[ SYSTEM ONLINE ]
SOFTWARE
ARCHITECT.
AI RESEARCHER.
Building recursive intelligence systems since 2017.
[ SECTION 02 — GENESIS : 2017–2019 ]
FROM ARCHITECTURE
TO INTELLIGENCE.
After two decades building software systems and fifteen years designing their blueprints, the architecture began to evolve. In 2017, the rigid rules of traditional software gave way to something more fluid — logic that could learn, adapt, and respond.
[ SECTION 02 — GENESIS : 2017–2019 ]
THE FIRST
AGENT.
2019. The first conversational AI. Not a chatbot reading scripts — a system that understood context, maintained state, and generated responses that felt considered. The chatbot became a coding agent. The agent became a collaborator.
[ SECTION 02 — GENESIS : 2017–2019 ]
THE PARADIGM
SHIFT.
The transition wasn't gradual. It was a phase change. Twenty years of engineering discipline didn't become obsolete — it became the foundation for building systems that could think.
[ SECTION 03 — DIFFUSION & FINE-TUNING ]
THE LATENT
SPACE.
From noise to structure. Hundreds of fine-tuned models. The art of teaching machines to see.
AUTO-REGRESSION
Custom GPT Fine-Tunes
Instruction tuning, RLHF alignment, and domain-specific model adaptation across multiple architectures.
DIFFUSION
SDXL Custom LoRAs
Trained hundreds of LoRA adapters for Stable Diffusion — style transfer, subject injection, composition control.
FINE-TUNING
LoRA & QLoRA Sweeps
Full parameter, LoRA, and QLoRA fine-tuning pipelines with hyperparameter optimization.
QUANTIZATION
GGUF, GPTQ, AWQ
Model compression and quantization for edge deployment without sacrificing output quality.
MULTI-MODAL
Vision-Language Models
Integrating visual understanding with language reasoning across unified architectures.
AGENT FRAMEWORKS
ReAct & Tool-Use
Building autonomous agents with chain-of-thought reasoning, tool selection, and multi-step execution.
VOICE
Whisper & TTS
Fine-tuned speech recognition and text-to-speech systems for conversational AI interfaces.
EMBEDDING
Custom Embeddings
Domain-specific embedding models optimized for semantic similarity and retrieval precision.
[ SECTION 04 — RAG & VECTOR RETRIEVAL ]
NAVIGATING
VECTOR SPACE.
Every piece of knowledge, every conversation, every insight — encoded as a point in high-dimensional space. The challenge isn't storage. It's retrieval. It's knowing which memories matter for this exact moment.
[ SECTION 04 — RAG & VECTOR RETRIEVAL ]
COMPLEX RAG
ARCHITECTURES.
From simple semantic search to multi-hop reasoning chains. From single-vector lookups to hybrid retrieval systems that combine dense embeddings, sparse keywords, and graph-based relationships. The labyrinth has many paths — the architecture chooses the right one.
[ SECTION 05 — ACTIVE RESEARCH ]
RECURSIVELANGUAGE MODELS.
Evolving Memory Architecture
The current frontier. Systems that don't just retrieve and respond — they reflect, evaluate, and evolve. Each interaction loop feeds back into the core, making the architecture smarter, more contextual, more human. This isn't prompt engineering. This is memory engineering.
[ SECTION 06 — CORE INTERFACE ]
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