Rakesh Navale
Exploring, researching AI, LLM, Security, MCP, and Developer productivity
Exploring how AI, LLMs, and platform engineering come together to build safer, more productive developer experiences. I work on model-context protocols (MCP), secure knowledge workflows, and tooling that turns complex systems into reliable, explainable workflows for engineers.
Latest Insights
View All Posts →The Hidden Cost of Non-Compliant MCP Servers
MCP compliance is not a checkbox — it is a direct economic and safety variable. This post quantifies how non-compliant MCP servers drain token budgets,...
96GB Mac Studio vs Windows PC: Multi-Agent LLM Orchestration on Real Hardware
I ran the same multi-agent collaboration task on a 96GB Mac Studio with 4 local LLMs and a Windows PC with 3. Same models, same...
Fine-Tuning Local LLMs: Methods, Tools, and What It Actually Costs
Pre-trained models get you 80% of the way. Fine-tuning closes the gap for your specific domain, tone, and task patterns. This post covers the practical...
Running LLMs Locally: A Practical Guide to Models, Hardware, and Getting Started
The range of open-source language models you can run on your own hardware in late 2025 is remarkable. This post covers which models are available,...
AI Security Is No Longer Optional
AI moved from cute demos to critical infrastructure in under three years. Along the way, it picked up a real attack surface: data leakage, deepfakes,...
Technical Expertise
Distributed Systems
Designing and implementing large-scale distributed architectures with focus on reliability, consistency, and fault tolerance.
AI/ML Platforms
Building production-grade machine learning infrastructure and platforms for model deployment, serving, and monitoring.
Performance Engineering
Optimizing system performance through profiling, bottleneck analysis, and implementing efficient algorithms and data structures.
Cloud Architecture
Architecting cloud-native applications with modern DevOps practices, containerization, and orchestration platforms.
Featured Work
Knowledge & Documentation Hub
Built a vector-based knowledge hub with LLM-driven retrieval, integrated into developer tools to surface contextual documentation and recommendations.
Model Context Protocol (MCP)
Designed a secure, role-aware protocol to provision context to LLMs and agents, enabling safe, contextual AI interactions across tools.
Cloud-Ready Developer Environments
Delivered Windows/Linux dev VMs and a lifecycle CLI to automate provisioning, customization, and onboarding for engineering teams.
Copilot Connectors
Built integrations that expose semantically indexed code and knowledge context to Copilot and other LLM interfaces for in-flow assistance.