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Put technical notes on the website. (#177)
Publish technical notes on the website.
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‎docs/README.md‎

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## To Add A Banner At the Top Of A Page use
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```docsy
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```docsy {"id":"01J49N8FNVE9SFP3DPTM3F52A1"}
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{{% pageinfo %}}
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This is a placeholder page that shows you how to use this template site.
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{{% /pageinfo %}}
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```
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```sh {"id":"01J49N9KR89D85Z84YDEVAC563"}
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To run hugo locally
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```
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```sh {"id":"01J49NA03YRB6QCR5CGWN31V0K"}
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hugo serve -D
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```

‎docs/content/en/docs/blog/_index.md‎

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---
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title: Blog
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menu: {main: {weight: 30}}
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weight: 30
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---
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Blogs related to Foyle.

‎docs/content/en/docs/tech-notes/_index.md‎

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---
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description: Technical Notes About Foyle
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title: Tech Notes
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weight: 40
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---
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# Objective:
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**Describe** how we want to use technical notes and establish guidelines on authoring technical notes (TNs) for consistency.
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# Why TechNotes?
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We use TNs communicate key ideas, topics, practices, and decisions for the project. TNs provide a historical record with minimal burden on the authors or readers. We will use TNs in Foyle to document our team engineering practices and technical “stakes-in-the-ground” in a “bite-sized” manner. Use a TN instead of email to communicate important/durable decisions or practices. Follow this up with an email notification to the team when the document is published.
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TNs are not meant to replace documents such as User Guides or Detailed Design documents. However, many aspects of such larger docs can be discussed and formalized using a TN to feed into the authoring of these larger docs.
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## Guidelines
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1. All TNs have a unique ID that TNxxx where xxx is a sequential number
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1. Keep TNs brief, clear and to the point.
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1. Primary author is listed as owner. Secondary authors are recognized as co-creators.
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1. Allow contributors to add themselves in the contributors section.
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1. TNs are considered immutable once they are published. They should be deprecated or superceded by new TNs. This provides a historical timeline and prevents guesswork on whether a TN is still relevant.
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1. Only mark a TN published when ready (and approved, if there is an approver).
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1. If a TN is deprecated mark it so. If there is a newer version that supersedes it, put the new doc in the front matter.
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Happy Tech-Noting

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tech_notes/tn001_logging.md renamed to docs/content/en/docs/tech-notes/tn001_logging.md

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# Logging
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---
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title: TN001 Logging
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description: Design logging to support capturing human feedback.
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weight: 1
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author: "[Jeremy Lewi](https://lewi.us/about)"
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date: 2024-04-10
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---
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* **Author**: Jeremy Lewi
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* **Last Updated**: 2024-04-10
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* **Status**: Being Drafted
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## Objective
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One of the key goals of Foyle is to collect human feedback to improve the quality of the AI.
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The key interaction that we want to capture is as follows
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*. User asks the AI to generate one or more commands
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*. User edits those commands
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*. User executes those commands
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* User asks the AI to generate one or more commands
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* User edits those commands
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* User executes those commands
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In particular, we want to be able to link the commands that the AI generated to the commands that the user ultimately
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executes. We can do this as follows
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*. When the AI generates any blocks it attaches a unique block id to each block
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*. When the frontend makes a request to the backend, the request includes the block ids of the blocks
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*. The blockid can then be used as a join key to link what the AI produced with what a human ultimately executed
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* When the AI generates any blocks it attaches a unique block id to each block
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* When the frontend makes a request to the backend, the request includes the block ids of the blocks
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* The blockid can then be used as a join key to link what the AI produced with what a human ultimately executed
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## Implementation
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tech_notes/tn002_learning.md renamed to docs/content/en/docs/tech-notes/tn002_learning.md

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# Learning from human feedback
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* **Author**: Jeremy Lewi
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* **Last Updated**: 2024-04-23
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* **Status**: Publish
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---
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title: TN002 Learning from Human Feedback
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description: Allow Foyle to learn from human feedback.
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weight: 2
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author: "[Jeremy Lewi](https://lewi.us/about)"
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date: 2024-04-23
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status: published
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---
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## Objective
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tech_notes/tn003_learning_eval.md renamed to docs/content/en/docs/tech-notes/tn003_learning_eval.md

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---
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runme:
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id: 01HWP5TGX8T7WR6FDJNH0T5MAY
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version: v3
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title: TN003 Learning Evaluation
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description: Measure the efficacy of the learning system.
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weight: 3
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author: "[Jeremy Lewi](https://lewi.us/about)"
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date: 2024-04-27
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status: published
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---
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# Learning Evaluation
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* **Author**: Jeremy Lewi
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* **Last Updated**: 2024-04-27
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* **Status**: Published
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## Objective
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Measure the efficacy of the learning system.
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## TL;DR
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The key hypothesis of Foyle is that by using implicit human feedback, we can create an AI that automatically learns
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about your infrastructure. In [TN002_Learning](tn002_learning) and [PR#83](https://github.com/jlewi/foyle/pull/83)
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about your infrastructure. In [TN002_Learning](../tn002_learning/) and [PR#83](https://github.com/jlewi/foyle/pull/83)
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we implemented a very simple learning mechanism based on query dependent few shot prompting. The next step is to
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quantiatively evaluate the effectiveness of this system. We'd like to construct an evaluation data set that consists of
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queries whose answers depend on private knowledge of your infrastructure. We'd like to compare the performance of

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# Integration With Runme.Dev
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* **Author**: Jeremy Lewi
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* **Last Updated**: 2024-05-07
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* **Status**: Published
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---
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title: TN004 Integration with Runme.Dev
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description: Integrate Foyle's AI capabilities with runme.dev.
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weight: 4
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author: "[Jeremy Lewi](https://lewi.us/about)"
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date: 2024-05-07
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status: published
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---
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## Objective
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