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Fixing some Markdown issues

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Design.md
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@ -3,34 +3,34 @@ The purpose of this document is to align on the initial design of Reply-O-Meter.
## Overview
The following is a product-oriented view of all the stages that need to happen in the final V1 product:
1. *Data Ingestions*
*Data Ingestions*
Input: raw photos
Output: List of Artifacts that link the raw photo with a textual representation
a. Ingest all of the raw data files. Photos of letters, postcards, and photos.
b. Digitization: conversation of the data to a textual representation.
c. Normalization: translation of all material to one internal language (e.g. English)
d. Artifacts: creating Artifacts from joint raw material. Example: photos of person and the name/time from the photo's backside.
1. Ingest all of the raw data files. Photos of letters, postcards, and photos.
2. Digitization: conversation of the data to a textual representation.
3. Normalization: translation of all material to one internal language (e.g. English)
4. Artifacts: creating Artifacts from joint raw material. Example: photos of person and the name/time from the photo's backside.
2. *Data Processing*
*Data Processing*
Input: List of Artifacts
Output: Graph of Entities (Person, Location, and Event)
a. Metadata (move to Ingestino?): for each Artifact, extract the Metadata on the Entities that it refers to, such as Person, Event, and Location.
b. Reconciliation: Create and/or Update existing Entities based on the information from the new Artifacts.
1. Metadata (move to Ingestino?): for each Artifact, extract the Metadata on the Entities that it refers to, such as Person, Event, and Location
2. Reconciliation: Create and/or Update existing Entities based on the information from the new Artifacts.
3. *Browser*
*Browser*
Input: Graph of Entities
Output: Updates to Artifacts and/or Entities
a. Feedback: User may correct any of the Ingestion or Processing steps, which will retrigger the rest of the flow.
i. Will trigger model training/tuning if relevant.
1. Feedback: User may correct any of the Ingestion or Processing steps, which will retrigger the rest of the flow.
2. Tuning: Will trigger model training/tuning if relevant.
4. *Story Creator*
*Story Creator*
Input: Graph of Entities
Output: Story
a. Chat Agent: User can chat with an Author to create stories based on the known Entities.
1. Chat Agent: User can chat with an Author to create stories based on the known Entities.
5. *Graphic Artist*
*Graphic Artist*
Input: Story, Graph of Entities
Output: Graphic Novel
a. People: Given a style, create visual representation for each Person across their life.
b. Supporting Material: Gather maps of the Location and the relevant Events.
c. Create a graphic novel based on the story.
1. People: Given a style, create visual representation for each Person across their life.
2. Supporting Material: Gather maps of the Location and the relevant Events.
3. Create a graphic novel based on the story.

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README.md
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@ -1,3 +1,3 @@
# ReplyOMeter
Please read the [PR-FAQ][PR-FAQ.md] document first.
Please read the [PR-FAQ](PR-FAQ.md) document first.