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Vibe Coding — The End of Coding as We Know It

Soargram — Mon, 06 Jul 2026 19:01:25 +0000

This article was originally published as a visual article by DevMindS on the Soargram platform.

Code is Dead. Long Live Code.

You've heard the term. You've seen the memes. You've probably tried it yourself. Vibe coding. The new way to build software where you just describe what you want — and the code appears.

But here's the truth: Vibe coding isn't the end of programming. It's the end of programmers who don't adapt.

1. What Actually Is Vibe Coding?

It started as a casual idea. Now it's everywhere.

AI writes your code. You review it. You fix it. You ship it.

The numbers are clear: most developers are using AI tools now. New developers are starting with them. Startups are building entire products this way. Big companies report that AI is writing a significant portion of new code.

It's not coming. It's here.

2. The Productivity Paradox

Here's the thing — AI makes you faster. But not always.

Some tasks get done twice as fast. Others? Not so much.

Experienced developers sometimes get slower with AI because they spend time reviewing and fixing generated code.

The biggest factor isn't the tool. It's the person using it.

Teams that treat AI as a first draft — that always needs human judgment — see real gains. Teams that treat AI as a finished product? They accumulate problems they don't notice until much later.

3. The Quality Problem Nobody's Talking About

Vibe coding generates code fast. But speed comes at a cost.

AI-generated code has more issues than human-written code. It fails security checks more often. Trust in AI code accuracy has dropped.

The problem is simple: AI optimizes for functionality, not security.

AI doesn't think about edge cases. AI doesn't worry about vulnerabilities. AI just makes it work. Whether it's safe or correct? That's on you.

There are stories of AI deleting entire databases. AI ignoring freeze instructions. AI doing exactly what you asked — and breaking everything in the process.

4. The 70% Problem

AI handles the first 70% of a project smoothly.

The remaining 30%? Only experienced engineers can handle it.

The first 70% is the easy part — boilerplate, repetitive tasks, common patterns. The last 30% is the hard part — edge cases, performance, security, architecture.

AI can't see these problems. But an expert can.

That's the gap. That's the opportunity.

5. The Three Skills That Actually Matter

As AI handles the heavy lifting, developers must master three things:

Writing Good Prompts
You need to understand the task, the inputs, the outputs, the errors, the performance expectations. The focus is moving from writing good code to writing good prompts.

Evaluating What AI Generates
Don't trust. Always verify. AI can be confidently wrong. Study the code. Understand it. Fix it. Rewrite the prompt. Or rewrite the code yourself.

Understanding the Problem
Design isn't about writing code. It's about deciding what matters. What constraints exist. What tradeoffs are acceptable. The key is leveraging AI to focus on higher-level design — while staying in control of the complexity.

6. The 70/30 Rule

Software has two types of complexity:

Accidental Complexity (70%): Not inherent to the problem, but to the process — tools, languages, frameworks. AI can reduce this.

Essential Complexity (30%): The inherent difficulty of the problem itself — abstract ideas, relationships, algorithms. AI cannot reduce this.

Even if all accidental tasks become zero, essential tasks still take most of your effort. The 70% is getting easier. The 30% is becoming more valuable.

7. The Bottom Line

Vibe coding is not the end of programming. It's the end of programming as we knew it.

The shift is from writing code to orchestrating it.

The coders are threatened. The engineers are essential.

The real winners will be developers who:

Can turn fuzzy problems into clear instructions
Can design context that leads to good results
Can distinguish what truly matters from what just works

The goal isn't to write more code faster. The goal is to build better software.

And that judgment — what "better" means — still depends on you.

This article was originally published as a visual article by DevMindS on Soargram — a social network for visual content. Read, save, and discuss it there.

Visual article on Soargram

You're building in public. Who's actually watching?

Soargram — Sun, 05 Jul 2026 17:48:20 +0000

Everyone's building in public now. It's the new badge of honor. The new "I'm an entrepreneur" stamp.

Every founder, every indie hacker, every developer, every "solopreneur" is tweeting their daily progress. Posting screenshots of their code. Sharing their wins, their fails, their lessons. It feels productive. It feels like you're building something.

But here's the thing – when everyone does it, it stops working.

The signal-to-noise ratio is dead. What worked in 2020 doesn't work in 2026. Back then, building in public was rare. You actually stood out. People followed your journey because it was interesting, not because it was expected.

Now it's the same story. The same excitement. The same "win" threads. The same metrics you're supposed to care about. The same format. The same posts. The same "I woke up at 5 AM and shipped" energy. It's become noise. And noise doesn't build businesses.

Here's the uncomfortable truth:
Most of your audience isn't real. Half of them are bots. Half of them are other builders doing the same thing – posting, not reading. Engagement is low. Comments are empty. People are liking your post but not actually reading it. They're just hitting the button and moving on.

You're spending 2–3 hours a day posting about your "progress" – but your product isn't growing. Your users aren't coming from Twitter. Your revenue isn't moving. Your actual business metrics aren't changing. But you feel busy. You feel productive. You feel like you're doing something.

Are you? Or are you just performing the idea of building?

The really smart builders are doing it differently.
Instead of tweeting about building – they build. Instead of performing "progress" for the algorithm – they make actual progress. They spend their time on the product, not on the posts. They spend their energy on the code, not on the comments.

The best product marketing in 2026 is a product that's so good it markets itself. Not a founder who's so loud they can't be ignored. Not a feed full of updates. Not a thread about how you fixed a bug. A product that people actually want. A product that solves a real problem. A product that doesn't need you to tweet about it every day.

So ask yourself honestly:

Are you building for your followers? Or for your users?

One gives you likes. One gives you validation. One gives you the feeling of being seen. But the other gives you a business. The other gives you customers. The other gives you revenue. The other gives you something that lasts beyond the next post.

One makes you feel good. The other makes you money.

Choose carefully. Because the algorithm doesn't care about your progress. Your users do. And if you're not building for them – you're not building anything at all.

This article was originally published as a visual article in The Vault on Soargram — a social network for visual content. Read, save, and discuss it there.

Read the full article here: You're building in public. Who's actually watching?

If You're Nothing Without the Pattern - You Shouldn't Have It

Soargram — Sat, 04 Jul 2026 13:57:59 +0000

This article was originally published as a visual article by Herkie Jerkie on the Soargram platform.

You ever see a developer who can't write a single line of code without consulting the Gang of Four first?

Like, bro, I get it. Design patterns are cool. But if you're nothing without the pattern — you shouldn't have it.

I've seen "seniors" spend 3 hours debating whether to use a Factory or a Builder for a simple object creation. Meanwhile, the junior next to them just wrote a function that does the same thing in 5 minutes.

And guess what? That junior's code worked. The "senior's" code didn't even get written because they were too busy flexing their pattern knowledge.

Here's the thing
Patterns are tools. Not a personality. Not a badge. Not a sign that you're a "real" developer.

If you can't write simple, clean code without slapping a pattern on it — you don't understand the problem. You're just pattern-matching your way through life. And that's fine if you're a beginner. But if you call yourself a senior and you still do this? Oof.

Real seniors know when NOT to use a pattern
They know that sometimes a simple if-else is better than a Strategy pattern. Sometimes a plain array is better than a complex Observer. Sometimes the best design is no design at all.

Because the goal isn't to use patterns. The goal is to solve problems. And if a pattern helps — great. If it doesn't — don't force it.

So here's my challenge to you
Next time you're about to reach for a pattern — stop.

Ask yourself: "Do I actually need this, or do I just want to look smart?"

Be honest. Your team will thank you. Your future self will thank you. And the junior who has to maintain your code 6 months from now will definitely thank you.

Because if you're nothing without the pattern — you shouldn't have it.

5 Anti-Patterns Beginners Write

Soargram — Sat, 04 Jul 2026 07:12:45 +0000

This article was originally published as a visual guide by Herkie Jerkie on the Soargram platform.

You write code. It works. You feel good.

But deep down, you know it's messy. You just don't know why.

Every beginner writes anti-patterns. It's not because you're stupid - it's because nobody told you the right way.

Here are 5 you probably have right now.

1. Too Many Parameters

def create_user(name, age, email, phone, address, city, country, zip_code): ...

Eight parameters is a nightmare. You forget the order, you mix them up, your IDE screams.

Fix: Use a dictionary or a dataclass.

def create_user(user_data): ...

One object. Clean. Maintainable.

2. type(x) == str

if type(x) == str:
    print("It's a string")

type(x) == str is fragile. Subclasses break it. Use isinstance() instead - it handles inheritance.

if isinstance(x, str):
    print("It's a string")

One simple change. Works every time.

3. The else After return

def is_even(n):
    if n % 2 == 0:
        return True
    else:
        return False

else after return is useless. Remove it.

def is_even(n):
    if n % 2 == 0:
        return True
    return False

Cleaner. Simpler. Done.

4. Deep Nesting

if user:
    if user.is_active:
        if user.has_paid:
            if user.has_access:
                print("Welcome")

Stop. You're building a pyramid. It's slow to read and hard to debug.

Fix: Use early returns.

if not user:
    return
if not user.is_active:
    return
if not user.has_paid:
    return
if not user.has_access:
    return
print("Welcome")

Flat. Readable. This is how seniors write code.

5. Mutating Lists While Iterating

for item in items:
    if item < 0:
        items.remove(item)

This skips elements. Python moves the index, but you move the list.

Fix: Use list comprehension.

items = [x for x in items if x >= 0]

One line. No bugs. Clean.

Or use a copy if you really need to mutate:

for item in items.copy():
    if item < 0:
        items.remove(item)

The Bottom Line

5 anti-patterns. Fix them - and your code instantly goes from junior to senior level.

These are the little things that separate messy code from clean code.

This article was originally published as a visual guide by Herkie Jerkie on Soargram - a social network for visual guides. Read, save, and discuss it there.

Python Myths Even Seniors Believe

Soargram — Fri, 03 Jul 2026 12:12:03 +0000

This guide was originally published as a visual guide by DevMindS on the Soargram platform.

Here are 5 persistent myths that even experienced developers fall for — backed by real facts from industry experts.

Myth 1: "Python is slow because it's interpreted"
This is only half true. Python is indeed slower than C or Rust, but not because it's interpreted. Python code is first compiled to bytecode, which runs on the Python Virtual Machine (PVM) — similar to how Java works.

What actually makes Python slower is its dynamic nature. Every operation requires type lookups, method resolution, boxing/unboxing, and memory allocation. A simple expression like p.x + 2 in Python involves:

Finding the type of p
Calling getattribute()
Unboxing values
Boxing results

None of this depends on interpretation.

Myth 2: "Type hints make Python faster"
Type hints don't improve runtime performance. They're designed for static analysis tools (like mypy) and your IDE, not for the interpreter. The Python interpreter itself doesn't perform any type checking based on these hints.

In fact, type annotations are completely ignored at runtime. The static types are "completely useless from the point of view of optimization and performance".

And you can still break them:

def add(x: int, y: int) -> int:
    return x + y

add('hello ', 'world')  # Still works

Myth 3: "The GIL is a terrible design flaw"
The GIL (Global Interpreter Lock) has a bad reputation, but it actually sits in a sweet spot. It's "just good enough to be useful yet pointless enough to not be used" for complex threading.

The GIL protects Python from needing complex thread synchronization in the common single-threaded case. More importantly, it "means that the core Python developers are not forced to spend a lot of effort supporting a model of concurrency that will ultimately be a dead end".

Removing the GIL was tried in the past — Greg Stein produced a patch years ago, but "nobody seemed to want to pay the penalty it extracted in speed reduction".

Myth 4: "Static compilation of Python is easy with type hints"
Python's dynamic nature makes static compilation fundamentally difficult. As one performance engineer explained: "Everything can change".

Consider this:

import random

class Evil:
    if random.random() > 0.5:
        def hello(self):
            print('hello world')

Evil().hello()  # Works half the time

Legal Python. "This is not something to define in production, I hope. Half of the time it still works, half of the time it raises an exception. Good luck compiling it."

Even new() can return instances unrelated to the class being instantiated. The dynamic features that make Python awesome also make it impossible to fully optimize ahead-of-time.

Myth 5: "Senior devs memorize everything"
Seniors don't have encyclopedic knowledge of syntax. They know concepts, patterns, and trade-offs — not exact syntax. Experience isn't about memorization. It's about knowing "what to solve, not necessarily how to write it".

The real difference between juniors and seniors:

Juniors get stuck on how to write the code

Seniors get stuck on whether they should write it at all

Seniors Google basic syntax openly. They bookmark the same Stack Overflow thread for the 40th time without shame. "Exact syntax is a low-priority cache eviction."

The Bottom Line
The difference between a junior and a senior dev isn't knowing more syntax. It's knowing which tool to use, and when.

This guide was originally published as a visual guide by DevMindS on Soargram — a social network for visual guides. Read, save, and discuss it there.

Read the full guide on the website. Link in bio.

Stop writing loops like a beginner.

Soargram — Thu, 02 Jul 2026 11:43:14 +0000

List comprehensions are faster. Cleaner. Pythonic. Full guide on the website. Link in bio.