u/Saurabh4266
Turns out, writing the code was the easy part.
I recently had two PRs merged into FastAPI one improving the API documentation and another fixing a bug in SSE/JSONL streaming.
They ended up teaching me two completely different things.
📚 Documentation PR #15930
Added an API reference page for fastapi.sse, covering the newer SSE support.
🐛 Bug-fix PR #15937
Fixed status_code being ignored for SSE and JSONL streaming endpoints, where responses could incorrectly remain 200 even when another status code was declared.
What I found interesting was that neither contribution was simply about writing code.
The documentation work was about making an existing API easier to understand.
The bug fix was about understanding how several pieces of the framework interact and why the generated OpenAPI schema and runtime behavior could diverge.
That made one thing pretty clear to me:
Open source isn't just about adding code. Sometimes it's about making existing code easier to understand, and sometimes it's about finding the small assumption that's quietly breaking things.
PRs:
#15930 — https://github.com/fastapi/fastapi/pull/15930
#15937 — https://github.com/fastapi/fastapi/pull/15937/
For people who contribute to mature open-source projects: what's one thing you learned that you wouldn't have learned from building projects alone?
Also, I’m thinking of starting a small community for people who actually build and contribute to open source sharing projects, finding contributors, reviewing PRs, and pushing each other to contribute consistently.
If you'd genuinely like to build something like that together, DM me. Maybe we can turn it into a community where contributing isn't a once-in-a-while thing, but a habit. 🚀
Do you think we're early in the universe's story, or incredibly late? Why?
reddit.comHow are you actually testing AI agents before putting them in production?
I've been building with AI agents/chatbots and I'm curious how other developers are handling testing.
A chatbot can pass all the normal tests and still completely fail when a real user gives it something unexpected.
How do you currently test for things like:
- unexpected user inputs
- hallucinations
- prompt injection
- tool/function misuse
- weird edge cases
- context failures
Do you use an existing evaluation/testing tool, maintain your own test suite, manually test everything, or mostly fix issues after they happen?
I'm particularly interested in what people building agents for real users or clients are doing.
How are you actually testing AI agents before putting them in production?
I've been building with AI agents/chatbots and I'm curious how other developers are handling testing.
A chatbot can pass all the normal tests and still completely fail when a real user gives it something unexpected.
How do you currently test for things like:
- unexpected user inputs
- hallucinations
- prompt injection
- tool/function misuse
- weird edge cases
- context failures
Do you use an existing evaluation/testing tool, maintain your own test suite, manually test everything, or mostly fix issues after they happen?
I'm particularly interested in what people building agents for real users or clients are doing.
How are you actually testing AI agents before putting them in production?
I've been building with AI agents/chatbots and I'm curious how other developers are handling testing.
A chatbot can pass all the normal tests and still completely fail when a real user gives it something unexpected.
How do you currently test for things like:
- unexpected user inputs
- hallucinations
- prompt injection
- tool/function misuse
- weird edge cases
- context failures
Do you use an existing evaluation/testing tool, maintain your own test suite, manually test everything, or mostly fix issues after they happen?
I'm particularly interested in what people building agents for real users or clients are doing.
👋 Welcome to r/BuildPublicAI - Introduce Yourself and Read First!
Hey everyone! I'm u/Saurabh4266, a founding moderator of r/BuildPublicAI.
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