From 1a79dcadd28d760e96ad67e7244cce80b88072c8 Mon Sep 17 00:00:00 2001 From: Nubenetes Bot Date: Mon, 25 May 2026 15:56:26 +0200 Subject: [PATCH] feat: integrate Stanford CS229 LLM video and bump to 2.1.1 --- CHANGELOG.md | 5 +++++ data/inventory.yaml | 16 ++++++++++++++++ v2-docs/videos.md | 10 ++++++++++ 3 files changed, 31 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index e055d8eb..63f93081 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [[2.1.1]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.1) - 2026-05-25 + +### Added +- **Stanford CS229 LLM Session**: Integrated the Stanford CS229 lecture on "Building Large Language Models" into the V2 Elite Video Hub. The architectural summary focuses on LLM orchestration, alignment (DPO/RLHF), and systems optimization for 2026 AI infrastructure. + ## [[2.1.0]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.0) - 2026-05-25 ### Added diff --git a/data/inventory.yaml b/data/inventory.yaml index 294d0b70..048728ab 100644 --- a/data/inventory.yaml +++ b/data/inventory.yaml @@ -282186,6 +282186,22 @@ https://www.youtube.com/embed/6eBSHbLKuN0: v1_locations: [] video_order: 25 year: N/A +https://www.youtube.com/embed/9vM4p9NN0Ts: + ai_summary: | + This Stanford CS229 technical deep-dive deconstructs the transition from raw autoregressive language models to instruction-tuned assistants, focusing on the systems orchestration required for 2026 AI infrastructure. It explores critical patterns in tokenization (BPE/Sub-word), parameter-efficient fine-tuning (PEFT/LoRA), and the shift from RLHF to Direct Preference Optimization (DPO) to simplify model alignment pipelines. For cloud architects, the lecture provides a foundational framework for optimizing the "Compute-to-Token" ratio and managing memory constraints (KV Cache) in distributed distributed inference environments, while advocating for LLM-as-a-Judge automated evaluation loops for scalable model governance. + category: AI and Future Operations + description: Featured video in the Top Videos & Clips section. + health_score: 100.0 + is_enriched: true + is_featured_video: true + last_checked: 0.0 + stars: 0 + status: online + technology: LLM Architecture & Post-Training + title: 'Stanford CS229: Building Large Language Models (LLMs)' + v1_locations: [] + video_order: 26 + year: N/A https://www.youtube.com/kubernetescommunity: content_hash: 5654ed1b031bb525606fadef419e20044ee5e80a0915535c7ae4e75f0c909d9d health_score: 100.0 diff --git a/v2-docs/videos.md b/v2-docs/videos.md index b629b2bf..ab4030ae 100644 --- a/v2-docs/videos.md +++ b/v2-docs/videos.md @@ -62,6 +62,16 @@ Welcome to the **Agentic Video Hub**. This section presents a logical, architect +??? note "🎬 Stanford CS229 | Machine Learning | Building Large Language Models (LLMs) | `LLM Architecture & Post-Training`" + !!! info "Architectural Summary" + This Stanford CS229 technical deep-dive deconstructs the transition from raw autoregressive language models to instruction-tuned assistants, focusing on the systems orchestration required for 2026 AI infrastructure. It explores critical patterns in tokenization (BPE/Sub-word), parameter-efficient fine-tuning (PEFT/LoRA), and the shift from RLHF to Direct Preference Optimization (DPO) to simplify model alignment pipelines. For cloud architects, the lecture provides a foundational framework for optimizing the "Compute-to-Token" ratio and managing memory constraints (KV Cache) in distributed distributed inference environments, while advocating for LLM-as-a-Judge automated evaluation loops for scalable model governance. + +
+ + + +
+ ## Architecture and Cloud Strategy ??? note "🎬 Kubernetes for SysAdmins | Kelsey Hightower at PuppetConf | Talk & Demo | `Kubernetes`" !!! info "Architectural Summary"