Saving a Tweet as a PDF: Overcoming CORS, Tokens, and Unicode Hurdles
A developer shares the technical challenges of turning X posts into clean PDFs, tackling browser CORS limits, token generation, and font rendering issues.
Stock photo for illustration only, not from the actual event
- Fetching tweets directly from the browser fails due to strict CORS policies.
- Server-side fetching is required, using tokens derived from tweet IDs.
- Unicode and emoji font limits in PDFs are solved via browser canvas rendering.
- Engagement metrics display only available real data to maintain accuracy.
Building a simple tool to paste a Twitter or X post link and receive a clean, complete PDF containing text, author info, and images without stitching screenshots together sounds like a quick afternoon project. In reality, it turned into a series of stubborn technical hurdles.
The obvious first attempt involved fetching tweets directly from the syndication endpoint used by X's own embed widgets. However, the Access-Control-Allow-Origin header strictly permits requests only from X's embed origin, causing browser CORS blocks. Older conversation endpoints now return empty bodies, and the official API requires a paid key.
Because CORS only applies to browsers, server-to-server communication bypasses the restriction entirely. Utilizing a WordPress stack, the developer set up a small REST endpoint where the browser queries the internal server, which in turn fetches data from X.
Cross-Origin Resource Sharing CORS is a fundamental browser security mechanism designed to prevent unauthorized cross-site data access. Utilizing a backend server proxy is a standard industry pattern to securely bridge client applications with restricted external APIs.
The endpoint requires a token query parameter derived from the tweet ID, computed using formulas mirrored from X's client-side embedding logic to successfully authorize data retrieval.
Stock photo for illustration only, not from the actual event
Handling images introduced another challenge, which was solved by having the server fetch image bytes and return them as base64 data URLs, completely eliminating cross-origin security errors.
Typography proved to be the toughest wall. Using jsPDF worked well until encountering characters outside Latin-1, such as Devanagari, Arabic, CJK scripts, or emojis, which rendered as broken characters or empty boxes. The workaround that actually succeeded involved rendering text using the browser's native canvas—which already supports system fonts and emojis—and dropping it into the PDF as a crisp image.
Regarding engagement counts, the public endpoint supplies genuine like and reply totals, but omits reposts and views. To remain honest and clean, the tool displays strictly available metrics rather than fabricating data points.
Source: Dev.to
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