Engineering is simple.
As simple as A, B, C.
An open AI engineering curriculum, free to read end-to-end. Twenty lessons, thirty deep articles, real projects — built by Divyanshu Singh Chouhan as an alternative to formal B.Tech in AI / Data Science.
- Lessons
- 20
- Modules
- 4
- Articles
- 30+
- Study notes
- 40
Lesson preview
Zero to deployed AI app
The transformation arc
Four modules. One continuous shape.
Each module produces a working artefact and a verified milestone. The course is designed so each step makes the next one obvious.
- A
- B
- C
- D
The actual stack
Tools you will use, not just hear about.
These are the technologies the course works with directly. No marketing logos. If a tool is on this wall, you will configure it, run it, and ship something with it.
Antigravity
AI IDE
Gemini
Model API
TypeScript
Type Safety
Node.js
Runtime
Docker
Containers
SQLite
Local Storage
Git
Version Control
Cloudflare
Edge & Tunnel
Learning shape
Read, build, review, then ask sharper questions.
Every lesson is written first. Videos can support a written lesson, but the core understanding has to survive on the page. This is what makes the curriculum verifiable and reviewable.
01·Read
Written lessons are the primary product
Every lesson explains the module clearly enough on the page that you understand the value before opening a terminal — or paying for anything.
02·Build
Each lesson produces an artefact
Code, configuration, terminal output, a repository — something verifiable that did not exist before the lesson.
03·Review
Recall locks understanding
After every lesson there is a short review note you use to write back what was learned, so the lesson sticks in long-term memory.
04·Apply
Articles deepen the model
Pair each lesson with the matching engineering blog article, glossary term, or reading path. The articles teach the deeper why; the lessons teach the doing.
From the engineering blog
Free deep-dives, written for engineers.
The blog is the public surface of the curriculum. Each article is a deep model that pairs with a specific lesson — read the article, then apply it hands-on.
aiClaude Code vs Codex CLI: A Same-Repo Workflow Test
A practical comparison of Claude Code and Codex CLI for real repository work, focused on context handling, patches, tests, review, and supervision cost.
14 min read
devopsPodman vs Docker for Developers in 2026
A developer-first comparison of Podman and Docker focused on local workflow, rootless containers, Compose habits, team fit, and real migration trade-offs.
9 min read
aiClaude Opus 4.7 vs GPT-5.5 — Which AI Coding Agent Should Developers Choose?
A source-backed developer comparison of Claude Opus 4.7 and GPT-5.5, focused on tool surfaces, repo evaluation, supervision cost, and coding-agent trade-offs.
9 min read
Public knowledge base
Everything useful starts on the page.
ABCsteps is built as a readable engineering library first: lessons, articles, glossary notes, and reading paths that explain the work before asking anyone to commit to anything.
- 20 written lessons across coding, cloud, full-stack systems, and AI product practice
- 30+ engineering articles paired with the curriculum topics
- Glossary and reading paths that turn confusing terms into usable mental models
- No signup, no email gate, no paywall before a learner can judge the quality
Curriculum
20 written lessons
A complete path from first app to AI-enabled cloud product, with labs and review notes.
Blog
30+ deep articles
Explanatory engineering articles on Docker, Git, APIs, AI integration, SEO, and deployment.
Glossary
Plain definitions
Every core term is explained in simple language and connected back to lessons and articles.
Paths
Curated sequences
Ordered reading tracks help learners study containers, APIs, editors, mobile, and version control.
Connected learning
Lessons, articles, and glossary terms reinforce each other.
Google sees value when a site helps people complete a task. ABCsteps is wired for that: every major concept has a lesson route, a deeper article, and a reference path so learners can move from reading to building without getting lost.
Read
Article first
Use the blog to understand the model behind a topic before opening the hands-on lesson.
Explore
Build
Lesson next
The curriculum turns the idea into a concrete artefact: code, repo, API, deployment, or AI feature.
Explore
Reference
Glossary always
When a term blocks understanding, the glossary gives a short definition and points back to the right page.
Explore
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.
Begin the path
Your first lesson is one click away.
The 20-lesson curriculum is free to read end-to-end — no signup, no email gate, no paywall. Start with the first lesson, then use the articles and glossary whenever a concept needs more depth.
Free curriculum · 30+ articles · Glossary · Reading paths