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feat: insert modality hub tables across 94 project READMEs and concept docs
Add "Learn Your Way" navigation tables to all 34 concept docs, 15 level-0 projects, 15 level-1 projects, 15 level-2 projects, and 15 level-10 projects. Each table links available modalities (concept, project, walkthrough, quiz, flashcards, diagram, browser) with em-dash for missing content. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎concepts/api-basics.md‎

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An **API** (Application Programming Interface) is a way for programs to talk to each other. When people say "API" in web development, they usually mean a web API — a server that accepts HTTP requests and returns data (usually JSON).
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<!-- modality-hub-start -->
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### Learn Your Way
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| Read | Build | Watch | Test | Review | Visualize |
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|:---: | :---: | :---: | :---: | :---: | :---:|
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/api-basics-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Visualize It
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See how Python works with JSON data, the format APIs use to communicate:

‎concepts/args-kwargs-explained.md‎

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`*args` and `**kwargs` let a function accept any number of arguments. `*args` collects extra positional arguments into a tuple. `**kwargs` collects extra keyword arguments into a dictionary. Together, they make functions flexible without requiring a fixed parameter list.
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### Learn Your Way
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| Read | Build | Watch | Test | Review | Visualize |
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/args-kwargs-explained-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Why This Matters
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You will see `*args` and `**kwargs` in almost every Python library. Understanding them is essential for writing decorators, creating wrapper functions, and understanding how frameworks like Flask and pytest work under the hood. They also appear in function signatures on docs.python.org — you need to read them confidently.

‎concepts/async-explained.md‎

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Async lets your program do other work while waiting for slow operations (network requests, file reads, database queries).
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### Learn Your Way
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| Read | Build | Watch | Test | Review | Visualize |
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/async-explained-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Visualize It
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See how `async` and `await` allow coroutines to run concurrently:

‎concepts/classes-and-objects.md‎

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> **Try This First:** Before reading, try this in Python: `class Dog: pass` then `my_dog = Dog()` then `print(type(my_dog))`. You just created a class and made an object from it.
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### Learn Your Way
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| Read | Build | Watch | Test | Review | Visualize |
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/classes-and-objects-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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A **class** is a blueprint for creating things. An **object** is a thing created from that blueprint.
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## Visualize It

‎concepts/collections-deep-dive.md‎

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Python's `collections` module provides specialized container types that go beyond the built-in `list`, `dict`, and `set`. They solve common patterns like counting items, creating lightweight objects, and handling missing dictionary keys — all with less code and better performance than rolling your own solution.
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### Learn Your Way
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| Read | Build | Watch | Test | Review | Visualize |
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/collections-deep-dive-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Why This Matters
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Every program needs to store and organize data. The built-in types handle most cases, but they have gaps. Need to count how often each word appears? `Counter`. Need a dict that automatically handles missing keys? `defaultdict`. Need a lightweight immutable object with named fields? `namedtuple`. Learning these tools saves you from writing (and debugging) boilerplate code.

‎concepts/collections-explained.md‎

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> **Try This First:** Before reading, try this in Python: `fruits = ['apple', 'banana']; fruits.append('cherry'); print(fruits)`. What does the list look like after `append`?
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### Learn Your Way
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| Read | Build | Watch | Test | Review | Visualize |
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| **You are here** | [Projects](#practice) | [Videos](videos/collections-explained.md) | [Quiz](quizzes/collections-explained-quiz.py) | [Flashcards](../practice/flashcards/README.md) | [Diagrams](diagrams/collections-explained.md) |
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Python has several ways to group multiple values together.
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## Visualize It

‎concepts/comprehensions-explained.md‎

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A comprehension is a one-line way to build a list, dictionary, or set from another sequence. Instead of writing a loop that appends to a list, you describe what you want in a single expression. Comprehensions are one of Python's most distinctive features.
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### Learn Your Way
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/comprehensions-explained-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Why This Matters
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Comprehensions make your code shorter, faster, and more readable — once you learn the pattern. They show up everywhere in Python: filtering data, transforming lists, building dictionaries from pairs, and more. Understanding them is essential from Level 2 onward.

‎concepts/context-managers-explained.md‎

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A context manager is something that sets up a resource when you enter a block and cleans it up when you leave — even if an error occurs. The `with` statement is how you use them. If you have ever written `with open("file.txt") as f:`, you have already used one.
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### Learn Your Way
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/context-managers-explained-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Why This Matters
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Resources like files, database connections, and network sockets need to be properly closed when you are done with them. Forgetting to close a file can corrupt data. Forgetting to close a database connection can exhaust the connection pool and crash your server. Context managers make cleanup automatic — you cannot forget.

‎concepts/dataclasses-explained.md‎

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A **dataclass** is the easy way to create a class that holds data. Instead of writing `__init__`, `__repr__`, and `__eq__` yourself, Python generates them for you.
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/dataclasses-explained-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## The problem dataclasses solve
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Here is a plain class that stores a person's information:

‎concepts/debugging-methodology.md‎

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Debugging is the systematic process of finding and fixing bugs. It is not about staring at code until you see the problem — it is a repeatable method that works on any bug, in any language. The best debuggers are not the smartest programmers; they are the most methodical.
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| **You are here** | [Projects](#practice) | — | [Quiz](quizzes/debugging-methodology-quiz.py) | [Flashcards](../practice/flashcards/README.md) | — |
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## Why This Matters
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You will spend more time debugging than writing new code. A systematic approach turns frustrating hours of "why does this not work?" into a predictable process. The method described here — Reproduce, Isolate, Hypothesize, Test, Fix, Verify, Prevent — works for everything from a typo to a race condition.

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