"To build trust, you have to show your work. We don't hide behind 'Magic Algorithms.' We use the best-in-class open source and API tools available today."
When you paste a YouTube link into TubeScripts, it feels like magic. You click a button, and 3 seconds later, you have a full transcript, a blog post, and a Twitter thread.
But it isn't magic. It's a carefully orchestrated symphony of three cutting-edge technologies working in parallel.
We believe that transparency builds authority. Whether you are a developer curious about our stack or a customer who wants to know their data is safe, here is exactly how TubeScripts works under the hood.
The Architecture: Speed & Security First
We faced a challenge: How do we process massive video files without making the user wait 10 minutes?
The answer was a Serverless Architecture. We don't have a dusty server room somewhere. We run entirely on the Edge network.
Our Tech Stack
Why this matters to you: By using Vercel Serverless Functions, your request is processed instantly by the server closest to your physical location. This ensures near-zero latency, whether you are in New York or Nairobi.
1. The Extraction Engine (Supadata)
The first step is getting the text. We partnered with Supadata, a specialized API that interacts directly with YouTube's internal data streams.
Instead of downloading the video file (which is slow and bandwidth-heavy), we extract the caption track data directly.
This allows us to "watch" a 2-hour podcast in approximately 1.5 seconds. We then normalize this data into a clean JSON format with timestamps, stripping out the "umms" and "ahhs" where possible.
2. The Brain (Groq & Llama 3)
Once we have the raw text, we need to make it useful. This is where TubeScripts shines.
Most AI tools use standard models that are slow and expensive. We use Groq, the world's fastest AI inference engine.
- The Hardware: Groq uses LPUs (Language Processing Units) instead of GPUs. They are designed specifically for text.
- The Model: We run Llama 3 (70B), an open-source model by Meta that rivals GPT-4 in reasoning capabilities but runs 10x faster.
When you click "Generate Blog Post," we aren't just asking the AI to summarize. We send a complex "System Prompt" that instructs the AI to act as a professional copywriter, ensuring the output matches the tone and structure of a human writer.
3. The Privacy Layer
Building in public means being honest about data.
Because we use a serverless architecture, we do not store your video content.
When you process a video, the data flows through our secure function, is processed by the AI, and is delivered to your browser. Once you close the tab, that data is gone from our active memory. We believe this "Ephemerality by Design" is the future of user privacy.
Why "Building in Public" Matters
We share our stack because we are confident in our product. In the SaaS world, trust is the ultimate currency.
By showing you the engine, we hope you see that TubeScripts isn't just another wrapper. It's a highly optimized tool built by engineers who care about speed, quality, and privacy.
We are constantly shipping updates. If you have a feature request or a technical question, we answer them personally. That's the difference between a faceless corporation and a founder-led tool.