Adnan Naous · Computer Science Student
Currently focused on Java, Python, computer science fundamentals,
software engineering, and AI-assisted development.
This repository records my real learning: original reasoning, corrections, experiments, projects, and progress—without presenting generated material as personal mastery.
Explore the journey · Current focus · Recent learning · Progress
I am Adnan Naous, a computer science student building a durable record of how I learn—not just a gallery of finished results. The repository is designed to preserve useful attempts, misunderstandings, corrections, explanations, practice, and applied work as they develop over time.
Everything is organized so that a visitor, human contributor, or AI assistant can understand the journey from the repository itself without needing private conversations or previous chat history.
Progress here means documented understanding and reproducible evidence. It does not mean claimed expertise.
| Destination | What you will find |
|---|---|
| Learning | Structured learning entries organized by computer science subject. |
| Projects | Self-contained applications and experiments that apply learned concepts. |
| Challenges | Practice problems with attempts, corrections, reasoning, and validated solutions. |
| Notes | Concise, searchable references and concept explanations organized by subject. |
| Resources | Evaluated books, courses, documentation, papers, and other learning sources. |
| Progress | Evidence-based learning logs, milestone reviews, blockers, and next steps. |
| Changelog | Notable changes to the repository and its structure. |
| Agent rules | The operational source of truth for AI agents and contributors. |
These are current priorities and roadmap areas, not claims of mastery.
| Priority | Area | Current direction |
|---|---|---|
| 1 | Java | Fundamentals and object-oriented programming |
| 2 | Python | Language fundamentals and practical problem-solving |
| 3 | Git and GitHub | Version control, collaboration, and repository workflows |
| 4 | Data Structures and Algorithms | Core structures, algorithmic reasoning, and practice |
| 5 | Databases | Data modeling, querying, and database fundamentals |
| 6 | Operating Systems | Processes, memory, filesystems, and system concepts |
| 7 | Computer Networks | Network models, protocols, and communication fundamentals |
| 8 | Software Engineering | Design, testing, maintainability, and development practices |
| 9 | Artificial Intelligence | Foundations and responsible use of AI-assisted tools |
| 10 | Practical Project Development | Applying concepts through increasingly complete projects |
This repository separates authorship and evidence so that polished documentation never misrepresents what I personally understood.
| Layer | How it is treated |
|---|---|
| My original understanding | Preserved when it has educational value, including uncertainty and useful mistakes. |
| Faithful English version | Translates and clarifies my words without making the technical depth appear stronger. |
| Understanding assessment | Identifies correct points, misconceptions, gaps, and unconfirmed ideas. |
| Academic explanation | Adds verified technical context in a clearly separate section. |
| Source-assisted material | Labels what came from a lesson, document, screenshot, article, video, or other source. |
| Revisions and corrections | Retains the reasoning behind meaningful changes instead of rewriting history. |
AI may assist with organization, translation, review, technical validation, and academic context. It must not invent my understanding, confidence, experience, progress, or achievements. Small learning events remain concise rather than being inflated into generic chapters.
For the complete protocol, see Learning Capture and Authorship Protocol.
This table lists recent learning only after a corresponding repository entry exists.
| Date | Topic | Area | Status | Entry |
|---|---|---|---|---|
| 26 Jul 2026 | HCIA Datacom Day 1 | Computer Networks | Exposed | View entry |
| 24–26 Jul 2026 | Ubuntu VMware developer environment | Operating Systems | Practiced | View entry |
| 21 Jul 2026 | Data quality in AI learning | Artificial Intelligence | Introduced | View entry |
| 20–21 Jul 2026 | Hackathon preparation, HCIA-AI V4, and first certificate | Progress / Artificial Intelligence | Practiced / Introduced | View entry |
| 12–16 Jul 2026 | openEuler and Linux foundations | Operating Systems | Introduced | View entry |
Progress is recorded with evidence and descriptive learning-depth labels rather than arbitrary percentages.
| View | Purpose | Current state |
|---|---|---|
| Progress guide | Defines how evidence-based progress is recorded. | Available |
| Learning log | Will contain dated learning and review entries. | Awaiting first entry |
| Roadmap | Shows the current learning priorities. | Active and expected to evolve |
| Milestones | Will link achievements to documented evidence. | No milestones claimed yet |
Learning depth may be labeled Exploring, Introduced, Developing, Practiced, Applied, Reviewed, or Verified. Higher-confidence labels are used only when supported by explanation, practice, or project evidence.
Technical results use separate validation labels: Tested, Partially tested, Untested, or Deprecated.
Nothing is featured yet. This section will highlight strong learning entries and completed or meaningfully developed projects only after they exist and have supporting evidence.
Adnan-Naous-Journey/
├── learning/ # Subject-based learning entries
├── projects/ # Self-contained practical builds
├── challenges/ # Exercises, attempts, and corrections
├── notes/ # Concise subject references
├── resources/ # Reviewed learning sources
├── progress/ # Logs, evidence, and milestones
├── templates/ # Reusable documentation templates
└── archive/ # Superseded material retained for context
Each concept should have one canonical location. Related entries should link to that source instead of duplicating it.
The repository is tool-independent and can be used by Codex, Claude Code, Cursor, Antigravity, other AI agents, or human contributors.
- AGENTS.md is the operational source of truth.
- CONTRIBUTING.md defines contribution and Git workflow rules.
- Documentation is normally written in English.
- AI agents may not commit, push, merge, deploy, or delete substantial content without explicit approval.
- Raw conversations, secrets, private data, and invented sources or achievements do not belong here.
- Technical claims and code should be validated in proportion to their risk.
- Tested results must be distinguished from assumptions and incomplete checks.
- Existing related material should be reviewed before new files are created.
- Attempts, mistakes, corrections, and personal reasoning should remain visible when educationally valuable.
- No project, exercise, resource, or learning milestone is treated as complete without evidence.
Computer Science Student
This repository is continuously evolving as new concepts are studied, questioned, practiced, corrected, and applied.
Learning · Projects · Challenges · Notes · Progress · Repository rules