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Validr – The fastest data validation library in python

Hacker News

Validr – The fastest data validation library in python

Validr is implemented by [Cython]( http://cython.org/ ) since v0.14.0, it's 5X faster than original pure python implemented. Now, it's 10X faster than [jsonschema]( https://github.com/Julian/jsonschema ), 40X faster than [schematics]( https://github.com/schematics/schematics ). benchmark result in travis-ci: json:loads-dumps 1000 jsonschema:draft3 180 jsonschema:draft4 184 schema:default 41 schematics:default 51 validr:default 2384 validr:use-refer-merge 2106 voluptuous:default 100 GitHub: https://github.com/guyskk/validr

Visit product View on Hacker NewsMar 22, 2017

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Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

21%21% predicted probability of success on Indie Hackers, 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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