add. page. basemin - overview

Author: svfcode

blog/config.yaml

@@ -11,7 +11,15 @@ permalinks:
 DefaultContentLanguage: ru
 
 markup:
+  tableOfContents:
+    startLevel: 2
+    endLevel: 4
+    ordered: false
   goldmark:
+    parser:
+      attribute:
+        block: true
+        title: true
     renderer:
       unsafe: true
     extensions:

blog/content/page/playlists/index.en.md

@@ -16,9 +16,15 @@ menu:
   - 🏷️ Terms: language model, dataset, parameters, AI/ML/NN, foundation models
   - 📊 Difficulty: basic
   - 📋 Prerequisites: none
+- **[AI basics – overview](/en/p/ai-basics-overview/)**
+  - 📋 Overview: what the «Bare minimum» series covers and how the material is organized
+  - 📊 Difficulty: basic
+  - 📋 Prerequisites: none
 
 ## 👥 On Their Shoulders
 
+> *Short digests from interviews, podcasts, and conferences on AI*
+
 - **[The Future of AI: Why Scaling Alone is No Longer Enough](/en/p/ai-future-beyond-scaling/)**
   - 🎬 [Video](https://youtu.be/5mUEOx3uLDc) · reading 12 min / video 8 min (original 52 min)
   - 👥 Participants: Nicholas Thompson, Eric Xing, Yoshua Bengio, Yuval Noah Harari, Yejin Choi
@@ -28,6 +34,8 @@ menu:
 
 ## 📐 Essential Mathematics
 
+> *Machine learning relies on mathematics that grows out of the school curriculum; to understand how ML works, you need a solid grasp of school-level math.*
+
 - **[Essential Mathematics — Overview](/en/p/math-essentials-overview/)**
   - 📋 Overview: which math areas are needed for AI/ML and why
   - 🏷️ Linear algebra, calculus, probability and statistics

blog/content/page/playlists/index.md

@@ -16,9 +16,15 @@ menu:
   - 🏷️ Термины: языковая модель, датасет, параметры, ИИ/МО/НС, фундаментальные модели
   - 📊 Сложность: базовая
   - 📋 Необходимые знания: нет
+- **[Базовый минимум про ИИ – обзор](/p/ai-basics-overview/)**
+  - 📋 Обзор: что входит в цикл «Базовый минимум» и как устроена подача материала
+  - 📊 Сложность: базовая
+  - 📋 Необходимые знания: нет
 
 ## 👥 На их плечах
 
+> *краткая выжимка из интервью, подкастов и конференций про ИИ*
+
 - **[Будущее ИИ: почему одного масштабирования уже недостаточно](/p/ai-future-beyond-scaling/)**
   - 🎬 [Видео](https://youtu.be/5mUEOx3uLDc) · чтение 12 мин / видео 8 мин (оригинал 52 мин)
   - 👥 Участники: Nicholas Thompson, Eric Xing, Yoshua Bengio, Yuval Noah Harari, Yejin Choi
@@ -28,6 +34,8 @@ menu:
 
 ## 📐 Необходимая математика
 
+> *В машинном обучении используется математика, которая опирается на школьную программу; чтобы понять, как устроено МО, нужно хорошо знать школьную математику.*
+
 - **[Необходимая математика — обзор](/p/math-essentials-overview/)**
   - 📋 Обзор: какие разделы математики нужны для ИИ/МО и зачем
   - 🏷️ Линейная алгебра, анализ, вероятность и статистика

blog/content/post/ai-basics-intro/index.en.md

@@ -7,6 +7,68 @@ tags:
   - Artificial Intelligence
   - Machine Learning
   - База
+image: cover.jpg
 ---
 
-Introduction to basic concepts of artificial intelligence. You can expand the content here.
+This is the first article in the “Bare minimum” series — a concise look at how AI works. Each piece will cover one idea or concept; I’ll try to keep them short and in order.
+
+We’ll start with common terms, then talk about prompt engineering, RAG systems, and agents that can handle complex multi-step tasks.
+
+**Video:** [Watch on YouTube](https://youtu.be/z9VBZn0XcVk)
+
+## What a language model is
+
+A language model is a system that, given what has already been said, predicts the next word. And so on in a loop:
+
+> The → The cat → The cat sat → The cat sat on → The cat sat on the → …
+
+Filling its own context with words it has just produced.
+
+## Where we run into them
+
+Any chatbot — whether it’s a support “assistant” that drives us crazy until we ask for a human, or “smart” chats like DeepSeek or ChatGPT — relies on a language model.
+
+The only difference between a support bot and a “smart” one from DeepSeek is scale: the scale of the dataset and the server capacity needed to process it.
+
+## Parameter scale
+
+That leads to **parameter scale**:
+
+- **1–7 billion** — models you can run on a local laptop
+- **Trillions** — models that need server clusters
+
+Models in the trillion-parameter range are called **foundation** models: they have absorbed not just statistics of word sequences, but knowledge encoded in language. Language is not only a medium for communication but also a record of collective experience.
+
+## AI, ML, and neural networks — what’s the difference
+
+**AI (artificial intelligence)** is the broadest term: systems and programs that behave “intelligently” — they solve tasks that usually need human intelligence (speech understanding, chess, image recognition, decision-making, and so on). AI can be built without machine learning (e.g. rules, expert systems).
+
+**ML (machine learning)** is a subset of AI: a way to build AI so the system learns from data instead of hand-written rules. The goal is to find patterns in examples (data) and use them for predictions or decisions. ML includes more than neural networks: decision trees, linear models, clustering, and others.
+
+**NNs (neural networks)** are a subset of ML: models inspired by neurons in the brain (layers, weights, activations). One of the most powerful ML tools, especially for images, text, and speech. **Deep learning** is ML with deep (many-layer) neural networks.
+
+The relationship: **AI ⊃ ML ⊃ NN** — neural networks are a kind of machine learning, and machine learning is one way to implement artificial intelligence.
+
+## Practice what you learned
+
+Short games help you check how well you’ve absorbed the material.
+
+Quiz:
+
+<div style="margin: 1.5em 0;">
+  <iframe
+    src="/games/ai-basics-quiz.html"
+    style="width: 100%; max-width: 672px; height: 540px; border: none; border-radius: 12px; display: block; margin: 0 auto; background: #0f1117;"
+    title="Quiz: test your AI basics"
+  ></iframe>
+</div>
+
+Sort the shelf — drag statements onto the right shelves (AI, ML, NN, parameter scale):
+
+<div style="margin: 1.5em 0;">
+  <iframe
+    src="/games/ai-basics-shelves.html"
+    style="width: 100%; max-width: 840px; height: 720px; border: none; border-radius: 12px; display: block; margin: 0 auto; background: #0f1117;"
+    title="Sort the shelf"
+  ></iframe>
+</div>

blog/content/post/ai-basics-intro/index.md

@@ -72,7 +72,3 @@ image: cover.jpg
     title="Разложи по полочкам"
   ></iframe>
 </div>
-
-## Дальше в цикле
-
-В следующих статьях разберём: как от простых моделей пришли к трансформерам, что такое токены и параметры, чем инструктивные и мультимодальные модели отличаются от «просто текстовых», и зачем это всё нужно.