Albert Tsai@Tsaialbert

Today, we are excited to introduce our project: a Developer Resource Translator powered by Meta's LLaMA. This tool allows developers around the world to easily translate technical documentation and resources into multiple languages, breaking down language barriers in developer communities. We all want to make our tools inclusive, especially Meta pays huge attention to making things accessible to everyone. But inclusivity in developer docs comes at a heavy cost of man hours. Thousands of Volunteers put so many man-hours into translating Llama docs, React docs, or any other open-source docs into non-English language. Maintaining the docs requires a huge effort. I run into this problem while contributing to We all want to make our tools inclusive, and Meta pays huge attention to ensuring accessibility for everyone. However, inclusivity in developer documentation comes at a significant cost in man-hours. Thousands of volunteers dedicate countless hours to translating Llama docs, React docs, and other open-source documentation into non-English languages. It requires substantial effort to maintain these documents. I personally encountered this issue while contributing to the https://github.com/javascript-tutorial/hi.javascript.info repository. I was translating into Hindi three years ago, but I didn't have enough bandwidth. With our tool, Developer Resource Translator, powered by Meta's Llama, users can easily translate any document into any language. We currently work with Markdown files and can directly pull files from your GitHub repository, providing users with the ability to make changes. https://github.com/javascript-tutorial/hi.javascript.info repo. I was translating into Hindi three years ago. With our tool, Developer Resource Translator AI powered by Meta's Llama, users can easily translate any document into any language. We currently work on Markdown files. We can directly pull files from your GitHub repo and provide users with the ability to make changes.

Personal CIA is a web application that generates customized market results based on your query. By allowing you to filter your target and choose a desired search timeframe, it provides more precise outcomes. To enhance user prompts, we used ChatGPT, which we then fed to AutoGPT for autonomous research. Users can track AutoGPT's progress and view results when the research is complete. If the search is successful, the dashboard displays the reach; otherwise, no results are shown. We chose this project to reduce the time and cost of conducting market research and provide more organized and well-researched results for better market understanding

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