Ground it
Documents, embeddings and vector search.
AI ENGINEER · BUILDER · EXPLORER
Thoughtful code.
Intelligent
possibilities.
I’m Paladugu Ganesh Naidu, creator, owner and developer of TuxNest. I build local-first AI engineering tools alongside applied ML, full-stack systems and security-aware software.
Paladugu Ganesh NaiduGenAI · full-stack · security
Paladugu Ganesh NaiduCreator · Owner · Developer of TuxNest
Ideas into working systemsRAG & local LLMsAgent workflowsGPU inferenceSecurity-aware design
01 / MY PRODUCT · CREATED AND DEVELOPED BY ME
TuxNest✳Your models.
Your code. Visible evidence.
TuxNest is my local-first AI engineering environment for VS Code. I created and develop it to help developers understand repositories, plan changes, review edits, execute approved commands and verify results with local Ollama or their own remote GPU host.
Created, owned and developed by Paladugu Ganesh Naidu.Product direction · VS Code extension · local and remote model workflows
Get TuxNest ↗AskPlanAgentLOCAL OLLAMA ●
✓
Inspect the workspaceContext before code
✓
Prepare a reviewed proposalMulti-file changes · snapshot checks
+ reviewable changes with explicit approval+ validation after accepted edits
What would you like to build?↑
Workflow illustration · see the product site for current details
02 / SELECTED WORK
A few things
I’ve put into the world.
01 / RAG / LOCAL LLM↗
Hybrid RAG API
Retrieval, local inference and memory. A modular API built with FastAPI and Ollama.
02 / APPLIED MACHINE LEARNING↗
CloudCostAI
Turning cloud usage into cost predictions with applied machine learning and a Flask interface.
03 / RESEARCH / 3D GIS↗
3D ULPIN
Exploring a volumetric extension to India’s land parcel identification framework.
04 / SECURITY ENGINEERING↗
NmapAnalyzer
Network scan analysis, structured reporting and security automation with C and MySQL.
03 / FROM MODEL TO SYSTEM
The useful part
is how it all connects.
Data becomes context. Context reaches a model. Tools turn an answer into action. Evaluation closes the loop.
Run it
Local inference, GPU compute and model serving.
Connect it
FastAPI, tools, agents and MCP.
Measure it
Quality, latency, memory and reproducibility.
GPU COMPUTE Original hardware imagery: NVIDIA DGX Spark. Visual references to compute infrastructure; no affiliation implied.
04 / LEARNING IN PUBLIC
Always building.
Always becoming.
A dependency-based roadmap from neural network foundations to production GenAI systems. Every concept earns its place through a working project.
Explore the roadmap ↗FOUNDATIONS ✓NEURAL NETWORKS ✓ACTIVATION FUNCTIONS ↗PYTORCH → TRANSFORMERSRAG → AGENTS → PRODUCTION