honorable_judge

4 ms·

1.
by honorable_judge 1y ago
Yea - that's right. Its this separation of concerns that I think will help people break through the confusion of building agents.
2.
Show HN: LMM for LLMs – A mental model for building LLM apps
6 points by honorable_judge 1y ago|2 comments
3.
by honorable_judge 2y ago
Envoy is compatible with OTel out of the box. That's a big plus for observability. Plus Envoy is designed for high-load dataplane (in the request path worklaods) and used in every modern stack. There are several advantages on using Arc
4.
by honorable_judge 2y ago
Arch moves the critical but crufty work around safety, observability, and routing of prompts outside business logic. Its a uniquely intelligent infrastructure primitive, engineered with purpose-built fast LLMs [3] for tasks like intent dete
5.
Show HN: Intelligent proxy for human-in-the-loop agents written in Rust(github.com)
3 points by honorable_judge 2y ago|1 comments
6.
Envoy (Proxy) but for AI Agents(github.com)
4 points by honorable_judge 2y ago|0 comments
7.
by honorable_judge 2y ago
For a programming language mostly out of favor, the effort to get a framework right and find the right audience to use this over existing options will be tricky. Good luck!
8.
by honorable_judge 2y ago
Its a proxy - built on Envoy. I think its fairly clear that this is a separate process. As far as I can tell, you create a config file, boot up archgw, and in the config have it point to endpoints where prompts get forwarded.
9.
by honorable_judge 2y ago
Found it interesting (for the use case) - the work to export chat and then map is exactly that: work
10.
by honorable_judge 2y ago
With all the focus on language specific frameworks - this out of process architecture choice is an interesting one. On one hand, it helps you side step the "is this functionality available on js, java, etc" question, and on the ot