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Learn how to go from toy models to scalable AI solutions that serve real users.
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Most developers think neural networks require Python and heavy libraries. Not true. With TensorFlow.js, you can train and deploy models right in the browser. In this post, I’ll walk you through building a basic image classifier using your webcam — no server, no Python, just JavaScript.
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Choosing the right inference format is critical. Should you convert your PyTorch model to ONNX, or rewrite it using TensorFlow.js? I benchmarked both using real-world web apps. Spoiler: there's a clear winner for latency, and a different one for ecosystem support.
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Developers are shifting to JS-first prototyping for AI. Why? Fewer dependencies, faster iteration, and seamless integration into fullstack apps. This post explores how to design a training pipeline using Node.js, GPU acceleration with WebGL, and serverless deployments via Vercel or Cloudflare Workers.
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We combine modern AI principles with deep software craftsmanship. You don’t get generic advice — you get hands-on, battle-tested insights based on real production environments.
Unlike marketing-driven blogs, CodexMind is built by a coder for coders. Our stack-first approach means you'll understand not just what to use, but why — from JavaScript-based training loops to GPU-optimized inference strategies.
We focus on ethics, performance, and maintainability — building AI that works not only in demos, but in real apps.
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This blog saved us weeks of dev time. The TensorFlow.js tips are pure gold.
Finally, an AI blog that doesn’t assume you have a PhD in math. Clean and practical.
As a front-end dev, I was afraid of neural nets. CodexMind made them feel like a puzzle I can solve.
I subscribed to the paid plan just to support the work. Worth every cent.