Build. Debug. Deploy.
Practical programming tutorials, AI and GenAI guides, developer tools, and interview preparation—written for developers who want to build real things.
Explore Topics
Tap to browse
Featured Reading
Start here
Bonsai 2 27B: 5.9GB Ternary AI Model
Explore ternary weights, 262K context, multimodal input, tool calling, benchmarks, hardware considerations, and local setup.
AI/ML Roadmap 2026
A practical path from Python and mathematics to machine learning, deep learning, LLMs, RAG, AI agents, and MLOps.
Latest Articles
Fresh from the blog
RAG System Design Interview Questions and Answers
Architecture, retrieval, chunking, evaluation, security, latency, cost and production design.
Multithreading vs Multiprocessing in Python
Understand threads, processes, the GIL, CPU-bound vs I/O-bound work, and practical examples.
Top RAG Interview Questions and Answers
Core RAG concepts, retrieval strategies, embeddings, vector databases and troubleshooting.
How to Use Claude Code with OpenRouter
A practical developer guide to connecting a coding workflow through a model gateway.
MiniMax M3: 1M Context and Multimodal AI
Model capabilities, context length, coding workflows and practical trade-offs.
Calling Gemini API from Java
Set up the Google GenAI Java SDK and make your first Gemini API call.
Useful Resources
Quick tools and learning destinations
Learn by Path
Build your skills step by step
PythonPython programming, decorators, concurrency and practical coding.
Generative AILLMs, model APIs, agentic workflows and AI developer tools.
RAGRetrieval, embeddings, vector search and production architecture.
AI / MLMachine learning foundations through modern AI systems.
Interview PrepTechnical interview questions for developers and AI engineers.
“Learn the concept. Build the example. Debug the problem. Deploy the solution.”
Why GangForCode?
GangForCode focuses on practical developer knowledge: explanations that connect concepts to code, current AI engineering workflows, useful browser tools, and interview preparation that goes beyond one-line answers.