Stop watching other people buildStart building yourself
Everyone around you is starting to build with AI. You are still watching tutorials.
Live · Challenge starting 25th of September 2026
30
Days
4
Weeks
8
Live sessions
24/7
Discord community
∞
Lifetime access
100
Interview questions
30
AI system design patterns
30
AI-DSA problems
10
Proofs by Day 30
7-Day
Full refund
Cohort Alumni's Work Here:
AI is not a feeling
But a number
3.2x
Roles asking for applied AI skills are growing more than three times faster than roles that do not. Every month you wait, the gap gets wider, not narrower.
No portfolio, no callback
Recruiters and hiring managers increasingly screen out candidates who cannot point to something they actually built and shipped, regardless of how many courses are listed on their resume.
Job Window is open
AI engineering is still young enough that a strong 30-day portfolio genuinely stands out. In two years, “I took a course” will not be a differentiator. Right now, it still can be, if the course produces something real.
“Before the Accelerator, I was more focused on the model itself. After the program, I started looking at AI applications as complete software systems.”Doan Thinh, Software Engineer at NVIDIA
Let's name what you are actually thinking
“I am not technical enough for this.”
You do not need a computer science degree. You do not need to already understand machine learning. You need to be able to write a few lines of Python and follow instructions. That is genuinely it.
What people assume they need
What Build30 actually requires
“I do not have time for this.”
Two hours a day. Not four. Not a weekend disappearing. Two hours, most days, for thirty days. That is one lunch break and one evening, or one early morning before anyone else is awake.
Some days will take less. A few will take more. The two-hour minimum is built around a real job, a real family, and a real life.
“I have started things like this before and never finished.”
You have probably started a course before and quietly stopped by week two. That is not a character flaw. That is what happens when nobody notices you stopped.
In Build30, someone notices. Your build goes into a public thread, every day, next to everyone else's. Two live sessions a week put you in a room with people building the exact same thing you are, the same week you are. This is not designed to be easy to quit without anyone seeing.
“Himanshu emphasizes building, testing ideas, and connecting AI to actual problems, not just following the latest tools and trends.”Hamad, Senior Manager
Typical course, alone
Build30, in public
Build30 AI Challenge Is for you if..
Different starting lines but same finish line: shipped and proven, 30 days from now.
You are a beginner, starting from zero
You have never shipped anything independently. Maybe you have taken a tutorial or two and understood them while watching, then froze the moment the video ended and you had a blank file in front of you. You are not behind. You have just never had thirty days of small, ordered steps with someone checking in. That is what this is.
You are a developer who knows code but not AI
You can write software. You have never built anything with an LLM, an embedding (a way of turning text into numbers a computer can compare), or an agent (an AI that can take actions, not just answer questions). The gap is not ability, it is exposure. Thirty ordered days closes that gap faster than a scattered pile of tutorials ever will.
You are the person who never shipped anything
You understand the concepts. You could probably explain RAG (a system that lets AI search your own documents before answering) to someone else. You have never built one yourself, end to end, deployed, with your name on it. That gap between understanding and having built is the whole reason Build30 exists.
“An exceptional AI leader, developer, facilitator, and coach.”Dr Kulvinder Panesar, AI Leader
You will have 10 Proofs after 30 days
None of what follows is about being the smartest person in the room. It is about being the person who did not stop on Day 4, when stopping would have been easy and nobody would have noticed.
30 live GitHub commits
A public commit history showing 30 consecutive days of AI engineering work. Every date stamped. Every build named. Harder to fake than any resume line.
A working knowledge of the full AI engineering stack
Not just RAG. Not just agents. Every major component: how LLMs work, embeddings, vector databases, agents, evals, guardrails, FastAPI, Docker, fine-tuning, system design.
100 AI engineering interview questions, answered
A searchable bank of the questions that come up at every AI engineering interview, with frameworks and model answers you wrote yourself.
$ pytest problems/ -v
30 passed in 4.82s
30 technical interview problems, solved
Real coding and system-design problems pulled from actual AI engineering interviews, worked through in your own repo. Not LeetCode trivia.
30 design patterns for AI systems
The patterns interviewers actually probe for: RAG architectures, agent orchestration, fallback chains, caching, guardrail placement. You will have built variations of most of them.
30 AI Data Structures & Algorithms (AI-DSA) Library
The DSA you actually needed, not LeetCode filler: MinHeaps, DP, graph traversal, BM25/RRF ranking. Searchable, with complexity and where it shows up in a real AI system.
30 published LinkedIn posts
Not about learning AI. About what you actually built. The habit starts here and it compounds long after Day 30.
4 published Substack articles
Your “30 days of building AI” story. Public. Permanent. Shareable. Your first pieces of long-form technical writing.
github.com/you/build30
README · 30 commits · 4 builds pinned
A GitHub profile that speaks for itself
Not another empty account with two tutorial repos. A build30 repository with a proper README, 30 commits, and documented builds a recruiter or client can actually evaluate.
buildership.io/builders/you
This is not a symbolic badge. It is a real URL with your name, your repos, and your proof, live for as long as Buildership exists.
A permanent listing in the Buildership Showcase
Every graduate's repositories, case study, and Demo Day link live permanently at buildership.io/builders.
“Himanshu’s approach is grounded in engineering rather than hype. He consistently emphasizes understanding the underlying systems, making good architectural decisions, and actually shipping working software.”Doan Thinh, Software Engineer at NVIDIA
Free Books
Free download
AI System Design Playbook
A practical, brief walkthrough of how 18 AI systems work that survive production, from Perplexity, vLLM, Cursor, OpenAI Realtime, Anthropic, Stripe, GitHub Copilot, Llama Guard, DeepSeek-V3, Apple PCC, Uber DeepETA, Databricks Genie, to Netflix.
Download the PlaybookGet it delivered on your email inbox.
Free download
AI Data Structures and Algorithms Handbook
A practical reference of 30 AI-specific data structures and algorithms, the patterns behind vector search, token routing, caching, and inference at production scale.
Download the HandbookGet it delivered on your email inbox.
From the Buildership community
In Their Own Words
Hamad
Senior Manager
“I have known Himanshu for three years. What I have always appreciated is the focus on practical action rather than treating AI as a purely theoretical topic. He emphasizes building, testing ideas, and connecting AI to actual problems, not just following the latest tools and trends.
Doan Thinh
Software Engineer at NVIDIA
“One of the projects I’m most proud of from the Accelerator was building and shipping a production-oriented AI application end to end. What made it valuable wasn’t just getting an LLM to work. I had to think through the complete engineering lifecycle, system architecture, API design, retrieval, evaluation, containerization, deployment, and reliability. That moved my understanding of AI from using AI models to actually engineering an AI system that can be deployed and maintained in the real world.
Dr Kulvinder Panesar
AI Leader
“Neural Vault is my proudest achievement from the Accelerator. It is an on-premise solution that delivers personal data privacy, source validation, AI productivity, and cost and speed optimization, all in one, for a specific use case. A close second is the Dev Auto Agent, where I worked on extended architecture and orchestration for human-in-the-loop monitoring and multi-agent production builds. That opened the doors for varied future projects.
Curriculum Roadmap
Every day there will be 1 Project Build, 3 interview questions, 1 technical Problem, 1 AI System Design Pattern, and 1 AI-Data Structures & Algorithms.
Week 0: Zero-to-One
Start from wherever you actually are. Zero prior AI experience required.
Included free with every enrollment
We do not throw you into Day 1 unprepared. Before your first build begins, you unlock our 4 Foundation Modules designed to close every prerequisite gap over the opening weekend:
15 core commands + fixing real errors
Lists, dictionaries, functions, and JSON
APIs, HTTP requests, and .env security
Intuitive spatial geometry
Friday, September 25 at 7:30 PM IST · Join Himanshu live for Day 0 Orientation.
Week 1: Foundations, Tokenization, Embeddings, & Your First Vector Store
First 3 Days text content is free for everyone to access. Unlock all inside the Season 1 live cohort.
Build
Build a simple character-level and word-level tokenizer with encode(text) and decode(token_ids) methods, and prove why the model cannot count the letter 'r' in 'strawberry'.
Interview questions
- Explain why an LLM fails when asked to count the number of 'r's in the word 'strawberry'.
- What is Byte-Pair Encoding (BPE), and why is it preferred over simple word-level splitting?
- Why does the same sentence translated into Hindi or Japanese consume significantly more tokens than in English?
Technical problem
Write build_vocab_and_encode(corpus) -> tuple[dict, list[int]] that assigns integer IDs to unique words and returns the vocab table plus the encoded sequence.
AI System Design Pattern
Strategy Pattern
AI-Data Structures & Algorithms
Bi-Directional Lookup Map (Two-Way Hash Table)
Build
Build a functional, file-backed TinyVectorDB class with add_document(id, text, metadata), query(search_text, top_k), and save_to_disk(filepath).
Interview questions
- What is the time complexity of brute-force nearest neighbor search across N vectors of dimension D?
- Why does an in-memory vector store become impractical at 10 million vectors?
- What is the purpose of storing metadata alongside vector embeddings?
Technical problem
Write top_k_search(query_vec, doc_vectors, k) -> list[int] returning the indices of the K highest-scoring vectors.
AI System Design Pattern
Pipeline Pattern
AI-Data Structures & Algorithms
Top-K Selection via Sorting / Priority Queue
Paper
Attention Is All You Need
Deep dive
Self-Attention: how Q, K, and V let every token look at and update its meaning from surrounding tokens
Every day: 30-min Tech Support calls · 9:00 PM IST · on Discord
Week 2: Real Search, Production RAG 2.0, & Structured Extraction
These 4 unlock inside the Season 1 live cohort.
Build
Build a Hybrid Search module that runs vector similarity search and exact-keyword search in parallel, merges both ranked lists with Reciprocal Rank Fusion (RRF), and returns the top 5 results.
Interview questions
- Why does vector embedding search often fail on queries like 'Error code 0x80070005' or 'Part #AB-9128'?
- What is Reciprocal Rank Fusion, and why is it preferred over simply adding similarity scores together?
- How does the smoothing constant k (typically 60) in RRF prevent top ranks from dominating scores?
Technical problem
Implement Reciprocal Rank Fusion to merge two ranked document-ID lists into a single fused ranking.
AI System Design Pattern
Chain of Responsibility
AI-Data Structures & Algorithms
Rank Aggregation via Hash Map Accumulation
Build
Build an AI Math Assistant that gives an LLM access to calculate(expression) and get_stock_price(ticker) tools, parses tool-call requests, executes them locally, and returns a verified answer.
Interview questions
- How does native model function calling differ from asking an LLM to write code in plain text?
- How do you prevent an LLM from calling dangerous tools or passing malicious parameters to a local function?
- What happens if a tool function throws an exception mid-execution, and how should that be reported back to the model?
Technical problem
Build a ToolRegistry class with a @register_tool decorator and an execute(tool_name, **kwargs) dispatcher.
AI System Design Pattern
Command Pattern
AI-Data Structures & Algorithms
Function Lookup Registry (Dispatch Table)
Paper
Retrieval-Augmented Generation for Knowledge-Intensive NLP (Lewis et al., 2020)
Deep dive
The 3 fatal RAG failure points: bad retrieval, context truncation, and hallucinated generation
Every day: 30-min Tech Support calls · 9:00 PM IST · on Discord
Week 3: Autonomous Agents, WASM Sandboxes, Guardrails, & Automated Evals
These 4 unlock inside the Season 1 live cohort.
Build
Build a two-agent system: a Coder Agent drafts a solution, a Reviewer Agent critiques it against quality criteria, and it loops back for edits until approved (max 3 rounds).
Interview questions
- When is a multi-agent system genuinely better than a single well-prompted model with few-shot examples?
- How do you prevent multi-agent loops from blowing up token budgets and cost?
- What is the difference between a hierarchical multi-agent system and a choreographed state-machine system?
Technical problem
Write a coordinator loop alternating Generator and Evaluator agents, tracking revision history, and stopping on 'APPROVED' or after 3 attempts.
AI System Design Pattern
Mediator Pattern
AI-Data Structures & Algorithms
Message-Passing State Graph
Build
Build an Input/Output Security Guardrail that scans for prompt-injection patterns, masks PII with regex/heuristics, and checks outgoing responses for leaked system instructions.
Interview questions
- What is an indirect prompt injection attack, and how could it exploit an AI reading support emails?
- Why is client-side input validation insufficient to protect an LLM against jailbreaks?
- How does the Dual-LLM Guardrail pattern work, and what does it cost in latency?
Technical problem
Write mask_pii_entities(text) -> str, regex-redacting emails, 16-digit card numbers, and US phone numbers.
AI System Design Pattern
Proxy Pattern
AI-Data Structures & Algorithms
Regex Finite State Matcher
Paper
ReAct: Synergizing Reasoning and Acting (Yao et al., 2022)
Deep dive
Grounding thoughts in real tool observations to stop hallucination cascades
Every day: 30-min Tech Support calls · 9:00 PM IST · on Discord
Week 4: Serving Optimization, FDE Skills, & Capstone System Design
These 4 unlock inside the Season 1 live cohort.
Build
Add a streaming GET /stream?prompt=... endpoint to your FastAPI app that streams tokens in real time via Server-Sent Events.
Interview questions
- When would you use Server-Sent Events instead of WebSockets for an AI chat application?
- What is Time-to-First-Token, and why does streaming drastically improve perceived latency?
- How do you safely close an upstream LLM generation stream if a user closes their browser tab?
Technical problem
Write an async generator fake_token_stream(text) yielding SSE-formatted tokens with a simulated 50ms delay.
AI System Design Pattern
Observer Pattern
AI-Data Structures & Algorithms
Async Generator Stream Buffer
Build
Create a complete System Architecture Blueprint and a 2-page technical design document for an enterprise AI document assistant serving 10,000 corporate users.
Interview questions
- Walk through your system design for an AI support bot handling 10,000 concurrent requests per minute without hitting rate limits.
- Where would you place a caching layer in an enterprise RAG architecture, and what is your invalidation strategy?
- In the PagedAttention paper, how does virtual memory pagination prevent GPU memory fragmentation?
Technical problem
Write estimate_cluster_capacity(total_users, queries_per_day, avg_tokens), calculating required QPS, daily token consumption, and monthly hosting cost.
AI System Design Pattern
CQRS Pattern
AI-Data Structures & Algorithms
Consistent Hashing Ring
The finish line
Publish your final technical case study on Substack, post your complete 30-day GitHub build history on LinkedIn/Twitter/X, submit your portfolio to the Buildership Showcase, and graduate.
Every day: 30-min Tech Support calls · 9:00 PM IST · on Discord
Each day's content is scoped for that single day. You are not expected to preview all thirty days at once, understand where you will be on Day 22 while you are still on Day 3, or feel behind because the schedule looks long when viewed all at once. Open one day. Do that day. Close it.
Want the Complete 30-Day Technical Specification?
Get the Free Notion Roadmap100% Free. Instant Access. Zero Spam.
Pick your level of support:
Every builder gets all 30 days, 8 live sessions, and the full Discord community. The VIP tier adds 1:1 time with Himanshu and a fast-track into the Accelerator.
Complete 30-Day Production Experience
$199 one-time
Increases to $249 on September 25 at midnight.
Applications close in
7-day full refund. No questions asked.
What you receive
30 Daily Implementation Manuals
30 Verified Reference Repositories
8 Live 90-Minute Interactive Build Sessions
Lifetime Recording Access
Daily Live Debug Support
24/7 Co-Working Community
The Complete 100-Question Interview Bank
30 AI Data Structures & Algorithms Library
30 AI System Design Pattern Swipe File
Live Demo Day Presentation
Permanent Public Portfolio Listing
Bonus Stack Included
1:1 Fast-Track & Career Accelerator Package
$499 one-time
Increases to $599 on September 25 at midnight.
Applications close in
Protected by the same 7-day full money-back guarantee.
What you receive
Everything Included in the Live Cohort
4 Private sessions 45-Minute 1:1 System Architecture & Resume Audit
100% Tuition Credit for AI Engineer HQ Cohort 3
Guaranteed Priority Seat in Accelerator Cohort 3
Personal GitHub Capstone Code Review
Private 30-Minute Mock System Design Interview
1-Year Paid Substack Membership Included
Permanent VIP Alumni Role
Bringing a team into the Live Cohort? Email for team seats at connect@himanshuramchandani.co.
100% Tuition Credit Guarantee
When you join Build30 Season 1 or VIP Accelerator today, 100% of your fee is credited toward your tuition for AI Engineer HQ Cohort 3. Plus the Early Access Grant: complete the 30 days, build your 30 systems, and your entire Build30 enrollment fee is deducted from your Accelerator seat. You effectively get Build30 for free.
Which tier is right for you?
Every deliverable in the Live Cohort, plus every exclusive VIP asset, side by side.
| Deliverables & Features | Live Cohort | VIP Builder Tier |
|---|---|---|
| Price (Locks until Friday, Sep 25 at Midnight) | ₹14,999 / $199 | ₹39,999 / $499 |
| Late Price (Sep 25 onward, Season 2 pricing) | ₹19,999 / $249 | ₹49,999 / $599 |
| Total Seat Allocation | Standard Capacity | STRICTLY 10 SEATS ONLY |
| 30 Daily System Builds, Manuals, & Reference Code | ||
| 8 Live 90-Minute Interactive Sessions with Himanshu | ||
| Full 1080p HD Recordings & Timestamped Notes | (Lifetime Access) | (Lifetime Access) |
| Daily 30-Minute Tech Support Calls on Discord (9:00 PM IST) | ||
| 24/7 Co-Working Voice Rooms & Dedicated Build Channels | ||
| 100 AI System Design Interview Questions & Model Answers | ||
| 30 AI Data Structures & Algorithms (AI-DSA) Library | ||
| 30 AI System Design Pattern Cards | ||
| Live Demo Day Presentation (October 4, 2026) | ||
| Permanent Listing on buildership.io/builders Directory | ||
| 7-Day 100% Money-Back Guarantee (No Questions Asked) | ||
| 4x Private 1:1 Milestone Sessions with Himanshu | (0 Sessions) | (4 Full Sessions) |
| Personal GitHub Pull Request Code Reviews | (Weekly Reviews) | |
| Live 30-Minute Mock System Design Interview on Excalidraw | (In Session 4) | |
| 100% Tuition Credit ($499) toward AI Engineer HQ Cohort 3 | (Guaranteed Seat) | |
| 1-Year Paid Substack Newsletter Membership ($150 Value) | (Included Free) | |
| Private VIP Channel Access with Direct DM Access |
Wait, there's more:
Every bonus below is included free the moment you join Season 1.
Bonus 1: Agentic Systems Blueprint
Implementation ManualDon't guess how to structure your agents. This is the exact step-by-step blueprint for designing autonomous systems that don't get stuck in loops.
Bonus 2: AI Math Blackbox Decoded
Rapid Reference GuideMost engineers fear the math. You don't need a PhD. This guide strips away the academic fluff and gives you the raw logic you need to understand Transformers and Attention mechanisms instantly.
Bonus 3: AI Production Architectures Swipe File
Visual Architecture LibraryStop designing from scratch. Here are 14 battle-tested architectures for RAG, Voice, and Multi-Agent systems. When you have a new project, open this, pick a pattern, and build.
Bonus 4: AI Engineer's Second Brain
Searchable Knowledge BaseYou can't memorize everything. This is your encyclopedic reference for every edge case, library, and tool in the AI stack. When you get stuck, the answer is in here.
Who is actually running this
Meet Your Instructor
Himanshu Ramchandani
Founder, Buildership & Dextar
100+ AI projects delivered · 10,000+ professionals trained · 30,000+ people follow his work
Himanshu Ramchandani is an AI engineer, Microsoft MVP, and the person 50,000-plus AI builders follow for engineering content that skips the theory and shows the actual build.
He has trained over 10,000 engineers, built and shipped production AI systems through his consulting practice, Dextar, and holds a graduate research background in knowledge tracing, the same discipline behind adaptive learning systems, which is part of why Build30's daily structure is built the way it is, calibrated pacing, not just a curriculum dump.
He created the PILOT framework, the five-step build loop behind every day of this cohort, and has kept a public build practice for years, teaching in public rather than only teaching from a stage.
During Season 1, I am building alongside you and posting my own progress every week. See the Founder's Build Log below.
“He brings a comprehensive, 360-degree approach to his course delivery, balancing collective group instruction with individualized support.”Dr Kulvinder Panesar, AI Leader
Questions about Build30
When does Season 1 start?
Can I do this alongside a full-time job?
What if I am in a different time zone and cannot attend the 7:30 PM IST Live Sessions?
What computer specifications do I need? Can I use Windows without a GPU?
Do I have to pay extra for OpenAI, Anthropic, or cloud API keys during the 30 days?
What if I get stuck on a day's idea?
How quickly do I get help if my code throws an error late at night?
What if I miss a day?
“Progress does not require perfection.”Hamad, Senior Manager
Is there a certificate or verifiable credential at the end?
Is my portfolio page permanent?
What if it's not what I expected?
Build Consistency in yourself
Questions? Reply on connect@himanshuramchandani.io
“Ask yourself one question: do I need more information, or do I need a system that will push me to actually build and ship?”Doan Thinh, Software Engineer at NVIDIA