Latest Posts
Architecting the Industrial Edge AI Gateway
A deep dive into deploying GRU-based inference engines to the extreme edge. Learn how we achieved sub-100ms anomaly detection using TFLite Micro and FreeRTOS isolation on ESP32-WROVER hardware.
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IIoT Edge AI Devices: Solar Farm Management
How edge gateways bring inverter, weather, battery, cooling, fire panel, and EV charger data from a solar-plus-storage site onto one secure timeline — plus where control belongs and how the same data supports carbon credits.
IIoT Edge AI Devices: Production Optimization on the SMT Line
How edge gateways bring PLC, reflow oven, sensor, and building data from a surface-mount line onto one secure timeline — and why trend-based maintenance comes before AI.
Traditional Algorithms vs. Edge AI Models: Choosing the Right Approach at the Edge
A practical comparison of rule-based logic and quantized Edge AI models for embedded and IoT systems — pros, cons, and real examples of when each one wins.
Building an LLM-Powered Digital Twin for Physical AI
An LLM-powered Digital Twin that combines real-time IoT data, system-level rules, and a LangChain-based reasoning agent to explain what is happening, why it is happening, and what should be done next.
LangChain: The Missing Glue for Real-World AI Applications
LangChain is the orchestration layer that turns LLMs into actual systems. Learn how it handles memory, tools, and agents to build real-world AI applications.
Understanding RAG: The Bridge Between LLMs and Your Data
Retrieval-Augmented Generation (RAG) is an architecture used to optimize the output of an LLM by referencing a trusted knowledge base outside of its training data.
Why I'm Building Physical AI in 2026
The next real leap in AI isn't a smarter model — it's intelligence that can sense, decide, and act in the physical world. Here's why I'm building Physical AI, and what that means in practice.