Fastest search engine in the world

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Hi! According to my benchmark tests I've just built the fastest free-text search engine in the world [0]. However, providing proof of that as well as making people care has proven to be a near impossible task. I could use some help from fellow programmers to both work on the formal proof but also to test this code against the Big5's offerings of full-text search. Would you care to go fetch an amount of common crawl data to test the abilities of ResinDB? If not, then do you perhaps have another strategy to convince people when you have invented something big? Is writing papers and formally proving things a way into the market place? If so, how do you figure MongoDB overthrew it's previous market place ruler? It had no formal proof of anything. The market loved it though, for it's speed. I would love for formal proof to be a success indicator. Diverting people from using Lucene/Elasticsearch would then be a breeze. Why ResinDB? Because why use a cloud-based luxury cruiser when the most precise relevance, the fastest querying and most energy-aware choice is provided by using an on-premise kayaak. ResinDB on-premise, a word2vec search engine implementation, is the fastest (local) information retrieval system known to me. How about men? [0] https://github.com/kreeben/resin

Visit product View on Hacker NewsJul 15, 2017

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

87%87% 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.

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% 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 · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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

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

Correct prediction on native model

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