skip to navigation
skip to content

Planet Python

Last update: December 13, 2025 07:43 PM UTC

December 13, 2025

Ahmed Bouchefra

Let’s be honest. There’s a huge gap between writing code that works and writing code that’s actually good. It’s the number one thing that separates a junior developer from a senior, and it’s something a surprising number of us never really learn.

If you’re serious about your craft, you’ve probably felt this. You build something, it functions, but deep down you know it’s brittle. You’re afraid to touch it a year from now.

Today, we’re going to bridge that gap. I’m going to walk you through eight design principles that are the bedrock of professional, production-level code. This isn’t about fancy algorithms; it’s about a mindset. A way of thinking that prepares your code for the future.

And hey, if you want a cheat sheet with all these principles plus the code examples I’m referencing, you can get it for free. Just sign up for my newsletter from the link in the description, and I’ll send it right over.

Ready? Let’s dive in.

1. Cohesion & Single Responsibility

This sounds academic, but it’s simple: every piece of code should have one job, and one reason to change.

High cohesion means you group related things together. A function does one thing. A class has one core responsibility. A module contains related classes.

Think about a UserManager class. A junior dev might cram everything in there: validating user input, saving the user to the database, sending a welcome email, and logging the activity. At first glance, it looks fine. But what happens when you want to change your database? Or swap your email service? You have to rip apart this massive, god-like class. It’s a nightmare.

The senior approach? Break it up. You’d have:

Then, your main UserService class delegates the work to these other, specialized classes. Yes, it’s more files. It looks like overkill for a small project. I get it. But this is systems-level thinking. You’re anticipating future changes and making them easy. You can now swap out the database logic or the email provider without touching the core user service. That’s powerful.

2. Encapsulation & Abstraction

This is all about hiding the messy details. You want to expose the behavior of your code, not the raw data.

Imagine a simple BankAccount class. The naive way is to just have public attributes like balance and transactions. What could go wrong? Well, another developer (or you, on a Monday morning) could accidentally set the balance to a negative number. Or set the transactions list to a string. Chaos.

The solution is to protect your internal state. In Python, we use a leading underscore (e.g., _balance) as a signal: “Hey, this is internal. Please don’t touch it directly.”

Instead of letting people mess with the data, you provide methods: deposit(), withdraw(), get_balance(). Inside these methods, you can add protective logic. The deposit() method can check for negative amounts. The withdraw() method can check for sufficient funds.

The user of your class doesn’t need to know how it all works inside. They just need to know they can call deposit(), and it will just work. You’ve hidden the complexity and provided a simple, safe interface.

3. Loose Coupling & Modularity

Coupling is how tightly connected your code components are. You want them to be as loosely coupled as possible. A change in one part shouldn’t send a ripple effect of breakages across the entire system.

Let’s go back to that email example. A tightly coupled OrderProcessor might create an instance of EmailSender directly inside itself. Now, that OrderProcessor is forever tied to that specific EmailSender class. What if you want to send an SMS instead? You have to change the OrderProcessor code.

The loosely coupled way is to rely on an “interface,” or what Python calls an Abstract Base Class (ABC). You define a generic Notifier class that says, “Anything that wants to be a notifier must have a send() method.”

Then, your OrderProcessor just asks for a Notifier object. It doesn’t care if it’s an EmailNotifier or an SmsNotifier or a CarrierPigeonNotifier. As long as the object you give it has a send() method, it will work. You’ve decoupled the OrderProcessor from the specific implementation of the notification. You can swap them in and out interchangeably.

A quick pause. I want to thank boot.dev for sponsoring this discussion. It’s an online platform for backend development that’s way more interactive than just watching videos. You learn Python and Go by building real projects, right in your browser. It’s gamified, so you level up and unlock content, which is surprisingly addictive. The core content is free, and with the code techwithtim, you get 25% off the annual plan. It’s a great way to put these principles into practice. Now, back to it. —

4. Reusability & Extensibility

This one’s a question you should always ask yourself: Can I add new functionality without editing existing code?

Think of a ReportGenerator function that has a giant if/elif/else block to handle different formats: if format == 'text', elif format == 'csv', elif format == 'html'. To add a JSON format, you have to go in and add another elif. This is not extensible.

The better way is, again, to use an abstract class. Create a ReportFormatter interface with a format() method. Then create separate classes: TextFormatter, CsvFormatter, HtmlFormatter, each with their own format() logic.

Your ReportGenerator now just takes any ReportFormatter object and calls its format() method. Want to add JSON support? You just create a new JsonFormatter class. You don’t have to touch the ReportGenerator at all. It’s extensible without being modified.

5. Portability

This is the one everyone forgets. Will your code work on a different machine? On Linux instead of Windows? Without some weird version of C++ installed?

The most common mistake I see is hardcoding file paths. If you write C:\Users\Ahmed\data\input.txt, that code is now guaranteed to fail on every other computer in the world.

The solution is to use libraries like Python’s os and pathlib to build paths dynamically. And for things like API keys, database URLs, and other environment-specific settings, use environment variables. Don’t hardcode them! Create a .env file and load them at runtime. This makes your code portable and secure.

6. Defensibility

Write your code as if an idiot is going to use it. Because someday, that idiot will be you.

This means validating all inputs. Sanitizing data. Setting safe default values. Ask yourself, “What’s the worst that could happen if someone provides bad input?” and then guard against it.

In a payment processor, don’t have debug_mode=True as the default. Don’t set the maximum retries to 100. Don’t forget a timeout. These are unsafe defaults.

And for the love of all that is holy, validate your inputs! Don’t just assume the amount is a number or that the account_number is valid. Check it. Raise clear errors if it’s wrong. Protect your system from bad data.

7. Maintainability & Testability

The most expensive part of software isn’t writing it; it’s maintaining it. And you can’t maintain what you can’t test.

Code that is easy to test is, by default, more maintainable.

Look at a complex calculate function that parses an expression, performs the math, handles errors, and writes to a log file all at once. How do you even begin to test that? There are a million edge cases.

The answer is to break it down. Have a separate OperationParser. Have simple add, subtract, multiply functions. Each of these small, pure components is incredibly easy to test. Your main calculate function then becomes a simple coordinator of these tested components.

8. Simplicity (KISS, DRY, YAGNI)

Finally, after all that, the highest goal is simplicity.

Phew, that was a lot. But these patterns are what it takes to level up. It’s a shift from just getting things done to building things that last.

If you enjoyed this, let me know. I’d love to make more advanced videos like this one. See you in the next one.

December 13, 2025 05:52 PM UTC

Hugo van Kemenade

Steering Council results

The Python Steering Council 2026 election results are in and congratulations to the new Python Steering Council!

Welcome Savannah for the first time, and thank you to Greg Smith and Emily Morehouse for four and three years’ service each.

Three are starting their sixth terms, and four members have been or are release managers.

The chart above only covers the Steering Council years. Let’s also not forget Guido van Rossum’s BDFL years:

The same chart but including Guido’s 28 year stint as BDFL.

December 13, 2025 02:40 PM UTC

Ahmed Bouchefra

A Pythonista’s Guide to the 2026 Code Rush

Look, we know the truth. Python is the best language ever written. It reads like English, it runs the AI revolution, and it doesn’t force us to worry about memory pointers or semi-colons.

But even I have to admit: the industry in 2026 is getting crowded. The “job market is brutal” chatter isn’t wrong. While we sit comfortably at the top of the TIOBE index, the ground is moving. New tech is pushing for raw speed and type safety, and “just knowing Python” might not be the golden ticket it was five years ago.

So, how do we—the whitespace-loving, bracket-hating crowd—stay on top? We don’t abandon ship. We fortify.

Here is how the rest of the programming ecosystem looks through snake-tinted glasses, and what you should actually bother learning to keep your edge.

1. Python: Still the King, But Watch the Throne

Let’s get the validation out of the way first. Python is still the engine of the modern world. Stack Overflow’s 2025 survey has us at nearly 58% usage. We aren’t going anywhere.

The Elephant in the Room (The GIL): We have to talk about the Global Interpreter Lock. It’s that annoying guardrail that stops Python from using multiple CPU cores at once for a single process. It’s why the “speed freaks” make fun of us.

Does it matter? Mostly, no. For 90% of apps, developer speed beats execution speed. But in 2026, efficiency is starting to count again. If you are building high-scale systems, Python is strictly the glue code. You need a partner language for the heavy computing.

2. The “Friends” We Can Tolerate

If you have to step outside the Python ecosystem, you want languages that don’t make you miserable.

Rust: The Best Friend You’re Jealous Of

If you learn one other language this year, make it Rust.

Why? Because Rust is what Python wants to be when it grows up and hits the gym. It gives you memory safety (no segfaults!) and C++ speed, but the tooling is actually modern.

For us, Rust is the perfect backend companion. Tools like Ruff (the super-fast Python linter) and Polars (the pandas alternative) are written in Rust. Writing Python extensions in Rust using PyO3 is a superpower. You write the slow parts in Rust, wrap them up, and call them from Python. You look like a genius optimization engineer, but you still get to write .py files most of the day.

TypeScript: The Only Sane Way to Do Frontend

I know, I know. We hate JavaScript. It’s messy and weird.

But unless you are using HTMX or Streamlit for everything (which, respect), you eventually have to touch the browser. TypeScript is the answer. It brings sanity to the chaos. It has types (like Python’s Type Hints, but actually enforced), so the code doesn’t explode at runtime.

Think of TypeScript as the “Pythonic” way to write JavaScript. It catches your mistakes before you push to prod. If you are doing full-stack, this is non-negotiable.

3. The “Necessary Evils”

Go: The Boring Plumber

Go (Golang) is… fine. It’s Google’s language for cloud infrastructure. It’s very simple, very fast, and very boring.

I see Go as the “anti-Python” in philosophy. Python is about expression and “one obvious way to do it.” Go is about “copy-paste this error check three times.” But, if you work in DevOps, Docker, or Kubernetes, you have to read Go. It’s a great paycheck language, even if it lacks soul.

Java: The Corporate Suit

Java is still everywhere in big banks and legacy enterprise systems. It’s verbose and heavy. Unless you are specifically targeting a job at a Fortune 500 bank or building Android apps (and even then, use Kotlin), you can probably skip this. Let the enterprise devs handle the boilerplates.

4. The “Don’t Bother” List (For Us)

The Strategy: The T-Shaped Pythonista

So, what’s the play? Do you drop Python?

Absolutely not. You double down on Python, but you stop being a “one-trick pony.”

  1. The Core: Be a master of Python. Know the internals. Use Type Hints. Understand asyncio deeply.
  2. The Edge: Pick Rust as your performance weapon. When Python is too slow, don’t complain—rewrite that specific function in Rust.
  3. The Reach: Learn TypeScript just enough to not break the frontend.

That is how you survive the shift. You don’t chase every trend. You keep your home base in Python, and you selectively raid the other villages for their best tools.

December 13, 2025 12:00 AM UTC

Armin Ronacher

Skills vs Dynamic MCP Loadouts

December 13, 2025 12:00 AM UTC

December 11, 2025

Python Software Foundation

Announcing Python Software Foundation Fellow Members for Q3 2025! 🎉

December 11, 2025 03:54 PM UTC

Django Weblog

Django Code of Conduct Transparency Report 2025

The Code of Conduct working group received 4 reports and met 12 times in 2025. This transparency report is a brief account of how those reports were handled. This year’s number is lower than previous years in part because of the formation of the Online Community Working Group which handles moderation on our official spaces and has been able to act directly on smaller scale infractions. In some cases we received additional reporting while investigating initial reports, but have not counted those as separate instances.

This working group conducts business in several ways. It has online meetings, typically once per month. It also discusses issues in a Slack channel, but most cases are handled in the meetings. The group welcomed three new members this year: Ariane Djeupang, Natalia Bidart, and Priya Pahwa. Natalia was selected by the new Online Communities Working Group as their liaison to the Code of Conduct Working group; Ariane and Priya were elected by the working group. The group also saw Jay Miller step down this year. We all want to thank Jay for his continued role in our community and for all the work he did with the Code of Conduct group.

It was the group’s intention to work with a consultant to update our Code of Conduct and processes. We reached out to two consultants to help with that work, but unfortunately we weren’t able to engage either to get that work completed. We hope to progress with that in 2026. In the meantime, we made a few internal process tweaks - creating up a new “ask CoC” channel with key stakeholders to discuss moderation and CoC enforcement, and having our team set up as moderators in GitHub until we find a better model.

Two reports from late 2024 carried into this year. Two reports resulted in suspensions from the relevant platforms. Another was shared with local event organizers.

Finally, this section provides a brief summary of the kinds of cases that were handled:

December 11, 2025 03:19 PM UTC

PyCon

PyCon US 2026 - Registration, Hotels, Travel Grants & More!

December 11, 2025 02:48 PM UTC

Daniel Roy Greenfeld

Adding Type Hints to my Blog

Using pyrefly to identify type failures on this site and then fixing one of them.

December 11, 2025 08:25 AM UTC

December 10, 2025

Real Python

Python Inner Functions: What Are They Good For?

Learn how to create inner functions in Python to access nonlocal names, build stateful closures, and create decorators.

December 10, 2025 02:00 PM UTC

Eli Bendersky

Revisiting "Let's Build a Compiler"

There's an old compiler-building tutorial that has become part of the field's lore: the Let's Build a Compiler series by Jack Crenshaw (published between 1988 and 1995).

I ran into it in 2003 and was very impressed, but it's now 2025 and this tutorial is still being mentioned quite often …

December 10, 2025 12:41 PM UTC

Real Python

Quiz: Python Inner Functions: What Are They Good For?

Test inner functions, closures, nonlocal, and decorators in Python. Build confidence and learn to keep state across calls. Try the quiz now.

December 10, 2025 12:00 PM UTC

Ahmed Bouchefra

The Efficient Way to Learn Python in 2026 (5 Prompts + A Free Book)

I see two types of learners in 2026, and honestly, both of them are doing it wrong.

The first group tries to learn solely through AI. They ask chatbots to “write a script,” copy-paste the result, and feel productive. But the second they hit a bug the AI can’t fix, they freeze. They have no foundation. They built a house on sand.

The second group goes the old-school route. They buy a massive, 800-page programming textbook. They read it cover to cover, highlighting every line. By Chapter 4, they are bored. By Chapter 7, they quit. It’s too slow for the pace of 2026.

Here is the secret I’ve found after years in this industry: The real growth happens when you combine the two.

A book gives you the structure—the “what to learn” and the “why.” The AI gives you the speed—the “how.”

If you want to master Python this year, you shouldn’t just read a book; you should interact with it. I recommend using the 10xdev python book as your primary roadmap. It’s structured for the modern developer, not the academic.

But don’t just read it passively. Use the following 5 AI prompts to turn that static text into a living, breathing course.

The “Book + Prompt” Methodology

The concept is simple. You read a section of the 10xdev python book to understand the core concept. Then, you immediately use an AI agent (ChatGPT, Claude, etc.) to test, expand, and apply that knowledge.

This keeps you moving fast without losing depth. Here are the specific prompts to make that happen.

1. The “Pre-Flight” Primer

Most people get stuck because they dive into a complex chapter without knowing why it matters. Use this prompt before you start a new chapter to prime your brain.

The Prompt:

“I am about to read the chapter on [Insert Topic, e.g., Asynchronous Programming] in the 10xdev python book (link: https://10xdev.blog/pybook).

Your Goal: Give me a 3-bullet point summary of why this specific concept is used in modern 2026 software development. Context: Don’t explain how to do it yet. Just tell me what problems it solves so I know what to look for while I read the book.”

Why this works: It builds a mental hook. When you eventually read the technical details in the book, your brain already knows where to file the information. You aren’t just memorizing; you are solving a problem.

2. The “Feynman” Stress Test

The ultimate test of understanding is whether you can teach it. After you finish a section, don’t just move on. Force yourself to explain it back to the AI.

The Prompt:

“I just finished the section on [Insert Topic, e.g., Decorators] in the 10xdev python book (https://10xdev.blog/pybook).

My Task: I am going to write a short paragraph below explaining this concept as if I were teaching a junior developer. Your Job: Critique my explanation. Did I miss any edge cases? Did I use the terminology correctly?

My Explanation: [Type your summary here…]”

Why this works: This is the fastest way to find holes in your knowledge. If you can’t explain it simply, you don’t understand it. The AI acts as your safety net, catching misunderstandings before they become bad habits.

3. The “Translator” Prompt (Theory to Practice)

Sometimes, a book example might not click. Maybe the 10xdev python book uses a “Bank Account” analogy, but you care about “Video Games.” Use AI to translate the book’s logic into your language.

The Prompt:

“The 10xdev python book (https://10xdev.blog/pybook) explains the concept of [Insert Concept, e.g., Object-Oriented Inheritance] using an example about [e.g., Bank Accounts]. I am struggling to visualize it.

Task: Explain this exact same concept, but use an analogy involving [Choose one: RPG Video Game Characters / Managing a Pizza Shop / A Spotify Playlist]. Output: Write a Python code snippet that mirrors the structure used in the book, but applied to this new analogy.”

Why this works: It makes the abstract concrete. By seeing the same logic applied to a domain you love, the concept sticks.

4. The “Modern Context” Checker

Technology moves fast. While the 10xdev python book is excellent, new tools appear every month. Use this prompt to ensure you are connecting the book’s foundational wisdom with the absolute latest 2026 tools.

The Prompt:

“I am reading the section in the 10xdev python book (https://10xdev.blog/pybook) about [Insert Topic, e.g., Web Scraping].

Question: The book covers the foundational logic well. But for a startup building in late 2026, are there new AI-specific libraries (like Crawl4AI or updated LangChain tools) that I should use alongside these principles? Output: Show me how to apply the book’s logic using the most modern tool available today.”

Why this works: It bridges the gap between “Foundational Principles” (which rarely change) and “Tooling” (which changes constantly). You get the best of both worlds.

5. The “Implementation Sprint” Prompt

Passive reading is the enemy. You need to build. Use this prompt to turn a chapter of the book into a mini-project.

The Prompt:

“I want to practice the skills from Chapter [X] of the 10xdev python book (https://10xdev.blog/pybook), which covers [Insert Topic, e.g., API Integration].

Task: Design a tiny coding challenge for me that uses these exact concepts. Constraints:

  • It must be solvable in under 60 minutes.
  • It must result in a working script, not just a function.
  • Do not write the code for me. Just give me the requirements and the steps.”

Why this works: It forces you to close the book and open your IDE. You stop being a student and start being a developer.

Why This Approach Wins

The developers who get hired in 2026 aren’t the ones who memorized the documentation. They are the ones who understand systems.

The 10xdev python book provides the system architecture—the mental model of how professional Python code is structured. The AI provides the infinite practice and instant feedback.

If you rely on just one, you are slow or shallow. If you use both, you are unstoppable.

Your Next Step:

  1. Go get the 10xdev python book.
  2. Open Chapter 1.
  3. Keep ChatGPT open in the next tab.
  4. Run Prompt #1.

That’s how you go from “learning to code” to “being a developer in the age of AI.”

December 10, 2025 12:00 AM UTC

Seth Michael Larson

Extracting Nintendo Switch “Play Activity” with OCR

December 10, 2025 12:00 AM UTC

December 09, 2025

PyCoder’s Weekly

Issue #712: Quantum Computing in Python, DataFrame Libraries, Django 6, and More (Dec. 9, 2025)

December 09, 2025 07:30 PM UTC

EuroPython Society

General Assembly 2025

We’re excited to invite you to this year’s General Assembly meeting! We’ll gather on Wednesday, 17 December 2025 20:00 CET, held online via Zoom. EPS membership is required to participate and additional joining instructions will be shared closer to the date.

You can

December 09, 2025 02:39 PM UTC

Django Weblog

Online Community Working Group GitHub repo and project

The Online Community Working Group has introduced a new GitHub repository designed to manage and track ideas, suggestions, and improvements across Django's various online community platforms.

Introducing the Online Community Working Group Repository

Primarily inspired by the rollout of the New Features repository, the Online Community Working Group has launched their own version that works in conjunction with the Online Community Working Group Ideas GitHub project to provide a mechanism to gather feedback, suggestions, and ideas from across the online community and track their progression.

The primary aim is to help better align Django's presence across multiple online platforms by providing:

  1. Centralisation: A community-platform-agnostic place to collect feedback, suggestions, and ideas from members of any of Django's online communities.
  2. Visibility: With a variety of platforms in use across the community, some of which require an account before their content can even be read, discussions can happen in what effectively amount to private silos. This centralised repository allows all suggestions and ideas to be viewed by everybody, regardless of their community platform of choice.
  3. Consistency: A suggestion for one platform can often be a good idea for another. Issues and ideas raised centrally can be assessed against all platforms to better align Django's online community experience.

How to use the Online Community Working Group Repo

If you have an idea or a suggestion for any of Django's online community platforms (such as the Forum, Discord, or elsewhere), the process starts by creating an issue in the new repository.

You'll be asked to summarise the idea, and answer a couple of short questions regarding which platform it applies to and the rationale behind your idea.

The suggestion will be visible on the public board, and people will be able to react to the idea with emoji responses as a quick measure of support, or provide longer-form answers as comments on the issue.

The Online Community Working Group will review, triage, and respond to all suggestions, before deciding whether or how they can be implemented across the community.

Existing Online Communities

Note that we're not asking that you stop using any mechanisms in place within the particular community you're a part of currently—the Discord #suggestions channel is not going away, for example. However, we may ask that a suggestion or idea flagged within a particular platform be raised via this new GitHub repo instead, in order increase its visibility, apply it to multiple communities, or simply better track its resolution.

Conclusion

The Online Community Working Group was relatively recently set up, with the aim of improving the experience for members of all Django's communities online. This new repository takes a first step in that direction. Check out the repository at django/online-community-working-group on GitHub to learn more and start helping shape Django's truly excellent community presence online.

December 09, 2025 02:00 PM UTC

Real Python

Using Functional Programming in Python

Boost your Python skills with a quick dive into functional programming: what it is, how Python supports it, and why it matters.

December 09, 2025 02:00 PM UTC

PyCharm

We’re excited to announce that PyCharm 2025.3 is here! This release continues our mission to make PyCharm the most powerful Python IDE for web, data, and AI/ML development. It marks the migration of Community users to the unified PyCharm and brings full support for Jupyter notebooks in remote development, uv as the default environment manager, […]

December 09, 2025 10:40 AM UTC

Python Bytes

#461 This episdoe has a typo

Topics include PEP 798: Unpacking in Comprehensions, Pandas 3.0.0rc0, typos, and.

December 09, 2025 08:00 AM UTC

Armin Ronacher

Let’s Destroy The European Union!

December 09, 2025 12:00 AM UTC

December 08, 2025

Hugo van Kemenade

Steering Council election

The Python Steering Council election is on!

This year six candidates are running for the five seats. See PEP 8107 for links to their nomination statements and PEP 13 for more on Python language governance.

I made a chart to show when the nominations arrived during the nomination period: six is fewer than in previous years, but they didn’t all wait until the last three days like in 2023 and 2024.

This year we’re also using a new voting system: with “Multi-winner Bloc STAR” you give between 0-6 stars per candidate.

If you’re in the core team, remember to vote by Friday!

December 08, 2025 07:50 PM UTC

PyCharm

PyCharm 2025.3 – Unified IDE, Jupyter notebooks in remote development, uv as default, and more

December 08, 2025 04:57 PM UTC

Real Python

Lazy Imports Land in Python and Other Python News for December 2025

PEP 810 brings lazy imports to Python 3.15, PyPI tightens 2FA security, and Django 6.0 reaches release candidate. Catch up on all the important Python news!

December 08, 2025 02:00 PM UTC

Reuven Lerner

30 things I’ve learned from 30 years as a Python freelancer

30 years! It’s hard to believe, but it was in December 1995 (i.e., 30 years ago) that I went freelance, giving up a stable corporate paycheck. And somehow, I’ve managed to make it work: During that time, I’ve gotten married, bought a house, raised three children, gone on numerous vacations, and generally enjoyed a good […]

The post 30 things I’ve learned from 30 years as a Python freelancer appeared first on Reuven Lerner.

December 08, 2025 11:36 AM UTC

Ahmed Bouchefra

Is AI Taking Your Dev Job? Here’s The Fix (Plus a 100% Free Python Course)

Things feel different in tech right now, don’t they?

A few years back, landing a dev or data role felt like winning the lottery. You learned some syntax, built a portfolio, and you were set. But in 2025, that safety net feels thin. We all know why.

Artificial Intelligence isn’t just a buzzword anymore. It’s sitting right there in your IDE.

You might be asking: Is my job safe?

Here is the honest answer. If your day-to-day work involves taking a clear set of instructions and turning them into code, your role is shaky. We have tools now that generate boilerplate, write solid SQL, and slap together UI components faster than any human.

But here is the good news.

The job isn’t disappearing. It’s just moving up a level. The industry is hungry for people who can think, design, and fix messy problems. To survive this shift, you need to stop acting like a translator for computers and start acting like an architect of systems.

You need future proof coding skills.

The Shift: From “Code Monkey” to Problem Solver

I remember my first real wake-up call as a junior dev. I spent three days writing a script to parse some logs. I was so proud of my regex. Then, a senior engineer looked at it, shook his head, and said, “Why didn’t you just fix the logging format at the source?”

I was focused on the code. He was focused on the system.

That is the difference. AI can write the regex. AI cannot see that the logging format is the actual problem.

Here is how you make yourself indispensable in 2025.

1. Think in Systems, Not Just Syntax

Most of us learned to code by memorizing rules. “Here is a loop,” or “Here is a class.”

But real software engineering is about managing chaos.

Take Object-Oriented Programming (OOP). It’s not just about making a class for a “Car” or a “Dog.” It’s a way to map out a complex business problem so it doesn’t collapse under its own weight later. AI can spit out a class file in seconds. But it lacks the vision to plan how twenty different objects should talk to each other over the next two years.

Or look at Functional Programming. It sounds academic, but for data roles, it’s vital. It teaches you to write code that doesn’t change things unexpectedly. When you are processing terabytes of data, “side effects” (random changes to data) are a nightmare. Learning to write pure, predictable functions keeps your data pipelines from exploding.

2. Don’t Wait for a Ticket

The average developer waits for work to be assigned. The indispensable developer goes hunting for it.

Every company is full of waste. The marketing team manually fixing a spreadsheet every Monday. The operations guy copy-pasting files between folders.

This is your chance.

You need an automation-first mindset. Learn to write scripts that touch the file system, scrape messy data, and handle errors gracefully. If a network connection drops, a bad script crashes. A good tool waits, retries, logs the issue, and keeps going.

AI can write the script if you tell it exactly what to do. But you are the one who has to notice the inefficiency, talk to the marketing manager, and design the tool that actually helps them.

3. Treat Data Like Gold

In 2025, data literacy isn’t optional.

You need to know your Data Structures. I’m not talking about passing a whiteboard interview. I mean knowing the trade-offs.

These small choices add up. They determine if your application runs smoothly or crawls to a halt. AI often defaults to the simplest option, not the best one.

A Gift to Get You Started

Talking about these concepts is easy. Doing the work is harder.

I want to help you take that first step. I found a resource that covers these exact mechanics—from the basics of variables to the bigger picture of OOP and file handling.

It is called the Python Complete Course For Beginners.

It’s a solid starting point to build the technical muscle you need to stop just “writing code” and start building systems.

I have a coupon that makes it 100% free. These coupons don’t last long, so grab it while you can.

Click here to access the free course

You can find the link to access the course for free in the bottom of the post.

The Bottom Line

Don’t let the headlines scare you. The demand for engineers who can solve fuzzy, real-world problems is higher than ever.

The code is just a tool. The value is you.

Level up your thinking. Master the tools that let you control the machine, rather than compete with it.

Stay curious,

Boucodes and Naima / 10xdev blog Team

December 08, 2025 08:46 AM UTC