Show HN: Dank-AI – Ship production AI agents 10x faster | Hacker News Reader

Show HN: Dank-AI – Ship production AI agents 10x faster

dank-ai.xyz

8 pointsdeltadarkly8 comments

Interesting to see anync event driven stateless runtimes used here this is becoming a trend for multi/sub agent workflows and using queuing systems in some cases like https://deliveryhero.github.io/asya/ (for kubes) lot of different approaches attempting to deal with fan-in conflict locks who would have thought a swarm of idiot savants would introduce coordination problems lol

We think that using event based agents will eventually become the standard. Currently most AI agent frameworks are essentially a single runtime and use "agents" as nodes in a "workflow". This design pattern is fine for most non-developers to automate tasks locally. However we believe that this pattern doesn't scale well especially for developers. Which is why our agents operate their own runtimes as microservices, and "workflows" are the abstraction layer above agents that allow you to connect and attach agents together to do real work (Workflows to-be-announced soon). We separate "workflows" & agents as different layers in the stack, we think this design pattern is the future of agentic.

Dagger (in Docker) had a idea like this while and Pydantic is using external state savers like Temporal.io there are a lot of directions good luck on yours !!

Pydantic also has a first party integration with DBOS, which doesn't require an external state server.

https://ai.pydantic.dev/durable_execution/dbos/

Super cool to see something out there for JS developers. Feels a lot closer to how you’d actually ship agent systems in production.

They have a stat on there that says 98% of developers use JavaScript.

Wrapper for LLM wrappers

Gave it a try, it was pretty quick to setup and deploy an agent. Reminds me a lot of vercel. It would be cool if you could use this to deploy agents built on other frameworks as well

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