merelydev

Open source should not be a threat just like Linux isn't really a threat to Mac and Windows. It all depends on who will get the network effects first (if there even is network effects in LLMs). People pay for windows because of its strong network effects eg Microsoft Office and countless software that run on windows. Even though Linux is free most businesses today don't use it on the desktop.

Opensource also means do it yourself and most businesses don't have time or expertise to do that, but this very issue creates a market for 3rd party providers that use open models to compete against Anthropic. So hardware and compute availability becomes somewhat of a moat.

The business feels like like web hosting before cloud computing. Web hosting in itself does not really have a moat, but than again AWS appeared with their abstractions created lock-in and network effects and disrupted the hosting industry.

Tomato tomato. The point is both are centralized, unlike textbook capitalism and the free-market economy.

> what does a world look like if the cost of having something done falls to a few cents of electricity and its burden?

There will be an oversupply, demand will be there too, but no one can afford to buy since most jobs have been replaced by AI. So the price of goods and services should go down, or some form of UBI will have to be implemented which is just socialism for the masses. Its either that or total anarchy (also socialism).

Isn't Communism just extreme oligarchy.

I don't understand all you wrote, but I guess you are saying AI leads to communism, so what happens to the investors money in a communist economy?

Exactly my point, if you trust the server's js code to do encryption than might as well trust server side encryption.

How is the js delivered to the client-side and how do you ensure that the server has not tampered it?

A bad day for opensource, but I guess this is how we get stronger patch by patch.

Or don't want to crash stock price.

That is the classic problem of decentralized networks, if it is big enough there will be spam at which point you will need moderators to sieve it out. But I think AI can also be helpful here instead of having centralized moderators we can have AI (hopefully self-hosted/decentralize) moderate your network graph for bots/spam.

I can see Deepseek becoming the standard and other providers will build on Deepseek. Like the Linux kernel and its distros.

I realize now that I phrased it wrong, I should have said the largest employers are the government, with very little private companies. At least in my country (Namibia) this is true. Unemployment is high because the youth is waiting for the government to create jobs.

> The main culprit of poverty in Africa (and other countries) is the widespread corruption. The main culprit of poverty in Africa (and other countries) is the widespread corruption.

I disagree with this. The core culprit is that most people in Africa depend on the government, most are employed by it. So effectively the government is just taxing itself with no new money coming in. The solution is a revolution in SMEs, the entrepreneurial spirit and a large private sector. Basically more decentralization.

I didn't know this, thanks. It seems Firefox does support PWAs on Android but not on desktop: https://developer.mozilla.org/en-US/docs/Web/Progressive_web...

PWAs are awesome but Firefox does not support them, you can only install them with Chrome based browsers. So it is still largely Googles ecosystem and they can fuck with it like they are doing with android apps.

> Seems like a solvable problem though by generating synthetic data that’s guaranteed to be accurate through linters, compilers and tests.

This makes sense and is very insightful, thank you. But with that solution it seems that only LLM companies will be in the position to create new languages.

> I used an LLM to write assembler in my made up API so I don’t think what you say is true.

I think you are underestimating the amount of data/context that is required to use a battle tested general purpose language, for both humans and LLMs. The ecosystem requires official docs, stack overflow answers, blog posts, tutorials, existing source code, subreddits, issue/PR discussions of undocumented features, obscure mailing list threads with rare insights, books, youtube videos, benchmarks, tests suits... and the ecosystem of libraries for the language that also need their own official docs, stack overflow answers...

You also need the collective audit by the community and assurance that this language has been used in production by countless others.

Agreed, said something similar yesterday:

"LLMs dont create anything new, if programmers stop reading the code technology will be forever frozen to 2022, no new programming languages, operating systems, concurrency primitives, databases, networking protocols, UI frameworks everything will be based on the training data and future generations will forget about all the primitives we now take for granted.

If someone creates a new programming language/ framework or new better way to do async or whatever, no one will use it because it is not in the training data and it wont take off because everyone is using LLMs. It will be like using the same Lego pieces over and over."

https://news.ycombinator.com/item?id=49854536

> For a human vs human game sure but at the very top level? No of course not because it's all done by AI.

Glad that you finally agree,this is what I was saying the whole time.

> This is trivially not true, as how has AI managed to become better than humans if the knowledge of how to do so never existed in the training data.

AI can do more work, faster, AI it only needs sufficient compute and data. But that does not mean it is more intelligent than humans, it still uses the same code, algorithms, frameworks, protocols etc etc that are in the training data, sourced from human open source code on the web.

> I guess I'm contesting that idea you are putting forward that the data from the games these humans are playing, which are at a vastly lower level that the top AIs are meaningfully important for helping the AIs to improve at the top level.

I never said human games are meaningfully important for training AI. Human games are still important for the advancement of chess, maybe Magnus Carlson can learn from games between two Super AIs but most humans still learn from games by humans, Grandmasters are continuously developing the opening, middle-game and end-game systems, adding to the chess knowledge base. Every serious chess player still reviews and study games by prominent Grandmasters, every serious chess player documents their own games, writing down every move. All rated games are recorded and added to the chess database that every player can review and study.

>Does it? Or will AI be able to run its own experiments and find better/more efficient abstractions...

Only if it is in the training data.

> LLMs receive new data via input context, not just training data.

Be more specific about the "new data". If everyone is using LLMs for work (generating code), especially the juniors who won't get the chance to learn from first principles, LLMs will be training on the data they generated. How will new code enter the system at large enough quantity that it can be used for training?

> It's _really not clear_ whether 2026 LLMs will be useless. To believe that reflects an enormous misunderstanding.

They won't be useless, they will just be frozen knowing only whats in their training data. No new programming languages will emerge, in 2526 they'll still be using Rust and javascript, same exact code from 2022 which dominates the training data.

> If this were true then it would be impossible for these models to ever exceed the top human level as there would exist no training data that allows them to exceed the top human level.

Of course AI exceeds humans at chess, I never denied that. I am saying because chess is primarily a human vs human game, humans will always be learning and playing chess, their games will add to the chess knowledge base, AI also adds to this knowledge base. But programming is not primarily a human vs human activity so there is a risk programmers will forget how to code and all software will be stuck in 2022 because of the training data, this stifles innovation.

Human games databases might not contribute to AI training but they do contribute to the chess database itself like it has always been, AI doesn't change that fact.

I am not talking about the advancement of AI, I am talking about the advancement of chess.

Two things can be true AI drastically contribute to the advancement of chess and humans playing against each other also contribute (even if slowly) to the advancement of chess as it has always been since the invention of the game. The point is that because chess is primarily a human vs human game humans will always have the knowledge of chess, unlike with programmers who are giving it up to prompting, and programming being much more complex than chess (checkmate and win) will be stuck in 2022 because of the training data.

> Pre training data is in large part synthetic these days

How much of that data can lead to innovation? Can you predict all innovation map it out on paper.

> Computer Chess progress has nothing to do with human vs human activity.

The point is that humans will always be learning chess because it primarily a human vs human activity they will be contributing games to the chess database, unlike with programmers who are stopping to code and only prompting, generating code stuck in 2022.

> AlphaGo Zero used no human game data at all.

Sure, but that instance of AlphaGo is still dependent on its training, its intelligence, so it is a question of is that the best and only way to win a game of Go. Just a few weeks ago, a Go Grandmaster found a way to beat one of the strongest Go AIs.

So a specific instance of an LLM might be the smartest based on what we know and need today but that is not the limit of how far we can go, this is why it is important for humans to always have an intimate connection with the code, math, science, chess etc for progress to continue.

My point is that LLMs depend on training data so the code they produce will be stuck in 2022, no new languages, techniques beyond that because new techniques are not in the training data (at least not enough of it for training because most coders are now using LLMs).

Chess/Go continues to progress because it is primarily a human vs human activity, people will always be learning to play chess and chess will continue to develop.

False dichotomy. Chess/Go can still be played between two humans and there is allot of value in that because humans compare each other to other humans, when you see a skillful Grandmaster play you know they are good compared to yourself or the average human, that is why people still watch, play chess/go and train hard to get good. Programming is different because you are creating something not necessarily trying to win a game.

That is like saying what if music is already good enough.

LLMs dont create anything new, if programmers stop reading the code technology will be forever frozen to 2022, no new programming languages, operating systems, concurrency primitives, databases, networking protocols, UI frameworks everything will be based on the training data and future generations will forget about all the primitives we now take for granted.

If someone creates a new programming language/ framework or new better way to do async or whatever, no one will use it because it is not in the training data and it wont take off because everyone is using LLMs. It will be like using the same Lego pieces over and over.

It's more like fuzzing, but you don't need AI for that, just need enough compute.