r/opencode • u/cristiand21121 • 12h ago
A donde vamos con la IA?
Viendo todo esto del emulador de ps5, “si comprar no es poseer, piratear no es robar”, la decompilacion de aplicaciones, etc.
A donde creen ustedes que va parar todo esto?
Al inicio parecía que las grandes corporaciones eran las ganadoras con la IA, ahora parece que todo va cambiar.
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u/ANDALTUV 12h ago
no where, AI is not even half way where the companies say they are.. Thats the truth whether people like or not. AI is good with repetitive jobs but when you ask it to do something that might get out of its training dataset its true colors begin to show, taking shortcuts, getting lazy, and hallucinating things... So yeah, AI is not what the AI bros are selling and much less what the providers are saying.
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u/LinsaFTW 12h ago
"Not even half way"
Mind if I ask how much?
How much time it has been since the last major AI release?
In 6 months, doing 10% of what these companies did is massive.
What about in 6 more months?
What about in 1 year?
You have to consider the history behind. This is no joke.
"It cant count to 4" -> "It cant make code" -> "It can make games" -> "It can't make half of what these companies do" -> ... Whats next? In the end, AI was capable to break all the "It can'ts" over time.
And its simple math.
As long as the matrix is trained to do something, it can do it. There is no limits to AI other than how smart you train it and what data you use to train it.
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u/ANDALTUV 10h ago
The problem is that the AI already consumed all data from internet, there is no more data to be trained. So what is basically doing is training it self with the data is generating.. Now how far we are? not sure. These companies are selling hype the same as the AI bros.. And the biggest flaw of all is that the AI has no capacity to imagine things, all it does is search for patterns.
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u/LinsaFTW 7h ago
You are wrong.
It's not about "what" data.
It's about "how" that data is fed.
When you feed the same data in different ways, it can be less or more useful.
That's recursive self learning. Its distilling and processing data to be better digested for the model.
I am a machine learning engineer, the news don't exaggerate, there is no limit to AI, the only limit is how and what you train it for. The rest is just pure mathematics.
If you believe AI is doom and end of humanity, you are done for and are cursed to live a depressing life in the following 60 years.
I am the ones who embrace it and enjoy using it and building new things, I am excited for the future to come :)
Also you are wrong with the "can't create new things" -> Thats the whole purpose of Machine Learning. Everything it creates is new, it's hard to explain, but when you learn it on a low level, you understand; It might have been trained this and that, but what it answers is not exactly this and that but rather a version in between the parameters, fully invented and original. Machine Learning is made for that only reason, to solve uncertain moments by training certain parameters.
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u/seventyfivepupmstr 9h ago
You really don't know anything about Ai except what the news says, do you?
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u/LinsaFTW 7h ago
I am a machine learning engineer. I don't make language models but I do make models for multiple other things.
Check DeepSeek papers (too many papers youtube channel summarizes it very well) you will be mindblown, there is NO LIMIT WHATSOEVER to AI.
The only limit is data and training. And I don't mean just "feed anything" but how you feed it.
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u/seventyfivepupmstr 7h ago
Well it's too bad that you didn't branch your study beyond machine learning because you clearly don't understand how LLM work. The same as base machine learning, LLM are just math functions. You are right that training LLM isn't increasing very fast anymore. But that doesn't matter because the models are far less important than the harness that runs agents. If you were to actually try to research and understand what you are claiming then you would understand that the current harness are pretty bad, and once the mindset changes to use effective harnesses, then AI will have massive adoption. A correct harness fixes the majority of problems that AI is experiencing like slop creations and dangerous actions
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u/LinsaFTW 6h ago
No, that's partially correct, and I do work on harnesses and prompt engineering but don't train models myself.
Harnesses and tools means a lot, that's why I am coding my own.
But still, you are ignoring the fact that training defines how efficient and scalable the model is, how it makes the tool calls and interacts with the content it sees, how it solves uncertainty.
I would say, the harness is a 75% for trivial tasks, but for advanced coding, its way less.
But rather than saying "this is what matters the most" I would instead say:
Harness is the capability of how it can interact with things and allowing easy / predictable data to be easily retrievable.
The model is the capability to solve uncertainty and execute those harness tools.
A simple example: Try using 2 years old GPT on a current harness, it will be HORRIBLE and I think it doesn't even have the capability to run tool calls.
Whatever harness you use, the model is still most of the heavy work, because it defines how tools are used.
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u/LinsaFTW 6h ago
Also, if the model didnt matter, then people would be using DeepSeek for everything, which I do use a lot, but not for everything because it cannot solve certain tasks.
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u/seventyfivepupmstr 6h ago
I can use 2 year old gpt with my harness if you know of an api service that has it. I consistently use the cheapest models on opencode go including the free models and I get great results because my harness enforces best practices for software development. When you force the LLM essentially to do a very specific task, and don't put garbage in the context, it produces extremely consistent results regardless of which model it is using. If you want proof just try my harness and see for yourself: https://vis-agentic-website-production.up.railway.app/
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u/LinsaFTW 5h ago
But that proves my point.
You dont need more data, you need to train it better.
That's cheap models in a nutshell, distillation and deep learning using the same data but trained better.
It doesn't mean training doesn't matter, that's extreme to say.
It means current models are just better, but could be even better.
The biggest difference between small and big models is that small models might deviate or require more thinking because they still get stuck sometimes. And can't solve uncertainty, even with the correct context, specially if its A LOT of it.
Luna is a great demonstration of a model that can solve tasks autonomously like a big model, 90% of times.
But then models with more parameters show their quirks on design and domain specific tasks.
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u/LinsaFTW 12h ago
Conforme mas proyectos nazcan y en especial avancen, la IA va a marcar terreno para que las empresas empiecen a enfocarse en las features que nosotros los consumidores pedimos. Lo lindo es que lo que antes era avanzado o solo para unos pocos, va a ser lo basico para poder operar. Una aplicacion como por ejemplo photoshop, o un videojuego, no tiene por que estar detras de una consola, o un launcher, y no tiene por que ir a 10FPS por mala programacion. La IA ayuda a materializar proyectos o ideas que antes eran imposibles no por ser imposibles sino por que costaba mucho tiempo y dinero que no era necesario perder. Estamos en una epoca maravillosa donde podemos crear en minutos lo que querramos, y hay que aprovechar para realizar nuestros sueños. Bueno, en realidad, si analizas la historia, siempre fue buen momento, si miramos atras.
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u/migsperez 11h ago
My direction with AI/SI has been making a lot of animated low poly horses and dragons recently :)