DEV Community: Ehsan Morad

AI-Powered Malware

Ehsan Morad — Thu, 23 Jul 2026 02:15:09 +0000

The era of traditional malware is ending. Enter AI-Powered Malware.

We all know how AI accelerates productivity, but what about the dark side? Hackers are now leveraging artificial intelligence to build the next generation of cyber threats.

Let us do a quick comparison between Traditional Malware and AI-Powered Malware:

Traditional Malware:

Static Code: Has a distinct signature and is highly predictable.
Hacker-Dependent: Requires manual updates by the malware author to evade antivirus software.
Easier Detection: Security tools identify them based on fixed, known signatures.

AI-Powered Malware:

Polymorphic: Mutates its code in real-time, leaving no fixed signature for detection.
Adaptive: Learns from defensive systems and automatically adjusts its infiltration paths.
Hyper-Smart Phishing: Uses deepfakes and highly personalized text to take social engineering to a completely new level.

The Takeaway:
We are no longer dealing with static malicious code, but an adaptive enemy. Defending against these evolving threats requires equally smart Defensive AI.

What do you think is the biggest challenge for organizations against these intelligent threats? Employee awareness or infrastructure upgrades?

Share your thoughts below.

#CyberSecurity #ArtificialIntelligence #Malware #Tech #AI #InfoSec #CyberThreats

Why are fewer people learning Perl today?

Ehsan Morad — Wed, 15 Jul 2026 14:54:23 +0000

In the 90s, Perl was the "duct tape of the internet." Today, it has faded from the spotlight. Why?

1️ Readability Crisis: Perl’s philosophy was "There’s more than one way to do it." This freedom often turned code into unreadable noise. Python won over large engineering teams by prioritizing clean, readable code.
2️ Steep Learning Curve: Heavy reliance on Regex and context-sensitive variables makes Perl notoriously difficult and time-consuming for newcomers to master.
3️⃣ Long Uncertainty: The 15-year wait for Perl 6 caused a massive developer migration to Ruby, Python, and Node.js.

💡 The Hidden Truth: Perl isn't dead; it’s just Legacy.
It still quietly runs critical infrastructure in bioinformatics, banking, and telecom. The real issue is the retirement of original developers, creating a massive demand for maintaining or migrating these legacy codebases.

The Verdict:
If you're a beginner, stick to Python or JavaScript. But if you're a seasoned developer looking for a lucrative niche market, mastering the migration of legacy Perl code to modern languages (like Python) is an absolute goldmine.

💬 What’s your experience with legacy Perl codebases? Let me know in the comments!

Perl #Python #Programming #LegacyCode #SoftwareEngineering #DevCommunity

AI in Manufacturing: Revolutionizing Protein Engineering and Production

Ehsan Morad — Mon, 13 Jul 2026 23:46:08 +0000

The integration of Artificial Intelligence (AI) into biotechnology and manufacturing, particularly regarding proteins, has sparked a massive transformation in the pharmaceutical and food industries. AI not only revolutionizes protein design but also optimizes their complex industrial production.

In the design and development phase, AI tools like AlphaFold predict the 3D structures of proteins with stunning accuracy. This allows scientists to design novel recombinant proteins for biologics (such as therapeutic antibodies and vaccines), a process that previously took years.

During biomanufacturing, AI plays a crucial role in optimizing bioreactors and fermentation processes. Machine learning algorithms monitor parameters like temperature, pH, and nutrients in real-time, adjusting conditions for maximum cell growth and protein yield, significantly reducing waste.

Furthermore, AI aids in quality control and purification by identifying impurities during production. Ultimately, combining AI with protein manufacturing accelerates the production of novel drugs and paves the way for sustainable alternative proteins in the food industry.

AI in the Pharmaceutical Industry: Transforming Drug Discovery and Production

Ehsan Morad — Mon, 13 Jul 2026 23:40:56 +0000

Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, fundamentally changing traditional processes of drug discovery, development, and manufacturing.

Drug Discovery: AI algorithms can analyze millions of chemical compounds in a fraction of the time, identifying promising molecules for treating diseases and reducing discovery time from years to months.

Targeted Drug Design: By analyzing protein structures and cell receptors, AI designs drugs that specifically target diseased cells, minimizing side effects.

Clinical Trials: AI assists in selecting the right patients, predicting outcomes, and optimizing trial protocols, thereby accelerating the drug approval process.

Smart Manufacturing: On the production line, AI monitors drug quality, ensures compliance with manufacturing standards, and minimizes production waste.

Ultimately, this technology not only reduces drug development costs but also increases hope for treating previously incurable diseases.