> 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.
> An important mechanism here is that even if a crawler doesn’t have a billing relationship with Cloudflare, and thus couldn’t be charged for access, a publisher can still choose to ‘charge’ them. This is the functional equivalent of a network level block (an HTTP 403 Forbidden response where no content is returned) — but with the added benefit of telling the crawler there could be a relationship in the future.
IMO this is why this will not work. If you're too small a publisher, you don't want to lose potential click-through traffic. If you're a big publisher, you negotiate with the main bots that crawl a site (Perplexity, ChatGPT, Anthropic, Google, Grok).
The only way I can see something like this work is if a large "bot" providers set the standard and say they'll pay if this is set up (unlikely) or smaller apps that crawl see that this as cheaper than a proxy. But in the end, most of the traffic comes from a few large players.
Nice idea but the results weren't comprehensive for the book I tested. I find that chatgpt with web search enabled does a better job with the simple prompt "Create a relationship diagram for the characters in <BOOK TITLE> using mermaidjs. Include the relationships between people."
The mention of Apple Notes search makes me frustrated haha! I’ve been dreaming of Apple integrating something like this one day. Looking forward to trying out your tool in the meantime.
What are the long-term plans given the likelihood of Apple having on-device LLMs and integrating them throughout their tools in 202X?