DEV Community: UWINTWALI Jean de Dieu

The Dawn of Agentic AI: An autonomous future

UWINTWALI Jean de Dieu — Thu, 11 Sep 2025 09:03:16 +0000

If you don't know what agentic systems are, don't worry. It is made for you still.

I've been following this quietly exciting technology unfold, and I can't help but feel amazed. Agentic artificial intelligence. The words may sound grand, perhaps even intimidating, but at their core, they are surprisingly simple.

✔️ An AI agent is like a digital helper. It reads information, makes decisions, and acts on its own, without needing someone to guide every step.

Now imagine not just one of these helpers, but many of them working together. They coordinate, share tasks, and multiply what any single agent could do. This collaboration of digital minds is what we call an agentic system. One worker can fix a leak, but an entire team can build a house. That is the quiet power of many working in harmony.

I have watched the rise of these AI agents not as a distant observer, but as someone full of curiosity, the way you watch a new horizon slowly reveal itself. Headlines talk about autonomous assistants capable of knowing your schedule, suggesting meals, booking flights, even arranging hotel rooms. For a long time, it sounded like science fiction, but it is quietly becoming part of our daily lives.

Take Incredible.one, for example. A young company leading this revolution. They have built a platform where ordinary people, not just engineers, can summon these digital agents. The beauty lies in the simplicity.

You write your request in plain words, and the system turns it into action.

Want to sort urgent emails, plan a meeting, analyze sales data, or post project updates? These agents can do it, connecting to the apps you already use daily such as:

☑️ Gmail & Outlook: Seamless email communication and management
☑️ Notion & Google Drive: Intelligent document organization and collaboration
☑️ GitHub: Automated code management and development workflows
☑️ Google Sheets: Smart data analysis and spreadsheet operations
☑️ And more…

I tried it myself, and it was quietly transformative. A simple prompt to send an email became an eye-opening experience. Just reach https://www.incredible.one/ and prompting like "send email that says brbr... to given address". The AI proved itself capable of so much more. and can handle Advanced Email Capabilities:

☑️ Reading and understanding context across entire threads
☑️ Managing multiple recipients with intelligent routing (To, CC, BCC)
☑️ Handling attachments, downloading, analyzing, organizing
☑️ Tracking conversations and providing thoughtful summaries
☑️ Composing replies that maintain the natural flow of dialogue

It wasn't just convenient. It was a glimpse into a new way of working, a small but profound shift in how we relate to our digital world.

This is what agentic AI offers. Not just technology, but transformation. Not just tools, but teams. And as I write this, I feel the rare thrill of witnessing history not as something far away, but as something alive, unfolding around us.

The future of work, communication, and creativity is already arriving. It is not coming. It is here. And it carries the name of agentic systems.

The Questions We Must Ask Ourselves

The question before us now is not whether this technology will change our world, it already is. The question is how we will choose to use it.

Will we use these agentic systems to:

▪️ Create more time for what truly matters: human connection, art, reflection, the simple moments that make life worth living?
▪️ Amplify our creativity rather than replace it, letting the agents handle the mundane while we focus on the inspired?
▪️ Bridge gaps between cultures, languages, and communities, making technology more accessible to everyone?
▪️ Design a world where technology serves humanity, not the other way around?

Or will we:

🔸 Use them to accelerate the pace of our already frantic lives, creating even more efficiency without purpose?
🔸 Allow them to disconnect us further from the tangible, human experiences that give life meaning?
🔸 Create new divides between those who understand this technology and those who don't?
🔸 Let the convenience blur the line between assistance and dependency?

What kind of partnership do we want with our digital agents?

One where they extend our capabilities while preserving our autonomy?
One where they handle the mechanics so we can focus on the meaningful?
One where they learn from us, but also teach us to be better, more intentional humans?

And perhaps most importantly:

❔ How do we ensure that as these systems become more powerful, they remain reflections of our best selves rather than amplifications of our worst tendencies?
❔ How do we build a future where agentic systems don't just make us more productive, but make us more human?

The dawn of agentic AI is indeed here, but the story of how it will shape our society, "that story is still being written". We are not just witnesses to this technological revolution; we are its authors. And every choice we make about how we use, regulate, and relate to these systems will determine whether this new chapter in human history is one of liberation or limitation.

The tools are in our hands. The question is: what will we build with them?

Enjoy Intelliget tools..!

TypeScript: Extending Built-in Types

UWINTWALI Jean de Dieu — Mon, 21 Jul 2025 10:37:55 +0000

Hey devs!!

Ever wanted to extend JavaScript's built-in types and make TypeScript fully aware of it?

Here’s a detailed guide I wrote: Demo

Happy Coding!

The SQL JOIN Cheatsheet You’ll Actually Use

UWINTWALI Jean de Dieu — Wed, 16 Jul 2025 06:00:33 +0000

When you're deep in a data problem, the last thing you want is to hesitate over INNER JOIN vs LEFT JOIN. SQL JOINs are fundamental but their subtleties often trip up even experienced developers.

To make this clearer, I’ve created a visual and code-based cheatsheet you can refer to in seconds, not minutes.

Visuals on the Gist

Why This Exists

Most JOIN tutorials either oversimplify or overwhelm. I wanted something between:

Code you can read like a sentence, paired with visuals that explain themselves.

This cheatsheet includes:

Clear examples for each JOIN type
Short comments that get to the point
Visuals that require no explanation

For Developers Who Don’t Like to Guess

If you already know SQL, this isn’t a lesson—it’s a reference.

You won’t find long paragraphs or bloated descriptions here. Just clean queries and concise visuals to validate your logic when it matters.

Use It Your Way

Keep it open when writing queries
Share with teammates
Fork and extend with your own examples

It’s open and ready to grow with you.

Explore the full cheatsheet here:

Visit my the Gist

If it helps you move faster and code with confidence, it’s done its job.

Why Numpy is faster than Pure Python: A Speed Comparison

UWINTWALI Jean de Dieu — Fri, 11 Jul 2025 12:52:36 +0000

Have you ever wondered why data scientists and numerical computing enthusiasts swear by Numpy?

Today, I ran a simple experiment to compare the speed of Numpy versus Pure Python for vectorized operations and the results were mind-blowing!

The Experiment: Numpy vs. Pure Python

I wrote two functions performing the same task, adding two arrays element-wise, one using Pure Python and the other leveraging Numpy.

Here's the code:

import numpy as np
import time 

size_of_vec = 10000

def python_version():
    time_1 = time.time()
    x = range(size_of_vec)
    y = range(size_of_vec)
    z = [x[i] + y[i] for i in range(len(x))]
    return time.time() - time_1

def numpy_version():
    time_1 = time.time()
    x = np.arange(size_of_vec)
    y = np.arange(size_of_vec)
    z = x + y
    return time.time() - time_1

a = numpy_version()  # Numpy time
b = python_version() # Pure Python time

c = b / a
c = int(c)

print(f"Numpy Version: {a}")
print(f"Pure Python Version: {b}")
print(f"Numpy is {c} times faster than Pure Python!")

The Results? Numpy Dominates!

Running this code, I found that Numpy was significantly faster, sometimes 100x or more than Pure Python, especially as the array size grows.

Numpy Version: Completed in microseconds.
Pure Python Version: Slower due to Python’s dynamic typing and loop overhead.

Why is Numpy So Much Faster?

Vectorized Operations: Numpy performs operations in optimized C/Fortran under the hood, avoiding Python’s slow loops.
Memory Efficiency: Numpy arrays are contiguous blocks in memory, while Python lists are flexible but slower.
No Type Checking: Numpy enforces fixed data types, reducing overhead.

Key Takeaway

For small arrays, the difference might seem negligible.

But as data scales, Numpy’s speed advantage becomes undeniable.

If you're working with numerical data, Numpy isn’t just an option, it’s a necessity for performance!

Next time you crunch numbers, let Numpy do the heavy lifting! 💪