deltadarkly

3 ms·

1. 1.▲
   by deltadarkly 7mo ago

   I built this because I wanted to make Python agents easier to deploy as microservices, so they’re cheaper to host, easier to scale horizontally, and have good visibility once they’re running. dank-py takes an existing agent project, inspect
2. 2.▲
   Show HN: dank-py – turn existing Python agents into microservices in 2 commands(github.com)

   4 points by deltadarkly 7mo ago|1 comments
3. 3.▲
   by deltadarkly 9mo ago

   I have a lot of DevOps exp so I used to do this for all my agents. But it was so time consuming and tedious lol I wanted to automate it.
4. 4.▲
   by deltadarkly 9mo ago

   A problem I keep running into, and hear from others, is teams embedding agents directly into their backend because it’s easy. But in production, debugging becomes painful and scaling gets expensive. Running agents as separate services fixes
5. 5.▲
   Show HN: Vercel for AI agents, one-click production deployment for any framework(cloud.ai-dank.xyz)

   4 points by deltadarkly 9mo ago|3 comments
6. 6.▲
   by deltadarkly 9mo ago

   I’ve been building a platform called Dank AI (www.ai-dank.xyz), basically a “Vercel for AI agents.” You define an agent in JavaScript with our framework, connect a GitHub repo, and it deploys to a production URL in one click (containerized,
7. 7.▲
   Show HN: Opensource AIchatbot stack (agent-memory-frontend) one click deployment(github.com)

   3 points by deltadarkly 9mo ago|2 comments
8. 8.▲
   by deltadarkly 11mo ago

   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 m
9. 9.▲
   Show HN: Dank-AI – Ship production AI agents 10x faster(dank-ai.xyz)

   8 points by deltadarkly 11mo ago|8 comments