In the past few years we've seen ample evidence that crypto is largely a complete scam, and likewise strong evidence that none of crypto's hoped for value will come to fruition (we didn't see it useful for fighting inflation, it's not being used to avoid sanctions, it certainly isn't being used as a currency, etc).
The fact that crypto still has any market value, and that companies like coinbase not only exist but have had a stellar year defies the imagination.
I get a few years back when there was still a lot of speculation/optimism, but clearly today everyone see that it is just a con. Today even my most cynical view of markets seems naive.
Not to my knowledge, which is why I used the word "suspected" since I think this falls on the "makes intuitive sense, but would not surprise me in the least if it turned out to be completely incorrect" category of hypotheses.
I consider "suspected" to be the least level of evidence while still taking something into consideration as a potential cause. A suspected murderer might not even have been arrested, let alone convicted.
We do know that sulphur emissions have a global cooling effect, and we do know that sulphur emissions recently were reduced, so it's a reasonable hypothesis from first principles.
To be clear, I'm absolutely not promoting increased sulphur emissions as a solution to our climate problems. Moreso pointing out that all those emissions are potentially masking the true severity of our current predicament.
I never claimed that they "require a traditional publisher", in fact I explicitly point out that you can pay for these yourself (though I can't imagine putting together a good team of editors without having prior publishing experience).
My point was that, in response to the parent claiming there's nothing traditional publishers offer, these are things that traditional publishers do in fact offer an author. If you write for a traditional publisher you mostly have to just worry about writing, and, unfortunately, marketing these days.
Which is fascinating because sulphur emissions counteract (mask might be a better term) global warming. Reduction in sulphur emissions is suspected to be one of the main culprits of this years sudden rise in Earth sea-surface/land temperature this year.
Wild when you see just how much emissions are still being released and still presumably cooling the Earth, meaning the effects of climate change we're seeing now are still likely a dampened version of the true long term impact.
I know plenty of authors and none of them are subsidized by wealthy families. All of them do it part time in the evenings out of a labor of love.
It is worth pointing out that there's nothing particular odd if it were the case that writing was subsidized by wealthy families. For the vast majority of the history of writing, writing was subsidized an left to monks, philosophers or aristocrats. It's only been in the relatively recent time period that writing was a potential occupation for anyone interested with enough skills/talent.
> only reason for using a traditional publisher is the cash advance then?
A few really important things come to mind:
- Editing. I'm not talking about mere copy editing which you can get done reasonably cheaply, but rather having an editor that is reading through everything and giving feedback is hugely important.
- Layout and printing of the book There's a lot that happens between writing and having a polished book in your hands. You can contract all this out but it adds a lot of work.
- Distribution. While the burden of marketing a book has increasingly fallen upon the author these days, if you want your book to be on the shelf at your local Barnes & Noble, then your much better off going with a traditional publisher.
- Prestige. Like it or not, the vast majority of people on Earth still look down upon self publishing. For some types of books this is less important: technical books and fantasy fiction books can go without in many cases (but if you want to use your book for credibility in something like consulting you'll still want a traditional publisher). But if you want to write on a serious topic it helps a lot to have an academic press publish your work, or if you want to really pursue writing literature you at least want some publisher that is recognized in your relevant community.
Currently I think the only really good use cases for self publishing are the fantasy fiction and niche technical book markets assuming you already have an audience. And even in those cases there are plenty of reasons to go with traditional publishers over self publishing.
I've always found it fascinating that geophysicist and earlier advocate for Bayesian methods, Sir Harold Jeffreys, didn't believe in continental drift and plate tectonics because he felt there was no known source of energy on the Earth massive enough to explain this movement. [0]
He remained an opponent until death (at which point continental drift was widely accepted) which is both a testament to the literally unbelievable energy behind seismic activity and the importance of updating your Bayesian priors as you gain new information.
> And now Google is unusable: using LLMs even just as a compressed form of documentation is a good idea.
Beyond all the hype, it'd undeniable that LLMs are good at matching your query about a programming problem to an answer without inundating you with ads and blog spam. LLMs are, at the very least, just better at answering your questions than putting your question into to google and searching Stack Overflow.
About two years ago I got so sick of how awful Google was for any serious technical questions that I started building up a collection of reference books again just because it was quickly becoming the only way to get answers about many topics I cared about. I still find these are helpful since even GPT-4 struggles with more nuanced topics, but at least I have a fantastic solution for all those mundane problems that come up.
Thinking about it, it's not surprising that Google completely dropped the ball on AI since their business model has become bad search (i.e. they derive all their profit from adding things you don't want to your search experience). At their most basic, LLMs are just really powerful search engines, it would take some cleverness to make them bad in the way Google benefits from.
> A lot to be said for not defaulting to data frames, in both r and python
I would even add especially in Python. The main issue I have found is that pandas heavy code is just not as easy to integrate into other Python tools/features/abstractions as code using mostly numpy, dictionaries and various comprehensions to do the vast majority of your work.
As a heavy pandas user for several years, I decided about a year ago to not import pandas by default and instead treat most data problems like regular python problems. I've been genuinely surprised as how much easier it is to create useful abstractions with the code I've been writing, and also how much easier it's been to onboard non-DS devs into the code base.
There are a few obvious cases when Pandas is very helpful, and I'll pull it out in those places, but I've been able to do a tremendous amount of data work in the last year and used very little pandas. The result is that I have an actual codebase to work with now rather than a billion broken notebooks.
Recent experience at a fairly young startup has shown me that open office culture has also started to breed a very different type of programmer.
People will often be pairing nearly all day long, any claim that you need a moment to focus and think about a problem is met with perplexity, every idea should be shipped to prod asap, while tests exist the idea of performing basic QA/manual testing on your own work is only used in the most extreme cases.
Contemporary startup engineering culture is best described as frenetic. It certainly feels hyper productive (if not extremely exhausting for a more traditional, introverted programmer), but I've started to notice a fairly large amount of that "productivity" is fixing mistakes a more focused programmer would have avoided.
I suspect the long-term impact of open offices my be even more deleterious than it's impact on the focus of individual programmers.
It's worth pointing out that most of the best science happened before peer review was dominant.
There's an article I came across awhile back, that I can't easily find now, that basically mapped out the history of our current peer review system. Peer review as we know it today was largely born in the 70s and a response to several funding crises in academia. Peer review was a strategy to make research appear more credible.
The most damning critique of peer-review of course is that it completely failed to stop (and arguably aided) the reproducibility crisis. We have an academic system where the prime motivation is the secure funding through the image of credibility, which from first principles is a recipe for wide spread fraud.
I'm pretty sure Sam Walton didn't establish Walmart with the hope of being acquired.
It's bizarre that we live in a time where we can't even fathom a business that is fundamentally very profitable, we just envision growing the company until it's attractive enough for someone else to take on the unsustainable cost of running the business: either get acquired by a large company or hoist your debt onto the public market.
Investment really did used to be about more than a complex "greater fool" game.
As an ex-smoker, I also used to just toss cigarette butts anywhere without thought. Decades after quitting it's still surprising to me that I ever thought this was normal (I've never, otherwise, been a big litterer).
At least when I began smoking in my early teens this was just what smokers did. When you were done smoking you just tossed the butt on the ground and stomped it out. I remember being annoyed when someone scolded me for littering, at the time it somehow felt different but I admitted the logic didn't quite work out.
I suspect a lot of this behavior originated from behaviors established before filtered cigarettes were the norm. Just tobacco rolled in thin paper, especially when burnt to the end before it burns your fingers, will likely break down after the first heavy rain. Filtered butts however, hang around for a long time.
> I can see how it feels like cheating to coax the model to produce the answer you want. But... it's not!
If it's for a single example, it is absolutely cheating. As an AI engineer this is a particular point of frustration where people complain because a large system can't return the result they want, when they were able to get the answer they wanted on their own with a lot of prompt hacking.
Each prompt is basically a point in latent space, and if you're "tweaking" the prompt what you're really doing is just re-rolling the dice until you land in a neighborhood closer the answer you want. You're not better at prompting, you just got lucky and are confusing that for insight.
Now if you're specific prompting trick works across a suite of evaluations, then you are probably on to something. But what people are doing in most cases is equivalent to performing some ritual before pulling the handle on a slot machine and then, when they finally win, claiming that they finally stumbled upon the correct ritual.
A good QA person is to a software developer as a good editor is to a writer. Both take a look at your hard work and critique it ruthlessly. Annoying as hell when it's happening, but in my experience well worth it because the end result is much higher quality.
I might just be too old, but I remember when QA people didn't typically write tests, they manually tested your code and did all those weird things you were really hoping users wouldn't do. They found issues and bugs that would be hard to universally catch with tests.
Now we hoist QA on the user.
Working with younger devs I find that the very concept of QA is something that is increasingly foreign to them. It's astounding how often I've seen bugs get to prod and ask "how did it work when you play around with it locally?" only to get strange looks: it passed the type checker, why not ship it?
Programmer efficiency these days is measured in PRs/minute, so introducing bugs is not only not a problem, but great because it means you have another PR you can push in a few days once someone else notices it in prod! QA would have ruined this.
> don’t waste your time writing your own neural net and backprop.
I don't think you should be combining writing a neural network with doing backprop since I don't know anyone working with serious ML who is not using some sort of automatic differentiation library to handling the backprop part for them. I'm not entirely sure people even know what they're saying when they talk about backprop these days, and I suspect they're confusing it with gradient optimization.
But anyone seriously interested in ML absolutely should be building their own models from scratch and training them with gradient descent, ideally start with building out your own optimization routine rather than using a prepackaged one.
This is hugely important since the optimization part of the learning is really the heart of modern machine learning. If you really want to understand ML you should have a strong intuition about various methods of optimizing a given model. Additionally there are lots of details and tricks behind these models that are ignored if you're only calling an api around these models.
There's a world of difference between implementing an LSTM and calling one. You learn significantly more about what's actually happening by doing the former.
I have a few friends who had major liquidity events happen to them and they each own 3-5 homes as investment homes. I believe these properties were paid full in full and in cash, so no mortgage.
This small proportion of my friends is mortgage free, but they own multiple houses so a fairly large proportion of my friends houses are mortgage free.
As this other comment pointed out [0] 40% of homes are mortgage free, but that says nothing about home owners.
Anecdotally I know that none of my neighbors are mortgage free. However I also have a few friends that had major cash windfalls things like startup exits or time working in hedge funds, each of them owns 3-5 houses outright as investments.
So, with this small sample I can easily see that of the total number of homes owned by friends/neighbors, 40% being mortgage free sounds about right, but far less of my friends/neighbors are mortgage free.
Glad to see that it's been watered down to "just as bad as" arguments.