Snoop Hawk – Zapier for Web Research

Hacker News

Hey Everyone, I wanted to share something I've been working on for the past month. Snoop Hawk lets you automate web research. Think visual unit tests. ;) You can set up jobs on any website and have an AI extract insights from it. What's really powerful is that you can schedule these jobs, and soon, set off triggers to notify you of changes. Think design reviews after code changes, competitor analysis, or automated monitoring. This may not be the audience for it, since y'all are technical and might give me the whole "Well, I can set up a cron job and hook to an API" spiel, but I appreciate any feedback you have. :) Bilal

Visit product View on Hacker NewsMay 14, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code · Missing: mac, agents, macos

88%88% 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 · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, soon · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

44%44% 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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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