๐ ๐๐๐ถ๐น๐ ๐ ๐ช๐ฒ๐ฏ ๐ฃ๐ฎ๐ด๐ฒ ๐ฆ๐๐บ๐บ๐ฎ๐ฟ๐ถ๐๐ฒ๐ฟ ๐ช๐ถ๐๐ต ๐๐ I was onboarding a new Python library. The docs were scattered across 12 HTML pages. I spent hours clicking back and forth, copying snippets. I thought: "There has to be a better way. Why can't I just dump all these pages into an AI and get a clean summary?" So I tried exactly that. I built a web page summarizer with AI. Here's what I learned:
- Manual copy-paste doesn't scale.
- Automating with Python and openai works, but has limitations.
- Cost adds up fast.
- Context loss across chunks is a problem.
- Hallucinated details can lead to errors. I ended up ditching my homegrown summarizer for most real work. But it's useful for quick exploration. You should use this approach when:
- You're exploring a massive codebase or documentation set.
- You're trying to figure out if a library does something before committing to deep reading.
- You want to generate a brief summary for a large internal wiki. Avoid it when:
- You need precision.
- The content is highly interconnected.
- You're on a tight budget. Source: https://dev.to/__c1b9e06dc90a7e0a676b/i-built-a-web-page-summarizer-with-ai-and-why-you-might-not-want-to-26fi Optional learning community: https://t.me/GyaanSetuAi