Behavioral Interview Prep for Engineers

This repository provides a comprehensive collection of educational materials and strategies designed to assist technical professionals in preparing for the various stages of the software engineering interview process. It covers core competencies including algorithmic problem-solving, behavioral interview techniques, system design architecture, and general career development. The content is organized into structured study plans and tactical guides that address specific interview formats, ranging from initial phone screens to final onsite sessions. It includes resources for mastering data structures and coding patterns, frameworks for structuring behavioral responses, and guidance on navigating professional job searches, including resume optimization and compensation negotiation. The repository also features company-specific question banks and practical advice for managing different interview environments.

This project is a comprehensive set of roadmaps and curricula designed for technical, behavioral, and architectural interview mastery. It provides structured guides, frameworks, and checklists for mastering algorithmic coding, system design, and behavioral questions. The resource is distinguished by specialized study paths, including a frontend engineering curriculum and a dedicated system design framework for architecting scalable systems. It also features a behavioral interview playbook that utilizes a standardized response method to align professional experience with company values. The guide covers a broad range of preparation capabilities, including technical assessment strategies for algorithmic problems, communication skills for live coding, and career planning for salary benchmarking and company research. It also provides guidance on the operational logistics of interviewing and post-interview communication. The content is delivered via markdown-based files for structured accessibility.

This project is a comprehensive career transition guide and job navigator designed for software engineers moving from the private sector into government and public service roles. It serves as a structured resource for evaluating job eligibility, comparing professional benefits, and selecting government positions that value technical backgrounds. The repository provides specialized guidance for the public sector recruitment process, including a handbook for navigating political reviews, medical screenings, and background vetting. It also features a civil service exam guide with study methodologies for administrative aptitude tests and essay examinations, alongside resources for mastering behavioral interviews and professional presence. The project covers a broad range of career planning capabilities, including the analysis of job postings, the creation of study schedules for working professionals, and the evaluation of risks associated with employment transitions. It also includes a curated collection of study materials, candidate forums, and personal narratives from programmers who have transitioned into the public sector.

Career-ops is an AI-driven job search automation system designed to manage the entire application lifecycle, from discovery to tracking. It functions as a career copilot that utilizes autonomous agents to identify vacancies, evaluate professional fit, and generate tailored application materials. The project distinguishes itself through a multi-archetype persona management system and writing style calibration, allowing users to maintain different professional identities and a consistent voice across documents. It employs a multi-dimensional weighted scoring system to evaluate job suitability and utilizes headless browser automation to scan career portals while evading bot detection. The system covers broad capability areas including ATS-optimized resume and cover letter generation, automated job application tracking with status normalization, and professional interview preparation through behavioral story banking. It also includes tools for salary negotiation strategy and career networking automation to manage outreach cadences. The software is delivered as a containerized execution environment to ensure consistent runtime and dependency management across different operating systems.

CS-Base is a comprehensive educational platform and technical repository designed to support software engineers in mastering backend architecture, artificial intelligence engineering, and career development. It functions as a centralized knowledge hub that combines illustrated theoretical tutorials with practical, project-based learning to bridge the gap between foundational computer science concepts and professional industry requirements. The project distinguishes itself by integrating a robust career mentorship framework with advanced AI engineering resources. It provides users with tools for resume optimization, interview simulation, and personalized study planning, while simultaneously offering deep-dive technical curriculum on topics such as retrieval-augmented generation, autonomous agent orchestration, and distributed system design. By synthesizing these domains, the platform enables developers to build production-grade applications while preparing for high-stakes technical hiring processes. Beyond its educational focus, the repository serves as a technical reference for implementing complex software patterns. It covers a broad capability surface including concurrency management, memory optimization, and secure system architecture, providing structured guidance on how to apply these principles within modern development workflows. The project is documented through a collection of technical guides, curated question banks, and project templates available directly within the repository.

This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologies for cognitive learning, such as spaced repetition, the Feynman technique, and information structure mapping using MECE models. The technical surface covers a wide range of computer science and engineering domains. It includes deep dives into distributed systems architecture, machine learning workflows, and frontend component design. Practical application is supported through algorithmic problem sets, JavaScript implementation exercises, and system design blueprints for scalable web applications. The project is primarily implemented as a collection of Jupyter Notebooks.

InterviewGuide is a comprehensive technical interview preparation platform that covers the full spectrum of software engineering recruitment, from foundational computer science concepts through to offer negotiation. It provides structured learning paths across algorithms, operating systems, databases, networking, and programming languages, with a particular emphasis on C++ and Go. The platform aggregates real interview experiences and company-specific questions from major tech employers, offering candidates a searchable database of past written exam problems and detailed accounts of actual interview processes. The project distinguishes itself through its integrated approach to the entire job-seeking lifecycle, combining algorithm practice with resume optimization tools that target automated screening systems, mock interview simulations with expert feedback, and campus recruitment navigation that maps the annual hiring cycle from summer internships to spring recruitment. It includes a curated algorithm problem set with over 300 interview-focused problems filterable by topic and difficulty, alongside high-frequency question collections for last-minute preparation. The platform also offers structured study plans that combine technical topics with real interview questions, peer learning cohorts for shared progress tracking, and downloadable PDF compilations of common technical interview knowledge points for offline study. Beyond core interview preparation, the repository covers system design principles for building scalable distributed systems, database internals including MySQL and Redis, operating system concepts from process management to memory allocation, and networking fundamentals spanning HTTP, TCP/IP, and DNS. It includes project-based learning modules for building web applications and microservices using Go, as well as practical exercises in Linux and network programming. The platform also addresses career transition guidance for newcomers, internship readiness assessment, and offer comparison strategies to help candidates make informed decisions about competing job offers.

This project is an engineering career ladder framework and professional development planning tool. It provides a methodology for defining seniority levels and the specific requirements needed to achieve promotions through a competency-based performance rubric. The framework focuses on separating people management duties from technical leadership to clarify organizational accountability. It utilizes an organizational role definition model to distinguish between individual contributor and manager responsibilities, preventing conflict and aligning professional goals. The system covers engineering performance management by measuring growth across dimensions such as system ownership, technical influence, and people leadership. It also includes a technical leadership framework to establish benchmarks for balancing managerial duties with architectural oversight.

This project is a collection of reference materials and educational guides providing theoretical foundations and practical patterns for algorithms, artificial intelligence, and professional technical interviews. It serves as a computer science study guide and a practical reference for solving computational problems through curated notes. The resources provide a learning path for machine learning, covering the mathematical foundations and architectures used to build large language models. It also functions as a technical interview preparation resource, containing common software engineering and artificial intelligence questions with detailed answers. The content is organized into study guides for core algorithms and data structure mastery, alongside technical analyses of artificial intelligence concepts. It also includes guidance on software engineering best practices, such as coding styles and naming conventions.