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System Design Interview Prep

Build intuition for designing scalable distributed systems from scratch.

  1. 1Fundamentals & CAP theorem
  2. 2Load balancing & caching
  3. 3Database sharding
  4. 4Microservices patterns
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71 articles

Distributed Systems

Deep dive into the theory and practice of building reliable distributed systems.

  1. 1Consensus algorithms
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34 articles

Python Engineering

From Python basics to production-ready engineering patterns.

  1. 1Python fundamentals
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31 articles

ML & AI Engineering

Navigate the landscape of machine learning and modern AI systems.

  1. 1ML fundamentals
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  5. 5Sparse MoE & advanced topics
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Softmax Function Explained: From Raw Scores to Probabilities

TLDR: Softmax converts a vector of raw scores (logits) into a valid probability distribution by exponentiating each value and dividing by the total. Subtracting the max before exponentiating prevents floating-point overflow. Temperature scaling contr...

·21 min read·65 views
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NoSQL Partitioning: How Cassandra, DynamoDB, and MongoDB Split Data

TLDR: Every NoSQL database hides a partitioning engine behind a deceptively simple API. Cassandra uses a consistent hashing ring where a Murmur3 hash of your partition key selects a node — virtual nodes (vnodes) make rebalancing smooth. DynamoDB mana...

·22 min read
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Java 21 to 25: Virtual Threads, Pattern Matching, and Structured Concurrency

TLDR: Java 21 LTS makes virtual threads a production-ready replacement for bounded thread pools — your newFixedThreadPool(200) can become newVirtualThreadPerTaskExecutor() and handle 10× the concurrency with no architectural changes. Pattern switch w...

·21 min read·2 views
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Java 14 to 17: Records, Sealed Classes, Text Blocks, and Pattern Matching

TLDR: Java 14–17 ran a deliberate four-release preview-to-stable conveyor belt. Records replaced 50-line POJOs with one line. Text blocks ended escape-sequence chaos in multi-line strings. Sealed classes turned "please only subclass these types" comm...

·24 min read
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HyperLogLog Explained: Counting Billions of Unique Items with 12 KB

TLDR: HyperLogLog estimates the number of distinct elements in a dataset using ~12 KB of memory regardless of cardinality — with ±0.81% error. The insight: if you hash every element to a random bit string, the maximum length of leading zeros you obse...

·17 min read
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Count-Min Sketch Explained: Frequency Estimation at Streaming Scale

TLDR: Count-Min Sketch (CMS) is a fixed-size d × w counter matrix that estimates how often any element has appeared in a stream. Insert: hash the element with each of the d hash functions to get one column per row, increment those d counters. Query: ...

·21 min read·2 views
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Clock Skew and Causality Violations: Why Distributed Clocks Lie

TLDR: Physical clocks on distributed machines cannot be perfectly synchronized. NTP keeps them within tens to hundreds of milliseconds in normal conditions — but under load, across datacenters, or after a VM pause, the drift can reach seconds. When s...

·18 min read·4 views
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Bloom Filters Explained: Membership Testing with Zero False Negatives

TLDR: A Bloom filter is a bit array of m bits + k independent hash functions that sets k bits on insert and checks those same k bits on lookup. If any checked bit is 0, the element is definitely not in the set — false negatives are mathematically imp...

·18 min read
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Stale Reads and Cascading Failures in Distributed Systems

TLDR: Stale reads return superseded data from replicas that haven't yet applied the latest write. Cascading failures turn one overloaded node into a cluster-wide collapse through retry storms and redistributed load. Both are preventable — stale reads...

·23 min read
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Split Brain Explained: When Two Nodes Both Think They Are Leader

TLDR: Split brain happens when a network partition causes two nodes to simultaneously believe they are the leader — each accepting writes the other never sees. Prevent it with quorum consensus (at least ⌊N/2⌋+1 nodes must agree before leadership is g...

·20 min read·6 views
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SQL Partitioning: Range, Hash, List, and Composite Strategies Explained

TLDR: SQL partitioning divides one logical table into smaller physical child tables, all accessed through the parent table name. The query optimizer skips irrelevant child tables entirely — a process called partition pruning — turning a 30-second ful...

·23 min read·2 views
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