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Since September 2026
Instructor since September 2026
Linux & How Computers Really Work, for Cybersecurity Students
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From 33 £ /h
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Most cybersecurity courses skip the part everything else sits on: how Linux, processes and memory actually behave. You're expected to "just know" it. I teach that part, one to one, at your pace.

Who I am: a backend team lead with 7+ years in production, and a CTF player. My team placed 9th of 200+ at Standoff 15, a three-day live offensive-security competition, and I've published Hack The Box writeups and a malware analysis. I also mentored groups of 14-17 students at LANIT-Tercom's developer summer camps in 2021 and 2022.

What we can cover (you pick):
- Linux fundamentals: shell, files and permissions, users, processes, services, logs, package managers
- Linux for security: what attackers and defenders actually look at on a box
- How computers work: boot, CPU and memory, processes and threads, syscalls, file systems
- Your first TryHackMe / Hack The Box machines, coached step by step (legal platforms only)
- Privacy basics: threat modelling, passwords and 2FA, what a VPN does and doesn't do
- "I'm stuck on this uni topic": concepts explained, not answers handed over

How lessons work:
1. You tell me what you want to understand or what you're stuck on.
2. We share a screen and you drive. I ask questions, point and explain. I only take the keyboard to show something, then hand it back.
3. After the lesson you get a written recap: what we covered, commands, links and one small task for next time.

I won't do graded homework for you. I'll make sure you understand it well enough to do it yourself.

Good fit for: cybersecurity and CS students, self-taught people moving into security who keep hitting gaps in the basics, and developers who want to stop being scared of the terminal.

Send me a message with what you're working on and we'll plan your first lesson around it.
Extra information
Online via video call with screen sharing. Have a computer ready: Linux, macOS, or Windows with WSL or a free VirtualBox VM. Not sure how to set it up? We'll do it together in the first lesson.
Location
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Online from Sweden
Age
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Duration
60 minutes
The class is taught in
English
Russian
Spanish
Availability of a typical week
(GMT -04:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
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Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
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- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
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Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
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- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

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- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.
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عبدالرحمن
A comprehensive course in Linear Programming and Linear Optimization, based on the renowned reference:

Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to Linear Optimization. Athena Scientific.

The course covers the first five chapters in their entirety, forming the foundation upon which everything that follows in mathematical improvement is built. The concepts are explained in clear Arabic with step-by-step solved examples, illustrations, and practice exercises.

The five lectures:

Lecture 1 — Introduction to Linear Optimization:

What is optimization? And why do we need it?

Variables, decisions, functions, and constraints

The general formula for linear programming

Standard Form

Reducing any issue to the standard formula

Piecewise Linear Convex Functions

Graphing and solution

Fundamentals of Linear Algebra

Analysis of algorithms and number of operations

Lecture 2 — Linear Programming Basics (LP Basics):

Practical examples: food issues, production, scheduling, networks, pattern classification

Converting problems to standard format

Remove free variables

Slack and Surplus variables

convex and concave functions

Absolute values in objective functions

Graphical solution in 2D and 3D

The five possible solutions

Lecture 3 — Geometry of Linear Programming (LP):

Polyhedron and Polytope

Hyperplanes & Halfspaces

Extreme Points

Vertices

Basic Feasible Solutions

Active Constraints

Degeneration

The presence of extreme points

Visual examples in 2D and 3D

Lecture 4 — The Simplex Method:

Optimality Conditions

Reduced Costs

Development of the Simplex algorithm

Simplex Tableau

Pivoting

The Revised Simplex Method

Cycling and how to avoid it

Bland's Rule

Finding a basic, possible, initial solution

Two-Phase Simplex Method

Big-M Method

Mathematical aptitude and average behavior

Klee-Minty cube

Lecture 5 — Duality Theory:

The original (Primal) and dual problem

The relationship between variables and constraints

Weak Duality Theory

Strong Duality Theory

Conditions for Complementary Slackness

Dual variables as marginal costs

Shadow Prices

Dual Simplex Method

Geometric interpretation of duality

Applications in Economics and Finance

Advantages:

🎯 Explanation based on an accredited academic reference

📐 Illustrations for each geometric concept

🧮 Solved examples with numbers

💻 Examples in Python using gurobipy

📊 Comparison tables between different formulas

🔗 Linking theory to practical applications

📝 Exercises at the end of each lecture
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Similar classes
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Raouf
Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
verified badge
Laroussi
Session 1: Revolutionizing your Scientific Writing with LaTeX & AI
Duration: 2 Hours | Level: Beginner | Tools: Overleaf + AI**

First Hour: Foundations and Cloud Environment (60 min)

1. Introduction to LaTeX Philosophy (15 min)

- The "WYSIWYM" concept:** Explain the difference between Word (*What You See Is What You Get*) and LaTeX (*What You See Is What You Mean*). Why content takes precedence over form.
- Key advantages:** Unrivaled typographic quality, automatic reference management, stability on long documents (theses), and free of charge.
- The structure of a file:** Distinction between the **preamble** (the brain: settings and packages) and the **body of the document** (the heart: text).

2. Immersion in Overleaf (25 min)

- Configuration:** Creation of an account and first project "Blank Project".
- Exploring the interface:** The file panel (left), the code editor (middle) and the PDF preview (right).
- Real-time collaboration:** How to share a project and leave comments (like on Google Docs).
- History and versions:** How to revert to a previous version in case of a compilation error.

3. Practical Workshop: My First Document (20 min)

* Writing basic commands: `\documentclass`, `\usepackage[french]{babel}`, `\title`, `\author`.
* Compilation of the document and observation of the result.
* Structuring: Use of `\section` and `\subsection`.

Second Hour: Mathematics and the Magic of AI (60 min)

4. The Power of Mathematics (20 min)

- Mathematical modes:** Difference between the text (`$...$`) and the centered block (`\[...\]`).
- Essential syntax:** Fractions `\frac{}{}`, exponents `^`, indices `_`, and roots `\sqrt{}`.
- Introduction to AMS packages: Why amsmath and amssymb are essential for professional rendering.

5. From hand to screen: AI at the service of LaTeX (30 min)

- Presentation of OCR tools:** Use of **Mathpix Snip** (the leader) or models like Gemini/ChatGPT to transform a photo into code.
- Concrete demonstration:
1. Take a picture of a complex handwritten formula (e.g., an integral with matrices).
2. Use AI to generate the corresponding LaTeX code.
3. Correction and insertion: Learn to check the AI-generated code before copying and pasting it into Overleaf.

6. Conclusion and Q&A (10 min)

* Summary of achievements.
* Resources for further exploration
* Definition of the exercise for the next session.
verified badge
عبدالرحمن
A comprehensive course in Linear Programming and Linear Optimization, based on the renowned reference:

Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to Linear Optimization. Athena Scientific.

The course covers the first five chapters in their entirety, forming the foundation upon which everything that follows in mathematical improvement is built. The concepts are explained in clear Arabic with step-by-step solved examples, illustrations, and practice exercises.

The five lectures:

Lecture 1 — Introduction to Linear Optimization:

What is optimization? And why do we need it?

Variables, decisions, functions, and constraints

The general formula for linear programming

Standard Form

Reducing any issue to the standard formula

Piecewise Linear Convex Functions

Graphing and solution

Fundamentals of Linear Algebra

Analysis of algorithms and number of operations

Lecture 2 — Linear Programming Basics (LP Basics):

Practical examples: food issues, production, scheduling, networks, pattern classification

Converting problems to standard format

Remove free variables

Slack and Surplus variables

convex and concave functions

Absolute values in objective functions

Graphical solution in 2D and 3D

The five possible solutions

Lecture 3 — Geometry of Linear Programming (LP):

Polyhedron and Polytope

Hyperplanes & Halfspaces

Extreme Points

Vertices

Basic Feasible Solutions

Active Constraints

Degeneration

The presence of extreme points

Visual examples in 2D and 3D

Lecture 4 — The Simplex Method:

Optimality Conditions

Reduced Costs

Development of the Simplex algorithm

Simplex Tableau

Pivoting

The Revised Simplex Method

Cycling and how to avoid it

Bland's Rule

Finding a basic, possible, initial solution

Two-Phase Simplex Method

Big-M Method

Mathematical aptitude and average behavior

Klee-Minty cube

Lecture 5 — Duality Theory:

The original (Primal) and dual problem

The relationship between variables and constraints

Weak Duality Theory

Strong Duality Theory

Conditions for Complementary Slackness

Dual variables as marginal costs

Shadow Prices

Dual Simplex Method

Geometric interpretation of duality

Applications in Economics and Finance

Advantages:

🎯 Explanation based on an accredited academic reference

📐 Illustrations for each geometric concept

🧮 Solved examples with numbers

💻 Examples in Python using gurobipy

📊 Comparison tables between different formulas

🔗 Linking theory to practical applications

📝 Exercises at the end of each lecture
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