devbas

I believe there is a big opportunity for LLM guardrails due to the non- deterministic nature of the Transformer architecture.

However, the just announced Claude Cowork still warns humans to stay in control: https:// claude.com/blog/cowork-research-preview I assume this is because their non-human guardrails are not good enough yet to fully validate the output of an LLM.

What non-human guardrails does Axonflow employ to enforce a policy rule with X% confidence on a prompt / LLM output?

The introduction seems to have AI sprinkled all over it: ..we embarked on a significant journey, ..in this monumental upgrade.

Beginners question but what is its purpose?

Personally I really enjoy doing analog photography. As the feedback loop is very long, technique is important. Also, since the number of pictures is limited, I always have to ask myself the question: is there actually a picture here?

I usually consider programming books as a reference and don't "read" them. My way of getting value out of these books is remembering the high-level contents and diving deeper when there is the need (e.g, a coding problem that I need to take care of)

So sorry to hear about your losses. Sending you a virtual hug through cyberspace.

Because the syntax is relatively complex and it is difficult to judge which endpoints and definitions to use.

Did we hug it to death? Got an error:

> Could not reach Cloud Firestore backend. Backend didn't respond within 10 seconds.

Great idea! Did you deploy a speech-to-text pipeline to achieve this? I always thought it would be relatively expensive to do podcast-to-text translation at scale (compared to the gains) but maybe I just didn't optimize it well enough :)