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Making the Python back end for my new webapp
An in-depth look at the technical challenges and solutions in creating the FastAPI backend for YouTubeTranscriptOptimizer.com, a powerful tool for transforming YouTube content into polished written documents and interactive quizzes.
Because the next stage of the pipeline is starting with nicely written text in full sentences, it's much easier for the model to focus on creating sensible section headings, deciding what should be shown as a numbered list or with bullet points, where to use bold or italics, etc. That means that in a fully async system, we can generate a ton of these requests and dispatch them at the same time using asyncio.gather() or similar with a semaphore and retry logic to avoid problems with rate limits, and process even a large input document shockingly quickly, as long as we are careful about preserving the order of the chunks and putting them back together correctly. Although I generally just go with Python because I'm fastest with it, and I have historically made web apps using FastAPI along with HTML templating libraries like Chameleon mixed with vanilla JS for dynamic client-side effects, I recently used NextJS for a small project for tracking whether anyone notable has starred or forked one of your GitHub repos (source available here.
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