The Night I Realized My Code Was Working Against Me

It was 1:14 a.m. My automation script had been running for hours or at least, I thought it had.

When I checked the logs, nothing made sense. The code wasn't broken. It was just bad. Nested loops, repeated logic, a couple of "temporary" fixes I'd promised myself I'd clean later (you already know how that ends).

That's when I had my moment not the kind where you throw the laptop out the window, but the quiet kind, the one where you realize:

"I don't need to write more code. I need to write smarter code."

Since then, I've collected a handful of Python tricks that completely changed how I automate things. They're small. But each one made me faster, cleaner, and a little more dangerous in a codebase.

Let's get into them.

1. Forget Loops — Try Vectorized Thinking with Numexpr

At some point, every Python dev writes loops they shouldn't. I used to write for-loops for everything data cleaning, calculations, even filters. Until I met numexpr.

Numexpr evaluates numerical expressions as strings and it's lightning fast because it uses multiple cores without you doing anything fancy.

import numexpr as ne
import numpy as np

a = np.arange(1e6)
b = np.arange(1e6)
result = ne.evaluate("2 * a + 3 * b")

This line alone replaced an entire nested loop in one of my scripts.

Why it matters: You're not writing automation for elegance. You're writing it for speed. And nothing feels better than slicing runtime from 40 seconds to 3.

Pro tip: Think like a vector, not like a loop.

2. Stop Using print() for Debugging — Use IceCream Instead

You know the moment debugging at 2 a.m., juggling print statements like confetti. Then I found IceCream, and it instantly changed my debugging habits.

from icecream import ic

x = 42
y = "automation"
ic(x, y)

Output:

ic| x: 42, y: 'automation'

It tells you the variable name and value, inline.

Why it matters: Debugging is half your life as a programmer. IceCream makes it human again less time squinting, more time fixing.

3. Logging That Talks Back — Structlog

Standard logging is fine until you need real context. I was once debugging a serverless automation where logs from multiple threads overlapped. That mess? Structlog fixed it.

import structlog

log = structlog.get_logger()
log.info("Processing started", user="Mahad", task="Data Cleaning")

Structlog lets you log structured, contextual info that's easy to filter later perfect for async automations or microservices.

Why it matters: Logs are your story when you're not in the room. Make sure they're clear enough for someone else to debug your chaos.

4. The One-Liner That Automates My Terminal: Invoke

This one felt illegal the first time I used it. You can automate shell commands directly from Python safely with Invoke.

I used it to chain deploy scripts, run linting, and even clean data folders automatically.

 from invoke import task

@task
def clean(c):
    c.run("rm -rf data/temp/*")

Now, I just type:

invoke clean

and my workspace resets.

Why it matters: Automation doesn't stop in your Python file. It should extend to your workflow.

5. Real Parallelism with Joblib

For months, I thought multiprocessing was the answer. Then I discovered Joblib. It's minimal, elegant, and crazy fast for parallel tasks.

Here's how I parallelized an API scraping task:

from joblib import Parallel, delayed
import requests

def fetch(url):
    return requests.get(url).status_code

urls = ["https://example.com" for _ in range(10)]
results = Parallel(n_jobs=4)(delayed(fetch)(url) for url in urls)

Pro tip: Most Python scripts are slower than they need to be because we write them linearly. Joblib makes your CPU work like it's 2026.

6. Caching Like a Pro with CacheTools

If you ever wrote an automation that hits the same API multiple times, you know the pain. I once built a job parser that fetched identical data over and over. Total waste.

Then I added cachetools, and the problem disappeared.

from cachetools import cached, TTLCache

cache = TTLCache(maxsize=100, ttl=300)

@cached(cache)
def fetch_user(id):
    print("Fetching user...")
    return {"id": id, "name": "Mahad"}

Now it only fetches once every 5 minutes automatically.

Why it matters: Good automation isn't about speed; it's about efficiency. Cache your intelligence.

7. Clean Automation UIs with Textual

If your automations live in the terminal, this is a game-changer. Textual lets you build interactive terminal apps dashboards, monitors, and even mini GUIs all in Python.

I built a CLI dashboard to monitor my running automations and log stats live.

from textual.app import App
from textual.widgets import Header, Footer, Static

class Dashboard(App):
    def compose(self):
        yield Header()
        yield Static("Automation running...", id="status")
        yield Footer()

Dashboard().run()

It felt like I built a control center inside my terminal.

Why it matters: Automation isn't just backend wizardry. When you can see what's happening, you control it better.

Final Thoughts: Simplicity Is Power

Every one of these tricks started as a frustration. A bottleneck. A "why does this take so long?" moment.

But here's the truth I've learned after years of building automations: You don't get faster by adding complexity. You get faster by mastering simplicity.

Automation isn't about writing more scripts. It's about writing fewer and smarter ones.

So next time your code feels clunky, ask yourself:

"Am I solving the problem… or just managing it?"

If it's the second one it's time to upgrade your toolbox.

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