PO

POC to scrape and structure HTML into JSON for RAG

Hacker News

POC to scrape and structure HTML into JSON for RAG

Hey all, I built a quick PoC that scrapes a webpage, sends the content to Gemini Flash, and outputs a clean, structured JSON — ready for RAG workflows. In my case, I’ll use this structured data to enhance models by integrating external knowledge sources during the generation process. Curious if you think this has potential or if there are any use cases I might have missed. Happy to share more details if there's interest!

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Actual performance

9points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, gemini · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
20%20% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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