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│ I build mobile apps that ship, AI systems that think, │
│ and developer tools that save time. │
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role:
- AI Systems Engineer
- Software Architect
- Mobile Developer
base: Sana'a, Yemen
current_focus:
- Production products across mobile, games, and developer tooling
- AI infrastructure, RAG, memory, and MCP systems
- Compiler-style generation, code intelligence, and structured workflows
- Clean architecture, scalable UX, and failure-resistant system design
highlights:
- Published Flutter package with 7,000+ downloads
- Built mobile commerce apps, AI tooling, code retrieval systems
- Shipped game work with Unity
ai_workflow_stack:
primary: Claude Code Opus 4.6
secondary: Codex ChatGPT 5.3
motto: "Build useful systems. Keep them fast, clean, and reliable."╔══════════════════════════════════════════════════════╗ ║ ROADMAP ║ ╚══════════════════════════════════════════════════════╝ 2020 │ ▼ Began building video games as a solo Unity developer │ ▼ 2020–2023 │ ▼ Built games independently using Unity, C#, and game-focused systems thinking │ ▼ 2021 │ ▼ Graduated in Information Technology Engineering │ ▼ 2023 │ ▼ Expanded into Flutter and AI engineering │ ▼ 2023–Present │ ▼ Building production mobile apps, developer tools, code intelligence systems, RAG pipelines, and AI infrastructure
Featured Start Order
1.liquid_glass_easy ·2.Hamsa ·3.Bolt AI ·4.Memory System for AIOther listed projects are private repositories.
Number one cross-platform Flutter package for interactive liquid glass effects with 7,000+ downloads on pub.dev. → View on pub.dev
📱 Mobile Projects
> Large-scale beauty and makeup shopping platform serving Saudi Arabia, Yemen, Oman, and UAE with isolated data, pricing, and currency. > Built guest cart merge, secure multi-step checkout, and market-specific payment flows. > Delivered glass morphism UI, hero animations, RTL Arabic support, real-time order tracking, and push notifications. > Premium fashion shopping app with polished browse-to-checkout flows. > Added search, filters, wishlist, secure checkout, real-time tracking, and push notifications. > Built smooth glass morphism UI and full RTL Arabic support.Production e-commerce app where I patched critical security, maps, and performance issues for a large live user base. → View on Google Play
Implemented shared cart feature enabling multiple users to collaborate on a single pharmacy order. → View on Google Play
Grocery and daily essentials e-commerce app with fast product browsing, cart management, and real-time order tracking. → View on Google Play
Improved location storage and fixed currency switching through REST API optimization. → View on Google Play
Integrated custom real-time push notifications for order status updates. → View on Google Play
Electronics commerce app for Arduino boards, microcontrollers, and maker hardware. Added dynamic language and currency switching, secure payments, and real-time backend sync.
⚡ Bolt AI — Flutter Clean Code Generator Using Compiler Architecture
╔══════════════════════════════════════════════════════════════╗
║ THE IDEA ║
║ ║
║ Most AI code generation tools produce raw code directly ║
║ from prompts. Bolt AI treats Flutter generation like a ║
║ real compiler problem: blueprint in → validated arch out. ║
╚══════════════════════════════════════════════════════════════╝
CORE PIPELINE
─────────────────────────────────────────────────────────────
YAML Blueprint → Parse → Validate → Transform
↓
Production-ready Flutter code
↑
Emit ←
INTERNAL STRUCTURE
─────────────────────────────────────────────────────────────
lib/compiler/
├── ir/ Intermediate Representation
├── parsers/ YAML + JSON blueprint parsing
├── validators/ semantic + architecture validation
├── transformers/ IR transformation layer
├── emitters/ 19 code emitters
├── pipeline/ orchestration and factories
└── compiler.dart entry point
GENERATED FEATURE SHAPE
─────────────────────────────────────────────────────────────
lib/features/product/
├── domain/
│ ├── entities/
│ ├── repositories/
│ └── usecases/
├── data/
│ ├── models/
│ ├── datasources/
│ └── repositories/
├── presentation/
│ └── bloc/
├── di/
├── product.dart
└── product_registry.dart
- Built around an Intermediate Representation — reasons about structure before generating Dart files.
- Semantic validators check types, duplicates, naming rules, reserved keywords, and required fields.
- Architecture validators enforce clean architecture boundaries, repository rules, use-case design, and presentation-layer consistency.
- Emitter layer generates entities, models, repositories, data sources, BLoC files, DI modules, registries, endpoints, and barrel files.
- Supports single-feature compilation and batch generation across multiple blueprints.
- Exposes compiler operations through MCP tools:
compile_blueprint,validate_blueprint,list_blueprints, andpreview_ir. - Turns Flutter clean architecture from repetitive manual setup into a reproducible compiler workflow.
🧠 Memory System for AI
╔══════════════════════════════════════════════════════════════╗
║ THE IDEA ║
║ ║
║ Most AI assistants forget useful context between runs or ║
║ rely on one flat memory store. This system models memory ║
║ the way humans do: episodic, semantic, and procedural. ║
╚══════════════════════════════════════════════════════════════╝
SYSTEM STRUCTURE
─────────────────────────────────────────────────────────────
MCP Client → mcp_memory_server.py → MemoryManager
├── VectorEngine episodic memory
├── StructuredStore semantic memory
└── FileStore procedural memory
MEMORY HIERARCHY
─────────────────────────────────────────────────────────────
Episodic Memory
├── storage: ChromaDB
└── use: experiences, lessons, summaries
Semantic Memory
├── storage: SQLite
└── use: facts, preferences, project details
Procedural Memory
├── storage: Markdown files
└── use: patterns, skills, reusable workflows
RETRIEVAL MODEL
─────────────────────────────────────────────────────────────
Memory → question embedding
→ answer embedding
Query → search both indexes
→ merge matches
→ return compact relevant memory
- Dual-index Q&A search — a memory can be retrieved whether the query matches the question or answer side.
- Episodic memory powered by vector search, semantic memory by structured facts, procedural memory by file-based patterns and skills.
- Uses Sentence-Transformers
all-mpnet-base-v2embeddings and token-efficient retrieval. - Includes project tagging, similarity thresholds, fact indexing, and stateless tool design.
- Exposes 20+ MCP tools for storing, recalling, deleting, and managing memories, skills, and patterns.
- Compatible with Cursor, Claude Desktop, Kiro, Antigravity, and other MCP clients.
- Makes long-running AI assistants more reliable through structured memory instead of raw context accumulation.
🔍 AST-Based Code RAG — Flutter / Laravel
╔══════════════════════════════════════════════════════════════╗
║ THE IDEA ║
║ ║
║ Standard code RAG splits source files like text documents. ║
║ This project treats code as structured logic with ║
║ relationships, flow, metadata, and audience-aware retrieval.║
╚══════════════════════════════════════════════════════════════╝
INDEXING FLOW
─────────────────────────────────────────────────────────────
Source Files → AST Parser → Structured Extraction
↓
Cross-file Analysis
↓
LLM Enrichment → 3 Named Vectors → Qdrant
CHUNK MODEL PER-CHUNK REPRESENTATION
────────────────────────── ──────────────────────────────────────
One point per: Chunk
├── class method ├── description vector
├── standalone function ├── developer-questions vector
├── class-level unit ├── user-questions vector
└── config / route block ├── calls / called_by
├── routes / models / tables
Never whole-file chunks └── raw source payload
Never mid-function splits
QUERY FLOW
─────────────────────────────────────────────────────────────
User question → query expansion → vector + BM25 hybrid retrieval
↓
merge + rerank
↓
call-graph expansion
↓
context builder → final answer
- Closes the semantic gap at index time, not query time.
- Stores enriched semantic descriptions, developer questions, user questions, and graph relationships — raw source kept in payload for answer-time context.
- Retrieval combines dense vectors with BM25 hybrid search, then expands through
callsandcalled_byneighbors for flow awareness. - Each chunk carries rich payload metadata: parameters, return types, visibility, routes, models, tables, git blame, and commit history.
- Qdrant used with named vectors and payload-aware search; incremental re-indexing keeps the index current.
- Includes a RAGAS-style evaluation setup using LLM-as-judge scoring for faithfulness, relevancy, precision, and recall.
- Designed for serious codebase understanding across frameworks such as Laravel and multi-project retrieval workflows.
🧬 DNA-Inspired Multi-Agent Architecture
╔══════════════════════════════════════════════════════════════╗
║ THE IDEA ║
║ ║
║ Most multi-agent systems use optional pipelines: ║
║ planner → coder → reviewer. ║
║ This project asks: what if agent relationships were ║
║ structurally enforced, the same way DNA base pairing ║
║ is enforced in biology? ║
╚══════════════════════════════════════════════════════════════╝
AGENT MAPPING
─────────────────────────────────────────────────────────────
A = Architect T = Tester
G = Generator C = Critic
TWO-STRAND MODEL
─────────────────────────────────────────────────────────────
Generative strand: A ──────────► G
│ │
Verification strand: T ◄────────── C
A pairs with T · G pairs with C · Pairing is mandatory
SYSTEM FLOW
─────────────────────────────────────────────────────────────
Raw input
→ A builds structured specification
→ T validates specification
→ G generates from verified spec
→ C critiques against that spec
→ Merge mechanism
→ Final output
SEQUENCE-AS-CONFIGURATION
─────────────────────────────────────────────────────────────
ATGC → analyze-first GCAT → generative-first
TACG → constraint-driven CGTA → adversarial-first
Same agents · Different reasoning strategy
- Maps DNA base-pairing rules into an AI collaboration model where complementary roles are enforced at the architecture level.
- A-T acts as the fast structural pair for requirements and validation; G-C acts as the deeper generation and critique pair.
- Key architectural rule: silence is invalid — critique is mandatory and generation is tied to a verified specification.
- Creates a parallel dual-track reasoning model instead of a loose sequential pipeline.
- The sequence itself becomes a programmable reasoning layer — same four agents, different cognitive strategies.
- Includes experiment artifacts and scored runs comparing structured DNA-style reasoning against other approaches.
- One of the clearest examples of thinking about AI systems: not just prompts and agents, but rules, structure, constraints, and failure-resistant collaboration.
| Project | Description | Link |
|---|---|---|
| ✨ liquid_glass_easy | #1 cross-platform Flutter package for interactive liquid glass effects · 7,000+ downloads | pub.dev |
| 🎮 Keep Flip | Paid Steam puzzle game built with Unity and fluid-physics gameplay | Steam |
| 🏆 Implant Mission | Audience Choice Award winner in a game jam | — |