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Web3privacynowplatform/scoringmodel/Readme.md

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 # Scoring model analytics based on a 50+ privacy projects [survey](https://docs.google.com/spreadsheets/d/1JWpAsGL10UTsVeuIVbouzUxRjaSPUAamxcbFljXuUWE/edit?usp=sharing)
 
+**17 Oct, 2023 update**
+
+**2 DO list**
+
+_Analyze_
+- Analyze latest privacy scoring assessment [here](https://mirror.xyz/0x0f1F3DAf416B74DB3DE55Eb4D7513a80F4841073/E9QPx9iKgPXPqEsAN-YklipSRJy9VTBMOLwwEcqqVpU)
+- Analyze [Data manifesto made during ETHRome](https://github.com/web3privacy/data/tree/main#readme)
+- Analyze pull requests by Railgun, Waku, Lit, Discreet, Webb [here](https://github.com/web3privacy/web3privacy/blob/main/Web3privacynowplatform/Brief.md)
+
+_Create_
+- Github page in this [repo](https://github.com/web3privacy/web3privacy/tree/main/Web3privacynowplatform/scoringmodel)
+- Enrich scoring model MVP with the new parameters that could be easily automated or quickly manually aggregated (if not - move data set into a backlog)
+- Try to apply previous scoring model approach (with % &/or semaphore take: green, yellow, red) to new parameters & observe how scoring model could change (write down potential changes & implications)
+
+_Delivery_
+- Final page will be breaken down into:
+  - Updated scoring model MVP data set (without scoring approach)
+  - Simulation of the old MVP + new MVP: how scoring could change with new parameters
+ - Backlog (data that can't be quickly parsed)
+
+# Backlog
+
 **Approach**:
 - we made a survey on how privacy projects recommend analysing if they are private or not.
 - answers were assembled into 1 Excel sheet to be further analysed