Granian is a fast ASGI server written in Rust that’s built to run Python web apps with less overhead and better performance.

If you’ve used Uvicorn or Gunicorn before, you can think of Granian as a step up. It feels familiar, but once you run it, you can tell it’s doing things more efficiently.

The easiest way to think about it is as a performance upgrade you can drop right into your stack. You don’t have to change your app.

You just swap the server, and your app can handle requests and concurrency in a much smoother way. It makes better use of your hardware without touching your Python code.

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Granian fits really well with how modern Python apps are built. Whether you’re using FastAPI, working with async code, or building APIs that need to scale, it gives you a more efficient engine underneath everything.

It handles connections cleanly, manages workers well, and helps avoid slowdowns that usually show up as your app grows.

You don’t need to rewrite anything. You don’t need to learn a new system. If you already understand ASGI, you’re already in a good spot.

In this article, I’ll walk you through a few useful things you can do with Granian that can improve how your backend runs. We’ll start a server, adjust workers for better performance, and look at how it handles async workloads.

What makes Granian stand out is that it stays focused on one thing. Running your Python app as fast and efficiently as possible. No extra layers. No complicated setup. Just solid performance with a clean developer experience.

Let’s start by installing Granian:

pip3 install granian

From there, you can spin up your server and let Granian do the work.

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It is a Python library focused on one clear job. It takes messy input data and turns it into structured, reliable data you can actually trust. And it does this without forcing you to write a bunch of messy if statements all over your code.

Data flows in from APIs, forms, databases, or direct user input. Marshmallow checks that data, cleans it up, makes sure it follows the rules you set, and shapes it exactly how you want it.

It also handles turning Python objects into JSON and JSON back into Python objects in a way that feels clean and organized instead of chaotic.

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It gives you a simple way to define schemas that describe what your data should look like. You can define fields, mark them as required, validate formats like emails or URLs, clean up inputs, and even customize the error messages your app returns.

Whether you are building a Flask API, working with FastAPI, loading configuration files, or cleaning data before storing it, Marshmallow helps keep everything consistent and predictable.

The first time you see schemas and nested fields, you might wonder if it is overkill. I had that thought too. But once your project grows past a few routes or a few data models, you start to see why it matters. It becomes the layer that keeps your app stable as it scales.

And it is not limited to simple data. It handles nested objects, lists of complex data, custom validation rules, pre-processing steps, and post-processing transformations. It works just as well in a small script as it does in a production API.

Today, I am going to walk you through three practical features in Marshmallow that can seriously improve how you handle data. You will see how to define schemas, validate input cleanly, and serialize data properly without repeating yourself.

If you do not have it installed yet, open your terminal and run:

pip3 install marshmallow

That is it. Let’s tighten up your data layer and make your application feel solid from the inside.

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Before long, the logs turn into a mess. They are inconsistent, hard to read, and not very helpful when something actually breaks.

Structlog is a Python library built around a simple idea. Logs should be data, not just text. Instead of writing long log messages and hoping they make sense later, structlog has you log clear key value information that both people and machines can understand.

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The result is cleaner logs, easier debugging, and systems that are much easier to work with as they grow.

At first, structlog can feel a bit unfamiliar. It does not look or behave exactly like the logging setup most people are used to. I will be honest, it took me some time before it really clicked.

But once it does, it is hard to go back. Your logs become consistent, searchable, and actually useful instead of something you scroll past and ignore.

In this article, we are keeping things simple. I will introduce structlog, explain how structured logging works in plain terms, and walk through three useful patterns that make structlog a solid upgrade over traditional logging.

If your logs have ever let you down right when you needed them most, this is for you.

Let’s get you set up with a quick terminal run:

pip3 install firefly

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And honestly, that’s fine. Until it’s not. That’s where ujson comes in.

Ujson, short for UltraJSON, is a fast JSON library written in C and built for performance. It does the same job as Python’s standard json module, but it runs noticeably faster, especially when you are working with large chunks of data or parsing JSON over and over again in a loop.

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The easiest way to think about ujson is as a drop in upgrade. The concepts are the same. The usage feels the same. You just spend less time waiting on JSON to finish its work.

What I like most about ujson is how little it asks from you. There is no new way of thinking about JSON. No complicated setup. You still load, dump, serialize, and deserialize data the same way you always have.

The real difference is that ujson is tuned for speed, which makes it a great fit for data pipelines, web services, scraping jobs, and any script where performance actually matters.

In this article, we are going to look at ujson and then walk through three practical functions that show where it’s good and how you can start using it right away.

If you work with APIs, scrape data, or move a lot of structured data around, this is one tool you will be glad you know about.

Let’s get you set up with a quick terminal run:

pip3 install ujson

If you spend most of your day in the terminal like I do, this one is going to be fun.

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It is a Python library that helps you understand what is actually going wrong in your app, not just that something failed.

At a basic level, sentry-sdk gives you real time error tracking and performance tracking. Instead of digging through logs or trying to recreate a bug on your laptop, Sentry shows you what happened, where it happened, and what led up to it.

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You can see stack traces, variable values, what the app was doing right before the error, and even which parts of your code are running slowly, all in one place.

I think of sentry-sdk as having eyes inside your production app. It sits quietly in the background while your code runs and lets you know the moment something feels off.

If an error happens, you see it right away. If a request suddenly slows down, you can tell exactly where the time is going. When you check later, the context is already there waiting for you.

The best part is how easy it is to set up. With just a few lines of code, Sentry starts catching errors automatically.

From there, you can add performance tracking, custom events, and extra details only where you need them. It works well for web apps, APIs, background jobs, and even small scripts where you just want to know what is happening.

In this article, we look at three practical features you can use right away to get better visibility, spend less time debugging, and catch problems before users start noticing them.

Let’s get you set up with a quick terminal run:

pip3 install sentry-sdk

If you spend most of your day in the terminal like I do, this one is going to be fun.

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Firefly is a Python library that lets you build clean, interactive dashboards and charts right inside your terminal. No browser. No web app. Just clear visuals, live updates, and a surprisingly polished experience in the place you already work every day.

It turns boring terminal output into something you can understand at a glance. Charts update in real time. Tables feel responsive. Dashboards finally look like dashboards.

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The easiest way to think about Firefly is this. It gives your terminal eyes. Instead of dumping numbers and hoping your brain connects the dots, you can see trends, progress, and system state right where your code runs. That alone changes how you debug, monitor, and explore data.

I did not expect to like it as much as I do. Most terminal tools get the job done but look rough. Firefly breaks that pattern. It feels thoughtful, well designed, and genuinely useful. Once you try it, going back to plain print statements feels old.

In this article, we are digging into Firefly and walking through three features that show how powerful terminal dashboards can be. No filler. Just practical examples you can drop straight into your workflow.

Let’s get you set up with a quick terminal run:

pip3 install firefly

If you spend most of your day in the terminal like I do, this one is going to be fun.

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JAX is a Python library that feels a lot like NumPy. You write the same kind of array code you already know. The difference is what happens behind the scenes. JAX is designed to run well on modern hardware like GPUs and TPUs. It also supports automatic differentiation and just in time compilation, without forcing you to rewrite your code from scratch.

If you have ever used NumPy and thought, this works great but I wish it were faster or easier to scale, JAX feels like the obvious next step. You keep writing familiar array based code, and JAX handles the heavy lifting. It moves work to accelerators and optimizes performance for you.

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What really makes JAX stand out is how cleanly it brings a few powerful ideas together. You get NumPy style APIs that feel natural. You get automatic differentiation, which makes working with gradients simple. And you get compilation through XLA, which can make your code much faster with very little extra work.

In this article, we are diving into JAX and looking at three practical features that show why so many people are moving past plain NumPy. No heavy theory. No vague explanations. Just clear examples that show how JAX works and when it makes sense to use it.

If you like clean math, fast code, and tools that can grow with you, this is worth checking out.

Let’s get it install via your terminal with pip:

pip3 install jax

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You grab some JSON from an API, pass it around as dictionaries, add a few keys here and there, and before long the code is hard to read and even harder to trust.

One small change in the data and things start breaking in places you did not expect. That is exactly the kind of problem dataclasses-json is meant to fix.

dataclasses-json sits in a really comfortable spot between clean Python code and real world data. If you already like using Python dataclasses, this library feels like it belongs there.

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It lets you take structured data like JSON from an API or a config file and turn it straight into proper Python objects without writing a bunch of extra glue code.

One of the best parts about this library is how little effort it takes to use. You do not have to learn a whole new system or way of thinking.

You write normal Python dataclasses, add a decorator, and suddenly you can turn JSON into objects and objects back into JSON with a single call. It handles nested data, optional fields, default values, and even custom behavior when you need it.

In this article, we are digging into dataclasses-json and looking at three practical features you can start using right away. These are the kinds of tools that quietly make your code better without making it more complicated.

If you work with APIs, config files, or any kind of structured data, this is a library that earns its place in your toolbox.

Let’s get it install via your terminal with pip:

pip3 install dataclasses-json

Let’s jump in and clean up how we work with JSON in Python.

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Aubio is a Python-friendly audio library that focuses on what people actually want to do. Detect tempo. Find onsets. Track pitch. Things you can use right away in real projects. It does not try to turn you into an audio engineer. It gives you useful building blocks and stays out of the way.

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The easiest way to think about Aubio is as an audio toolbox for getting answers, not theory. You give it sound and it tells you what is going on. Where the beats are. How fast the music is. What pitch is being played.

That alone opens the door to a lot of practical ideas like music analysis, audio tagging, smart editing tools, or even small experiments like a beat detector or a tuner.

In this article, we are going to dig into Aubio and look at three genuinely useful functions you can start using right away. No fluff. No academic side trips. Just practical audio analysis that actually works.

By the end, you will have a clear idea of what Aubio is good at and how it can fit into your own projects.

If you have ever wanted to work with audio but did not know where to begin, this is a solid place to start. Let’s get into Aubio and make audio feel approachable.

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Fire is a small Python library that turns your code into a command line tool with almost no effort. There is no boilerplate to write. No long argument parsing setup. No time wasted reading docs just to add flags.

Think of it as a direct connection between your Python code and the terminal. Instead of spending time wiring up argparse or click, you stay focused on the actual logic.

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If the function works in Python, it works from the command line too. That idea alone is what makes Fire feel so nice to use.

Fire keeps everything Python first. Function arguments become command line arguments. Methods turn into subcommands. Classes become full tools without extra work. If you already understand Python, you already understand how Fire behaves.

Whether you are writing small utilities, internal tools, automation scripts, or full command line apps, Fire keeps the code clean and easy to read. It is especially useful when you want power and flexibility without pulling in a big framework.

In this article, we are going to look at Fire the same way we always do. We will introduce the library, then walk through three genuinely useful things you can do with it right away.

Fire works so well because it stays out of your way. It lets Python stay Python and turns the terminal into a natural extension of your code. Once you see how fast you can go from idea to a working CLI, it all makes sense.

Let’s start by installing Fire and turning plain Python into something you can run, share, and actually enjoy using from the command line.

pip3 install fire

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SQLModel is a modern Python library built by the same person who created FastAPI, and it sits right between SQLAlchemy and Pydantic.

If you have ever thought that SQLAlchemy feels a bit heavy, or that Pydantic is great but does not actually talk to a database, SQLModel was made to solve that exact problem.

You define a class once and that class becomes your database table, your Python object, and your validation model all at the same time. There is no duplication and no separate files that slowly drift out of sync.

You describe your data once and then use it everywhere, whether you are saving to a database, validating input, or passing data around your app.

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What really sold me on it is how natural it feels to write. You use normal Python type hints, and suddenly those hints actually matter. They turn into database columns, they validate data, and they make your editor more helpful.

If you already use FastAPI, SQLModel feels like it was always meant to be there. If you come from SQLAlchemy, it feels familiar but simpler. If you come from writing raw SQL, it gives you structure without hiding what is really going on.

In this article, we are diving into SQLModel and three practical pieces of it that you can use right away. You will see how models, sessions, and queries work together in a way that feels practical and grounded, not theoretical.

These examples are not meant to cover everything. They are meant to give you a solid base so the next time you need to store, query, or validate data in Python, SQLModel feels like an easy choice.

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You write a normal Python file with regular functions, classes, or even just variables, and Fire turns it into a command-line tool right away. There’s no extra setup and nothing complicated to wire together.

If you’ve ever written a script and wished you could run it the same way you run any other command, Fire does that for you.

You point it at your code, and suddenly you have a tool that understands arguments, handles types, and even shows help text without you having to write anything extra.

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I’ve tried different ways of building command-line tools, and most of them felt heavier than they needed to be. Some made you write long configuration files, others relied on decorators everywhere, and a few needed so much setup that you’d forget what you were trying to do in the first place.

Fire skips all of that. You add one line, fire.Fire(), and your script is ready to use from the command line.

Today I’ll show you how Fire works, how it handles arguments, and how you can build simple or more advanced command-line tools without changing the structure of your program.

Once you see it in action, you’ll start noticing all the small scripts in your projects that would be far more helpful if they were command-line tools. To get started, install Fire:

pip3 install fire

Take a moment and get comfortable. Your Python files are about to turn into real command-line tools.

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Before any changes get committed, it checks everything to make sure you’re not adding sloppy formatting, forgotten print statements, or anything that breaks your team’s standards.

With pre-commit, you can automatically run tools like Black, Flake8, or isort before your code even hits the repository. It keeps your project clean without you having to remember to do it yourself.

I’ll be honest—I used to forget to lint or format my code all the time. I’d push changes and later realize half my files didn’t match our style rules. Once I started using pre-commit, that problem disappeared.

It quietly took care of everything each time I ran git commit. It’s one of those small tools that make a big difference, especially when you’re working with a team or managing a growing codebase.

Welcome to Pre-Commit. Check out other 3 Random Articles here.

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In this issue, we’ll walk through how pre-commit works, how to set it up in your own project, and look at some of its most useful hooks.

By the end, you’ll see that pre-commit isn’t just about enforcing rules—it’s about saving time, catching problems early, and keeping your workflow smooth and professional.

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It’s a solid Python library built for working with DNS, and it’s great for anyone who needs to look up domain names, check DNS records, or manage their own DNS data.

The easiest way to think about it is that it gives you one place to handle anything related to domain names. Whether you’re building tools, checking IP addresses, or automating parts of a server setup, dnspython handles the heavy lifting.

I used to think working with DNS meant you needed a deep networking background or a bunch of clunky tools. Once I tried dnspython, things started to make sense.

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You can look up domains, check A, MX, or TXT records, and even run reverse lookups with just a few lines of code. It’s also much easier to read and work with compared to raw sockets or command line tools like nslookup.

In this article, I’m going to walk you through how dnspython works, how to install it, and how to use its most helpful features. You’ll see how to run simple lookups, try more advanced queries, and understand how DNS fits together behind the scenes.

By the time you’re done, you’ll have a clear picture of how dnspython helps you explore and automate one of the internet’s core systems.

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Numba is a just-in-time compiler that takes normal Python functions and turns them into machine code that runs at incredible speed.

Basically it makes your programs run faster without needing to rewrite them in C or C++. I’ve used it on data-heavy projects where loops were slowing everything down, and Numba made them run for their lives with almost no extra work.

So, why use Numba? Think of it as a performance upgrade for Python. You just add a simple @njit decorator to your functions, and suddenly your loops, array operations, and math-heavy tasks start performing like compiled code.

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Numba is great for things like scientific computing, data analytics, and any project where speed matters. From NumPy arrays, running simulations to processing massive datasets, this will give you a huge performance boost.

Today, I’ll walk you through how to use Numba to turn your Python code into something that moves fast. Get ready as things are about to speed up.

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It’s the modern way to handle files and folders in Python, and once you try it, you’ll never want to go back to using just os or plain string paths. Pathlib treats file paths like real objects instead of basic strings, which makes your code cleaner, safer, and easier to read.

Whether you’re checking if a file exists, creating folders, reading files, or joining paths, Pathlib handles all of it smoothly across different operating systems.

I used to write long, messy path code like os.path.join(”folder”, “subfolder”, “file.txt”) or if not os.path.exists(...), but that’s in the past.

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Now, with Pathlib, I can just write Path(”folder”) / “subfolder” / “file.txt”—and it just works. It’s simple, clear, and built right into Python, so there’s nothing extra to install.

Think of Pathlib as your personal file system helper. It knows where your files are, tells you if they exist, creates folders when needed, and lets you open or write to files with very little effort.

In this article, I’ll walk you through a few practical and genuinely useful Pathlib functions that can make your daily coding smoother. From reading and writing files to navigating folders, Pathlib takes what used to be clunky code and turns it into something clean and natural to write.

Let’s jump in, you’ll see how much better file handling in Python can feel once you start working with Paths instead of plain strings.

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If you’ve ever wondered how computers can summarize text, translate languages, or even write code with such accuracy—this is the technology making it happen.

Transformers are basically the engine that powers today’s smartest AI language models. They help machines understand the meaning and context of words in a way that feels almost human.

Welcome to Transformers. Check out other 3 Random Articles here.

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What makes Hugging Face stand out is how easy they make it to use these advanced models. You don’t need to be an expert in deep learning to work with models like BERT, GPT-3, or DistilBERT.

With just a few lines of Python, you can load a model, run predictions, and see powerful results right away. It takes something that used to be locked away in research labs and puts it right into the hands of everyday developers.

In this article, I’ll show you how to get started with the Transformers library, go over a few of the key features that make it so effective, and walk you through running real NLP tasks in minutes.

Whether you’re building a chatbot, analyzing customer reviews, or generating text, this library gives you a clear window into what modern AI can really do.

Let’s get started with the basics by installing Transformers:

pip3 install transformers

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Basically it’s an open-source framework that helps devs connect LLM’s like GPT with real-world data, tools, and workflows all using Python. Ever hear the term, MCP?

Instead of manually wiring together APIs, prompts, and databases, LangChain gives you a clean, organized way to build smart applications that can think, remember, and interact with other systems.

Think of it as the bridge between language models and everything else. It lets you build chatbots that can pull in live data, summarize documents, run code, or even handle multi-step reasoning automatically.

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With LangChain, you’re not just sending one-off prompts—you’re creating AI agents that can plan, make decisions, and perform tasks dynamically. After spending some time exploring it, I can honestly say it’s one of the most exciting tools in the Python AI space right now.

Whether you want to connect a model to your own data, build AI assistants, or experiment with reasoning agents, this library makes it simple and flexible.

In this article, I’ll walk you through how to build your first chain, link it with data, and run your own small AI workflows.

What makes LangChain really stand out is how smoothly it blends the power of large language models with the flexibility of Python. You’ll see just how quickly you can go from a simple prompt to a fully working AI pipeline with only a few lines of code.

Let’s get started with the basics by installing LangChain and setting up your first chain.

pip3 install langchain-openai 

Once it’s up and running, you’ll see just how easy it is to make AI work for you.

Thank you guys for allowing me to continue to do work that I find meaningful. This is my full time job so I hope you will support my work. You can now subscribe on the substack app!

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It’s a lightweight Python tool that turns that rough data into something people can actually understand, by people I also mean me. Instead of showing hard-to-read numbers or timestamps, Humanize makes them sound natural more like something a person would actually say.

It could be file sizes, time differences, or large numbers, it helps your output feel friendly and familiar.

Seeing “uploaded 432000 seconds ago” or “file size: 108374182 bytes” doesn’t exactly make sense at first glance.

Humanize fixes that in seconds. It automatically changes that kind of data into “uploaded 5 days ago” or “file size: 103 MB.” It’s the kind of small detail that makes dashboards, reports, and apps easier for people to read and trust.

What I love about Humanize is how simple it is to use. There’s no setup or complex configuration—you just import it, call a function, and your data instantly looks better.

In this article, I’ll walk you through three useful functions from Humanize that can make your data look cleaner and your apps sound more natural.

To get started, just run:

pip3 install humanize

Now, let’s make your data sound like it’s speaking human.

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With just a few lines, your model can go live on the web with an easy-to-use interface anyone can try. That’s all Gradio, it connects your model to real people without all the extra work.

If you’ve ever finished training a model and thought, “Okay, what now?”, Gradio solves that problem.

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It lets you build a clean, interactive page where users can test your model by typing text, uploading pictures, or recording audio all without writing a single line of HTML.

Think of it like a faster, simpler version of Streamlit that’s focused just on showing off models. I love Streamlit, I really do. Gardio is a nice tool because it’s simpler for an MVP.

Often I’d use Flask or Streamlit to share projects, it worked, but it was a hassle. Gradio completely changed that. Now, I can go from having a model ready to having a live demo faster.

In this article, I’ll show you how to use Gradio to turn a simple Python function into an interactive web app. We’ll look at how it works, how to build your first interface, and a few tricks to make your app stand out.

Whether you’re showing a machine learning model, an image generator, or just a fun Python project, Gradio makes the process quick and enjoyable.

pip3 install gradio

Let’s jump in and see how a few lines of Python can turn into a real working web app right before your eyes.

Thank you guys for allowing me to continue to do work that I find meaningful. This is my full time job so I hope you will support my work. You can now subscribe on the substack app!

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