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Quixotic – a tool for wasting bot and LLM scraper time

Hacker News

Quixotic – a tool for wasting bot and LLM scraper time

I get a lot of bot traffic, most of which doesn't appear to respect robots.txt, so I made a tool to easily rewrite my content to serve to these bots instead. It consists of two components: quixotic - a command line tool that is static-site friendly to generate a copy of a website with some of the words replaced using a Markov generator. linkmaze - a web server that I can send the worst bots to. It generates 100% Markov content on the fly and with random links that also refer to linkmaze content.

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Actual performance

6points
2comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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