How To Develop End To End Software Using AI (Vibe Coding)

Explore how AI reshapes software jobs through consolidation, sparks new AI roles and skills, and shows how to secure work by mastering prompts and AI debugging.

AI reshapes data analysis, IT operations, and customer support, causing job cuts and transformations across industries. It automates finance, healthcare, manufacturing, and education tasks, impacting analysts, clerks, recruiters.

Explore how vibe coding uses AI agents and the Model Context Protocol to generate, test, and debug code in VSCode, embracing chat oriented programming and QC.

Watch a hands-on demo of vibe coding, AI agents, and MCP, exploring VS code and Xcode, free and paid tools, copilot modes, AI models, and MCP server integration.

Explore prompt engineering and context engineering to craft precise prompts, structure outputs, and debug with artificial intelligence, including best practices, live debugging, and iterative refinement.

Explore context engineering foundations, including system prompts, user prompts, memory (short-term and long-term), rag, and tools; learn best practices for selective context to improve accuracy and reduce cost and time.

Develop and refine AI-assisted debugging skills by applying prompt engineering with project context and error messages, fixing one error at a time through experimentation, model variation, and web search.

Explain what AI hallucination is, why it happens, and how to validate AI work and reduce hallucinations with guardrails, best practices, and code-generation examples across mature languages and task complexities.

Validate AI-generated software with exhaustive manual testing, unit and automation tests, AI-assisted and human code reviews, plus guardrails to reduce hallucinations and improve end-to-end quality.

Learn to apply ai to refine and expand product requirements, translate them into front-end ui plans, back-end microservices, and data schemas for an employee management dashboard.

Learn how a business analyst uses AI to generate actionable user stories with acceptance criteria, then export them in CSV format for end-to-end software development.

Generate AI-driven test cases from detailed requirements and user stories for front-end UI and back-end REST APIs, covering login, dashboard, and employee management scenarios, including happy paths and edge cases.

Learn to generate end-to-end technical specs with AI for tech lead roles, detailing frontend react SPA and backend java spring boot, with rest api definitions and sample requests.

Generate high level architecture diagrams with AI tools, iterating from basic requirements to detailed models, detailing a frontend in the browser, a Spring Boot microservice backend, and a MySQL database.

Learn to configure AI-powered IDE tools, scaffold a Spring Boot project with H2 memory database, and generate create, read, update, delete employee APIs with AI, including tests.

Follow a six-step, end-to-end approach to build an employee microservice in Java, mapping a JPA employee model and enabling CRUD via REST on an H2 database with unit tests.

Generate a Postgres rest api to create employees with six fields, excluding id which is auto generated, including data validations, using a three-layer controller, service, repository architecture and camel-cased json.

Build a Rest API in Java with Spring Boot, returning a list of employees via a camelCase response DTO, implemented in the controller and service, with empty-list behavior.

Patch rest api at /api/employee/{id} updates selected fields (name, status, department, salary, manager id) while fixing id and date of joining, with data validation and error handling.

Develop a rest api at /api/employee/{id} to perform a soft delete by setting status to not active, with data validation and error handling when the employee does not exist.

Generate unit tests for a Java Spring Boot employee microservice with AI, covering get, create, update, and delete APIs, using mojito, JUnit, and AssertJ.

Build a React frontend for an employee web app using vibe coding with AI. Review GitHub Copilot, Gemini Code Assist, Klein; set up project, add unit tests, test in Chrome.

Leverage ai to build an end-to-end employee management web app with CRUD APIs. Break requirements into five steps, from a dashboard to create, edit, delete pages and unit tests.

Create an end-to-end employee page in a React app, capturing name, date of joining, department, salary, manager id, and status, posting to the API with a 201 success modal.

Build an edit employee page with a pre-filled form for name, department, salary, manager ID, and status, then submit a patch to the local API and show a success message.

Implement delete employee on the dashboard with a per-row button; on 204, show a success model with 'employee deleted successfully' and refresh via Get Employee List API.

Generate and review AI-driven unit tests for a React web app, weighing immediate versus all-at-once testing, with setup files, and aiming for test coverage using Jest and React Testing Library.

Explore the future of AI through ANI, AGI, ASI, and quantum AI, examine job security and strategies to protect careers in a world shaped by AI and quantum computing.

Discover how ai reshapes jobs by excelling in generating creative content, code, images, and data insights, while humans remain indispensable in emotional intelligence, ethical judgment, and Moravec's paradox-related physical tasks.

Learn how AI disrupts work, why mastering AI and related tools keeps you indispensable, and how to upgrade skills, practice, and apply AI for repetitive tasks while safeguarding quality.