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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 #15595 · 04/01/2026, 11:30 AM

#go#distribution_spec#helm#kubernetes#oci#oci_distribution#opencontainers#zot Zot is a lightweight, production-ready OCI-native container registry for storing images, Helm charts, SBOMs, and other artifacts without vendor lock-in. It offers built-in authentication (OIDC, LDAP), storage options (S3, Azure), scanning, caching to cut Docker Hub limits/latency, and ARM/edge support as a single binary. You benefit by easily self-hosting a secure, scalable alternative to Docker Hub, saving costs, boosting speed, and enabling secret-less workflows on any device. https://github.com/project-zot/zot