Learn from builders too busy to be YouTubers. Our agent-powered content delivery lets experts skip the studio and focus on the content—giving you the granular, production-level details that usually get cut from polished tutorials.
Hyper-relevant, battle-tested, detail rich, Engineering project based courses. helps in bridging the gap between "completing a tutorial" and "owning a production system" for learners
Job Readiness > Completion Certificates
We measure success by how effortlessly learners can build systems and how clearly they can explain the trade-offs.
The 10x Production Pipeline
Human Architect
Curation of Logic
Experts blueprint the architecture and code, stripping away the academic fluff.
AI Agent Force
Automated Synthesis
Agents handle the detail-heavy audio, documentation, and scenario logic.
Outcome
Deploy-Ready Depth
Zero details skipped. No recording fatigue. Just deep technical mastery.
Build with production level code-bases curated by expert practitioners accelerated by our AI agents.
from langchain.text_splitter import RecursiveCharacterTextSplitter # Start with code: Building the ingestion pipeline text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200, length_function=len, is_separator_regex=False, ) docs = text_splitter.split_documents(raw_data) # Next step: Connecting to Production Vector DB vector_store = Chroma.from_documents(docs, OpenAIEmbeddings())
Why 200 overlap?
"In production, 20% overlap ensures semantic continuity. If a sentence is cut between chunks, the embedding model loses context. We use RecursiveCharacterTextSplitter because it prioritizes paragraph and sentence breaks over character counts."
Manual expert explanations suffer from production fatigue. High-stakes recording environments lead creators to skip "boring" but critical implementation details to save time.
AltGan solves this by separating curation from delivery. Our experts design the high-density logic, and our AI agents generate precise, consistent audio explanations that cover 100% of the technical surface area.
Expert: Alex Kar
Curated Content
We shift the focus from knowing to thinking. By simulating real production failures, we provide the 'battle-scar scenarios' of an experienced engineer to expedite your job readiness.
Expert Curation
Your Answer
Agent Assessment
Excellent reasoning.
You correctly identified the latency/accuracy trade-off. **Next Step:** To finish this exercise, explain how you would verify if the smaller model preserves semantic intent during high-load periods.
"In an era of information abundance where critical thinking is fading, AltGan replaces passive learning with active problem-solving. Through real-world project scenarios, our Assessment Agent guides learners to think, brainstorm, and defend their architectural & development decisions."
We shift the focus from knowing to thinking. By simulating real production failures, we provide the 'battle-scar scenarios' of an experienced engineer to expedite your job readiness.
Every course on our platform follows a strict, job-validated roadmap.
Face a "Day 1 on the Job" scenario. Your answers are assessed by our AI agents to map your specific technical gaps and establish a starting readiness score.
Reverse learning order. We start with the code. We explain the "why" and the theory only when you hit the technical constraints of the project.
A sidebar learning companion that knows the codebase you're building. Highlight any production decision to get an immediate architectural breakdown.
Engage in high-fidelity architectural defense. Our agents simulate real-world production crises, assess your logic in real-time, and guide you through multiple iterations until you achieve mastery.
Toggle the experience to condense your project notes into high-impact talking points and simulated behavioral questions used by top tech firms to finalize your readiness rating.
Mastered by active engineers. Designed for production.
The flagship track for engineers building production-grade LLM applications. Covers chunking strategies, vector optimization, and latency management.
Master rate-limiting, caching strategies, and database optimization for millions of concurrent users.
Go beyond theory. Learn how to handle consensus, state management, and partial failures in large-scale architecture.