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Comment by infecto | Hacker News Reader

There has generally always been some barrier. Computer access, internet access, books etc. If AI coding stays around, which looks like it will, it will just be the current generations barrier.

I don’t think it is sad at all. There are barriers to all aspects of life, life is not fair and at least in our lifetimes will never be. The best anyone can do is to help those around them and not get caught up the slog of the bad things happening in the world.

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Having been a poor person learning how to code I'd say there's a huge difference between just needing a computer vs needing that plus a dozens per month subscription.

I don't know that there's much we can do about that potentially becoming the new normal in the future, but it bums me out.

There are free and offline options, like Llama.cpp, but you will have to pay by giving up your privacy to Meta (or similar large companies)

How does using an offline model give up your privacy?

Also, running models locally requires good hardware to get acceptable performance. It's still a large barrier to entry.

Sure, for now, and maybe in the future. But it's possible that paid models will end up greatly outpacing free ones, and at some point the companies controlling them will stop burning billions of dollars per month and jack up prices.

No it’s not much different. I grew up poor. It was a struggle to have internet access.

But traditional barriers have been able to be knocked down more easily with charity, because it's easier to raise charity money for capex than opex.

It was common to have charity drives to get computers into schools, for example, but it's much harder to see people donating money for tokens for poor people.

Previous-generation equipment can be donated, and can still spark an interest in computing and programming. Whereas you literally now can't even use ChatGPT-4.

Small models and processors are going to continue improving to the point that you’ll be able to vibe code locally on your phone at some point.

When the iPhone came out, not everyone had a smartphone. Now 90% of the US has a smartphone, and many of these smartphones run generative local models.

"It's harder to convince other people to pay for this for me" is an insane criticism. Not every AI model needs a premium account, you can even run many excellent models locally if you don't want to pay for an internet connection.

At some point you just have to accept that yes things are easier if you have a little bit of spending money for things. That's not "sad" it's a basic fact of life.

You have been mean with your interpretation of my statement.

I am not saying, "It's harder to convince other people to pay for this for me".

I am saying, "It is harder for me to pay for this for someone else".

It probably is harder to convince others to pay for opex than capex - maybe that's a good thing, maybe not.

But it's certainly not any harder for you to donate this money to someone because they want to spend that money on tokens instead of a computer, for example.

I think you're still misinterpreting them. It's harder to donate tokens than it is to donate books or a computer. Not all donations are cash.

Though, in a sense, I am donating tokens. I run an LLM box at my apartment, and I let some people in my community have a free account through my hosted Open WebUI.

This feels like picking a random thing against LLMs to complain about. These tools are not even necessary today I am not sure why they would be necessary tomorrow beyond efficiency. If that day does come though, you would have to assume open source models would also be coming a long way.

I attribute my barriers to entry as things that forced me to really learn. All my family could afford was a 386 with 16MB of ram when 486s where pretty common. I had to really hack to make things work. Working under constraints meant I was exploring limits and understanding efficiency.

I still carry that in my day job which I _think_ helps me write better code - even in unconstrained systems, thinking in terms of memory and instruction efficiency can still help make better/faster code.

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