ThinkNet

Founded & Led by Naksh Gupta

Building open-source AI systems and exploring the path toward intelligent, world-aware machines.

🌍 Our Vision

ThinkNet aims to explore and build the next generation of AI — systems that don't just generate text or recognize images, but can understand the world, learn representations, predict consequences, reason about actions, and eventually interact with the physical world.

Our long-term direction spans:

LLMs → Multimodal AI → World Models → Embodied AI → Intelligent Agents

🧠 Our Current Stack

We work across:

• Large Language Models — Transformers, pre-training, fine-tuning, SFT & LoRA
• Generative AI — Diffusion Models & multimodal generation
• Computer Vision — CNNs, Vision Transformers, detection, segmentation & perception
• World Models — JEPA, latent-state prediction & action-conditioned prediction
• Embodied AI — perception, planning, control & robotics
• AI Agents — tool use, RAG, LangGraph & agentic systems
• ML Engineering — PyTorch, TensorFlow, Hugging Face, FastAPI & modern AI infrastructure

🚀 What We're Working On

🧠 ThinkNet LLM Series

Developing increasingly capable small language models, with a focus on efficient training, high-quality data and open research.

Upcoming: ThinkNet LLMs in the 100M–250M+ parameter range.

🌍 ThinkNet World Models

Researching JEPA-style representation learning and world models that can learn how the environment changes over time.

Current direction:

Observation → Latent State → Action → Predicted Future State

🤖 ThinkNet Embodied AI

Exploring systems that connect perception with action:

Vision → World Model → Planning → Control → Action

The long-term goal is to build AI that can move beyond screens and interact with the physical world.

👁️ Vision & Multimodal Models

Working toward models capable of understanding:

Images • Video • Text • Audio • Physical environments

🔬 Upcoming Research

Our roadmap includes experiments and models around:

• ThinkNet LLM
• ThinkNet Vision
• ThinkNet Multimodal
• ThinkNet World Model
• ThinkNet Embodied AI
• ThinkNet Agent

These projects are developed incrementally through open-source datasets, experiments, models and research implementations.

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🛠️ Our Philosophy

Learn → Build → Experiment → Open Source → Iterate

We believe meaningful AI progress comes not only from scaling models, but from discovering better representations, learning objectives, architectures and ways for machines to understand and interact with the world.

ThinkNet

Building intelligence. Exploring the world. Open-sourcing the journey.