Machina: Building a Mini Raft From Scratch in Java
Three nodes electing a leader and replicating a log over raw HTTP — what building consensus teaches that reading about it never does.
Read Post →Long-form notes on backend engineering, systems, and machine learning.
Three nodes electing a leader and replicating a log over raw HTTP — what building consensus teaches that reading about it never does.
Read Post →What you need to know about Docker as a software engineer. Learn Images, Containers, Dockerfiles, Compose, Volumes, and Networking fundamentals.
Read Post →Understanding how containers actually work. A deep dive into Linux namespaces, cgroups, and the kernel features that make containerization possible.
Read Post →Understanding the key differences between VMs and containers. When to use each, their architectures, and how they achieve isolation.
Read Post →Understanding JSON Web Tokens from structure to security: authentication flows, signature verification, and best practices for secure implementation.
Read Post →My personal notes and strategies for system design interviews. Contains company-specific insights and preparation materials.
Read Post →A complete guide for backend developers on working with frontend teams: API design, JSON responses, CORS, error handling, and best practices for seamless collaboration.
Read Post →Personal thoughts and reflections on my career path, challenges faced, and lessons learned that I keep for myself.
Read Post →A comprehensive guide to designing clean, consistent REST APIs: proper HTTP methods, status codes, why mutations should return resources, and security best practices.
Read Post →A deep dive into distributed version control, Git architecture, objects, and the elegant design that revolutionized software development.
Read Post →Understanding the complete journey from raw internet data to ChatGPT: pretraining, tokenization, fine-tuning, RLHF, and the magic of in-context learning. Based on Andrej Karpathy's comprehensive guide.
Read Post →How do LLMs convert text into numbers? A deep dive into Byte Pair Encoding (BPE), token IDs, and embedding vectors. Understanding the complete pipeline from raw text to neural network input.
Read Post →Global Vectors for Word Representation. A deep mathematical dive into how GloVe combines global co-occurrence statistics with vector space semantics to create powerful word embeddings.
Read Post →A deep dive into the Word2vec paper. Understanding CBOW, Skip-gram, Negative Sampling, and how neural networks learn semantic meaning from raw text.
Read Post →A comprehensive guide to embeddings, cosine similarity, pgvector, and building intelligent recommendation systems with vector databases.
Read Post →A comprehensive exploration of gradient descent, convergence theory, loss landscapes, and modern optimization algorithms that power neural network training.
Read Post →The finale of the series. We connect Entropy and Cross-Entropy to define KL Divergence as the "wasted bits" of our model.
Read Post →The cost of being wrong. Part 2 explains Cross-Entropy, how it differs from Entropy, and why it's the standard loss function for classification.
Read Post →Why is "surprise" the essence of information? Part 1 of the series explains Entropy, bits, and why we use logarithms to measure uncertainty.
Read Post →A deep dive into the mathematics behind neural network training, deriving gradients from scratch.
Read Post →The fascinating story of how machines discover what matters, without being told. Exploring automatic feature learning and why it revolutionized AI.
Read Post →A visual deep dive into Raft. Understanding leader election, log replication, and safety through interactive-style diagrams.
Read Post →How distributed systems agree when machines fail. Understanding the Bully Algorithm with a complete implementation walkthrough.
Read Post →Storing petabytes on thousands of unreliable machines. How Google built reliable storage from cheap, failing hardware with clear visual explanations.
Read Post →How Google processes terabytes with a simple programming model. Understanding Map, Reduce, and the execution model with clear visual diagrams.
Read Post →When one machine isn't enough. A comprehensive introduction to distributed systems, connecting concepts to networks and operating systems you already know.
Read Post →Understanding the fundamental principle behind parameter estimation: from intuition to mathematical derivation, with examples and applications in machine learning.
Read Post →Watch space bend, rotate, and stretch. Understanding how matrices transform space .
Read Post →A visual journey through vectors, linear combinations, span, and basis vectors. Understanding the geometric intuition behind linear algebra .
Read Post →Master systemd and systemctl: Learn service management, unit files, dependencies, troubleshooting, and advanced configuration for modern Linux systems.
Read Post →Understanding the life of a process: Signals, Inheritance (fork/exec), Zombies, and how Namespaces make Docker possible.
Read Post →How the Linux kernel boots. From BIOS/UEFI and GRUB to the Kernel, Init (Systemd), and User Space.
Read Post →A comprehensive guide to Linux system organization, processes, system calls, and the separation between kernel and user space.
Read Post →A deep dive into the Linux File System architecture. Understanding Inodes, Superblocks, Directory Entries, and the Virtual File System (VFS) .
Read Post →A practical guide to navigating and managing Linux systems from the command line.
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