Backend engineering, taught as a connected path
Learn backend engineering by following a roadmap, reading the guide, then practicing the idea.
Codefarm helps you move from scattered study to durable understanding across system design, Java, Spring Boot, and Kafka.
A simpler way to learn
Use the same rhythm for every topic so each page has a clear next step.
From the Codefarm blog
Recent write-ups on backend engineering, straight from this site.
Model Context Protocol (MCP) Explained: How AI Agents Actually Connect to Your Systems
MCP went from an Anthropic open-source release in late 2024 to the industry standard for connecting LLMs to tools and data. Here is how the protocol actually works — hosts, clients, servers, tools, resources, prompts — why OpenAI and Google adopted it, and what to watch out for before exposing your systems to an agent.
Java 25 LTS: What's New, What Matters, and What to Actually Use
Java 25 is the long-term-support release after Java 21, and the one most backend teams will standardize on next. Scoped values go final, compact object headers shrink heap for free, Project Leyden attacks warmup time, and Java quietly becomes scriptable. Here is what matters for production systems — and what's still preview.
Long Context vs RAG in 2026: Why 'RAG Is Dead' Keeps Being Wrong
With million-token context windows in Gemini, Claude, and GPT-4.1, 'just stuff everything into the prompt' sounds simpler than building a RAG pipeline. Here's why retrieval survives — cost, freshness, access control, and the physics of attention — and how long context actually changed RAG design instead of killing it.
Start here
Begin with the foundational guides every backend learner needs.
Networking Basics
OSI, DNS, TCP, and what really happens when you type a URL.
Client-Server Architecture
REST, statelessness, HTTP methods, and clean API thinking.
Basic System Concepts
Latency, throughput, scaling, load balancing, and caching.
Databases
SQL vs NoSQL, indexing, transactions, and ACID properties.
New: Applied AI Engineering guides
From transformer internals to production-grade agents — a 9-phase roadmap for building on LLMs.
Transformers & Attention, Explained Without the Math
How self-attention works and why every modern chat model is a decoder-only Transformer stack.
Pre-training, Post-training & Inference
The three-stage model lifecycle — and the actual, mechanical reason LLMs hallucinate.
The RAG Pipeline
Ingest, chunk, embed, index, retrieve — how to ground a model's answers in real, citable documents.
ReAct & Reasoning Loops
The Thinking-Acting-Observing loop that makes an agent's tool use reliable.
Practice after learning
The site is built for active recall and visual intuition, not just reading.
Your learning dashboard
Start exploring roadmaps, taking quizzes, and bookmarking content to see your progress here.
Latest from My Medium Blog
Fresh insights on backend development, system design, and engineering
Watch when video helps
Video lessons support the roadmap; the structured path still lives on Codefarm.
Ready to build real momentum?
Start with one roadmap, keep the guide beside it, and practice before moving on.