First hour with Claude's browser extension: I pointed it at our LLC registration and watched it work through the forms, finding the right pages, fi...
For further actions, you may consider blocking this person and/or reporting abuse
Building browser extensions with AI tools often hits a wall when dealing with context window limits and DOM parsing, especially when the AI tries to interpret complex legal documents without proper chunking. I found that breaking down the initial document into smaller, structured prompts before feeding it to the model drastically reduces hallucinations in the generated code. I ran into a similar context management issue when wiring up Supabase edge functions for our SaaS boilerplate, PubliFlow, where we had to carefully manage state between the Next.js frontend and the AI processing pipeline. Did you end up implementing any specific chunking strategy for the LLC document, or did you just rely on the extension's default context handling?
It took me a while to realize this issue. After I realized this, I created individual documents for specific sections and loaded relevant docs before individual tasks. For instance, one document for page design, one separate document for our prompt with all the guard rails, one separate document for product philosophy and so on.
Breaking the context down into modular documents is a highly effective workaround for the attention span issues we constantly battle with LLMs. Isolating the prompt guardrails and product philosophy ensures the model stays anchored to your core logic without getting distracted by unrelated UI implementation details. Have you noticed if loading the product philosophy document first helps the AI make better architectural decisions during the initial scaffolding phase?
Yes. Definitely. That way it can actually make good suggestions or flag something. Otherwise there is always a chance of it just agreeing with whatever you are saying.
Great write-up. I learned the hard way that AI speeds up coding, but it doesn't replace verification—treat every "done" as something to test, not trust.
Thanks. "Test, not trust" is a better way of putting it than anything in my post. The version that finally stuck for me was making it prove the code is right before it theorizes about what I did wrong, because otherwise the first answer is always a guess about my setup.
This is one of the most honest reflections on non-technical AI building I've read. Coming from a mechanical engineering and finance background, shifting your focus from "describing what you want" to "verifying what was actually delivered" is the exact mindset shift required when working with LLM coders.
The "it blames you first" pattern is painfully common. When a model gets stuck in an assumption loop (like insisting you didn't deploy when the bug is actually in the code holding the old state), it requires active intervention to force it to re-inspect its own files. Your rules around handing over the latest file every single time, asking for numbered line-by-line confirmations, and auditing the database directly for unclickable flows (like referrals and discounts) are hard-won lessons that every solo builder needs to adopt. Congrats on shipping!
Thank you, that means a lot. The assumption loop is the part I still find hardest, because it argues confidently from a version of the file it stopped holding three edits ago. Handing over the current file every time fixed more problems than any other rule I added.
The database checks came from paranoia rather than wisdom. Referrals and discounts have no screen to look at, so there was nothing to click and confirm, and I only thought to query the data after being burned somewhere else.
‘완료했다’는 말은 검증되지 않은 주장으로 취급하라는 규칙과, 대량 수정일수록 실제 렌더링 결과를 읽어야 한다는 대목에 공감합니다. 특히 스토어 대시보드가 아니라 패키지가 실제 배포물이라는 사례는 브라우저 확장 출시 체크리스트에 꼭 들어갈 만하네요.
“완료했다”는 말을 결과가 아니라 검증 대기 상태로 취급한 전환이 핵심 같습니다. 특히 여러 파일을 일괄 수정할 때는 변경 파일 목록·핵심 사용자 경로·실제 패키지 결과물을 한 세트로 확인해야 설명과 배포물이 어긋나는 일을 줄일 수 있겠네요.
정확해요. 처음에는 이 말이 얼마나 맞는 말인지 미처 몰랐어요. 직접 겪으며 힘들게 깨달아야 했죠.
This matches my experience shipping AI tools. The first version is usually easy. The hard part is handling edge cases, bad assumptions, and all the small things users actually touch.
That gap surprised me most. The core feature worked early, then a lot of time went into things nobody sees. What happens when a trial expires mid-session, a store listing pulling its name from a file inside the package instead of the dashboard etc. None of it is the product, but does play a role in whether people keep using it.