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OSS2026-08-28 · source-backed
440 stars, created August 11, covering 12 sections from model APIs through structured output, RAG, evals, agent loops, LoRA versus fine-tuning, security, LLMOps and serving, plus three case studies and a capstone (GitHub). The stance is that you write the agent loop, RAG and evals from raw API calls first so you understand what the frameworks do before reaching for them. Everything runs on the free Groq tier with no credit card, GPU sections on Colab's free T4. MIT.
Each link below shares sources, entities, or timing with this story.
NVIDIA partners with Groq / Shared entities / Same source domain / Shared topic / Earlier coverage
Linked by a graph relationship (NVIDIA partners with Groq); both cover August, GitHub; reported by the same outlet (github.com).
LangChain released LangGraph / Shared entities / Same source domain / Earlier coverage
Linked by a graph relationship (LangChain released LangGraph); both cover GitHub, LangChain, MIT; reported by the same outlet (github.com).
NVIDIA partners with Groq / Shared entities / Shared topic / Earlier coverage
Linked by a graph relationship (NVIDIA partners with Groq); both cover August, GitHub; overlapping topics (august, credit).
Linked by a graph relationship (NVIDIA partners with Groq); both cover August, Groq; overlapping topics (agent, august).
NVIDIA partners with Groq / Shared entities / Same source domain / Earlier coverage
Linked by a graph relationship (NVIDIA partners with Groq); both cover APIs, GitHub; reported by the same outlet (github.com).
Linked by a graph relationship (NVIDIA partners with Groq); both cover August, GitHub; reported by the same outlet (github.com).
Linked by a graph relationship (NVIDIA partners with Groq); both cover August, GitHub; reported by the same outlet (github.com).
Linked by a graph relationship (NVIDIA partners with Groq); both cover GitHub, MIT; reported by the same outlet (github.com).