DEV Community: Peter Ngotho

AI actually isn’t moving that fast.

Peter Ngotho — Tue, 14 Apr 2026 05:43:40 +0000

It just feels like it is because you’re watching the surface layer.

It’s not Claude vs ChatGPT.
It’s whether your product creates real business value.

It’s not OpenClaw vs Claude Cowork.
It’s whether AI can actually take actions on behalf of users.

It’s not vector databases vs Postgres.
It’s whether your system gets the right context at the right time.

It’s not which model is “best.”
It’s whether the model is embedded into a workflow people already use.

The biggest mistake people are making right now: Optimizing for demos instead of outcomes.

We’ve had:

Impressive copilots that no one opens twice
Agents that work until they hit real-world edge cases
Dashboards powered by AI that don’t change decisions

Claude isn't killing anything unless we actually trust it to!

The Evolution of Cybersecurity in the Age of AI

Peter Ngotho — Sat, 16 Nov 2024 12:29:33 +0000

**_AI and machine learning (ML) are now at the forefront of cybersecurity, enabling companies to detect and respond to threats in real-time.As technology advances, so too does the sophistication of cyberattacks. In the past, traditional cybersecurity focused on perimeter defense, using firewalls and antivirus software to protect against external threats. AI and machine learning (ML) have become crucial in detecting and responding to cyber threats. AI systems are capable of processing vast amounts of data at lightning speeds, enabling them to identify unusual patterns and behaviors that might indicate a cyberattack.

Role of AI in Cybersecurity

Threat Detection: AI algorithms analyze vast amounts of data to identify patterns and anomalies that may indicate a security breach. By continuously learning from new data, these systems can improve their accuracy over time, allowing for quicker detection of potential threats.
Automated Response: In addition to identifying threats, AI can automate responses to certain types of attacks. For example, if a system detects unusual login attempts, it can automatically lock accounts or alert administrators, reducing the response time significantly.
Predictive Analytics: AI-powered tools can predict potential vulnerabilities by analyzing historical data and current trends. This proactive approach helps organizations strengthen their defenses before an attack occurs.
Enhanced Security Protocols: With the integration of AI, security protocols can be dynamically adjusted based on real-time analysis of network traffic and user behavior. This adaptability ensures that security measures remain effective against evolving threats. Companies like Hexadecimal Softwares are leveraging AI to enhance cybersecurity by developing intelligent systems that can predict, prevent, and respond to cyber threats in real time. With a deep focus on integrating AI-driven security frameworks, Hexadecimal Softwares offers businesses tailored solutions that stay ahead of emerging threats. In conclusion,the future of cybersecurity is deeply intertwined with AI, offering new capabilities while posing new challenges. As the digital landscape continues to evolve and are leading the charge in integrating advanced AI technologies to safeguard businesses from the ever-growing risk of cyber threats._**

What is Pickle in python ?

Peter Ngotho — Sat, 16 Nov 2024 12:27:53 +0000

It is used for serializing and de-serializing a Python object structure.
Pickling: It is a process where a Python object hierarchy is converted into a byte stream and dumps it into a file by using dump function.This character stream contains all the information necessary to reconstruct the object in another python script.

Unpickling: It is the inverse of Pickling process where a byte stream is converted into an object hierarchy.While the process of retrieving original Python objects from the stored string representation is called unpickling.we use load function to do this.
First of all you have to import pickle module through this command:

import pickle
Pickle has two main methods. The first one is dump, which dumps an object to a file object and the second one is load, which loads an object from a file object.

Examples
1). Pickling.
import pickle

a=[1, 2, 3, 4, 5, 6, 7]

open the file for writing

pic_out=open("mypicklefile","wb")

write object to file mypicklefile

pickle.dump(a,pic_out)

pic_out.close()
2). Unpickling.
import pickle

open the file for reading

pic_in = open("mypicklefile", "rb")

load the object from the file into var a

a = pickle.load(pic_in)

print(a)
Output:[1, 2, 3, 4, 5, 6, 7]
Use cases of Pickling:

saving a program’s state data to disk so that it can carry on where it left off when restarted (persistence)
sending python data over a TCP connection in a multi-core or distributed system (marshalling)
storing python objects in a database
converting an arbitrary python object to a string so that it can be used as a dictionary key (e.g. for