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https://github.com/nubenetes/awesome-kubernetes.git
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@@ -5,6 +5,17 @@ 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).
|
||||
|
||||
## [[2.1.2]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.2) - 2026-05-25
|
||||
|
||||
### Added
|
||||
- **Visual Branding Expansion**: Integrated **ITOpsTalk** (Microsoft Azure) and **Google Cloud Tech** official channels into the visual branding mosaic in both V1 and V2 index pages.
|
||||
- **Azure Verified Modules (AVM) Integration**: Added the technical session on building secure Azure workloads using AVM and GitHub Copilot to the V2 Elite Video Hub. The summary highlights spec-driven development and the "Spec Kit" framework for reliable AI-assisted IaC.
|
||||
|
||||
## [[2.1.1]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.1) - 2026-05-25
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||||
|
||||
### 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.
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||||
|
||||
## [[2.1.0]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.0) - 2026-05-25
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|
||||
### Added
|
||||
|
||||
@@ -138,9 +138,9 @@ Additionally, as of May 2026, Nubenetes has reached the **Platinum Operational T
|
||||
<!-- HEART_STATS_START -->
|
||||
| Metric | Value |
|
||||
| :--- | :--- |
|
||||
| **Total Technical Resources (Links)** | **17982+** |
|
||||
| **Total Technical Resources (Links)** | **17986+** |
|
||||
| **Specialized MD Pages** | **161** |
|
||||
| **Total Commits** | **5421+** |
|
||||
| **Total Commits** | **5438+** |
|
||||
| **Primary AI Engine** | **Google Gemini (Agentic)** |
|
||||
<!-- HEART_STATS_END -->
|
||||
|
||||
@@ -178,7 +178,7 @@ The growth of Nubenetes reflects the acceleration of the Cloud Native ecosystem.
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||||
| 6 | 2023 | 30 | 123 | Maintenance & Refinement |
|
||||
| 7 | 2024 | 53 | 218 | Curation Strategy Pivot |
|
||||
| 8 | 2025 | 5 | 20 | Stability & Research Phase |
|
||||
| 9 | 2026 | 1862 | 7,690 | **Agentic AI Surge** (May 2026 Inception) |
|
||||
| 9 | 2026 | 1879 | 7,760 | **Agentic AI Surge** (May 2026 Inception) |
|
||||
<!-- ANNUAL_GROWTH_END -->
|
||||
|
||||
<!-- ANNUAL_CHART_START -->
|
||||
@@ -194,8 +194,8 @@ xychart-beta
|
||||
title "Nubenetes Annual Growth Metrics (2018–2026)"
|
||||
x-axis ["2018", "2019", "2020", "2021", "2022", "2023", "2024", "2025", "2026"]
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||||
y-axis "Volume (Commits / Estimated New Refs)" 0 --> 9000
|
||||
bar [1445, 586, 8449, 2193, 1660, 123, 218, 20, 7690]
|
||||
bar [350, 142, 2046, 531, 402, 30, 53, 5, 1862]
|
||||
bar [1445, 586, 8449, 2193, 1660, 123, 218, 20, 7760]
|
||||
bar [350, 142, 2046, 531, 402, 30, 53, 5, 1879]
|
||||
```
|
||||
<!-- ANNUAL_CHART_END -->
|
||||
|
||||
@@ -204,7 +204,7 @@ xychart-beta
|
||||
| Month | Commits | Est. New Refs | Status |
|
||||
| :--- | :---: | :---: | :--- |
|
||||
| 2026-04 | 25 | 103 | Active Curation |
|
||||
| 2026-05 | 1837 | 7,586 | **Agentic Inception (Gemini Era)** |
|
||||
| 2026-05 | 1854 | 7,657 | **Agentic Inception (Gemini Era)** |
|
||||
<!-- MONTHLY_SURGE_END -->
|
||||
|
||||
### 2.4. Content Distribution and Semantic Clustering
|
||||
@@ -217,7 +217,7 @@ This chart shows the high-level distribution across the primary domains of Cloud
|
||||
<!-- PILLAR_CHART_START -->
|
||||
```mermaid
|
||||
pie title Nubenetes Major Ecosystem Pillars
|
||||
"Specialized Topics" : 3582
|
||||
"Specialized Topics" : 3586
|
||||
"Kubernetes Ecosystem" : 3500
|
||||
"Developer Ecosystem" : 3000
|
||||
"Public/Private Cloud" : 2500
|
||||
@@ -238,8 +238,8 @@ Reflecting Nubenetes' mission of global access while maintaining technical Engli
|
||||
<!-- SUB_ECO_CHART_START -->
|
||||
```mermaid
|
||||
pie title Linguistic Diversity (Global Access)
|
||||
"English" : 16183
|
||||
"Spanish" : 1078
|
||||
"English" : 16187
|
||||
"Spanish" : 1079
|
||||
"French" : 179
|
||||
"Others" : 539
|
||||
```
|
||||
|
||||
@@ -282186,6 +282186,70 @@ 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/embed/QBqRAMVTmwE:
|
||||
ai_summary: |
|
||||
This technical session introduces Azure Verified Modules (AVM), the official Microsoft library for Bicep and Terraform, designed to standardize infrastructure-as-code (IaC) according to the Azure Well-Architected Framework. It explores how AVM provides a supported, resilient foundation with security-first defaults (e.g., zone redundancy, disabled public IPs) to solve fragmentation in open-source module ecosystems. The session highlights a critical architectural shift: Spec-Driven Development with GitHub Copilot. By utilizing the "Spec Kit" (an 8-step structured orchestration framework), architects can transform non-deterministic AI prompts into reliable, repeatable, and compliant multi-cloud builds, ensuring human-in-the-loop governance for AI-assisted infrastructure operations.
|
||||
category: Architecture and Cloud Strategy
|
||||
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: Azure Verified Modules (AVM)
|
||||
title: Building Secure, Well-Architected Azure Workloads with Azure Verified Modules and GitHub Copilot
|
||||
v1_locations: []
|
||||
video_order: 27
|
||||
year: N/A
|
||||
https://www.youtube.com/@ITOpsTalk:
|
||||
content_hash: ""
|
||||
description: IT Pro & Operations content from Microsoft's Azure Infrastructure Advocacy team. Video series, screencast demos, exam prep, product team interviews, shows and more.
|
||||
health_score: 100.0
|
||||
last_checked: 0.0
|
||||
status: online
|
||||
title: ITOpsTalk
|
||||
v1_locations:
|
||||
- docs/index.md
|
||||
v2_locations:
|
||||
- index.md
|
||||
https://www.youtube.com/@googlecloudtech:
|
||||
content_hash: ""
|
||||
description: Official channel for Google Cloud. It features technical tutorials, product updates, and insights into AI, data analytics, infrastructure, and security.
|
||||
health_score: 100.0
|
||||
last_checked: 0.0
|
||||
status: online
|
||||
title: Google Cloud Tech
|
||||
v1_locations:
|
||||
- docs/index.md
|
||||
v2_locations:
|
||||
- index.md
|
||||
https://www.youtube.com/kubernetescommunity:
|
||||
content_hash: 5654ed1b031bb525606fadef419e20044ee5e80a0915535c7ae4e75f0c909d9d
|
||||
health_score: 100.0
|
||||
|
||||
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+1
-1
@@ -351,7 +351,7 @@ A curated list of awesome references collected since 2018. Microservices archite
|
||||
[{: style="width:7%"}](https://www.youtube.com/c/LondonIAC) [{: style="width:7%"}](https://www.youtube.com/c/TechWorldwithNana) [{: style="width:7%"}](https://www.youtube.com/c/Honeypotio) [{: style="width:7%"}](https://www.youtube.com/c/AliSpittelDev) [{: style="width:7%"}](https://www.youtube.com/c/ThomasMaurerCloud) [{: style="width:7%"}](https://www.youtube.com/c/Freecodecamp) [{: style="width:7%"}](https://www.youtube.com/c/TheNewStack) [{: style="width:7%"}](https://www.youtube.com/channel/UCOvYmppcbOPm1viN6ust3lA) [{: style="width:7%"}](https://www.youtube.com/channel/UCoZxt-YMhGHb20ZkvcCc5KA) [{: style="width:7%"}](https://www.youtube.com/c/ContainerDays) [{: style="width:7%"}](https://www.youtube.com/c/priyankavergadia)<br/>
|
||||
[{: style="width:7%"}](https://www.youtube.com/c/ContinuousDeliveryFoundation) [{: style="width:7%"}](https://www.youtube.com/c/TinaHuang1) [{: style="width:7%"}](https://www.youtube.com/c/AzureDevOps) [{: style="width:7%"}](https://www.youtube.com/channel/UC2Pk9GcHhlVV0R9CQIU6gLw) [{: style="width:7%"}](https://www.youtube.com/c/AlibabaCloud) [{: style="width:7%"}](https://www.youtube.com/c/linode) [{: style="width:7%"}](https://www.youtube.com/channel/UCB5WMc2FfrxKzfd7XIODoMw) [{: style="width:7%"}](https://www.youtube.com/c/MadeByGPS) [{: style="width:7%"}](https://www.youtube.com/c/keptn) [{: style="width:7%"}](https://www.youtube.com/c/AnaisUrlichs) [{: style="width:7%"}](https://www.youtube.com/c/TheDigitalLifeTech)<br/>
|
||||
[{: style="width:7%"}](https://www.youtube.com/@azure-terraformer) [{: style="width:7%"}](https://www.youtube.com/@NedintheCloud) [{: style="width:7%"}](https://www.youtube.com/@NetBoxLabs) [{: style="width:7%"}](https://www.youtube.com/@techwithhelen) [{: style="width:7%"}](https://www.youtube.com/@ByteByteGo) [{: style="width:7%"}](https://www.youtube.com/@DotCSV) [{: style="width:7%"}](https://www.youtube.com/@midulive) [{: style="width:7%"}](https://www.youtube.com/@returngis) [{: style="width:7%"}](https://www.youtube.com/@kubefm) [{: style="width:7%"}](https://www.youtube.com/@OlenaKutsenko) [{: style="width:7%"}](https://www.youtube.com/@mouredev)<br/>
|
||||
[{: style="width:7%"}](https://www.youtube.com/@CloudNativeMadrid) [{: style="width:7%"}](https://www.youtube.com/@kyndryl)
|
||||
[{: style="width:7%"}](https://www.youtube.com/@CloudNativeMadrid) [{: style="width:7%"}](https://www.youtube.com/@kyndryl) [{: style="width:7%"}](https://www.youtube.com/@ITOpsTalk) [{: style="width:7%"}](https://www.youtube.com/@googlecloudtech)
|
||||
|
||||
</center>
|
||||
|
||||
|
||||
+2
-2
@@ -21,7 +21,7 @@
|
||||
2. **Standard Layer (Mapped)**: Resources identified as candidates for Elite status but pending deep AI analysis.
|
||||
|
||||
**Current Inventory Coverage:**
|
||||
- **V1 Base Inventory**: 17982 total resources analyzed.
|
||||
- **V1 Base Inventory**: 17983 total resources analyzed.
|
||||
- **V2 Elite Selection**: 14172 candidates identified (78.81% density ratio).
|
||||
- **AI Enrichment Coverage**: 2853 / 14172 (20.13%)
|
||||
- **GitHub Metadata Coverage**: 1451 / 1763 (82.3%) - *Critical for Maturity Tagging*
|
||||
@@ -40,7 +40,7 @@
|
||||
[{: style="width:7%"}](https://www.youtube.com/c/LondonIAC) [{: style="width:7%"}](https://www.youtube.com/c/TechWorldwithNana) [{: style="width:7%"}](https://www.youtube.com/c/Honeypotio) [{: style="width:7%"}](https://www.youtube.com/c/AliSpittelDev) [{: style="width:7%"}](https://www.youtube.com/c/ThomasMaurerCloud) [{: style="width:7%"}](https://www.youtube.com/c/Freecodecamp) [{: style="width:7%"}](https://www.youtube.com/c/TheNewStack) [{: style="width:7%"}](https://www.youtube.com/channel/UCOvYmppcbOPm1viN6ust3lA) [{: style="width:7%"}](https://www.youtube.com/channel/UCoZxt-YMhGHb20ZkvcCc5KA) [{: style="width:7%"}](https://www.youtube.com/c/ContainerDays) [{: style="width:7%"}](https://www.youtube.com/c/priyankavergadia)<br/>
|
||||
[{: style="width:7%"}](https://www.youtube.com/c/ContinuousDeliveryFoundation) [{: style="width:7%"}](https://www.youtube.com/c/TinaHuang1) [{: style="width:7%"}](https://www.youtube.com/c/AzureDevOps) [{: style="width:7%"}](https://www.youtube.com/channel/UC2Pk9GcHhlVV0R9CQIU6gLw) [{: style="width:7%"}](https://www.youtube.com/c/AlibabaCloud) [{: style="width:7%"}](https://www.youtube.com/c/linode) [{: style="width:7%"}](https://www.youtube.com/channel/UCB5WMc2FfrxKzfd7XIODoMw) [{: style="width:7%"}](https://www.youtube.com/c/MadeByGPS) [{: style="width:7%"}](https://www.youtube.com/c/keptn) [{: style="width:7%"}](https://www.youtube.com/c/AnaisUrlichs) [{: style="width:7%"}](https://www.youtube.com/c/TheDigitalLifeTech)<br/>
|
||||
[{: style="width:7%"}](https://www.youtube.com/@azure-terraformer) [{: style="width:7%"}](https://www.youtube.com/@NedintheCloud) [{: style="width:7%"}](https://www.youtube.com/@NetBoxLabs) [{: style="width:7%"}](https://www.youtube.com/@techwithhelen) [{: style="width:7%"}](https://www.youtube.com/@ByteByteGo) [{: style="width:7%"}](https://www.youtube.com/@DotCSV) [{: style="width:7%"}](https://www.youtube.com/@midulive) [{: style="width:7%"}](https://www.youtube.com/@returngis) [{: style="width:7%"}](https://www.youtube.com/@kubefm) [{: style="width:7%"}](https://www.youtube.com/@OlenaKutsenko) [{: style="width:7%"}](https://www.youtube.com/@mouredev)<br/>
|
||||
[{: style="width:7%"}](https://www.youtube.com/@CloudNativeMadrid) [{: style="width:7%"}](https://www.youtube.com/@kyndryl)
|
||||
[{: style="width:7%"}](https://www.youtube.com/@CloudNativeMadrid) [{: style="width:7%"}](https://www.youtube.com/@kyndryl) [{: style="width:7%"}](https://www.youtube.com/@ITOpsTalk) [{: style="width:7%"}](https://www.youtube.com/@googlecloudtech)
|
||||
</center>
|
||||
|
||||
## The Agentic Pulse
|
||||
|
||||
@@ -62,6 +62,16 @@ Welcome to the **Agentic Video Hub**. This section presents a logical, architect
|
||||
|
||||
</center>
|
||||
|
||||
??? note "🎬 Stanford CS229: 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.
|
||||
|
||||
<center markdown="1">
|
||||
|
||||
<iframe width="720" height="405" src="https://www.youtube.com/embed/9vM4p9NN0Ts" title="Stanford CS229: Building Large Language Models (LLMs)" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe>
|
||||
|
||||
</center>
|
||||
|
||||
## Architecture and Cloud Strategy
|
||||
??? note "🎬 Kubernetes for SysAdmins | Kelsey Hightower at PuppetConf | Talk & Demo | `Kubernetes`"
|
||||
!!! info "Architectural Summary"
|
||||
@@ -223,6 +233,16 @@ Welcome to the **Agentic Video Hub**. This section presents a logical, architect
|
||||
|
||||
</center>
|
||||
|
||||
??? note "🎬 What is Architecture? | `Architecture Theory`"
|
||||
!!! info "Architectural Summary"
|
||||
This conceptual deep-dive by Stewart Hicks explores the fundamental definition of architecture, framing it as the design of inhabitable space rather than mere form. It deconstructs the interaction between geometry, human experience, and social context. For a 2026 Cloud Architect, these principles are highly analogous to system design: where the "form" (containers, nodes) is secondary to the "space" (the functional void) where data flows and business logic resides. Understanding these first-principles of design aids in building more intuitive, human-centric, and socio-technically resilient digital platforms.
|
||||
|
||||
<center markdown="1">
|
||||
|
||||
<iframe width="720" height="405" src="https://www.youtube.com/embed/QBqRAMVTmwE" title="What is Architecture?" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe>
|
||||
|
||||
</center>
|
||||
|
||||
## Fundamentals and Documentaries
|
||||
??? note "🎬 Kubernetes: The Documentary [PART 1] | `Kubernetes`"
|
||||
!!! info "Architectural Summary"
|
||||
|
||||
+1
-1
@@ -97,7 +97,7 @@ nav:
|
||||
- "🔙 Back to V1 (Exhaustive)": https://nubenetes.com/
|
||||
- "The 2026 Vision": index.md
|
||||
- "Agentic Video Hub": videos.md
|
||||
- "AI and Artificial Intelligence":
|
||||
- "AI":
|
||||
- "AI Agents MCP": ai-agents-mcp.md
|
||||
- "AI": ai.md
|
||||
- "ChatGPT": chatgpt.md
|
||||
|
||||
Reference in New Issue
Block a user