PrajnaEdge is not navigated like a conventional website. Knowledge is represented through paths, structures, and transitions.
Matter
NodeClick a node name to enter that concept and continue along the awakened path.
ExplorationsStep-by-step learning experiences containing theory, architecture, and interactive elements.
SetuA Setu connects related concepts that can be explored independently from the main sequence.
VortexA Vortex opens a path toward another knowledge area, foundation, or future domain.
BlackholeA deeper exploration space opened through the Vortex, where you select the depth of investigation.
AntarguhaA concealed exploration existing outside the regular path, waiting to be discovered within the system.
For the best experience, open PrajnaEdge on a desktop or laptop screen.
PrajnaEdge
A curiosphere for curious minds who want to understand, experiment with, and experience technology.
Technology is a system of connections.
Modern technology is built from layers that continuously interact with one another. At the physical level, electronic devices transform electrical signals into digital information. Digital logic turns that information into computation, while processors, memory and communication interfaces provide the machinery needed to execute instructions and move data. As these components become part of embedded systems, they begin to interact with the physical world through sensors, controllers, actuators and real-time software.
But computation does not exist in isolation. Operating systems coordinate hardware and software, firmware gives specialized machines their behaviour, and communication protocols allow independent systems to exchange information. At the same time, machine learning is moving beyond the cloud into edge and on-device systems, where models must operate within real constraints such as memory, processing power, latency and energy consumption.
PrajnaEdge explores these connections as one continuous technology landscape — and takes them beyond explanation. From computing foundations and embedded systems to intelligent machines and edge AI, ideas can be understood, experimented with, and eventually turned into technology that can be experienced in the real world.
PrajnaEdge products and technology experiences are currently in development.
In Development
Playground
Experiment with intelligence beyond the cloud.
AI inference performed closer to where data is generated — optimizing model representation to reduce latency, memory footprint, and bandwidth requirements.
Run an optimized, trained model directly on physical embedded silicon.
Cloud AIComing Soon
Model Training & Representation
Explore how neural networks learn representations from data before compression and deployment. Future experiments will allow adjusting learning parameters—such as learning rate, epoch counts, and batch sizes—to observe loss trajectories and decision boundaries.
Edge AIComputer Vision
Image Classification
Can this image classifier maintain its intelligence while becoming small enough for the edge?
On-device AIAI-Thinker ESP32-CAM
On-device Image Classification
Run a fully quantized INT8 image-classification model directly on embedded hardware.
On-device AIComing Soon
On-Device Model Adaptation
Can a constrained edge device adapt or fine-tune model parameters directly using local data? Exploring on-device transfer learning and few-shot adaptation under strict compute, memory, and thermal limits.
Edge AI Playground
Image Classification
Can this image classifier maintain its intelligence while becoming small enough for the edge?
Choose an image
Upload an image
Supports JPG, JPEG, PNG
This classifier recognizes only Apple, Banana, and Orange. Other objects may be incorrectly classified as one of these classes.
Run a fully quantized INT8 image-classification model directly on embedded hardware.
This experiment investigates preparing a compact INT8 TensorFlow Lite model for direct inference on resource-constrained embedded hardware. The model uses the same CNN architecture as the Edge AI exploration, converted to a full INT8 representation to classify three classes: Apple, Banana, and Orange.
Practical guides to embedded systems and engineering.
Automotive · EmbeddedProtocol Debugging
CAN Bus Debugging: From Symptoms to Root Cause
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Edge AI Demonstrations
Deploying neural networks and intelligent decision loops on raw silicon targets.
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Operating Systems
The Rules of Fairness
How the Kernel turns difficult choices into predictable decisions.
KernelSchedulingPoliciesArchitecture
Policies vs. Mechanisms
To understand how a scheduler works, we must distinguish between two related concepts:
* The Mechanism (The Scheduler): This is the code that performs the transition from one Process to another. The technical details of this transition, known as a Context Switch, will be explored later in our Operating Systems journey.
* The Policy (The Ruleset): This is the algorithm or set of rules that determines which process from the Ready Queue should be selected next.
The mechanism is universal—it remains the same regardless of what programs you are running. The policy is configurable—it changes depending on what goals the system is trying to optimize.
Why Multiple Policies Exist
There is no single "correct" policy because there is no single engineering goal. Different computer systems prioritize different objectives:
* Fairness: Ensuring that every active process eventually gets a turn on the processor. No process should starve in memory indefinitely.
* Responsiveness: Prioritizing user interaction. A text editor or cursor should respond within milliseconds, even if heavy background calculations are running.
* Throughput: Maximizing the total number of processes completed per hour. This is vital for servers running background data-processing batches.
* Real-Time Behaviour: Guaranteeing that critical tasks execute within strict deadlines. An engine controller sensor loop must run on time, no matter what.
A scheduling policy is an expression of what a system values most.
Four Classic Scheduling Policies
Modern operating systems like Linux, Windows, or macOS use highly sophisticated scheduling architectures. However, these complex production schedulers are built upon a small set of classic, foundational concepts.
Before we can understand how modern schedulers combine these ideas, we must first understand the four classic policies that represent the historical foundations of scheduling:
1. First-Come, First-Served (FCFS)
* The Idea: "Respect arrival."
* The scheduler selects whichever process arrived first in the Ready Queue. It runs until it yields or terminates.
2. Shortest Job First (SJF)
* The Idea: "Finish quickly."
* The scheduler examines all ready processes and runs the one with the shortest estimated execution duration first.
3. Priority Scheduling
* The Idea: "Protect what matters most."
* Each process is assigned a priority value. The scheduler always executes the process with the highest priority first.
4. Round Robin (RR)
* The Idea: "Everyone deserves a turn."
* The scheduler gives each process a tiny, fixed slice of execution time (a quantum). When time runs out, the process is preempted and sent to the back of the queue.
These classic policies introduced different ways of thinking about fairness. Each solved a particular engineering problem, but each also revealed new, unforeseen limitations.
Before we understand how modern operating systems schedule processes, we must first understand these foundations.
Engineering concepts you don't just read — you experience.
Founded in 2026.
PrajnaEdge is a technology company exploring the space between understanding technology, experimenting with ideas, and turning them into things that can be experienced.
Our Mission
To make technology easier to explore, deeper to understand, and more exciting to experience.
Our Vision
To build a technology ecosystem where curiosity, experimentation and creation continuously lead to one another.
Where it began
Embedded Systems
PrajnaEdge began with Embedded Systems — exploring the foundations that connect hardware, software and intelligent computation.
The first technology universe is built around that foundation. The journey will expand as new ideas, experiments and products emerge.
I am the engineer behind the design, development, and content of PrajnaEdge. I build low-level systems where code directly controls hardware, bridging the gap between register-level silicon behavior and intelligent edge decision loops.
I am an Embedded Firmware Engineer focused on developing software for resource-constrained systems. My experience spans bare-metal firmware, device drivers, microcontroller peripherals, and communication protocols, working across the boundary between hardware and software.
My work has involved microcontroller-based systems, real-time behaviour, hardware interfaces, and communication technologies such as CAN, CAN FD, UART, SPI, and I²C. I am particularly interested in understanding systems from the lowest level upward—from registers and peripherals to intelligent edge systems.
ENGINEERING PHILOSOPHY
Engineering is not just about writing code; it is about managing constraints, timings, and physical hardware characteristics. True mastery of complex systems comes from understanding the interactions across different layers of the stack.
This conviction is why I built PrajnaEdge—to bridge the gap between conceptual theory and direct, register-level physical reality.
Where it all began — understanding the physical layer of computation. Circuits, signals, and systems gave me a mental model of how information moves through hardware.
⬡
Connects to Systems Understanding circuits directly enables writing firmware that talks to peripherals at the register level.
What it is
BTech in Electronics and Communication
Undergraduate foundation covering analog & digital circuits, signal processing, microprocessors, and communication systems.
CircuitsSignal ProcessingMicroprocessorsVLSI
What I did
Core Engineering Fundamentals
Studied semiconductor physics, digital logic design, and embedded microcontrollers. Built prototypes using 8-bit MCUs.
8051Logic DesignPCB Basics
What I learned
The Hardware Mental Model
Every software abstraction sits on physical reality. Understanding silicon teaches you why timing, power, and noise are first-class engineering problems.
Let's Connect
Interested in embedded systems, AI, or building something meaningful? I'd love to hear from you.
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Legal & Privacy
Last updated: September 2026
1. Terms of Use
Welcome to PrajnaEdge (prajnaedge.dev), an independent engineering and technology platform created and maintained by Devaharsha Meesarapu. By using this website, you agree to these terms.
Educational & Research Focus: PrajnaEdge publishes interactive technical explorations, architectural models, and simulation walk-throughs covering embedded systems, computer architecture, operating systems, and edge artificial intelligence. All materials, interactive tools, and code demonstrations are provided solely for educational and conceptual understanding.
Hardware & Firmware Disclaimer: Embedded programming interacts directly with hardware registers, physical voltages, and precise timing. While all writeups and code demonstrations are prepared with care, they are provided "as is" without warranty of any kind. You are responsible for reviewing component datasheets, circuit schematics, and electrical ratings before deploying code to physical microcontrollers or custom hardware.
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