Dr. Soumadip Biswas
Computer Science & Engineering | Student | Researcher | Educator

Engineering Handbook

A structured repository of technical principles, system configurations, and operational guidelines.

Volume I: Debugging & Program Understanding Foundations

Master the scientific method of software engineering by moving beyond guesswork to systematically isolate state, control execution, and deconstruct complex system failures.

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Isolating State: The Scientific Method of Debugging
Transitioning from trial-and-error programming to deterministic failure analysis.
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Deconstructing Stack Traces & Core Dumps
Analyzing memory snapshots and call stacks to identify the exact point of system failure.
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Memory State & Undefined Behavior
Understanding how memory allocation, pointers, and undefined behavior dictate system stability.
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Instrumentation & Execution Control
Moving beyond print statements: leveraging assertions, breakpoints, and dynamic analysis to command system execution.
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Concurrency & Distributed Faults
Deconstructing race conditions, deadlocks, and the systemic challenges of asynchronous execution.
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Volume II: Git & Engineering Discipline

Writing code is only a fraction of software engineering. The true discipline lies in how that code is tracked, integrated, and documented over time. This volume explores the transition from isolated programming to systemic collaboration, focusing heavily on the internal mechanics of Git, architectural branching strategies, and repository hygiene.

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The Shift to Engineering Discipline
The transition from writing isolated scripts to managing a collaborative, historical codebase.
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Git Internals & The Conceptual Model
Deconstructing Git as a content-addressable filesystem and a Directed Acyclic Graph (DAG).
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Branching as Architectural Design
Mapping team topology to repository structure through branching strategies, merging, and rebasing.
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Anti-Patterns & System Documentation
Identifying destructive version control behaviors and the engineering necessity of ADRs and comprehensive READMEs.
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Applied Labs & Evaluation Rubrics
Scenario-based exercises for resolving complex Git states and the standard rubric for evaluating repository hygiene.
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Volume III: Linux & Systems Literacy

Applications do not execute in a vacuum; they run on an operating system. An engineer who treats the OS as a black box will inevitably fail to scale or debug complex systems. This volume explores Linux from an architectural perspective, covering kernel boundaries, process lifecycles, and system observability.

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Linux Architecture & The Systems View
Understanding the OS as a resource allocator, the boundary between user and kernel space, and system calls.
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The Filesystem & Access Control
Deconstructing inodes, the Filesystem Hierarchy Standard (FHS), and resolving permission faults.
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Process Management & Observability
Deconstructing the process lifecycle, the fork/exec model, and system telemetry tools.
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Shell Automation & Network Foundations
Mastering standard streams, defensive scripting, and the fundamental architecture of Linux networking.
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Failure Modes & Applied Labs
Diagnosing catastrophic system failures, understanding the OOM killer, and the exit criteria for Linux systems literacy.
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Volume IV: API Engineering

An API is not simply a URL that returns data; it is a strict, legally and mathematically binding architectural contract between decoupled systems. This volume elevates API design from basic web routing to the engineering of robust, secure, and evolvable distributed state machines.

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Modules for this volume are currently in development.

Volume V: Databases as Engineering Systems

Code is ephemeral; data is permanent. The database is the ultimate source of truth and the most critical bottleneck in any distributed system. This volume shifts the perspective of a database from a simple storage mechanism to a complex, concurrent state machine governed by strict mathematical guarantees and physical I/O limits.

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Modules for this volume are currently in development.

Volume VI: Distributed Systems & Reliability

Navigate the chaotic reality of unreliable networks, unsynchronized clocks, and partial failures by mastering consensus algorithms, container isolation, and declarative orchestration.

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Modules for this volume are currently in development.

Volume VII: Emerging, Intelligent & Cloud-Native Systems

Transition from deterministic code to probabilistic, planetary-scale systems. Master the architectural trade-offs of Cloud elasticity, Machine Learning fragility, Edge computing, and adversarial consensus.

Select a module below to begin reading.

Modules for this volume are currently in development.