rgthelen

5 ms·

1.
by rgthelen 7mo ago
Thanks for following along since day 1!
2.
by rgthelen 7mo ago
webhooks are the part where most agent-built auth falls apart. Here's how Corral handles it: The server-express.ts template generates the webhook route with the raw body parser before express.json() (Stripe requires the raw body for si
3.
by rgthelen 7mo ago
Thanks! On auth providers — Corral already supports 9 OAuth providers out of the box (Google, GitHub, Apple, Discord, Microsoft, Twitter, Facebook, GitLab, LinkedIn) plus email/password, magic links, and email OTP. Adding a new one is
4.
by rgthelen 7mo ago
It 100% runs locally, is easy to deploy, and it wires it right into your app - even cooler, if you have a JS backend, no extra server, if you have python, go, ruby, etc, it will have your agent create a tiny side-car so you don't need
5.
Show HN: Corral – Auth and Stripe billing that AI coding agents can set up(github.com)
5 points by rgthelen 7mo ago|9 comments
6.
by rgthelen 11mo ago
The split model leaves too many holes to make it really useful for the community. When we add things like "authentication", we'll ship the plugins (like okta integration (for enterprises), etc. We will do our best to maintai
7.
by rgthelen 11mo ago
Yeah, we are in it to win it.
8.
by rgthelen 11mo ago
Oh man, yeah, big pivot. We still are Rownd ( https://rownd.com ); but we see the writing on the wall. SaaS Software that helps with "hard code" problems is going the way of the dodo. What used to take a few weeks and wa
9.
by rgthelen 11mo ago
We are adding continuous model fine-tuning soon, and being able to bring extra horsepower into training is an opportunity. There is also a genuine opportunity to do the same thing with shared resources inside an internal server or VPC; tim
10.
by rgthelen 11mo ago
Yeah, totally fair. The “horizontal orchestration” story only goes so far — at some point you need vertical depth. We’re starting with regulated enterprises (defense, healthcare, legal, fintech) where control and compliance actually matter.
11.
by rgthelen 11mo ago
Yes! Vulcan support is in dev (will be in the next versioned release, probably tomorrow). You can pull down the repo and run a few easy commands to get inference up and running. https://docs.llamafarm.dev/docs/models#l
12.
by rgthelen 11mo ago
Appreciate that — and totally agree. The “who cares / who pays” question is exactly why this hasn’t scaled before. Our bet is that the timing’s finally right: local inference, smaller and more powerful open models (Qwen, Granite, Deeps
13.
by rgthelen 11mo ago
Funny you bring it up. We shipped Vulkan support TODAY through a tight integration Lemonade ( https://lemonade-server.ai ). We now support AMD, Intel, CPU, and Cuda/Nvidia. Hit me up if you want a walk through - this is in d
14.
by rgthelen 11mo ago
Our business model is not to compete in open-source; we are just providing this to the community since its the right thing to do and as a signal to enterprises to reduce risk. Our goal is to target large enterprises with services and suppor
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by rgthelen 11mo ago
I hear you and valid. A mixture of Models is probably a better phrase - we are constantly moving between AI experts and very skilled Developers who use OpenAI endpoints and call it AI, so we are constantly working on finding the correct lan
16.
by rgthelen 11mo ago
Let me know when you open source it; I think there is a place for this and I think we could integrate it as a plug in pretty easily into the LlamaFarm framework :)
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by rgthelen 11mo ago
Yeah, I don't see why we could not integrate that. I think that is the next step as we move our workloads to production.
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by rgthelen 11mo ago
This looks awesome. Are you kind of like lemonade? Let's chat - robert@llamafarm.dev
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by rgthelen 11mo ago
Cool ideas, thank you.
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by rgthelen 11mo ago
What are a few use-cases you want to see this used for?
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by rgthelen 11mo ago
The hardest part, honestly, is the runtime. How do we make it super easy actually to deploy this. We are still working on that. Where do you see a few good places to focus at first? I was thinking AWS and Google, since both have good GPU
22.
by rgthelen 11mo ago
Yeah, that’s a fair framing — it is kind of an “application layer” for AI orchestration, but focused on ownership and portability instead of just convenience. Yeah, the beachead will be our biggest issue - where to find first hard-core user
23.
by rgthelen 11mo ago
Good question — that’s actually the sweet spot for LlamaFarm. You can wire things together yourself (LangChain, bash, Ollama, etc.), but LlamaFarm tries to make that repeatable and portable. It’s declarative orchestration for AI systems — y
24.
by rgthelen 11mo ago
I am kind of militant about this. The ability to run great AI models locally is critical, not just for this sector, but for innovation overall. The bar of "build a few datacenters" is far too high for all but the largest countri
25.
by rgthelen 11mo ago
Likewise, it's been fun building in the open on this one. You can download the CLI with just: curl -fsSL https://raw.githubusercontent.com/llama-farm/llamafarm/main/... | bash Then do LF init to get the
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by rgthelen 1y ago
Great job on this. I'll be following.
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by rgthelen 1y ago
I got it working locally on a Mac; but would love to hear where you want to deploy to next!
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Show HN: LlamaFarm – Working on binary AI Project deployment – (early preview)(github.com)
2 points by rgthelen 1y ago|1 comments
29.
by rgthelen 2y ago
This is what AI is now - sprinkled on top without any actual value.
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by rgthelen 2y ago
I used to work in aircraft maintenance on F-15s. We'd have entire toolboxes with cutouts, just like the example. I still use a "physical" checklist as I get ready in the morning - my wallet, keys, ear buds, and keys all have

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