Shadin Alharthy

Abdur Rahman Awan

Mohammed Al Ghaithi

Waleed Al Harthi 

Omar Alhendi

Ahmed Al Hasani

Al Azhar Al Mawali

Let’s get this out of the way: Docker is a crutch — and that’s exactly why I use it.

Not because I’m ashamed of being lazy, but because I’ve fully embraced it. I don't want to manually configure anything twice, I don't want to debug setup issues on someone else’s laptop, and I definitely don’t want to explain how to install PostgreSQL for the hundredth time.

I’ve been lazily docker-compose up-ing my way through projects for years. It works. Maybe not on your boutique dev setup with its exotic Mac chip and artisanal shell scripts — but it works in my container, and that’s the standard I care about.

Docker is the ultimate toolkit for developers who optimize for effortless repetition. Not lazy in the “I cut corners” way. Lazy in the “I eliminate pain forever” way. If you're setting things up manually over and over again, you're not being diligent — you're just a human CI/CD pipeline cosplaying as productivity.

🚀 “But Isn’t Docker Overkill for Small Projects?”

The point isn’t whether it’s necessary. The point is (one of several points actually) I never want to hear the words “It works on my machine” ever again — not from me, not from anyone on my team, and definitely not from that guy who still uses pip install directly on his host system like it’s 2009 or the guy who manually installs .Net SDK on his Windows Server like it's 1998.

Using Docker is pre-emptive laziness. I’m saving myself from future me, from debugging weird dependency issues, from trying to remember which version of PostgreSQL worked with that one Node.js project during Ramadan when sleep-deprivation hits harder than memory leaks. I snapshot my sanity in a docker-compose.yaml file and move on.

It also gives this beautiful consistency to everything I deploy, a database, a custom app I made, a video game server, a random "to-do" app form the internet, all deploy the exact same way, on any server, without any side configurations or dependencies.

There’s this idea that if something’s easy, it must be cheating. That if you use Docker, you're skipping the “real work” of setup, configuration, and elbow-grease engineering. But that's nonsense.Docker isn't a shortcut for the clueless — it's a tool for people who know exactly what pain they're avoiding. It’s the decision not to spend your afternoon debugging Python on a Windows box, or remembering which Linux distro has the right package name for libpq-dev.

The smartest devs I know aren’t scared of complexity — they’re just ruthless about where to put their energy. Docker lets them focus on what they’re building, not the environment it’s running in.Also, have you noticed that nearly all serious open-source projects have aDockerfile? Take a hint already.

Dockerizing everything might look like overengineering, but it’s just strategic laziness. It's a shortcut past pain, inconsistency, and future regret. If you're still hand-installing dependencies and configuring services from scratch, ask yourself this: Who’s really working harder here?

Now grab a flimsy plastic cup of chai karak, enjoy the microplastics and caffeine in your blood stream, and endure me for two more chapters as I spoon-feed you how to containerize like a lazy wa7sh.

You got this far, so you're clearly one of us now. Let’s clear up a pain point before it sneaks up on you: your OS.

 Yeah, it can run Docker using WSL (Windows Subsystem for Linux). It's installable via the Windows package manager — no sketchy .exes or weird tutorials required. WSL is great for development: spinning up containers while you build stuff, test APIs, or casually run a Postgres instance. But it’s not built for unattended production — unless you enjoy Docker silently dying during a system update.

 Now we’re talking. It’s the battlefield-tested choice for production deployments — the kind that restart themselves without a dramatic sigh. I go with Ubuntu. Why? Because it just works, and because I’m too lazy to argue with Arch users.

 Whether you use Windows or Linux, the Docker CLI works the same way. No surprises. docker ps behaves like docker ps. Your muscle memory remains unbothered. So master Docker on Windows, then log onto a Linux server and run Docker like you never even alt-tabbed.

Personally, I use Windows 🪟 as my everyday Docker playground, and Linux 🐧 to deploy things meant to run forever.

Follow the link and do the needful to install Docker, I trust this is not your first day using a computer?

https://apps.microsoft.com/detail/xp8cbj40xlbwkx?hl=en-GB&gl=GB

You may run into an error with Windows WSL, simply run 

 in Windows PowerShell/Terminal and you're good to go... after a reboot. See you soon!

 Docker Desktop for Windows is free for private use, paid for commercial use. Docker on Linux is free for anything. You can pull the Linux version to WSL or explore alternatives like Podman but if you're a noob; stick with Docker Desktop until you learn to walk.

So now I’m assuming you’ve got Docker installed and you're ready to make some magic. We’re not going to waste time with “hello world” — you deserve better. How about spinning up your own self-hosted Google Drive?

Sounds intimidating? It’s not. In fact, it’s just 2 steps. Seriously.

🪄 Step-by-Step: Instant Google Drive (Okay, Nextcloud)

Press Win + X and choose Terminal (or open it however you like).

docker run -d --name nextcloud -p 8080:80 nextcloud

Boom. That’s it. Maybe that was technically three steps — but if you can microwave popcorn, you can do this.

Congrats, you’ve just launched Nextcloud, your own personal cloud storage server. Docker pulled the official Nextcloud image from 

, the "app store" for containerized software, and started it inside a lightweight container on your machine.

To access it, fire up your browser (please not Internet Explorer) and head to:

You're now the proud owner of a private cloud — a DIY Google Drive, if you will. LITERALLY. (Well, kind of.)

Right now, the app is running on localhost, meaning your laptop is now the server. Yes, even if it’s balanced on a couch somewhere in Al Khoud.

With some additional setup (like Tailscale, Cloudflare Tunnels, or good old port forwarding with dynamic DNS), you could access this from anywhere in the world — from Salalah to Stockholm. But hey — that’s a story for a future episode of The 

docker stop nextcloud
docker rm nextcloud

That stops the container and removes it from your local environment. Simple. There are a few more advanced cleanup steps — like removing images and volumes — and if you're on Windows, you can do most of it with a few clicks in the Docker Desktop app. Alternatively, just having a cheat sheet like this open on a second screen is 90% of the battle, and it's relevant for both Windows and Linux.

Or better yet — print it, laminate it, and pin it on the fridge right next to those random souvenir magnets from that one Malaysia trip. Respect.

In the next chapter, we’ll go beyond solo containers and dive into spinning up and juggling multiple apps at once like a proper tech Jedi. Stay lazy — but smart.

So, you’ve installed Docker. You’ve spun up Nextcloud like it was a kettle of Karak. You’ve stopped and removed containers like a true system whisperer. But now it’s time to become what every lazy dev secretly dreams of:

Let’s juggle multiple containers using Docker Compose — the tool that lets you launch entire environments with a single docker-compose up. No more pasting long commands. No more remembering ports. No more pretending you don’t want to automate everything.

Let’s go back to Nextcloud — but this time, we’ll store its files and metadata in a proper PostgreSQL database.

We’ll also define volumes to keep your data safe across container restarts. Because even lazy devs respect persistence.

🧾 docker-compose.yaml (a.k.a. Dev Magic Scroll)

Here’s the full docker-compose.yaml — copy, paste, chill:

services:
  db:
    image: postgres:16
    container_name: nextcloud_db
    restart: always
    environment:
      POSTGRES_DB: nextcloud
      POSTGRES_USER: nextcloud
      POSTGRES_PASSWORD: supersecret
    volumes:
      - db_data:/var/lib/postgresql/data

  app:
    image: nextcloud
    container_name: nextcloud_app
    restart: always
    ports:
      - 8080:80
    environment:
      POSTGRES_HOST: db
      POSTGRES_DB: nextcloud
      POSTGRES_USER: nextcloud
      POSTGRES_PASSWORD: supersecret
    depends_on:
      - db
    volumes:
      - nextcloud_data:/var/www/html

volumes:
  db_data:
  nextcloud_data:

Save it somewhere, make sure to save it as a ".yaml" file, not ".txt". You can just paste the above in notepad and save it.

 anywhere empty inside this folder and select open "Terminal" or "PowerShell", then run this command.

Docker spins up both the Nextcloud app and the database, connects them, creates volumes, and exposes the web interface at:

You now have a cloud storage system running on top of a full database backend, all with one command. No setup screens. No fiddling with ports. No begging your Linux friend for help.

This works for 2 apps, this works for a thousand apps.. LET.. THAT.. SINK.. IN!

You just became someone who runs multi-container environments from a single file. If you were to connect to the Postgres database directly, you will find Nextcloud has all its data there.

You’ve joined the ranks of people who say things like “oh yeah, I just spun up a stack,” while secretly sipping icy Vimto until it turns white and tasteless.

This is what Docker Compose is for. Clear infrastructure. Minimal effort. Reproducible environments.

Even if your laptop’s buried under chargers, mango juice spills, and a dusty router from Omantel — your containers are running smooth.

Now that you’ve unlocked the power of Docker Compose, the real playground begins.

You could host your own password manager.

You could self-host RSS feeds, write code in the browser, automate your house, or run a VPN server from your closet.

You could even launch a private dev wiki that no one but you will ever read.

But that’s your journey now. I’ve just handed you the keys.

The Lazy Dev Diaries – Episode 1: But It Works in My Container!

 Interactive Power BI Dashboard with GIS Integration and Secure Data Governance

National-Level Projects Dashboard for Strategic Urban Development

In support of a national spatial strategy program led by a government urban planning authority, Rihal delivered an advanced Business Intelligence (BI) solution that enhances visibility into strategic project execution across Oman. The initiative focused on streamlining data analysis and reporting through a centralized Power BI dashboard tailored for multi-tiered urban planning: National, Structural, and Visionary.

The project’s primary goal was to equip the planning authority with a scalable, user-friendly tool for monitoring project KPIs in real time—ultimately driving smarter, data-backed decision-making at the national level.

As large-scale urban development projects gained momentum, the need for a structured data environment became urgent. The planning team required:

A real-time view of project completion status.

Clean, well-governed datasets to ensure trust in insights.

A cohesive visual interface aligned with internal brand guidelines.

Tools for GIS mapping and multidimensional reporting.

Rihal designed and implemented a full-scale Business Intelligence dashboard using Microsoft Power BI 365, enriched with GIS integration and powered by structured data governance through our proprietary 

: Ensuring high data integrity through rigorous validation.

: Fully compliant with the authority’s official branding and UX standards.

: Adding a spatial dimension to project tracking and reporting.

: Delivering secure data access, version control, and team collaboration features.

: Full implementation and go-live completed ahead of schedule.

: Cleaned, validated, and mapped datasets for reliable insights.

: Visually engaging dashboards improved internal and external reporting consistency.

: The client’s internal staff were trained to manage and evolve the dashboard independently.

: The dashboard was showcased at the COMEX Expo and received praise from ministers, government entities, and the general public.

: Faster insights into project performance and resource allocation.

: Streamlined information flow between departments.

: Clear ownership of data enhanced data governance culture.

: The system was designed to grow with the authority’s evolving needs.

This project demonstrates how government entities can adopt cutting-edge data solutions to drive national development goals. With the right mix of tools: Power BI, GIS, and secure data governance, urban planning authorities can make faster, better-informed decisions that shape the future of cities.

Oman Urban Planning Dashboard with Power BI & GIS

The Definitive Guide: Why Linux > Windows for Programming

In the world of software development, the choice of operating system can significantly impact productivity, efficiency, and creativity. While Windows has long been a dominant player in the consumer market, Linux has emerged as the undisputed champion for programmers. From its open-source roots to its unparalleled customization and performance, Linux offers a suite of advantages that make it the superior choice for coding. Let’s explore why Linux outshines Windows for programming, layer by layer.

1. Open Source Nature: The Power of Transparency

At the heart of Linux’s superiority is its open-source foundation. Unlike Windows’ closed proprietary system, Linux grants developers full access to its source code. This transparency fosters rapid bug fixes, as a global community of contributors can identify and resolve issues quickly. When a vulnerability or inefficiency arises, there’s no need to wait for a corporate entity to address it—developers can dive into the code themselves, modify it, and share improvements with the community.

This collaborative model also means Linux distributions evolve organically. Hundreds of active distributions—like Ubuntu for beginners, Fedora for cutting-edge features, or Arch Linux for minimalists—cater to specific programming needs. Whether you’re building embedded systems or deploying machine learning models, there’s a Linux flavor tailored to your workflow.

2. Unlimited Customization: Build Your Ideal Environment

Linux is a playground for developers who crave control. Its modular architecture allows programmers to swap out components like kernels, desktop environments, and development tools. Want a lightweight system without a graphical interface? Strip it down. Prefer a specific toolkit or window manager? Install it. This flexibility stands in stark contrast to Windows’ monolithic structure, where core components are locked behind proprietary walls.

For instance, developers working on low-level systems can customize the kernel for real-time operations, while web developers might optimize their environment for server compatibility. Package managers like apt (Debian) or pacman (Arch) simplify adding or removing software, ensuring your system remains clutter-free and purpose-built.

3. Performance: Speed and Efficiency Unleashed

When it comes to raw performance, Linux leaves Windows in the dust. Its lightweight design ensures smooth operation even on older hardware, freeing up resources for memory-intensive tasks like compiling code or running virtual machines. Linux’s file systems (e.g., ext4, Btrfs) handle small files and concurrent operations more efficiently than Windows’ NTFS, speeding up tasks like dependency installation or version control.

Process management is another area where Linux excels. The OS allocates CPU and memory resources more effectively, reducing lag during multitasking. Combined with faster security patches and minimal background processes, Linux ensures your machine dedicates its power to what matters: your code.

Linux’s security model is inherently robust. Its open-source nature means vulnerabilities are spotted and patched faster than in closed systems. Unlike Windows, which remains a prime target for malware due to its market share, Linux’s granular permission system and smaller attack surface make it resistant to common threats.

Developers also benefit from features like SELinux (Security-Enhanced Linux) and AppArmor, which restrict application privileges by default. This “least privilege” approach minimizes risks when testing untrusted code or managing servers.

5. Package Managers: Dependency Hell Solved

Imagine installing a programming language, framework, or tool with a single command—without hunting down installers or wrestling with dependencies. Linux package managers make this a reality. Tools like apt, dnf, and zypper provide centralized repositories for thousands of applications, handling dependency resolution automatically.

For developers, this means seamless setup of environments. Need Python, GCC, or Node.js? One command installs them—along with all required libraries. On Windows, this process often involves manual downloads, PATH variable tweaks, and compatibility checks.

6. Terminal Mastery: The Developer’s Best Friend

Linux’s terminal is a programmer’s superpower. Unlike Windows’ Command Prompt or PowerShell, shells like Bash and Zsh are deeply integrated into the OS, offering powerful scripting capabilities and access to Unix tools (e.g., grep, awk, sed). Automating tasks, parsing logs, or managing files becomes intuitive, saving hours over GUI-driven workflows.

The terminal also integrates seamlessly with development tools. Compilers, debuggers, and version control systems like Git feel native, enabling faster iteration.

Linux includes built-in SSH support, simplifying secure connections to remote servers. Developers can edit files, manage deployments, or debug live environments directly from their terminal. With cloud-based development and containerization on the rise, this native capability streamlines workflows.

Windows has introduced OpenSSH in recent years, but its implementation lacks the polish and integration Linux offers out of the box.

Most programming languages are developed on and for Unix-like systems. Python, Ruby, C/C++, and Java run natively on Linux, often with better performance and fewer quirks. Tools like GCC and Clang are optimized for the OS, while package managers simplify language runtime management (e.g., pyenv for Python versions).

Even languages tied to Windows, like C#, now cross-platform via .NET Core, run flawlessly on Linux.

9. Server Compatibility: Develop as You Deploy

Over 90% of cloud infrastructure and web servers run Linux. Developing on Linux ensures environment parity between your workstation and production, avoiding “works on my machine” pitfalls. Docker containers, Kubernetes clusters, and cloud services all align with Linux’s architecture, enabling smoother testing and deployment.

10. Cost and Licensing: Freedom Without Limits

Linux is free—no licensing fees, subscriptions, or artificial hardware restrictions. You can install it on any number of machines, from Raspberry Pis to enterprise servers, without worrying about compliance. This lowers the total cost of ownership, especially for startups or indie developers.

11. Community: Knowledge at Your Fingertips

Linux’s global community is one of its greatest assets. Forums, documentation (e.g., Arch Wiki), and platforms like Stack Overflow provide instant support. Whether you’re troubleshooting a kernel panic or optimizing a script, someone has likely solved your problem already.

From its open-source ethos to its unmatched performance and customization, Linux provides the tools and freedom developers need to innovate. While Windows has its place in gaming and enterprise environments, Linux remains the OS of choice for those who value control, efficiency, and a system that adapts to their needs—not the other way around.

For programmers, the choice is clear: Linux isn’t just an OS; it’s a catalyst for creativity.

Linux vs Windows: Which Is Better for Programming in 2025?

Recently, during a discussion on large language models, someone asked me, "What kind of AI is this AI?"It’s a deceptively simple question—one that reveals a widespread misunderstanding about the layers and nuances within the field of artificial intelligence.

Terms like AI, machine learning, deep learning, and generative AI are often used interchangeably. While it’s tempting to nod along and treat them as synonyms, each term represents a distinct concept that carries critical implications for how we understand, adopt, and integrate these technologies.

Understanding these distinctions isn’t just a concern for engineers or data scientists anymore. In an increasingly AI-driven world—where intelligent systems influence everything from work processes to creative expression—it’s essential for everyone. Mislabeling or oversimplifying AI can lead to poor decisions, especially when procuring solutions that might not meet an organization’s needs.

Let’s unpack the layers of AI to understand what they truly mean.

At its core, Artificial Intelligence (AI) refers to the broad field of computer science aimed at building systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, problem-solving, perception, and even creativity.

Any system—whether simple or complex—that mimics human cognition or behavior can be called an AI system. This includes everything from rule-based expert systems to state-of-the-art generative models.

Rule-Based AI is one of the earliest and most straightforward forms of artificial intelligence. It operates by following predefined if-then rules explicitly programmed by human experts.

These systems do not learn from data. Instead, they rely solely on encoded human knowledge to make decisions.

A basic temperature monitoring system might include rules such as:

IF temperature > 100°C THEN trigger alarm.

Such systems work well in narrowly defined scenarios but lack flexibility and adaptability.

Machine Learning: Teaching Machines to Learn

Machine Learning (ML) marks a pivotal shift from hard-coded logic to data-driven intelligence. Instead of writing rules manually, we train machines to learn patterns from large datasets.

By analyzing data, ML models uncover relationships and create internal representations that allow them to make predictions on new, unseen data.

Consider a spam detection system. A machine learning model is trained on thousands (or millions) of labeled emails—some marked “spam,” others “not spam.” The model learns to detect spam based on features like:

Keyword usage (e.g., “win money” or “free offer”)

Once trained, it can automatically classify new incoming emails with impressive accuracy.

Deep Learning is a specialized subset of machine learning that uses artificial neural networks, inspired by the human brain’s structure.

Input Layer – receives data (analogous to human senses)

Hidden Layers – process data via complex mathematical operations (analogous to reasoning)

Output Layer – delivers the result or prediction

When a network has more than one hidden layer, it is referred to as a deep neural network, hence the name deep learning.

These models excel at tasks involving complex patterns, such as speech recognition, image classification, and natural language processing.

A deep learning model can analyze an image and determine whether the person in it is male or female. It does this not through explicit instructions, but by learning from thousands of labeled images during training.

Generative AI: Creating, Not Just Predicting

Generative AI is a powerful extension of deep learning. Unlike traditional models that classify or predict, generative models are designed to createnew content—text, images, audio, code—based on the data they’ve been trained on.

These systems rely on advanced architectures, particularly the Transformer, which underpins Large Language Models (LLMs) like GPT.

Generative AI doesn’t just identify patterns—it learns to reproduce them in novel ways, enabling creativity at scale.

A marketing team can use GPT-based tools like ChatGPT-4o to generate multiple versions of product descriptions, each tailored for a specific audience segment. What once took days can now be done in minutes.

As we integrate AI deeper into our lives and organizations, understanding the type of AI we’re dealing with becomes more than a technical detail—it’s a strategic necessity.

Generative AI creates new content from learned patterns.

Labeling everything as “AI” oversimplifies the landscape. Knowing the difference ensures you choose the right tool for the right problem—and that’s where true intelligence begins.

4 Types of AI to Know in 2025

Why Automation Became a Must for Oman’s Telecom Sector

Oman’s telecommunications industry has been evolving rapidly. But with high-volume operations, legacy systems, and growing customer expectations, many providers face a tough challenge: How do you move faster, smarter, and more efficiently, but without burning out your teams or losing your edge?

This was the exact situation for one of Oman’s largest telecom providers.

To stay ahead of the curve and support the goals of Oman Vision 2040, they turned to Robotic Process Automation (RPA), and partnered with a leading Omani automation company to make it happen.

What followed was more than a digital upgrade. It was a company-wide shift that streamlined processes, boosted employee skills, and reduced costs while building a stronger, smarter workforce from the inside.

The Challenge: Manual Work, Fragmented Systems, and Slowed Growth

Before automation, the telecom company relied heavily on manual, repetitive workflows across departments like finance, customer service, HR, and network management. Some of the biggest pain points included:

Time-consuming data entry from disconnected systems

Delayed service delivery caused by manual handovers and human error

Heavy dependence on third-party vendors for routine tasks

Slower customer response times, affecting satisfaction and loyalty

The leadership team knew that traditional methods were no longer sustainable, and that to remain competitive, they needed to digitally transform and invest in scalable, intelligent solutions.

The Solution: A Custom RPA Strategy Built in Oman, for Oman

To drive this transformation, the telecom provider partnered with a local automation expert to roll out a fully customized RPA program. The strategy focused on long-term impact instead of quick wins. So, here’s how they did it:

They worked closely with department heads to identify high-impact processes that were ideal for automation—starting with finance, HR, and service delivery.

The first bots were deployed to handle tasks like invoice processing, employee onboarding, and service provisioning. Some tasks that once took hours were now completed in minutes—

3. Citizen Developer Training (A First in Oman)

In a groundbreaking move, the company launched a training program that taught non-technical employees how to build their own bots using low-code tools. This sparked a culture of innovation and gave teams more control over their own efficiency.

After early success, RPA was expanded across multiple departments, including technical operations, procurement, and customer servicewhile integrating smoothly with existing ERP and legacy systems.

The Results: Measurable ROI and Real Organizational Change

40+ processes automated across business functions

70%+ reduction in processing time for key workflows

Zero downtime from bots, thanks to reliable infrastructure

30+ citizen developers trained, many of them women in non-technical roles

Significant cost savings by reducing manual work and outsourcing

Wider Impact: Aligning with Oman’s National Goals

This wasn’t just a win for the company, it was a win for Oman’s digital economy. The initiative supported key pillars of Oman Vision 2040 by:

Upskilling Omani talent, reducing reliance on external consultants

Encouraging innovation from within, building a digital-first mindset

Contributing to a knowledge-based economy through technology adoption

Promoting inclusion, with many citizen developers being women in business roles

Here's The Gist: Automation Works Best When People Lead It

RPA isn’t just about saving time, it’s about unlocking potential.

By giving employees the tools to automate their own tasks, the company didn’t just become more efficient. It became more agile, more confident, and more prepared for the future.

This case proves that with the right mindset, the right partners, and a commitment to empowering people, automation can do far more than optimize, it can transform.

How RPA Transformed a Leading Telecom in Oman 

In today’s rapidly evolving digital landscape, data is often called the “new oil.” Yet, for many government and private entities in Oman, this valuable resource remains locked away in filing cabinets, paper archives, and isolated digital silos. As artificial intelligence (AI) transforms industries worldwide, the inability to access and leverage this data is becoming a critical barrier to progress—and a growing risk.

The Data Dilemma: Siloes and Physical Archives

Across Oman, countless organizations are sitting on decades’ worth of records—contracts, reports, citizen data, and more. Much of this information exists only in physical form, scattered across departments and locations. Even where digital records exist, they are often siloed, stored in incompatible systems that don’t communicate with each other.

This fragmented approach means that decision-makers lack a holistic view of their organization’s data. Opportunities for innovation, efficiency, and improved public services are missed simply because the right information is inaccessible or invisible.

With AI’s rise, the value of data has never been higher. Machine learning algorithms thrive on large, diverse datasets, uncovering patterns and insights that can drive smarter decisions, automate processes, and deliver better outcomes for citizens and customers alike. For Omani organizations, the data already exists—but it’s trapped.

Imagine the possibilities if government ministries could instantly analyze years of public records to predict service demand, or if private companies could mine historical sales data to forecast trends and personalize offerings. The potential is enormous, but only if the data is digitized, unified, and made AI-ready.

Failing to address this challenge carries significant risks:

Missed Opportunities: While other nations and companies harness AI to leap ahead, Omani organizations risk falling behind, unable to compete or innovate at the same pace.

Rihal Blogs

The Lazy Dev Diaries – Episode 1: But It Works in My Container!

Oman Urban Planning Dashboard with Power BI & GIS

Linux vs Windows: Which Is Better for Programming in 2025?

4 Types of AI to Know in 2025

How RPA Transformed a Leading Telecom in Oman

From Filing Cabinets to AI: Why Omani Organizations Must Digitize Their Data Before It’s Too Late

CodeStacker: Rihal’s Ramadan Tech Challenge Fueling Omani Innovation

Strategic Decision Making: Power BI

Rihal Blogs

.css-1enyoca{color:var(--text);-webkit-text-decoration:none;text-decoration:none;}.css-1enyoca:hover{-webkit-text-decoration:none;text-decoration:none;color:var(--text);}The Lazy Dev Diaries – Episode 1: But It Works in My Container!

Oman Urban Planning Dashboard with Power BI & GIS

Linux vs Windows: Which Is Better for Programming in 2025?

4 Types of AI to Know in 2025

How RPA Transformed a Leading Telecom in Oman

From Filing Cabinets to AI: Why Omani Organizations Must Digitize Their Data Before It’s Too Late

CodeStacker: Rihal’s Ramadan Tech Challenge Fueling Omani Innovation

Strategic Decision Making: Power BI

The Lazy Dev Diaries – Episode 1: But It Works in My Container!