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Deoding Complexity into Simplicity
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Deoding Complexity into Simplicity

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Ammar-Alnagar/README.md

⚙️ Ammar Alnagar — AI Systems Engineer | Cognitive Architect

I build AI that thinks, plans, and adapts — not just predicts.


🚀 Hey, I'm Ammar

I work at the intersection of large language models, cognitive science, and systems engineering. My focus is on building reasoning-first agents and modular multi-agent frameworks that go beyond prompt-response.

  • 🧠 Core interest: agents with belief modeling, memory, and long-term goals
  • 🔍 I care about making AI useful, interpretable, and actually smart — not just "chatty"

🧠 What I Work On

  • Cognitive agents — agents that track goals, beliefs, and mental states
  • Reasoning pipelines — LLM stacks designed for logic, not just fluency
  • Memory systems — long-term memory, self-reflection, and behavior updates
  • Tool-using agents — LangGraph, OpenAI Assistants, Google AI SDK, LangChain
  • Multi-agent systems — coordination, planning, modular workflows

🛠️ Projects

  • Zirel-0 – DeepSeek-R1-Zero finetuned for better reasoning and minimal hallucination
  • OR1 Behemoth – Qwen 3 72B trained for Rust — strongest Rust completion & Generation model available
  • CogniLink – LLaMA 3.3 70B with added reasoning + reflection, without losing creative edge
  • Manticore 32B – Reasoning-tuned Qwen3-32B with low-latency high-accuracy outputs
  • VisoLearn – Multimodal VLLM tutor for neurodivergent learners (ASD)
  • Qwen Griffon – Lightweight SLM with enhanced reasoning at low cost
  • Self-Learning RAG – Retrieval system that adapts to user context and evolves
  • Enlightener – RLHF-trained tutor agent with long-memory and personalized feedback
  • Immy – Emotion-aware real-time AI bear with adaptive state modeling
  • Wisal - a revolutionary, AI-powered educational platform designed specifically for children with Autism Spectrum Disorder (ASD) in real time.

🧰 Tools I Use a Lot

Languages: Python, Rust, C++, Java
Frameworks: PyTorch, Transformers, TRL, Unsloth, Accelerate , Burn , Lightning Agent Stacks: LangGraph, LangChain, OpenAI Agents framework, Google ADK (Google AI Agents) ,Pydantic AI Focus Areas: RAG, memory-augmented agents, symbolic logic, multi-agent systems , Small language models (Small VLMs too)


📡 Let’s Connect

LinkedIn Email Hugging Face


Building systems that learn, reason, and adapt. No fluff. Just logic.

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  1. Pyron Public

    Pyron is a Python-based agentic pipeline framework that allows a team of AI agents to work together to complete tasks. It is built using the Google Agents Development Kit and is designed to be easi…

    Python

  2. Re-AG-Reasoning Public

    ReAG is a Python SDK for building Retrieval-Augmented Generation (RAG) applications. It provides a flexible and easy-to-use client for querying large language models (LLMs) with your own data.

    Python 1

  3. Marla Public

    This project implements an agentic pipeline using the Google Agent Development Kit (ADK) framework. It features a master agent that supervises and delegates tasks to a team of specialized agents, e…

    Python

  4. RustaceanAI Public

    This is a full and complete AI system thats built in pure RUST. It will have options of using openai models , gemini models , openrouter , huggingface or even local models.

    Rust

  5. Ai-Agents.rs Public

    This project implements a modular multi-agent system in Rust. It demonstrates how agents can communicate, interact, and execute behaviors in a simulated environment. The system is designed to be ex…

    Rust 1

  6. SLRAG-with-COT Public

    Self Learning RAG With COT is a project that implements Retrieval-Augmented Generation (RAG) combined with Chain of Thought (COT) reasoning. This project aims to enhance the performance of language…

    Python 16 2