I have a note in my phone called “random” that’s been running for over a year. It’s a disaster. Half-finished thoughts, business ideas, a line I liked from a podcast, something I wanted to research, a task I never scheduled, all of it mixed together with zero structure.

Every few weeks I’d open it, feel briefly overwhelmed, and close it again without doing anything. The ideas weren’t bad. They were just unusable in the state they were in, scattered fragments instead of anything I could actually act on.

The problem was never that I lacked ideas. It was that raw thoughts and usable output are two completely different things, and I didn’t have a reliable way to turn one into the other.

These are the 10 prompts that finally fixed that, and none of them are “organize this for me.” That gets you a slightly tidier mess. These get you something you can actually use.

1. The Category Sorter

Before anything else, you need to know what kind of thoughts you’re even looking at.

Here’s a messy list of thoughts, ideas, and notes I’ve collected: [PASTE YOUR BRAIN DUMP]. Sort these into natural categories based on what they actually are, tasks, ideas worth developing, things to research, random thoughts with no clear action, and anything else that emerges. Don’t force categories that don’t fit.

The instruction not to force categories matters. A brain dump often has 2 things that don’t belong anywhere yet, and pretending they fit somewhere just creates false structure.

2. The “What’s Actually Actionable” Filter

Not everything in a brain dump deserves the same kind of attention. Some things needed action years ago and just never got flagged.

From this list, identify which items are things I could act on this week if I chose to, which are longer-term ideas with no immediate action, and which are just thoughts with no action attached at all. Be honest, don’t inflate vague thoughts into fake action items.

This prompt stops you from turning every stray idea into a guilt-inducing task. Some things on your list were never meant to become to-dos.

3. The Duplicate and Overlap Finder

Brain dumps accumulate the same idea written five different ways over months, because you forgot you already wrote it down once.

Look through this list and identify any ideas that are actually the same thought expressed differently, or ones that overlap significantly. Group them together and tell me which version is the clearest way to state the combined idea.

I found the same business idea, worded three different ways, spread across four months, in my own notes. This prompt is the only reason I noticed.

4. The Vague Idea Sharpener

Some entries in a brain dump are just a fragment, evocative but too vague to act on.

Here’s a vague idea I wrote down: [PASTE THE VAGUE ENTRY]. Ask me 3 specific questions that would help sharpen this into something concrete enough to actually act on or explain to someone else.

The point isn’t for the model to guess what you meant. It’s for it to ask the right questions so you can clarify it yourself, since you’re the only one who actually knows what was in your head when you wrote it.

5. The Priority Ranker

A sorted list is progress. A sorted and ranked list is something you can actually use tomorrow morning.

From this sorted list of ideas and tasks, rank them by a combination of how much impact they’d have and how much effort they’d take. Tell me the top 3 I should actually focus on first, and briefly say why the rest can wait.

Ranking forces a decision. Without it, everything sits at the same level of importance, which functionally means nothing is a priority at all.

6. The One-Paragraph Expansion

Some ideas deserve more than a fragment but don’t need a full plan yet, just enough shape to remember why you cared.

Take this one-line idea: [PASTE THE IDEA]. Expand it into a single clear paragraph that explains what it actually is, why it might be worth pursuing, and what the first small step would look like.

This is the difference between a note that means something to you today and one that’ll be meaningless gibberish when you reread it in six months.

7. The Connection Finder

Some of your best ideas aren’t new. They’re two existing scattered thoughts that were always meant to be one idea.

Look at this full list of ideas and thoughts. Are there any two or three that connect in a way I haven’t noticed, ideas that could combine into something stronger than either one alone? Point out the connection and explain why it works.

I’ve had two separate “someday” ideas turn into one actual project because of this prompt. They’d been sitting three months apart in the same note the whole time.

8. The Kill List

Half the value of cleaning up a brain dump is deciding what to actually let go of.

From this list, identify which items I should honestly consider deleting entirely, ideas that sounded interesting in the moment but don’t hold up, or tasks that are no longer relevant. Be direct, don’t just tell me to keep everything “just in case.”

Permission to delete matters more than it sounds like it should. Most people keep dead ideas around out of guilt, not genuine intent to revisit them.

9. The Turn-Into-a-Plan Prompt

For the ideas that survive filtering, ranking, and connecting, this is where it becomes real.

Take this idea: [PASTE THE DEVELOPED IDEA]. Turn it into a simple action plan with no more than 5 steps, the first of which should be something I could start within the next day, not the next month.

The constraint on the first step matters most. A ten-step plan starting with “research thoroughly” almost never gets started. A plan starting with something doable today usually does.

10. The Weekly Brain Dump Processor

The real fix isn’t a one-time cleanup. It’s a repeatable process so the mess doesn’t rebuild itself in another year.

Here’s everything I’ve added to my notes this week: [PASTE THE WEEK’S ENTRIES]. Sort, prioritize, and tell me the one thing from this week’s list that’s most worth carrying forward into next week, and the rest I can safely let sit or let go.

Running this weekly is what actually prevents the year-long backlog from forming again. A messy note isn’t the problem, an unprocessed one is.

If You Want More Prompts Built the Same Way

These 10 solve the brain dump problem specifically. But this same principle, prompts built for a specific job instead of generic requests, applies to basically everything you use ChatGPT for.

I put a full set of prompts built this way into the ChatGPT Prompt Playbook. It’s free.

Grab it here:

ChatGPT Prompt Playbook

Try it on whatever’s sitting in your own messy notes app right now. That’s usually the fastest way to see the difference.

10 Prompts for Turning a Messy Brain Dump Into Something Actually Useful was originally published in CodeToDeploy on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Eleven times. Eleven separate conversations, for eleven different kinds of tasks. A client email. A content caption. A quick research question. Another email, different client, different tone. A LinkedIn post. Something for a proposal. Back to email. A random “explain this to me” question that had nothing to do with work at all.

None of those tasks took long individually. Most were under five minutes. But something about the day felt exhausting in a way that didn’t match how much actual work got done.

I went looking for why, and it wasn’t the tasks. It was the gap between them.

The Cost Nobody Names

There’s a well-known concept in productivity research called context switching cost, the mental tax you pay every time you move from one type of task to a completely different one. Most people know this applies to deep work versus meetings, or coding versus email.

Almost nobody applies it to how they use ChatGPT.

Every time I opened a new chat for a new kind of task, I wasn’t just typing a new question. I was rebuilding an entire mental frame from scratch. What tone does this need? What’s the goal here? What context does the model need that it doesn’t already have? What did the last similar prompt I wrote even look like?

None of that shows up as “time spent.” It shows up as fatigue. By hour six of eleven context resets, I wasn’t tired from the work. I was tired from the switching.

Why This Is Worse With AI Than With Anything Else

Here’s the part that makes this specific to how we use AI tools, and not just a general truth about multitasking.

When you switch tasks in your own head, you at least carry your own memory with you. You remember roughly what worked last time you wrote a client email, even if you don’t consciously think about it.

When you switch chats with ChatGPT, you get none of that carryover unless you explicitly reconstruct it. Every new conversation starts at zero. No memory of your preferred tone. No memory of the structure that worked well last time. No memory of the specific phrasing that got you a good result three days ago for the exact same kind of task.

So the switching cost isn’t just cognitive, it’s rebuilding cost. You’re not just changing mental gears, you’re re-explaining yourself to a stranger, over and over, multiple times a day, for tasks you’ve genuinely done dozens of times before.

What I Actually Tracked

I paid attention for a full week after that Tuesday. Here’s roughly what the eleven-a-day pattern broke down into:

Client communication, several times a day, different clients, different tones each time. Content writing, captions, posts, occasional longer pieces. Quick research or fact-checking, usually mid-task, low effort but disruptive. Planning or prioritization, usually once a day, often at the start or end. Random personal questions, unrelated to work, just genuine curiosity.

Five distinct categories. Not eleven distinct problems, five categories repeating throughout the day. That distinction mattered more than I expected once I noticed it.

I wasn’t actually facing eleven new problems every day. I was facing the same five kinds of problems, over and over, and rebuilding my approach to each one from scratch every single time it came up.

The Fix Isn’t Fewer Tasks. It’s Fewer Rebuilds.

You can’t eliminate the different kinds of work you do in a day. The client emails still need writing. The content still needs to go out. The research questions still come up mid-task.

What you can eliminate is the rebuilding. If you already know your five (or eight, or twelve) recurring categories of AI tasks, you don’t need to reinvent your approach to each one every single time. You need a version that already works, sitting there, ready to adapt instead of construct.

That’s a completely different mental experience than starting from a blank chat window. It’s the difference between writing an email and filling in a template you already trust.

Where This Actually Led Me

I ended up doing something almost embarrassingly simple. I sat down and built out a proper prompt for each of my recurring categories, once, properly, instead of half-remembering an approach each time. Client tone. Content structure. Research framing. Planning format.

That’s the entire idea behind the Complete AI Prompt Library. It’s 8 resources bundled together, covering the actual recurring categories people run into, content, marketing, outreach, client communication, and more, so instead of reconstructing your approach from zero every time a familiar kind of task shows up, you’re adapting something that’s already built.

If eleven chats a day sounds a little too familiar, this might save you more time than it costs:

Complete AI Prompt Library

Worth trying even just on the one category that eats the most of your day. That’s usually where the fix is felt the fastest.

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After spending years writing Python, I stopped getting excited about libraries just because they were popular.

Early on, I used to install a new package whenever I discovered something interesting. A new library promised faster development, cleaner code, better automation, or some clever way to solve a problem I hadn’t even encountered yet.

Sometimes it worked.

More often, I ended up with another dependency, another API to remember, and another piece of code I had to maintain.

Over time, my approach changed.

I started paying attention to the libraries I repeatedly reached for when building actual projects. Not the ones that looked impressive in a tutorial, but the ones that saved me time when requirements changed, data got messy, an API failed, or a script suddenly needed to run automatically every morning.

Those libraries became my secret weapons.

They aren’t necessarily the most complicated Python packages. In fact, some of them are surprisingly simple.

But that’s exactly why I value them.

A good library doesn’t make your code look clever.

It makes a difficult problem feel ordinary.

Here are the Python libraries that earned a permanent place in my workflow.

1. pathlib — The Library I Use Before Reaching for Anything Fancy

One of the biggest improvements in my Python code wasn’t learning a sophisticated framework.

It was learning to handle files properly.

I used to see code like this everywhere:

import os
files = os.listdir("data")
for file in files:
    if file.endswith(".csv"):
        print(file)There’s nothing inherently wrong with it.

But once I started building automation-heavy scripts, file handling became more complicated.

I needed to create directories, move files, inspect extensions, build paths, find specific files, and make scripts work across different operating systems.

That’s when pathlib became one of my default tools.

from pathlib import Path
data_dir = Path("data")
csv_files = list(data_dir.glob("*.csv"))
for file in csv_files:
    print(file)The difference looks small.

In real projects, it isn’t.

pathlib gives me an object-oriented way to work with paths instead of constantly manipulating strings.

For example:

output = Path("reports") / "2026" / "summary.csv"

I don’t have to worry about manually constructing the correct path separator.

That becomes especially useful when you’re building automation.

Imagine a script that:

1. Finds newly downloaded CSV files.
2. Reads them.
3. Cleans the data.
4. Generates a report.
5. Saves the report into a dated directory.
6. Archives the original files.

The data processing might require Pandas.

The report might require another library.

But pathlib quietly holds the entire workflow together.

That’s the kind of library I appreciate more after years of development.

It doesn’t steal the spotlight.

It removes friction.

What I learned

Don’t underestimate boring problems.

File paths, directories, filenames, configuration files, and temporary files aren’t exciting.

But automation is mostly about connecting these boring pieces reliably.

And pathlib makes that work much cleaner.

2. Pandas — Not Because It’s Popular, But Because Real Data Is Messy

If you’ve worked with real-world data, you already know the problem.

The dataset in the tutorial is beautiful.

The dataset someone sends you on Monday morning is not.

I’ve received data with missing values, inconsistent column names, duplicate rows, unexpected types, strange dates, empty strings, and columns that apparently mean three different things depending on the row.

This is where Pandas became less of a data-science library and more of a general-purpose problem-solving tool for me.

A simple example:

import pandas as pd
df = pd.read_csv("customers.csv")
df.columns = df.columns.str.lower().str.replace(" ", "_")
df = df.drop_duplicates()
df["email"] = df["email"].str.strip().str.lower()None of this is particularly advanced.

That’s the point.

A large percentage of data work isn’t building sophisticated models.

It’s getting the data into a shape where sophisticated models can actually be useful.

One of my favorite Pandas features for automation is the ability to chain transformations.

cleaned = (
    df
    .drop_duplicates()
    .dropna(subset=["email"])
    .assign(email=lambda x: x["email"].str.lower().str.strip())
)

When a pipeline contains many transformations, this style can make the sequence easier to understand.

But Pandas also taught me something more important.

Automation exposes bad assumptions

A script that works once is easy.

A script that runs every day is different.

Suppose your code expects a column called:

customer_email

Then someone changes the export to:

email_address

Your notebook doesn’t care.

Your automated pipeline does.

That’s why I increasingly think about data validation before data processing.

Before transforming a dataset, I want to know:

• Are the expected columns present
• Are the data types reasonable?
• Are required values missing?
• Are there unexpected duplicates?
• Did the number of records suddenly change?

A professional data pipeline doesn’t just process data.

It checks whether the data makes sense.

That was a lesson I learned through projects rather than courses.

3. Requests — Because APIs Are Everywhere

Python becomes dramatically more useful when it can communicate with other systems.

That usually means APIs.

And for straightforward HTTP work, requests has been one of the libraries I keep coming back to.

import requests
response = requests.get(
    "https://api.example.com/users",
    timeout=10
)
response.raise_for_status()
users = response.json()That last part matters.

I’ve become much more careful about error handling as my scripts have become more automated.

This:

requests.get(url)

might be enough for a quick experiment.

This:

response = requests.get(url, timeout=10)
response.raise_for_status()

is much closer to what I want in an actual automation workflow.

Why?

Because APIs fail.

Servers become unavailable.

Requests time out.

Authentication expires.

Rate limits appear.

Responses change.

If nobody is watching the script when it runs, your code needs to know how to react.

That’s the difference between writing a script and building automation.

A useful automation system should answer questions such as:

What happens if the API doesn’t respond?

What happens if the response isn’t valid JSON?

What happens if the API returns zero records?

What happens if the request takes two minutes instead of two seconds?

Those questions aren’t particularly glamorous.

They’re also the questions that eventually show up in production.

4. Pydantic — The Library That Changed How I Think About Data Validation

I didn’t fully appreciate data validation until I started building systems where different pieces of software had to communicate with each other.

You receive data.

You transform it.

You send it somewhere else.

Then someone changes the input.

Suddenly your assumptions break.

Pydantic gives you a much cleaner way to define what your data should look like.

from pydantic import BaseModel
class Customer(BaseModel):
    name: str
    email: str
    age: int

Now the structure isn’t just living in my head.

It’s represented in code.

This becomes especially powerful when working with APIs, configuration, and AI applications.

For example, if an AI model is supposed to return structured information, I don’t want my entire application to blindly trust a block of generated text.

I want a defined structure.

Something like:

class Analysis(BaseModel):
    category: str
    confidence: float
    summary: str

Then I can validate the result before passing it deeper into the application.

This changed one of my habits.

I stopped thinking of validation as something I add after a system breaks.

I started treating validation as part of the system design.

Pro tip: The earlier you validate data, the easier it is to find where a bug actually started.

This becomes incredibly important in automated workflows because there might not be a human checking every intermediate result.

5. openpyxl — Because Excel Never Went Away

I have built plenty of systems where the final destination was not a database.

It was Excel.

You can spend an afternoon designing an elegant data pipeline and then discover that the person using the output wants an .xlsx file.

That’s real software development.

And instead of fighting that reality, I’ve learned to automate it.

openpyxl is one of the libraries I use when I need to work directly with Excel files.

For example:

from openpyxl import Workbook
wb = Workbook()
ws = wb.active
ws.append(["Name", "Revenue"])
ws.append(["Alice", 12000])
ws.append(["Bob", 15000])
wb.save("report.xlsx")

The interesting part isn’t creating a spreadsheet.

The interesting part is what happens when you combine this with other libraries.

Imagine:

API
 ↓
Python
 ↓
Pandas
 ↓
Data validation
 ↓
Analysis
 ↓
Excel report

Now you have an automated reporting system.

No copy-pasting.

No manually downloading data.

No opening Excel every morning just to perform the same five steps.

This is where I started seeing Python differently.

Python isn’t just a programming language for building applications.

It can be the glue between systems that were never designed to work together.

6. SQLAlchemy — When I Stopped Treating Databases Like CSV Files

There is a point in many Python projects where CSV files stop being enough.

Maybe the dataset gets larger.

Maybe multiple users need access.

Maybe you need transactions.

Maybe records need to be updated rather than recreated.

Maybe your application needs persistent state.

That’s when databases become unavoidable.

And SQLAlchemy became one of the tools that helped me work with databases more comfortably from Python.

A simplified example:

from sqlalchemy import create_engine
engine = create_engine("sqlite:///app.db")
df.to_sql(
    "customers",
    engine,
    if_exists="replace",
    index=False)

The important lesson wasn’t simply learning another library.

It was learning when to stop forcing one tool to do a job it wasn’t designed for.

I’ve seen projects where someone keeps everything in Pandas DataFrames long after the application has started behaving like a database system.

That’s usually a sign that the architecture needs to evolve.

Pandas is excellent for data manipulation.

A database is designed for persistent storage, querying, concurrency, and structured access.

Knowing the difference is more valuable than memorizing another hundred Pandas methods.

7. scikit-learn — The Library That Made Me Stop Overcomplicating Machine Learning

Machine learning has a strange effect on developers.

As soon as they hear “AI,” they sometimes assume the project needs a neural network.

I’ve made that mistake too.

Then I started building more practical systems.

A lot of problems don’t require a huge model.

Sometimes you need:

• classification
• clustering
• regression
• preprocessing
• feature selection
• dimensionality reduction
• similarity calculations

And scikit-learn handles an enormous amount of this work.

A basic pipeline can be surprisingly compact:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
model = Pipeline([
    ("scale", StandardScaler()),
    ("classifier", LogisticRegression())
])
model.fit(X_train, y_train)

What I particularly like is the emphasis on composable building blocks.

You can construct a workflow instead of scattering preprocessing and modeling logic throughout your project.

That matters when you eventually need to reproduce the process.

Because reproducibility is one of those things that sounds optional until you need it.

If preprocessing happens differently during training and inference, your model doesn’t have a machine-learning problem.

It has a software-engineering problem.

That’s another lesson real projects taught me.

8. sentence-transformers — When Search Became More Interesting Than Keywords

One of the biggest shifts in my AI projects happened when I stopped thinking about search as simply matching words.

Consider a knowledge base containing thousands of documents.

A user searches:

“How do I reduce memory usage in Python?”

A keyword search might look for “memory,” “Python,” and “reduce.”

But semantic search can represent the meaning of the query and compare it with representations of documents.

That’s where embedding models become useful.

With sentence-transformers, the workflow can be surprisingly straightforward.

from sentence_transformers import SentenceTransformer
Now those sentences have numerical representations that can be compared.

This opened up several projects for me:

• document search
• duplicate detection
• recommendation systems
• clustering
• semantic retrieval
• question-answering systems

But the deeper lesson was about representation.

The model isn’t magically “understanding” your database.

You’re creating a numerical representation that makes certain relationships computationally useful.

Once I understood that, many AI applications started looking less mysterious.

9. FastAPI — The Moment My Scripts Started Becoming Services

For a long time, I built Python scripts that solved a problem.

Then someone asked:

“Can we make this available to the rest of the application?”

That’s a different problem.

A script runs.

An API serves.

FastAPI became one of my favorite ways to turn Python logic into an accessible service.

A minimal endpoint can look like this:

from fastapi import FastAPI
app = FastAPI()
@app.get("/health")
def health():
    return {"status": "ok"}That’s tiny.

But the real value comes when you connect it to the systems you’ve already built.

For example:

User request
     ↓
FastAPI
     ↓
Validation
     ↓
Python logic
     ↓
ML / AI model
     ↓
Database
     ↓
JSON response

Suddenly a Python project isn’t just something you run from your terminal.

It’s something other applications can use.

This was one of the biggest transitions in my development journey.

I stopped asking:

“Can I write a script that does this?”

And started asking:

“Can I build a reliable service around this?”

That small change in thinking improved the architecture of my projects dramatically.

10. Rich — Because Developer Experience Matters Too

This one is less about data science and more about something I overlooked for years:

the experience of using my own tools.

When a Python script runs for several minutes, this:

Processing...

isn’t particularly helpful.

Rich lets you build much better terminal output.

For example:

from rich.progress import track
import time
for item in track(range(100)):
    time.sleep(0.01)Now I can see progress.

That sounds trivial.

It isn’t.

If you build automation tools that other developers use, observability becomes part of usability.

Progress indicators, tables, formatted errors, logs, and clear status messages make debugging significantly easier.

A technically correct tool can still be frustrating to use.

I’ve learned that developer experience isn’t decoration.

It’s part of engineering.

The Pattern I Eventually Started Seeing

After using all these libraries across different projects, I noticed something.

The libraries weren’t the real reason my projects improved.

The way I combined them was.

A typical automation project might look like this:

Pathlib
   ↓
Find incoming files
   ↓
Pandas
   ↓
Clean and transform data
   ↓
Pydantic
   ↓
Validate structure
   ↓
scikit-learn / AI
   ↓
Analyze data
   ↓
SQLAlchemy
   ↓
Store results
   ↓
openpyxl
   ↓
Generate report

No single library is revolutionary here.

Together, they create a system.

And that’s where I think many Python developers eventually reach a turning point.

You stop collecting libraries.

You start designing workflows.

The Biggest Mistake I Made With Python Libraries

For years, I thought becoming a better Python developer meant learning more libraries.

It doesn’t.

At least, not indefinitely.

There is always another package.

Another framework.

Another AI SDK.

Another tool promising to make your code faster.

If you chase all of them, you’ll spend more time learning tools than building things.

My approach now is much simpler.

When I encounter a problem, I first ask:

Can the standard library solve this?

If not:

Is there already a mature library for it?

Then:

Will adding this dependency actually make the system simpler?

That last question saves me surprisingly often.

A library should remove complexity.

If it introduces more complexity than it removes, I think twice before using it.

What Years of Python Actually Taught Me

The biggest change wasn’t that I learned more syntax.

It was that I became better at recognizing patterns.

I started recognizing when:

• a script should become a service
• a DataFrame should become a database table
• repeated manual work should become automation
• unvalidated input should become a schema
• keyword search should become semantic search
• a messy workflow should become a pipeline
• a quick experiment needed production safeguards

That is what I consider professional development.

Not knowing every Python library.

Knowing which problem you’re solving and choosing the simplest reliable tool for it.

The libraries above became my secret weapons because they repeatedly helped me do exactly that.

And interestingly, the more experienced I became, the less interested I was in using complicated tools just to prove that I could.

I wanted code that was easy to understand.

Easy to automate.

Easy to debug.

And, most importantly, easy to change when the requirements inevitably changed.

If I Had to Start Again

If I were starting Python development again today, I wouldn’t try to memorize dozens of libraries.

I’d build projects.

Then I’d let the problems introduce me to the libraries.

Build a file-processing automation.

You’ll meet pathlib.

Build a data-cleaning pipeline.

You’ll meet Pandas.

Connect it to an external service.

You’ll meet requests.

Build an API.

You’ll meet FastAPI and Pydantic.

Build a prediction system.

You’ll meet scikit-learn.

Build document search.

You’ll meet embeddings and sentence-transformers.

Automate reporting.

You’ll probably meet Excel tooling.

That’s a much better learning loop than randomly studying libraries.

Problem → project → limitation → library → solution.

That’s how most of these tools became permanent parts of my workflow.

Not because someone told me they were essential.

Because I needed them.

Final Thought

After years of Python development, I’ve become less impressed by code that looks complicated.

I’m much more impressed by code that quietly solves a real problem every day.

A script that saves someone two hours.

A pipeline that eliminates repetitive work.

An API that turns a useful model into a usable service.

A data-cleaning process that runs without someone opening a notebook.

Those are the projects that taught me the most.

And the libraries that made them possible became tools I could rely on without thinking twice.

That’s ultimately what I look for in a Python library.

Not whether it’s trendy.

Not whether everyone on Twitter is talking about it.

Not whether it has the longest feature list.

I ask a much simpler question:

Does this make the problem I’m solving easier?

If the answer keeps being yes, the library stays.

That’s how these Python libraries became my secret weapons.

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These Python Libraries Became My Secret Weapons After Years of Python Development was originally published in CodeToDeploy on Medium, where people are continuing the conversation by highlighting and responding to this story.