Paid academic course for AI-native engineering
Learn AI, ML, and cloud by building the small systems behind real apps.
ABCsteps is a human, practical engineering course by Divyanshu Singh Chouhan. Begin with a small app, then move through Git, Docker, APIs, databases, deployment, and AI integration step by step.
- Lessons
- 20
- Modules
- 4
- Notes
- 40
Academic course path
Zero to deployed AI app
The ABC method
A platform shape that can teach, publish, and monetize without hype.
Beginner-accessible path
Begin from zero without hiding the tools. Every module explains what is happening and why it matters.
Build-first curriculum
Each step connects concepts to a working system: game, terminal, GitHub, Docker, API, database, cloud, and AI.
Cloud-connected outcome
The end goal is not passive viewing. It is understanding how real applications are shipped and improved.
Technologies you will master
A truthful logo wall for the actual learning stack.
These are the tools and concepts used directly in the lessons.
AI Agents
assistance
TypeScript
language
Node.js
runtime
Docker
containers
SQLite
storage
Cloudflare
cloud
GitHub
portfolio
APIs
backend
Three.js
graphics
Nuxt
web
ML Basics
models
Prompting
ai craft
Ownership
Founder-led curriculum
Public lessons are authored around Divyanshu's teaching model, not anonymous scraped notes.
Clarity
No fake placement claims
Trust is built through lessons, articles, projects, and verified learner stories when they exist.
Access
Transparent syllabus
Learners can inspect the path, outcomes, and sample teaching style before they message Divyanshu.
Compliance
AdSense-safe layout
The site prioritizes original content, clear navigation, legal pages, and non-deceptive monetization.
Learning with Divyanshu
Read, build, review, and ask better questions.
The curriculum is designed so learners can make progress through written lessons, reviews, blog articles, and project steps. Videos can support the journey, but the core learning must be clear in writing.
Learn
Written lessons are the primary product
Every course page should explain the module clearly enough that a learner understands the value before enquiring.
Publish
Blog content supports the curriculum
Articles explain concepts deeply enough for search visitors and future AI citations.
Convert
Enrollment stays human-first
The honest conversion path is WhatsApp or call with Divyanshu, not a checkout flow pretending the operation is larger than it is.
Scale
Downloads before dashboard
PDFs, workbooks, command sheets, and rubrics can serve enrolled learners before a full portal or login system is worth building.
Course fee strategy
A clear paid course, with public proof before enrollment.
The site remains useful for search visitors through syllabus previews, blog articles, and transparent course details. Enrollment is paid, but the first conversion path is direct WhatsApp/call instead of a payment gateway.
Course enrollment
ABCsteps AI Engineering
INR 4,999
The complete 20-lesson course path with guided study structure, projects, review checkpoints, and a downloadable learner pack after enrollment.
- 20 lesson path
- Module labs and reviews
- Downloadable study pack
Founder support
Guided Cohort
Limited seats
A founder-led study group around the same curriculum, with direct review and structured accountability.
- Live study support
- Project feedback
- Portfolio direction
Advanced help
Mentorship
By application
For learners who need deeper help with AI products, cloud deployment, or project architecture.
- Architecture review
- AI/API guidance
- Next-path planning
Academic outcomes
What a learner should be able to prove after the course.
Outcome 01
Build a working app
Use AI assistance without losing understanding of files, code, and verification.
Outcome 02
Publish and explain work
Use GitHub, documentation, and written notes to show what was built and why.
Outcome 03
Deploy with cloud basics
Understand containers, tunnels, APIs, and deployment checks at a practical level.
Outcome 04
Add AI responsibly
Treat AI as an API-backed product feature with costs, errors, and limits.