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https://github.com/alifeee/blog
HTML blog
https://github.com/alifeee/blog
alpinejs anchorjs blog giscus google-fonts zero-md
Last synced: 1 day ago
JSON representation
HTML blog
- Host: GitHub
- URL: https://github.com/alifeee/blog
- Owner: alifeee
- Created: 2023-04-24T11:31:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-17T16:51:42.000Z (6 days ago)
- Last Synced: 2024-12-20T12:40:57.373Z (3 days ago)
- Topics: alpinejs, anchorjs, blog, giscus, google-fonts, zero-md
- Language: Jupyter Notebook
- Homepage: https://blog.alifeee.co.uk/
- Size: 203 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# alifeee's blog
This repo contains blog stuff that I write, as well as any code used to generate it. It can be found on [`blog.alifeee.co.uk`](https://blog.alifeee.co.uk)
## Markdown
Markdown parsing is done using [`markedjs`](https://marked.js.org/). Install and use it using:
```bash
# install
npm install -g marked
# use
marked file.md
```
...then I put the generated HTML into the file manually or use [a script](./2024/09/sellotape-dispenser/build.sh).
To add anchor link IDs, add the [`marked-gfm-heading-id`](https://github.com/markedjs/marked-gfm-heading-id) extension using:
```bash
npm install -g marked-gfm-heading-id
# add heading ID extension
# import { gfmHeadingId } from "marked-gfm-heading-id";
# marked.use(gfmHeadingId());
# to add to CLI:
sed -i '/import { marked } from /a import { gfmHeadingId } from "marked-gfm-heading-id";\nmarked.use(gfmHeadingId());' /usr/alifeee/.nvm/versions/node/v20.18.0/lib/node_modules/marked/bin/main.js
```
## RSS Feed
An RSS feed is generated using [Python](./utilities.py/rss_feed.py) via [workflow](./.github/workflows/rss.yml). You can find (and subscribe to it) here: .
## Similar posts
After every blog post there are a few "similar posts". The HTML for this section is automatically generated by the [similar_posts.yml workflow](./.github/workflows/similar_posts.yml) using the [embeddings.py](./utilities.py/embeddings.py) Python script. The way this is done is inspired by [Tom Hazledine's](https://tomhazledine.com) post: [TomBot2000: automatically generating related posts using LLMs](https://tomhazledine.com/llm-related-posts/). It uses OpenAI to generate a text embedding for each post and ranks them by similarity. The recommendations should update as new blog posts are posted.
