TolKiEm — Emotional Filter 作者: João Morais
Filters search results and social media by emotional intent. Promotes tolerance, kindness and empathy.
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扩展元数据
关于此扩展
TolKiEm is a free, non-profit browser extension that filters search results and social media feeds by emotional intent — not by topic, but by what the content leaves in the reader.
Every result is classified as Positive, Neutral or Negative based on a philosophical framework that asks one central question: does this content move the reader toward greater openness, awareness and empathy — or toward fear, division and resentment?
How it works
TolKiEm analyses the title and description of each result using an AI model trained on the DeRose table of constructive vs. destructive emotions, the Three Filters of Socrates (Truth, Goodness, Utility), and a persona that combines ethical, scientific and probabilistic thinking.
Positive results are highlighted — content that illuminates, humanises, builds bridges, informs with integrity.
Neutral results are shown as-is — factual, encyclopaedic, informative without transformation.
Negative results are dimmed — content that inflames, divides, dehumanises, or manipulates through fear and outrage.
On Google and Bing, TolKiEm also injects a box at the top of the page with positive results from its own search engine (tolkiem.org) — showing what a conscious, emotionally-aware search would surface for the same query.
Supported platforms
Google · Bing · DuckDuckGo · YouTube · TikTok · Reddit
Privacy
TolKiEm collects no personal data, no browsing history, no cookies. The classification cache is stored locally on your device. No tracking. No advertising. Ever.
About the project
TolKiEm (Tolerance · Kindness · Empathy) is a non-profit project inspired by J.R.R. Tolkien's vision of radically different peoples learning to coexist — and by the ethical philosophy of Professor DeRose, who authorised the use of his work in this project.
The name reveals itself in the capitals: Tolerance · Kindness · Empathy.
Every result is classified as Positive, Neutral or Negative based on a philosophical framework that asks one central question: does this content move the reader toward greater openness, awareness and empathy — or toward fear, division and resentment?
How it works
TolKiEm analyses the title and description of each result using an AI model trained on the DeRose table of constructive vs. destructive emotions, the Three Filters of Socrates (Truth, Goodness, Utility), and a persona that combines ethical, scientific and probabilistic thinking.
Positive results are highlighted — content that illuminates, humanises, builds bridges, informs with integrity.
Neutral results are shown as-is — factual, encyclopaedic, informative without transformation.
Negative results are dimmed — content that inflames, divides, dehumanises, or manipulates through fear and outrage.
On Google and Bing, TolKiEm also injects a box at the top of the page with positive results from its own search engine (tolkiem.org) — showing what a conscious, emotionally-aware search would surface for the same query.
Supported platforms
Google · Bing · DuckDuckGo · YouTube · TikTok · Reddit
Privacy
TolKiEm collects no personal data, no browsing history, no cookies. The classification cache is stored locally on your device. No tracking. No advertising. Ever.
About the project
TolKiEm (Tolerance · Kindness · Empathy) is a non-profit project inspired by J.R.R. Tolkien's vision of radically different peoples learning to coexist — and by the ethical philosophy of Professor DeRose, who authorised the use of his work in this project.
The name reveals itself in the capitals: Tolerance · Kindness · Empathy.
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权限与数据
必要权限:
- 访问您在 tolkiem.org 的数据
- 访问您在 www.google.com 的数据
- 访问您在 www.google.pt 的数据
- 访问您在 duckduckgo.com 的数据
- 访问您在 www.bing.com 的数据
- 访问您在 www.youtube.com 的数据
- 访问您在 www.tiktok.com 的数据
- 访问您在 twitter.com 的数据
- 访问您在 x.com 的数据
- 访问您在 www.facebook.com 的数据
- 访问您在 www.reddit.com 的数据
收集的数据:
- 开发者称此扩展无需收集数据。
更多信息
- 附加组件链接
- 版本
- 1.3.1
- 大小
- 22.23 KB
- 上次更新
- 1 个月前 (2026年4月25日)
- 隐私政策
- 阅读此附加组件的隐私政策
- 版本历史
- 添加到收藏集