adds complexity, but it's worth it....I'd not want to turn OpenClaw or the like free on any platform without guard rails....

"[Racism] is right twice per day" is the phrase that comes to mind.

Racism....where does that come into play with this?

I don't see anywhere where any specific race was targeted or any restrictions were based on skin color, creed or sex, etc.....it just says that H1B (which could be anyone in the world) would be more $$ to bring into the US.

You're really reaching on this one.

If I'm mistaken PLEASE cite and point out where this is meaded out by race in the exec order or rule....

The money is already earmarked to go to funding ICE and enforcement. Which, well, I'm not even sure how to word what that vibe is.

Hmm...how about:

Amazing!!

Fantastic!!

About fucking time...

Getting what I voted for...

Were you looking for something along those lines?

This is all moot because it's false that if you use AI you cannot gain copyright on the work. I literally hold a copyright registered with the US copyright office (I registered with them, despite not being a US citizen, because they're precedent-setting), fully disclosing the use of AI in the process, and directly speaking about the matter with a copyright examiner.

What the standard is is that raw, unfiltered outputs are not copyrightable (and even then, there's wiggle room; the argument put forward by the copyright office was "based on their current understanding" (as of several years ago) and presumed very minimal control by the user over the outputs (which was actually already obsolete by the time they put it out, but that's nitpicking)). But the human creative action you take with those outputs can cause the resultant product to be copyrightable. This can be things like sample mixing, mastering, adding vocals, etc, but can also even just be the selection process itself. Yes, assemblage, selection, curation, etc are all potentially copyrightable acts.

In my case, while the samples were (overwhelmingly) AI, the whole project was weeks of work, involving hundreds of generations, track splitting and reassembling, things down to the level of editing individual phonemes (or for example in one part, stripping the vocals and instruments from a sample down to just the residual "other" channel, throwing it into reverse, and then piling on progressively more reverb and volume up to a sudden cutoff to create a tension-point transition). No issues whatoever registering the copyright on it. I'm sure you could do far less and still have a copyrightable work.

I think people need to keep in mind that marketability (aka enjoyability), talent, and effort are three independent axes. You can be talented at one or more aspects of music production, a genuine skill (play a guitar, sing, etc), but put forth either high or low effort, and make good or terrible music, all independently. You can put forth a lot of effort, but you may or may not be talented, and it may or may not be good. You may make a great enjoyable marketable track, but may or may not have notable talents, and may or may not have put in a lot of effort.

In general: I judge how much I want to listen to it by its enjoyability; I attribute how much the author deserves credit / how much it's "art" by how much non-rote effort they put in; and I value the artist's skill based on their demonstrable talent. All of this independent of what specific tools were used. That said, knowledge of certain facts may colour one's enjoyability of a track. If you hate AI and know / suspect it's AI, that'll strongly colour your enjoyability of it. Or if you know the artist is a bad person or whatnot, that again can colour your enjoyability of a track that you'd otherwise enjoy in a vacuum.

One thing I've been thinking recently is being in the AI trade but only with high seniority, not common stock. So if there is a "pop", but the underlying business remains sustainable, the high seniority investors end up with the assets and make a mint when the market returns to balance, whereas the stockholders are the ones who suffer. This could mean a mix of convertible bonds / convertible ETFs, preferred shares, senior corporate debt, physical infrastructure (power suppliers, REITs, etc), BDCs and private credit vehicles, etc. Or possibly even common stock in some of the diversified giants (Google, Microsoft, etc), who - while they'd take quite a temporary hit in a crash - would survive and then buy up all the distressed players.

Oh, shoot, I forgot my "Claude-isms" ;)

Or simply debloat Brave...

I mean, Leopold was literally fired from OpenAI after one year.

(Though I would hardly call Altman "a genius". He's a rather sociopathic VC guy who happened to be in the right place at the right time)

I'm holding out for so many things.

New fabs are finally being built but they take like 3-5 years, so that's not going to solve anything any time soon :P

Situational Awareness is a "hedge" fund that did the opposite of hedging - they made a bunch of leveraged bets that were all linked to each other in typical market movements. It was headed by Leopold Aschenbrenner, a guy just a couple years out of college, whose employment career had been very brief stints at the FTX crypto exchange firm (until it collapsed) and at OpenAI (until he was fired a year later over an alleged information leak). No financial management experience whatsoever. But he was into Effective Altruism, writing AI whitepapers, all that sort of stuff that Silicon Valley tech bros like, and so when he started a hedge fund, $45B was quickly pumped into it.

Nah, this isn't a sort of bubble-popping event. It's a tangential player in the stock market, not some key participant in operations or loans in the AI ecosystem. My main concern: inference IS profitable. Very. But the heavy leverage of the industry is also very real, and can very much still spread like a contagion. And a likely trigger for that is inflation, triggered by trade wars and the ongoing Hormuz and Ukraine conflicts (or worse, new ones added to the list!). One, inflation directly increases their costs, but two, it causes central banks to raise rates. This starves companies of capital (both loans and equity), incl. to refinance existing liabilities, while also hindering income (e.g. new orders get put off or cancelled). It can easily flip a leveraged company into insolvency, and then that can ripple if there's nothing to stop it.

An investment opportunity can be brilliant, a massive world-changing field with huge margins, but still be a terrible investment. One, because the companies you invest in need to survive continuously and not get blipped out by an adverse market event until they have more ability to withstand them. And two, also, because there's a problem where investors don't merely value the whole market as if it has high odds of success, but value the specific player they're invested in with high confidence as if it will dominate said market. Which, obviously, all players combined cannot do.

More like heâ(TM)s lazy and his talent was being at the right place at the right time

I'm thinking "talent" may be a strong word here....

I mean, the words "rap" and "music" are mutually exclusive terms after all, eh?

That's not the problem. The problem is we already are in a scenario where we have two good jobs for every five people. A good job here is something that lets you afford a one-bedroom apartment by yourself in a city that consistently has employment.

Apparently you and those like you, ONLY consider a city to be the equivalent of of NYC....which is quite the outlier.

Most other cities across the US, with plenty of good jobs, allow you to afford MUCH better than a 1 room apartment.

Certainly! Your point raises a crucial, multifaceted issue that merits deeper examination. It's not just about detection accuracy, it's about the evolving interplay between human intention and algorithmic assistance — a dynamic that resists simple binary classification.

When we delve into the mechanics of modern AI tools, it becomes evident that the boundary between "correction" and "generation" is increasingly porous. Consider the following nuances:

Gradual authorship spectra: Text exists on a continuum — from fully human-crafted prose, to human writing with AI-suggested punctuation, to passages where the AI has restructured entire paragraphs. Detection tools, however, often operate on categorical assumptions that fail to capture this complexity.

The feedback loop phenomenon: Furthermore, as writers internalize AI-suggested patterns — Oxford commas, em dashes, and certain syntactic rhythms — their "human" baseline shifts. This creates a fascinating, almost paradoxical scenario: the more one learns from AI feedback, the more one's unassisted writing may trigger detection algorithms.

Contextual markers vs. stylistic choices: Notably, certain signals — like the increased use of "delve," "testament," or negative parallelism — may reflect genuine stylistic evolution rather than automated generation. It's a testament to how AI influences language culture broadly, not just individual documents.

In conclusion, the challenge you identify is not merely technical but philosophical — forcing us to reconsider what we mean by "authentic" voice in an era of human-AI collaboration. The question, ultimately, is not whether a tool assisted in the writing, but whether the ideas, arguments, and intellectual labor remain genuinely human.

I hope proves helpful! Please let me know if you would like to delve deeper into any specific aspect of these findings, or if you would like me to explore the broader implications for content strategy.