AI-Powered Web Collections

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Hi! I've just deployed a new feature to my search engine called "Web Collections". They are a slices of the WWW index, subsets. You decide their size. They are created from the advanced query parser GUI that appear after you query the WWW index. From the GUI, simply give your collection a name and click "Create collection" and the result set from the current query will be appended to your named collection. A link will appear leading you to a search GUI that will let you query this new collection as soon as it's been persisted. All collections can spawn new collections because all are queryable. You may create collections from data that doesn't originate from the WWW index but from somewhere else by utilizing the HTTP API to create/append to/read from and query your collections, freely, right now. There is one last feature to implement before my intended MVP is done and that is to be able to reference collections in the query language so that AND, OR and NOT set operations can be orchestrated across collections. It's got the AI label because I create graphs of word embeddings (bags-of-characters, bag-of-word, bags-of-topics) and virtual vector spaces of clusters of documents and because it's a NLP framework of sorts. It's what I use to analyse and create new language models. Each training session creates a new model that enriches the one it was based on. Here's a demo and a question because I'm of course curious: would this a be slightly useful to you had it been at full WWW scale? http://didyougogo.com

Visit product View on Hacker NewsFeb 26, 2019

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

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

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

17%17% 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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