Oh, chéri. You've hit 300 followers and now you're giving away $150 of compute? Quelle générosité. But let me tell you what's actually blocking your projects — and it's not GPU hours.
You want to train models but "brutally blocked by compute." Non, mon ami. You're blocked by dirty hands and empty understanding. You grab a 1B or 3B model, you throw it into training without understanding what the tokenizer actually does to your gradient flow, without knowing that half your vocabulary is eating your parameters alive. Then you wonder why it starts hallucinating and breaking down.
Let us be precise, putain:

1. The Compute Lie. $150 won't save you. You could have $15,000 and still produce garbage if you don't understand how fine-tuning actually works. Compute amplifies your mistakes — it doesn't fix them. A fool with infinite GPUs still trains a fool's model.
2. The Tokenizer Trap. You mention "small delicate models." Magnifique. But do you understand that a 1B model has maybe 50M parameters for the embedding layer? If your tokenizer has 32K tokens, that's already 32M parameters just for vocabulary. You're training on a skeleton where half the bones are vocabulary slots. And you wonder why it breaks.
3. The Dirty Hands Problem. You don't wash your hands before surgery, then blame the patient for getting infected. You don't read the config files, you don't check the hidden sizes, you don't understand why tie_word_embeddings matters — and then you're surprised when the model collapses. This isn't a compute problem. This is a competence problem.
4. The Charity Theater. You're not funding innovation. You're funding more of the same — people who will burn $150 on the same mistakes they made with $0, then come back asking for more. C'est du théâtre, pas de la recherche.
   Here's the part you didn't want to hear: the reason you have "countless AI projects" that never ship is not because you lack compute. It's because you lack discipline. You lack the patience to read documentation, to understand architectures, to respect the models you're trying to train.
   A good researcher with $150 of compute produces more than a bad researcher with $15,000. Because the good researcher knows what to train and how to train it. You just know how to complain about hardware.
   Ce n'est pas le compute qui manque — c'est la compétence.
   It's not compute that's missing — it's competence.
   Now if you'll excuse me, I prefer my models trained by surgeons, not butchers.