Building Artistic Intelligence — where research, systems, and creativity converge.

👤 Rahul Chaube (he/him)
🧠 AI Researcher • LLM Architect • Startup Founder
🎨 Creative Technologist • Systems Thinker

I don’t build tools.
I build intelligence systems.

My work lives at the intersection of:

• Foundational AI research
• Production-grade systems
• Creative & artistic intelligence

Old-school engineering discipline × forward-looking AI vision.

Artistic Intelligence is not “AI art”.

It is:

• Intelligence that understands context
• Systems that reason, adapt, and create
• AI that respects human aesthetics & intent
• Research that turns into real products

🏛️ AI Research Lab:
👉 https://www.artisticimpression.org/

🧩 Large Language Models

• From-scratch training pipelines
• Tokenization & long-context scaling
• SFT • RFT • Preference Learning
• Hallucination control & evaluation

👁️ Multimodal Intelligence

• Vision–Language reasoning
• Text → Image generation
• Context-aware perception

🤖 Agentic AI

• Tool-using agents
• Memory-driven reasoning loops
• Autonomous task execution

🏗️ Applied Intelligence

• Industrial AI
• Education AI
• Agriculture AI
• Human–AI interaction systems

Nepal’s first fully self-developed LLM

• Trained from scratch on open data
• Custom tokenizer & training infra
• Multi-phase roadmap → Phase 4 (2025)
• Research-first, deployment-ready

Universal open-source intelligence system

• Multimodal reasoning
• Voice-driven interaction
• Contextual memory
• Designed for accessibility & scale

Realistic text-to-image generation models

• Diffusion pipelines
• Prompt control & realism tuning
• Open research & reproducibility

• Kotlin ⭐ — primary language for system design & apps
• Python — AI research & experimentation
• Java — backend & structured systems
• JavaScript — product & frontend logic
• C / C++ — performance-critical components

• PyTorch — research & custom training loops
• TensorFlow — production ML pipelines
• CUDA — GPU-level experimentation (research usage)
• HuggingFace — internals, tokenizers, datasets (not wrappers)

• Docker — reproducible environments
• Linux (Ubuntu) — primary dev OS
• GitHub Actions — CI/CD & automation
• Google Colab / DGX A100 — large-scale training

• React — AI product interfaces
• Web APIs — system communication
• 🎙️ Voice Interfaces — speech-driven AI UX

• 🏭 CAVI-X — Context-Aware Visual Inspector
• 🎓 Flick AI — AI-powered learning platform
• 🌱 AgroSathi — Agriculture intelligence
• 🏫 CampusConnect — Smart campus ecosystem
• 🎤 Recono — Voice & speech recognition system

🏅 ICPC (LLM Training Problem)
⚡ HackAI by NVIDIA
🚀 Red Bull Basement — Top 15
📄 IEEE International Conference (Deep Learning)

• Research before hype
• Open systems over black boxes
• Design matters
• Intelligence should feel human

I collaborate on:

• LLM research
• Creative AI systems
• Deep-tech startups
• Open-source intelligence infra

If you’re serious about building intelligence, welcome.

Your support directly funds:

• GPUs
• Training experiments
• Infrastructure
• Open publications

☕ https://buymeacoffee.com/rahulchaube

Creative Intelligence • Research Systems • Build the Future