[bckr]

I think this is just the wrong way to go about learning things.

There are 3 things to learn at any given time:

1. That which never changes i.e. humans, yourself and others

2. What you need to know to succeed right now i.e. deeper in your current tools and systems, or those you'll need to use next month

3. Whatever intrigues you. Maybe this is what you're asking: what's new to be intrigued by?

To the latter, I would say to start learning machine learning if you haven't already.

[brad0]

For 1. specifically, I like this quote from Jeff Bezos:

> I very frequently get the question: "What's going to change in the next 10 years?" And that is a very interesting question; it's a very common one. I almost never get the question: "What's not going to change in the next 10 years?" And I submit to you that that second question is actually the more important of the two -- because you can build a business strategy around the things that are stable in time. ... [I]n our retail business, we know that customers want low prices, and I know that's going to be true 10 years from now. They want fast delivery; they want vast selection.

> It's impossible to imagine a future 10 years from now where a customer comes up and says, "Jeff, I love Amazon; I just wish the prices were a little higher." "I love Amazon; I just wish you'd deliver a little more slowly." Impossible.

https://www.inc.com/jeff-haden/20-years-ago-jeff-bezos-said-...

[azinman2]

Interesting, because I wish Amazon actually had better curation (aka less selection). There’s a lot of crap on there right now.

[tracker1]

I'd add that improving logistics in terms of bundling packages and slowing shipping to handle reduced shipments would do a lot in terms of reducing an ecological footprint (not that I'm big on woke environmentalism).

+1 on reducing options and improving curation... I've gotten in the habit of nearly always selecting "sold by amazon" option in searching... if nothing else, at least the return process is less likely to be a hassle.

[amerkhalid]

Funny because I think we are at a point where I wish averages prices were a little higher on Amazon, by eliminating all the junk and fake stuff off it.

[sunpar]

You're wishing for what you think the outcome of higher prices might be, not for higher prices themselves.

[elliotec]

You don't wish average prices were higher - you wish the junk and fake stuff were eliminated.

[derekp7]

But also with accepting the tradeoff of higher prices. In other words, lower prices and faster delivery don't trump quality. If they have to be fudged a bit a better guarantee of quality then that is acceptable.

[dredmorbius]

There's also Charlie Stross (@cstross), from "Dude, You Broke the Future" (2017):

When I write a near-future work of fiction, one set, say, a decade hence, there used to be a recipe that worked eerily well. Simply put, 90% of the next decade's stuff is already here today. Buildings are designed to last many years. Automobiles have a design life of about a decade, so half the cars on the road will probably still be around in 2027. People ... there will be new faces, aged ten and under, and some older people will have died, but most adults will still be around, albeit older and grayer. This is the 90% of the near future that's already here.

After the already-here 90%, another 9% of the future a decade hence used to be easily predictable. You look at trends dictated by physical limits, such as Moore's Law, and you look at Intel's road map, and you use a bit of creative extrapolation, and you won't go too far wrong. If I predict that in 2027 LTE cellular phones will be everywhere, 5G will be available for high bandwidth applications, and fallback to satellite data service will be available at a price, you won't laugh at me. It's not like I'm predicting that airliners will fly slower and Nazis will take over the United States, is it?

And therein lies the problem: it's the 1% of unknown unknowns that throws off all calculations. As it happens, airliners today are slower than they were in the 1970s, and don't get me started about Nazis. Nobody in 2007 was expecting a Nazi revival in 2017, right? (Only this time round Germans get to be the good guys.)

<http://www.antipope.org/charlie/blog-static/2018/01/dude-you...>

Multiple HN discussions: <https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...>

[yamrzou]

Betting on things that never change (2017) https://news.ycombinator.com/item?id=29289541 (2021)

https://news.ycombinator.com/item?id=33692661 (2022)

[c7DJTLrn]

That's why base skills like being able to confidently administrate Linux systems and maintain networks will be more valuable than knowing Kubernetes ten years down the line. Yes, the latter pays more right now and it will still be around, but it's a trend nevertheless. Linux isn't going anywhere.

[moneywoes]

Sales I guess? Best way to learn that

[eternityforest]

What's the best way to learn machine learning without a real GPU or a budget for cloud time? There's lots of demos and stuff that can run on low end CPUs, but is that close enough in terms of skill to what you'd actually be doing on the job to be worth it?

[hanrelan]

I think you can go far quite cheaply. Get your code working on smaller/toy models, and then when you want to test it on larger ones you can ship it over to a machine at one of the cheaper providers (vast.ai/jarvislabs etc) to give it a run before pausing/killing the machine.

I've been porting Stable Diffusion (which isn't a small model) over to Elixir and as part of doing that have been starting/stopping my jarvislabs machine when I start/stop building. I've been spending about $1/day without trying to be efficient.

Also, fast.ai is a great resource for learning ML, I highly recommend it.

[0x008]

Google colab?

[hlfshell]

Unfortunately this might not be the truth any longer, as it has adopted a new pricing model that is far stingier with gpu time and very confusing to properly track and predict.