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
