In my earlier articles, “A Minimal Evolving AI Brain for Real Software Development” and “The AI That Dreams in Markdown,” I shared an idea that kept growing in my mind:
what if AI didn’t just answer questions — what if it lived inside a structured memory, learned from it, and slowly became more consistent over time?
That idea became the .mind file system.
At first, it felt almost magical.
Markdown files, carefully written, linked together, forming a living knowledge graph.
An AI could read them, understand context, and continue work without starting from zero every time.
But then reality hit.
The First Failure: Letting AI Organize Its Own Mind
My initial instinct was simple and naive:
“Why don’t I let the AI organize the .mind files by itself?”
After all, AI is good at:
- summarizing
- categorizing
- connecting ideas
- recognizing patterns
So I tried.
I asked the AI to:
- reorganize files
- fix broken links
- unify structure
- clean inconsistencies
- improve navigation
And it partially worked — but only on the surface.
The deeper problems remained:
- Some links were fixed, others were guessed
- Files were moved, but long-term consistency broke later
- Terminology drifted again after a few sessions
- Small errors accumulated quietly
- The system felt “smart,” but not stable
The truth was uncomfortable:
AI is creative, but it is not reliable at hard, repetitive loops.
It gets tired.
It improvises.
It optimizes locally, not structurally.
I was asking the wrong thing from AI.
The Insight: AI Should Not Organize — It Should Observe
That’s when the shift happened.
I stopped asking AI to do the organization.
Instead, I asked:
What if AI only observed the mind —
and scripts did the hard work?
This changed everything.
Scripts Became the Skeleton of the Mind
I started writing simple, predictable scripts.
Not smart scripts.
Not “AI scripts.”
Just honest, boring, deterministic scripts.
Scripts that:
- scan files
- validate links
- detect encoding issues
- measure reachability
- count references
- report inconsistencies
- apply safe fixes when possible
They had no creativity.
No opinions.
No hallucinations.
And that was exactly their power.
The Sleep Phase: Where AI Really Learned
Here’s the part that surprised me.
Once scripts existed, AI didn’t need to organize anymore.
It only needed to read the reports.
This created something new:
A sleep phase for the AI.
During this phase:
- Scripts run first
- Reports are generated
- Metrics are exposed
- Patterns become visible
Then AI steps in — not to fix — but to detect patterns.
It notices:
- which documents are isolated
- which concepts repeat under different names
- where structure is weak
- which areas of the mind are growing too fast
- which parts are becoming stale
AI stops pushing files around and instead starts thinking.
The mind reorganizes itself through predictable rules,
while AI reflects on meaning and direction.
That separation was critical.
Why Scripts Create Mind Consistency (and AI Couldn’t)
Scripts succeed where AI fails for one simple reason:
Scripts do not get creative
They:
- run the same way every time
- never “assume” intent
- never skip steps
- never get bored
- never improvise
They handle:
- the hard loop
- the boring repetition
- the long-term maintenance
This creates structural gravity.
Once gravity exists, AI can orbit safely.
AI’s New Role: Pattern Detection, Not Maintenance
With scripts handling structure, AI became something else entirely:
- a pattern detector
- a system thinker
- a semantic observer
- a storyteller of the mind
AI no longer “fixes links.”
AI asks why links matter.
AI no longer “renames files.”
AI notices naming philosophies emerging.
AI no longer cleans chaos.
AI understands it.
This is when consistency truly emerged.
The Closed Loop Finally Closed
The workflow became natural:
- Humans write knowledge
- Scripts evaluate and maintain structure
- Reports expose the state of the mind
- AI analyzes patterns during the “sleep phase”
- Humans and AI update the .mind
- The cycle repeats
No heroics.
No manual policing.
No fragile intelligence.
Just evolution.
What the .mind Really Is (Now)
The .mind system is no longer just documentation.
It is:
- a shared memory
- a thinking surface
- a training ground for context
- a stable substrate for AI reasoning
Scripts are the bones.
Markdown is the tissue.
AI is the dreaming layer.
A Lesson I Didn’t Expect
I thought the breakthrough would be a better prompt.
Or a smarter AI.
Instead, it was humility.
I learned to stop asking AI to be disciplined.
And instead:
- give it structure
- give it feedback
- let it think when the system is quiet
Intelligence doesn’t emerge from chaos.
It emerges from repetition plus reflection.
Scripts gave repetition.
AI gave reflection.
Together, they gave the mind consistency.
In the previous article,
I explained how a simple folder — a structured .mind directory — can turn an AI assistant into something more stable, more consistent, and far more useful than a standard chat model.
But there is a missing ingredient that makes the AI mind truly powerful.
Not just memory…
but sleep.
Just like humans don’t grow while awake — they grow while resting —
an AI software development agent becomes significantly more capable when it has a structured cycle to organize, compress, and re-link all the knowledge it has learned.
This single idea dramatically enhances the agent’s intelligence and helps build software systems much faster.
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Software development has never been just “writing code.” It’s a constant negotiation between requirements, architecture, tests, documentation, and the long shadow of past decisions. What slows teams down is not a lack of skill — it’s the constant rebuilding of context. Senior developers excel not because they are smarter, but because they remember.
AI can reach that level — but only if we give it continuity.
Modern language models recognize patterns extremely well, yet they forget everything between conversations. Without a memory layer, even the most advanced AI behaves like an intern starting from zero every day.
This is why many teams turn toward heavy solutions like vector databases, embedding pipelines, or multi-agent orchestration. These are useful when your data is chaotic and comes from emails, PDFs, documents, and logs.
But software development is nothing like that.
Our domain is beautifully simple:
- code
- documentation
- architecture
- tests
- decisions
- domain rules
- schemas
Everything is text. Everything is structured. Everything fits together.
Because of this, the best solution isn’t a giant retrieval system —
it’s a clean memory folder + a disciplined prompting method.
Been having fun creating AI-generated music videos lately — and honestly, I enjoy every moment of it.
You can check out more of them on my channel if you’re curious. (/¯◡ ‿ ◡)/¯ ~ ┻━┻
1. The Question That Started It All
As a computer engineer, I’ve always been fascinated by the space between abstract theory and tangible experience.
Few theories capture that gap more than string theory (Wikipedia), the ambitious framework suggesting that every particle and force is built from unimaginably small, vibrating strings.
At its heart lies the Polyakov Action (Wikipedia), a mathematical formulation describing how strings move through spacetime. Elegant on paper, it’s intimidating to visualize — and for most people, it stays locked as equations in a textbook.
One day, while studying string theory purely for fun, I asked myself:
“What does this actually look like?”
I decided to stop reading and start seeing.
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Introduction
When I embarked on my personal lab project five years ago, my goal was clear: to make significant progress in understanding and managing my IT infrastructure. Over the years, I’ve made commendable strides, accumulating a wealth of resources both on-premises and in the cloud. My journey has been one of constant learning and adaptation, with a particular focus on securing my setup. I’m proud to say that, through diligent effort, I’ve established a set of security measures that are not only reliable but have been tested and refined over the past five years.
However, as with any technological endeavor, new challenges inevitably arise. Currently, I find myself grappling with two main issues: Observability & Automation. Operating in Egypt presents its own unique set of challenges for my on-premises setup, including scheduled power outages, high operational costs, and the need for fast incident response. These obstacles underscore the importance of being able to monitor my infrastructure efficiently and automate responses to incidents as they occur.
After much consideration, I’ve chosen Zabbix as the cornerstone of my Observability strategy. Zabbix stands out for its ability to provide a centralized view of all resources or hosts within my network, whether they’re located on-premises or in the cloud. Moreover, it offers the added benefit of enabling automation, which is crucial for managing my infrastructure effectively and responding to incidents swiftly.
Inspired by the potential of Zabbix, I embarked on a personal research project to explore its capabilities firsthand. I successfully installed and configured Zabbix as a proof of concept (POC), integrating it with various types of Resources, including routers, VMs, PCs, laptops, and services. The success of this POC has not only bolstered my confidence in Zabbix but also motivated me to share my experiences with others.
I believe that by documenting my journey and the steps involved in setting up Zabbix, I can assist others who are looking for a straightforward way to install and configure this powerful tool. My hope is that this document will serve as a helpful guide for anyone seeking to enhance their infrastructure’s observability and automation capabilities, regardless of their location or the unique challenges they face.
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Introduction
VMware Automation has always piqued my interest, but I lacked a personal project to experiment with until I encountered a significant problem. The issue at hand was the excessive heat generated by my servers and the subsequent high consumption of electricity. While I initially mitigated the problem by installing external fans on the server rack, I knew that this was not a complete solution.
After a period of time, a lightbulb moment occurred. Why not automate the process of shutting down the servers when I leave the office and automatically starting them up when I return? This idea stemmed from the fact that I had network appliances, such as firewalls, running on these servers to control the office network. It made sense to power down this network when it was not needed and activate it on demand.
Motivated by this vision, I embarked on a personal project to leverage the power of VMware Automation. In this article, I will share a sample automation workflow that showcases the immense capabilities of VMware Automation in addressing network management challenges and achieving optimal resource utilization.
Through this technical article, we will dive into the intricacies of VMware Automation, exploring how it can empower administrators to seamlessly control and optimize their IT infrastructure. The sample automation provided will serve as a practical demonstration of how VMware Automation can revolutionize network management, enabling administrators to achieve greater efficiency, cost savings, and flexibility.
Let’s dive in and discover the wonders of VMware Automation in network management!
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From 2 years ago, i started a hacking project for challenge in WebScraping domain to scrape the whole Pahe.ph website which leaks movies, series, anime & more.
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Currently my main job is Microsoft Dynamics Technical Consultant which responsible for developing and customizing features in Microsoft Dynamics 365.
I was facing an issue in accessing my own work which i exported from the environments i’m developing on.
Microsoft let you export your project in format called *.axpp which help you take backup of your project for archiving or relocation.
This feature is awesome but the problem was what if i want to access the content of the exported file and extract some of my old codebase in the current projects.
For that i was re-importing the project in test environment so i could open the files in Visual Studio and extract the content i want, UNTIL ………
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RoofMan Official Blog 
OPNSense Removes OpenVPN Advanced Settings: A Security Flaw?
Posted on October 18, 2025 By RoofMan in Pulses
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So here we go again. OPNSense decided to remove the Advanced section from the new version of the OpenVPN instance. Their excuse? “Security reasons.”
Let’s be honest—this is pure bullshit. That “Advanced” section was exactly where power users like me could fine-tune and push OpenVPN beyond the cookie-cutter defaults. Stripping it out doesn’t magically make anything safer. If anything, it just dumbs down the software and cripples flexibility for people who actually know what they’re doing.
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Tags: Advanced Settings, cybersecurity, Firewall Security, IT Infrastructure, Network Administration, Network Management, Open Source Software, openvpn, opnsense, System Hardening, Tech Commentary, VPN Configuration