thunderbird / thunderbird/thunderbird-android

Smart Email Summaries

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#8,453 5 comments 9 reactions 0 assignees View on GitHub

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type: enhancement
Dominant language
Kotlin
Stars
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Forks
2.8k
Avg merge
3d 3h
Merged PRs (30d)
57

Description

Checklist
  • I have used the search function to see if someone else has already submitted the same feature request.
  • I will describe the problem with as much detail as possible.
  • This issue only contains a request for one single feature, not multiple (related) features.
App version

8.0-b5

Problem you are trying to solve

Emails are messy, and the first few lines are a often very poor indicator of the contents of the message.
Unlike instant messaging, the content of the notification only serves as a prompt to read further, as opposed to an overview of the content

Compact and default view currrently do not provide a meaningful overview of the contents prior to opening the message.
Deciding on the value of the email currently requires clicking and reading all emails, making the inbox more of a to do list than a dashboard from which one can at a glance archive, keep or delete a message.

Suggested solution

Offer users the option to have an LLM parse the content of the email, and create a short digest, providing a more meaningful overview of the message.

Screenshots / Drawings / Technical details

The model chosen should be (in order of importance):

  1. fast: the model should quickly process incoming messages, allowing for the generated text to appear both in notifications and inbox, without unduely delaying notifications
  2. low-resources: the use of the LLM preview function should not unduely affect battery life
  3. lightweight: the model istelf should be reasonably small so as to not unduely impact the install size of thunderbird
  4. obedient: the model should follow instructions to ensure only a concise preview is returned
  5. accurate: this is last in the list, as the status quo of preview lines is often useless. anything reasonably relevant would be an improvement

There may be considerations,

  • how many of the stored emails to generate previews for
  • should a user be able to restrict function to different power-saving settings (charging, standard, battery saver)
  • should models be user loaded, or shipped with thunderbird
  • should the functionality be local only, or should there be support cloud and self hosted providers

For a minimum viable feature, there should be

  • user settings to enable swapping preview lines for digest lines
  • user setting to toggle/configure back processing of emails
  • user setting to enable swapping in notifications or only in dashboard
  • LLM backend to process an email into a digest

Risks involved:

  • Inaccuracies: Digest may be incorrect, causing the user to ignore import emails, or give urgency/legitimacy to unimportant or malicious emails
  • Feature creep: shipping this feature may create undesired focus on "AI" features, like LLM email co-writing, or LLM-reading, taking valuable focus and time away from valuable core issues
  • Ethics and public perception: "AI" is a currently divisive topic. Shipping such a feature may raise discussions on the ethics behind model training, and mozilla/thunderbird's inherent approval thereof
  • Future burden: Should legal issues be settled regarding the training or use of LLMs, there may be a requirement to remove or modify parts or all of this feature.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

No files, tests, or entry points are named. Start by scoping the proposed digest flow across inbox previews, notifications, settings, and background processing, then resolve the open choices around model hosting, power use, storage, and privacy. Done requires a defined MVP, accepted risks, and a testable implementation plan.

Written by the indexing model from the issue text.

Assessment

Tech stack
android, kotlin
Domain
ai, mobile
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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