Visual explainers
See the moving part before memorizing the term.
Each short walkthrough turns one distributed-systems or AI concept into a visible mechanism, so the tradeoff has somewhere to land.
Watch bursts get shaped instead of dropped blindly. Start with Rate Limiting Algorithms System Design · ~100sAll explainers
Available now
Rate Limiting Algorithms
Token Bucket vs Sliding Window — understand burst handling, accuracy trade-offs, and when to use each
CAP Theorem
Why distributed systems can't have Consistency, Availability, and Partition Tolerance all at once
Heartbeat & Failure Detection
How distributed systems detect node failures using periodic heartbeat signals and timeouts
Majority Quorum
Understanding R+W>N — how distributed systems guarantee reads see writes using quorum overlap
Lamport Clock
Understanding logical time — how distributed systems order events without synchronized clocks
Gossip Protocol
How distributed systems spread information efficiently using epidemic-style protocols
Circuit Breaker
How to prevent cascade failures in microservices using the circuit breaker pattern
Consistent Hashing
How distributed systems minimize data movement when nodes change using a hash ring
Raft Consensus Algorithm
How distributed systems elect leaders and achieve agreement across multiple nodes
Kafka Topic Partitioning
How Kafka distributes messages across partitions for parallelism and ordering
Eventual Consistency
How distributed databases synchronize without blocking writes
Backpropagation
How neural networks learn by propagating errors backward through layers
Write-Ahead Log (WAL)
How databases ensure durability and crash recovery using write-ahead logging
Consumer Group Rebalancing
How Kafka redistributes partitions among consumers when members join, leave, or fail
Producer Acknowledgments
Understanding Kafka's acks=0, acks=1, and acks=all for durability vs latency trade-offs
Attention Mechanism
How self-attention enables transformers to understand context by letting each token attend to all others
Two-Phase Commit (2PC)
How distributed systems achieve atomic transactions across multiple databases using the 2PC protocol
Exactly-Once Semantics
How Kafka achieves exactly-once processing using idempotent producers, transactions, and consumer isolation
Log Compaction
How Kafka's log compaction turns a topic into a key-value table by keeping only the latest value per key
Tokenization & Embeddings
How text becomes vectors — the pipeline from raw characters to dense numerical representations