Show HN : I have developed a robots.txt full text search engine.

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As a sub project of my pet project DNSDigger.com i have developed a search engine that index robots.txt (http://en.wikipedia.org/wiki/Robots_exclusion_standard). I actually don't know what poeple could use this for but i suspect poeple that are into SEO could use it and black/white-hat hackers(?). What i need help with is VPS/$$ for spidering. And as i sit on a considereble index of domainnames (though DNSDigger.com 300m hosts) i thought that if you want a TLD indexed sooner than later you could sponsor it with either VPS or Paypal. See it as a strange "kickstarter". And if you think the "design" suck (which it does because there is a lot of things i suck at and design is one of them) you are free to submit anything better. I can put up a banner for your service a limited time. I am adding new hosts/TLD all the time. And if you have to ask what this is you will not have any use for it even if you did ;)

Visit product View on Hacker NewsAug 2, 2013

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host, soon · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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