Code example that came up:
Code example that came up:
Works in READMEs, issues, PRs, and wikis.
No external tools needed, the diagram lives in your md, version-controlled with your code. 😍
Works in READMEs, issues, PRs, and wikis.
No external tools needed, the diagram lives in your md, version-controlled with your code. 😍
```python
if units == "f":
temp_str = f"{celsius}°F"
else:
temp_str = f"{celsius}°C"
# Later in another file...
units = console.input("Units? (c/f): ").strip() or "c"
# And in yet another place...
```python
if units == "f":
temp_str = f"{celsius}°F"
else:
temp_str = f"{celsius}°C"
# Later in another file...
units = console.input("Units? (c/f): ").strip() or "c"
# And in yet another place...
Instead of formatting timestamps all over your code, keep the formatting close to the data (encapsulation).
Instead of formatting timestamps all over your code, keep the formatting close to the data (encapsulation).
Then someone asked:
“Can I access this via a REST API?”
My answer used to be:
“No… I’d have to rewrite everything.” 😬
Then someone asked:
“Can I access this via a REST API?”
My answer used to be:
“No… I’d have to rewrite everything.” 😬
If your feature has a real lifecycle (draft → review → approved → published), model it as a **state machine**.
If your feature has a real lifecycle (draft → review → approved → published), model it as a **state machine**.
• `pip install orjson` - that's Rust.
• `pip install polars` - Rust.
• `pip install cryptography` - Rust.
• `pip install pydantic` - the core is Rust.
• `pip install ruff` - Rust again.
• `pip install orjson` - that's Rust.
• `pip install polars` - Rust.
• `pip install cryptography` - Rust.
• `pip install pydantic` - the core is Rust.
• `pip install ruff` - Rust again.
Combine `gh`, `fzf`, and your `$EDITOR` to create issues without leaving the terminal 👇
This gives you fuzzy search for assignees and your familiar editor for composing the issue body—much faster than the web UI.
Combine `gh`, `fzf`, and your `$EDITOR` to create issues without leaving the terminal 👇
This gives you fuzzy search for assignees and your familiar editor for composing the issue body—much faster than the web UI.
No, it's not. It's saving you from a 3 AM debugging session three months from now.
In Python, variables are references. You pass an object around, everyone shares it.
In Rust, by default, Ownership is exclusive.
No, it's not. It's saving you from a 3 AM debugging session three months from now.
In Python, variables are references. You pass an object around, everyone shares it.
In Rust, by default, Ownership is exclusive.
Most developers stay in "Passive Consumption" mode because it feels safe. You watch an instructor set up a pytest environment or configure a Docker container and think, "I get it."
Most developers stay in "Passive Consumption" mode because it feels safe. You watch an instructor set up a pytest environment or configure a Docker container and think, "I get it."
In Week 2 of our Pybites dev Rust cohort, we're tackling one of Rust's most powerful features: explicit error handling.
Here's the mental shift that changes everything:
In Week 2 of our Pybites dev Rust cohort, we're tackling one of Rust's most powerful features: explicit error handling.
Here's the mental shift that changes everything:
You’ve finally moved your logic into a proper src/ layout so you can structure your pytest tests properly.
You’ve finally moved your logic into a proper src/ layout so you can structure your pytest tests properly.
If you are still manually fixing spaces or imports, you are wasting brain cycles.
Your `.pre-commit-config.yaml` should be your best friend.
If you are still manually fixing spaces or imports, you are wasting brain cycles.
Your `.pre-commit-config.yaml` should be your best friend.
I just used it to send a weekly markdown report, which now looks so much better :)
`markdown-it-py` made it trivial 👇
Works great with SendGrid, SES, or any email service that accepts HTML content.
I just used it to send a weekly markdown report, which now looks so much better :)
`markdown-it-py` made it trivial 👇
Works great with SendGrid, SES, or any email service that accepts HTML content.
There is no better feeling than `git push` -> `Deployed`. 🚀
Last week, one of our PDC Foundations students went from "I've never packaged an app" to having their CLI tool installable via `pip` - we love seeing these lightbulb moments. 💡
There is no better feeling than `git push` -> `Deployed`. 🚀
Last week, one of our PDC Foundations students went from "I've never packaged an app" to having their CLI tool installable via `pip` - we love seeing these lightbulb moments. 💡
The real power of LLMs unlocks when you give them typed tools.
In Python, we can define a function and expose it to the model using Pydantic schemas.
The real power of LLMs unlocks when you give them typed tools.
In Python, we can define a function and expose it to the model using Pydantic schemas.
Coming from Python, you might think of Enums as just a list of named constants: `Color.RED`, `Color.BLUE`, etc.
Coming from Python, you might think of Enums as just a list of named constants: `Color.RED`, `Color.BLUE`, etc.
Most developers can write a script to fetch data from an API. They get it to work, then they move on.
Most developers can write a script to fetch data from an API. They get it to work, then they move on.
We've spoken with developers who’d watched multiple 10+ hour Udemy courses.
They knew the theory: Django, Docker, AsyncIO.
We've spoken with developers who’d watched multiple 10+ hour Udemy courses.
They knew the theory: Django, Docker, AsyncIO.
"I need a React frontend for my SaaS."
Do you? Or do you just want a smooth UI?
The complexity cost of a decoupled frontend is massive:
- State synchronization
- JWT/Auth handling
- API serialization overhead
"I need a React frontend for my SaaS."
Do you? Or do you just want a smooth UI?
The complexity cost of a decoupled frontend is massive:
- State synchronization
- JWT/Auth handling
- API serialization overhead
🔹 Learn the Python ORM to focus on next
🔹 Get type-safe models with Pydantic-style, high-performance validation
🔹 See how cleanly SQLModel plugs into FastAPI for real-world APIs
🔹 Learn the Python ORM to focus on next
🔹 Get type-safe models with Pydantic-style, high-performance validation
🔹 See how cleanly SQLModel plugs into FastAPI for real-world APIs
I’ve built a Rust Cohort for Pythonistas with Jim Hodapp, a 6-week, project-driven journey where you’ll:
Build a real JSON parser, in Rust 🏗️
I’ve built a Rust Cohort for Pythonistas with Jim Hodapp, a 6-week, project-driven journey where you’ll:
Build a real JSON parser, in Rust 🏗️