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Source channel @githubtrending · Post #14845 · Jun 20

#jupyter_notebook#ai#artificial_intelligence#chatgpt#deep_learning#from_scratch#gpt#language_model#large_language_models#llm#machine_learning#python#pytorch#transformer You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4]. https://github.com/rasbt/LLMs-from-scratch

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@githubtrending · Post #14859 · 06/24/2025, 11:30 AM

#typescript#cli#clustering#concurrency#dependency_injection#effect#error_handling#javascript#observability#opentelemetry#platform#schema#typescript#workflows Effect is a powerful TypeScript framework that helps you build reliable and complex applications by managing side effects like logging, network calls, and database operations in a safe and organized way. It uses a core `Effect` type to describe workflows that are lazy, composable, and type-safe, allowing you to handle errors and dependencies explicitly. The framework is modular, with many packages for AI, CLI tools, distributed computing, SQL databases, and more, making it flexible for various needs. Using Effect improves code quality, concurrency handling, and maintainability, helping you write robust TypeScript apps efficiently[1][2][4][5]. https://github.com/Effect-TS/effect