61–70 of 134 posts
Bit unrelated, I realized that z.ai gives you access to deepseek 4 flash. It's incredible how well it performs when given a detailed spec. I'm not sure I've seen a model one-shot like that, and I was already impressed by gemma 4's speed and efficiency.
How? Can you give details?
The big lab models are academically interesting but business wise they seem toast long term. There’s no way for these model companies to compete when the models are becoming a pure commodity and others offering options that are orders of magnitude cheaper. It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from high…
On token pricing, I think it's very much bottlenecked by hardware (the aggregate of compute) rather than the number of competing models. Assuming that the ceiling of the token price is determined by the economic value a unit of compute can provide, then the less efficient ones would be priced out of the compute allocation. It's not consumers bidding up a limited number of different models, but more like tokens of different models bidding up the limited computing resource. Less-intelligent tokens (which are generated by weaker models) are crowded out by smarter tokens from the limited compute. My prediction is that we'll see a meaningful downward pressure on token prices only when the new batches of next-generation hardware get mass-deployed.
The big lab models are academically interesting but business wise they seem toast long term. There’s no way for these model companies to compete when the models are becoming a pure commodity and others offering options that are orders of magnitude cheaper. It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from high…
Small models aren't going to take people's jobs. Agents using large models might. That's how their numbers make sense.
Earlier quoted context omitted.
Considering the state of the field ( RAG/retrieval/evaluation) I have 0 trust in it, even more if it's closed source with bullshit claim like that. Everything is vibe sloped to death, and dead after a few months to a couple of years (and not hard to be 100 cheaper than GPT-5.6 sol ... DS is basically free and I guess already 100 times cheaper or more, and here another slope ).
castform founder here. we should have made it more prominent on the blog but here's the full code example: https://github.com/castform-ai/benchmax/tree/main/examples/n...
Earlier quoted context omitted.
castform founder here. we should have made it more prominent on the blog but here's the full code example: https://github.com/castform-ai/benchmax/tree/main/examples/n...
Are you using blind chunking or section aware chunking?
Earlier quoted context omitted.
Are you using blind chunking or section aware chunking?
for the example here the chunking is section aware -> but the general training data synthesis pipeline is agnostic to type of chunking
Earlier quoted context omitted.
Considering the state of the field ( RAG/retrieval/evaluation) I have 0 trust in it, even more if it's closed source with bullshit claim like that. Everything is vibe sloped to death, and dead after a few months to a couple of years (and not hard to be 100 cheaper than GPT-5.6 sol ... DS is basically free and I guess already 100 times cheaper or more, and here another slope ).
castform founder here. we should have made it more prominent on the blog but here's the full code example: https://github.com/castform-ai/benchmax/tree/main/examples/n...
Earlier quoted context omitted.
castform founder here. we should have made it more prominent on the blog but here's the full code example: https://github.com/castform-ai/benchmax/tree/main/examples/n...
Thank you for the github link, what is the license? I went briefly over several repo, unless I missed it somewhere the license was often missing.