> The hardest part of QA still seems to be maintaining tests as the system evolves
That’s interesting. It may explain why so many companies now push “self-healing” tests with LLMs for small UI shifts. The teams I spoke with faced different challenges, so the toughest part varied by where they stood in their QA cycle.
> Curious whether you’re aiming...
I started with a broad “AI test everything” approach, but I learned fast that the intent problem I mentioned is tough to beat. The prototype looked great in demos, yell fell short when I dogfooded them on my other projects. And when I met with teams, I didn’t see clear market pull. What comes next is still open.
Hey everyone, I’ve put together this blog post summarizing what I’ve learned over the past month while building a QA-focused startup. I’m still developing my understanding of the space, so I’m also looking for gaps I might be missing. All feedback is welcome!
This is a super fun idea. As someone who just launched a chrome extension, I find it cool that with tweeks you are essentially create one but without having to go through the chrome web store. I wonder if there's any risk in you offer shared "tweaks" that goes against some web store policy.
Bundling and distribution. OpenAI has more paid subscribers than Anthropic. I started using Codex over Claude because Codex is included in my subscription.
Curious, how are you keeping the product data up-to-date? We built something similar for price alerts on specific URLs, that we use all the time, but have to poll it daily to see the price change (https://lowlow.bot). I imagine that would be a lot of $$ for every product on the Internet.
I’m curious how any project management to code agent workflow can be successful given how messy the process is in real life.
Especially discovering unknown unknowns that lead to changes in your original requirements. This often happens at each step of the process (e.g. when writing the PRD, when breaking down the tickets, when coding, when QAing, and when documenting for users).
That’s when the agent needs to stop and ask for feedback. I haven’t seen (any) agents do this well yet.