vishaal_007

7 ms·

1.
Choosing an LLM Framework in 2026(modelriver.com)
2 points by vishaal_007 6mo ago|0 comments
2.
by vishaal_007 6mo ago
We kept running into the same wall. The prototype worked great: chain a few calls together, get a response and it felt like we were basically done. Then real traffic showed up and we were spending more time on the stuff around the LLM call
3.
LangChain is powerful, but running it in production isn't(modelriver.com)
3 points by vishaal_007 6mo ago|1 comments
4.
by vishaal_007 6mo ago
I’m one of the people behind ModelRiver. We wrote this after running into the same pattern a few times: the AI feature worked fine in development, but once real traffic hit it, the problems were mostly infra problems, not prompt problems. P
5.
OpenAI-compatible apps break in predictable ways(modelriver.com)
1 points by vishaal_007 6mo ago|1 comments
6.
by vishaal_007 7mo ago
Co-founder here—it's just me and my partner bootstrapping this thing. We've been wasting tokens left and right just trying to debug our response parsing code. Not even the AI logic, mind you, just our own sloppy stuff and don&#x27
7.
We stopped paying OpenAI to debug our own code(modelriver.com)
2 points by vishaal_007 7mo ago|1 comments
8.
by vishaal_007 8mo ago
In our experience, it usually comes down to whether the request has user-visible state over time. If the response is something you can treat as atomic and either succeed or fail cleanly, it tends to stay simple. The requests that “grow” ten
9.
by vishaal_007 8mo ago
In practice, event-driven starts to feel like overkill when requests are short-lived and failures are cheap. If a call is fast, idempotent, and the user isn’t waiting on partial output, a simple sync request is usually the clearest solution
10.
by vishaal_007 8mo ago
I’m another founder on this. One thing that surprised us while building AI features was how often the hard problems weren’t about model choice, but about request lifecycle. Once you introduce streaming, retries, and multiple providers, a lo