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Thoth Machine Learning

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Thoth Machine Learning

We released Thoth ML, the machine learning module of project Thoth (https://github.com/trulia/thoth) to open source. Thoth is a real-time Solr monitor and search analysis engine. The Thoth ML module applies machine learning algorithms to the data collected by Thoth to gain useful insights. Currently, this module consists of the query time predictor and the query pattern recognition tool. The query time predictor receives a Solr search request and in real time (1-3 milli seconds) predicts if the query is going to be slow or fast. This prediction can be used by the search infrastructure in multiple ways. At Trulia, the slow or fast prediction is used to route some requests to either a slow pool or a fast pool. This is done to ensure that critical user queries don't have to compete with complex, time consuming queries for resources. This leads to instantaneous query execution and hence, a pleasant user experience. The query pattern recognition tool uses probabilistic topic modeling to find commonly recurring patterns in Solr search requests. These patterns can be quite useful as seen in the example here (https://github.com/trulia/thoth-ml/wiki/Query-Pattern-Recognition). Here's the Thoth Machine Learning repository link : https://github.com/trulia/thoth-ml

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% 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: mac, model, user · Missing: agents, macos, agent
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
43%43% 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 · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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