Learn how a non-engineer built a custom, high-quality website translation service using an AI-native code editor and GitHub Actions. This workflow replaces expensive localization software and agencies, saving over $140,000 annually while improving translation quality and speed.
with Claire Vo
Using an AI-native code editor like Cursor, build a small, custom server. This approach allows marketers and other non-engineers to build the first 90% of a functional tool before involving an engineering team for final polish and deployment.
Create a GitHub Action that automatically triggers whenever a developer changes or adds a text string in the website's codebase. This action will initiate the translation process.
For each target language, create a dedicated prompt file. This file should contain brand guidelines, a glossary of specific terms that should not be translated, and stylistic nuances to ensure the translation is context-aware and on-brand.
The GitHub Action sends the new text string and the corresponding language prompt file to an LLM via your custom server. The LLM returns the translated text, which is then automatically sent back to the codebase as a new commit.
For content managed in a CMS, add a 'Translate' button to the interface. Hook this button up to the same custom translation service to provide instant, on-demand translations for marketing content.
Follow Sahil Lavingia's method for delegating small but impactful maintenance tasks, like fixing an internal Slack report, to the AI agent Devin. This workflow shows how to use simple, clear prompts to fix code and update system prompts without distracting your engineering team.
Learn Gumroad CEO Sahil Lavingia's process for rapidly prototyping a UI with v0, then using the AI agent Devin to autonomously code, implement, and submit the feature for review, turning a multi-day task into a one-hour workflow.
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