I'm not very up to date on the Ai lingo, but is a Strands Decoder just a decision tree in the same vein as Jev ?

You can do this even more efficiently with the rust crate bonsai. You will have more control and you can create sceneries of logic. You can use incredibly cheap models this way and get way better results.

I built a real time character story. In which players interact and integrate fictional characters. There is no delay in response and the characters are all in character. Oh and it’s all voice.

It’s shocking how effective this is.

They keep referring to the solution as an agent, but it's actually just a single call to a model.

What I don't understand is why are they not offering the decision model via Bedrock yet. Sagemaker / Lambda are quite expensive if you're not using the model continuously.

People use machine learning models before and after LLM is invented.

> JSON key order changed the answer. Sending the same content with keys in a different order moved the output probabilities by up to 0.19

Isn't it detrimental?

No mention of quality, or at least I didn't see it. What seem to matter nowadays are speed and cost.

the biggest take sway here is that profit chances from the model provider to the generic computation provider.

the bubble is bubbling

50x cheaper, damn. I recall Chinese LLMs being similarly cheaper (and quite suitable for such simple tasks).

I think a decision model is superior here based on the task, and of course they weren't out yet when they started, but I'm wondering if they wouldn't have similar results (on price, probably not on speed) if they went with DeepSeek or something?

I don't mean send the data to china obviously — there must be an American provider that offers it?

Anyway it's moot in this case (decision model is a better fit) but it just makes me wonder who else is overpaying by 2 orders of magnitude.

This pattern is playing out a lot. Running LLM inference at scale on apps typically lacks ROI. But I increasingly see folks use LLMs to help improve classical techniques.

So there’s some LLM use to develop a new version of algo, but maybe 2% of what would have been used by putting LLMs in the path of every API call. That generally does have ROI.

Putting LLMs in the loop each time is generally a dumb move, unless you’re an AI bro trying to get everyone to use LLMs for all the things.