diff --git a/Web3privacynowplatform/scoringmodel/Product features/Readme.md b/Web3privacynowplatform/scoringmodel/Product features/Readme.md index f7cec9a..e941079 100644 --- a/Web3privacynowplatform/scoringmodel/Product features/Readme.md +++ b/Web3privacynowplatform/scoringmodel/Product features/Readme.md @@ -2,7 +2,9 @@ Here we prototype potential product features within our "l2beat for privacy" platform. All they are based on market survey available [here]((https://docs.google.com/spreadsheets/d/1JWpAsGL10UTsVeuIVbouzUxRjaSPUAamxcbFljXuUWE/edit?usp=sharing)) -1. **Validity track** ("yes/no" applicability like if "docs" exist or not). +# Validity track + +"yes/no" applicability like if "docs" exist or not. _Benefit_: easy to source manually & empower scoring model with the links. _Downside_: the existence of Git repo or docs doesn't guarantee the state of privacy within a certain solution. @@ -16,7 +18,7 @@ _Validity examples_ (yes, no): This could be broken down into 100% (4 yes, each - 25%; where 2 no & 2 yes = 50%) -2. **101 educational materials**. +## 101 educational materials. _Benefit_: significantly upgrades DYOR & flags main misconceptions about privacy execution plus 1 guide could serve all audiences _Downside_: too many content pieces to write or assemble to encompass the whole privacy services & nuances spectrum (needed to be broken down into delivery phases) @@ -32,7 +34,7 @@ _Selected 101s_: The scoring model could consist of passive & active parts. When passive means what could be automated or req no subjective gaze, active - for personal consideration (research). -3. **Checklists** (example: trusted sources list). +## Checklists (example: trusted sources list). _Benefit_: serves as a self-check navigation within the complex audit stream (and a predecessor of an actionable database) _Downside_: there will be always something missing from the list @@ -53,7 +55,7 @@ _Checklist MVP_: This is a mix of objective metrics & subjective takeaways (like an old product release can be a negative sentiment for privacy & vice versa). Case studies will help to empower these assumptions. If we will collect good case studies lib -> it will help people to have market benchmarks for every step of scoring system. -4. **Academy** (content from case studies to third-party services to check net data, leakages). +# Academy (content from case studies to third-party services to check net data, leakages). _Benefit_: this will be the backbone of our platform in the future that will bridge the gap between lack of privacy-centric education, lack of third-party services to check-up solutions & poor transparency on behalf of privacy-devs. _Downside_: usually takes too long time to deliver & many different contributors (should be simplified via tracks like Web3 Privacy 101).