PTENES
TRACK 1

🧠 Fundamentals

Before installing anything: why giving Claude Code memory changes the game, what a knowledge graph is (no mystery), what Graphify does, and why Obsidian is the right home for all of it. Each term is defined the first time it appears.

4
Modules
24
Topics
~3h
Duration
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doc.md guia.pdf README Source Knowledge graph node node node node node god node community (Leiden) Claudeconsult the map

↑ The track’s path in one image: documents become entities linked by relationships, grouped into communities, with a god node in the center—and Claude Code consults this map instead of rereading everything.

Trail map

Detailed content

1.1~40 min

🧠 Why Claude Code needs a second brain

The difference between a session that forgets everything and an agent with searchable external memory.

What it is:

Claude Code is a agent of AI that runs in your terminal—it reads files, edits code, and runs commands for you. “Memory” here means everything it can keep in mind to respond: what’s in the current conversation plus whatever you point it to.

Why learn:

Without external memory, each session starts from scratch on large projects. Understanding what it "remembers" is the first step toward giving it a persistent brain.

Key concepts:

Agent · session · context · external memory.

What it is:

A context window is how much text the model can read at once (measured in tokens — word fragments). It’s large, but finite: an entire repository rarely fits.

Why learn:

When the content won’t fit, the agent needs to choose what to read. A knowledge map helps it choose well instead of scanning everything blindly.

Key concepts:

Token · context window · cost · context selection.

What it is:

grep is a literal search for a word in the files. It finds where the term appears, but doesn't know what it relates with what or why.

Why learn:

That’s exactly the gap the graph fills: it stores the connections and the meaning, not just the text's location.

Key concepts:

Literal vs. semantic search · relationship · "map, not index".

What it is:

A "second brain" is an organized external knowledge base that you (or an agent) consult. Here, it's the graph + vault that hold a project's understanding.

Why learn:

It’s the course’s central idea: take knowledge out of the ephemeral session and put it somewhere persistent and navigable.

Key concepts:

PKM · external memory · persistence · searchable knowledge base.

What it is:

Claude Code already reads a file CLAUDE.md with instructions and its own memory. It’s great for rules, but it isn’t a map of an entire repository.

Why learn:

To find out where the native tool ends and Graphify+Obsidian begins — they complement each other; they don't compete.

Key concepts:

CLAUDE.md · native memory · limits · complement.

What it is:

Graphify creates the map; Obsidian stores it as navigable Markdown; Claude Code queries it in your project context. Together = a second brain.

Why learn:

It's the roadmap for the entire course. Knowing the destination makes each practical step that follows make sense.

Key concepts:

Stack · map → vault → agent · project context.

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1.2~45 min

🕸️ Knowledge graph, demystified

Nodes, edges, communities, and god nodes — the vocabulary that makes the rest of the course obvious.

What it is:

A graph are points (nodes) connected by lines (edges). Think of subway stations (nodes) and tracks (edges) between them.

Why learn:

It’s the structure Graphify produces. Without this mental picture, the rest becomes jargon.

Key concepts:

Node · edge · neighborhood · degree (number of connections).

What it is:

In Graphify, the nodes are entities (an idea, function, person) and the edges are relationships typed. Each one carries provenance: which file it came from.

Why learn:

The video says "concepts/connections"; the tool says "entities/relations". Knowing both terms avoids confusion in practice.

Key concepts:

Entity · typed relationship · provenance · reconciliation (merging aliases).

What it is:

A community is a group of highly connected nodes. The Leiden is the algorithm that finds these groups automatically.

Why learn:

Communities become the project’s “themes”—and, in Obsidian, named groups on the canvas. That’s how the graph becomes navigable.

Key concepts:

Community · clustering · Leiden · theme/topic.

What it is:

A god node is the most connected entity — the hub that touches many others. Example: "Context Window" pulling in dozens of related concepts.

Why learn:

These are the best starting points for asking the agent — and GRAPH_REPORT.md lists them for you.

Key concepts:

God node · high degree · hub · entry point.

What it is:

RAG is "searching for passages and giving them to the model." GraphRAG does this for a graph: beyond the passages, it provides the relationships between them.

Why learn:

It’s the difference between giving the agent a stack of pages and giving it an annotated map. A map gets you faster, better answers.

Key concepts:

RAG · GraphRAG · retrieval · structured context.

What it is:

With the map, Claude Code knows what's close to what. Ask about subagents → it finds "agent teams," "separate context," and so on, without scanning everything.

Why learn:

It’s the selling point of the entire stack — better answers in large repositories.

Key concepts:

Neighborhood · navigation · contextual answers · efficiency.

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1.3~40 min

⚙️ Graphify under the hood

What the tool does, how it extracts knowledge, what it produces—and the limitation Obsidian solves.

What it is:

Graphify is a CLI (terminal program, the package graphifyy) that also installs a skill (an instruction file) inside Claude Code.

Why learn:

The two sides do different things: the skill (/graphify) has Obsidian mode; the headless CLI has extract/query. You'll use both.

Key concepts:

CLI · skill · graphifyy · slash command.

What it is:

For code, Graphify reads the structure (the AST, the code “tree,” via tree-sitter)—fast and without an API key. For documents, use a LLM to extract the meaning.

Why learn:

Decide whether you need an API key and how "literal" or "semantic" the extraction will be.

Key concepts:

AST · tree-sitter · semantic extraction · LLM.

What it is:

Everything goes into one folder graphify-out/: o graph.json (the truth), the graph.html (interactive visual) and the GRAPH_REPORT.md (audit with suggested questions).

Why learn:

Knowing which file is which prevents panic on the first run and shows you where to explore.

Key concepts:

graph.json · graph.html · GRAPH_REPORT.md · cache.

What it is:

Graphify points both to a code base how much for one document corpus (PDFs, markdown, etc.). In the course, we use documents: the Claude Code docs.

Why learn:

The choice changes the type of graph and whether you take it to Obsidian or keep it in Graphify itself (the subject of Track 3).

Key concepts:

Code base · corpus · markdown · PDF.

What it is:

The skill has --obsidian (one Markdown file per node with backlinks + one canvas) and --wiki (articles by community, Wikipedia-style).

Why learn:

It's the video's secret sauce: turning the graph into a vault that Obsidian (and the agent) understands.

Key concepts:

--obsidian · --wiki · graph.canvas · backlink.

What it is:

On its own, the Graphify graph exists in isolation — it only knows about that corpus. It doesn’t connect to the rest of what you store.

Why learn:

That’s exactly why we bring it into Obsidian: to fit this knowledge into the broader context of your project.

Key concepts:

Silo · vacuum · broader context · integration.

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1.4~35 min

🔮 Obsidian, the vault that becomes memory

What a vault is, why Markdown works well with the agent, and how graph.json becomes a navigable brain.

What it is:

O Obsidian is a note-taking app. A vault is simply a folder of files markdown (plain text with formatting) that it opens.

Why learn:

Since it’s just a folder of files, Claude Code can read everything too—there’s no proprietary format in the way.

Key concepts:

Obsidian · vault · markdown · local folder.

What it is:

A wikilink é [[nome-da-nota]]: links one note to another. Obsidian creates the backlinks (who points here) by itself.

Why learn:

This is how the graph’s edges become navigable links. Each Graphify relationship becomes a [[...]].

Key concepts:

Wikilink · backlink · note · bidirectional link.

What it is:

Obsidian has a "graph view"—but it's only a drawing of the links between markdown notes. It's not Graphify's knowledge graph; it's a visual representation of it.

Why learn:

The video warns about this: "it’s not exactly a knowledge graph, it’s a bunch of connected markdown files". Knowing the difference prevents false expectations.

Key concepts:

Graph view · note links · representation · ≠ original graph.

What it is:

Markdown is plain text. The agent reads it, runs grep, follows the [[links]] — no binary layer in between.

Why learn:

That's why the vault is the ideal home: the same file works for you to read in Obsidian and for Claude Code to consult.

Key concepts:

Plain text · human- and machine-readable · no lock-in.

What it is:

O Canvas is a visual canvas; the plugins extend Obsidian. Graphify generates a graph.canvas with the communities already grouped.

Why learn:

Many people want the stack just for Obsidian's infrastructure (UI, add-ons). Knowing what's available helps you decide.

Key concepts:

Canvas · plugin · UI · add-ons.

What it is:

The Obsidian export reads the graph.json and writes one Markdown file per node. It’s the bridge between the graph world and the notes world.

Why learn:

Completes Track 1: you understand the two shores and the bridge. Track 2 crosses that bridge in practice.

Key concepts:

graph.json → .md · bridge · regeneration · practical next step.

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