Read. Try. Keep what helps.
Find a clear explanation, a reusable template or a tool to help you move forward. Our current collection focuses on practical AI, career skills and learning at work.
The reading shelf
65–72 of 108 guides
For developers2 minDevelopers and QA engineers
Generate test cases, write test code, create realistic test data, and explore edge cases with AI — while keeping tests meaningful.
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For developers2 minFront-end developers
Accessibility essentials for AI interfaces: streaming text, focus management, screen reader announcements, and clear controls.
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For developers2 minDevelopers and platform teams
Trade-offs between hosted APIs and self-hosted open-weight models: control, privacy, cost, quality, and operational load.
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For developers2 minDevelopers
What MCP is, how clients and servers fit together, and what to consider before connecting AI assistants to your tools and data.
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Students & freshers2 minCollege students
Project ideas at three levels — beginner, intermediate, advanced — with what makes each credible to recruiters.
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Students & freshers2 minStudents
Use AI as a tutor — explanations, practice questions, feedback — while following your institution's rules and actually learning.
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Students & freshers2 minStudents seeking internships
Build a focused skill set, a project that fits the internship, and an application that shows evidence — with India-specific places to look.
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Students & freshers2 minFreshers and students
Set up a clean GitHub profile, pin the right repositories, and write READMEs that make your work easy to evaluate.
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A plain-text task, context, format and constraints template, with a practice example and an answer checklist.
Learning files published for each webinar. Share feedback or skip it to get the kit.
Explore your next step, prepare your application and practise for interviews. Each tool explains its current capabilities.
