Coding interview roadmap

A 13-pattern roadmap for algorithm interviews, plus an AI infra track. Each pattern has a recognition checklist, Python templates and graded practice. Free to start: no credit card, no install. Pro is $19.99 a month or $129 a year.

Algorithm patterns, in order

• Array & Hash Table: Master array manipulation and hash table techniques for efficient lookups and storage
• Two Pointers: Learn efficient two-pointer techniques for array and string problems
• Sliding Window: Master sliding window technique for substring and subarray problems
• Binary Search: Learn binary search and its variations for efficient searching
• Linked List: Master linked list manipulation and traversal techniques
• Stack: Learn stack data structure for LIFO operations
• Binary Tree: Master binary tree traversal and manipulation
• Dynamic Programming: Master dynamic programming for optimization problems
• Graph: Learn graph algorithms and traversals
• Greedy: Master greedy algorithms for optimization
• Backtracking: Learn backtracking for combinatorial problems
• Heap: Master heap/priority queue data structure
• Bit Manipulation: Learn bit manipulation techniques

AI infra track

• Math Foundations: Matmul shapes, block matrices, softmax as a distribution, quantization error, lossless speculative decoding and the chain rule, checked with small tensors.
• Memory & FLOPs Accounting: Count parameters, FLOPs, KV cache bytes and training memory before you optimize anything.
• Numerics & Hand-Written Backward: Stable softmax, LSE, cross-entropy gradients and LayerNorm/RMSNorm backward, checked against autograd.
• Transformer Inference Building Blocks: RoPE in both layouts, GQA without copies, KV cache and sampling, checked against a full forward.
• GPU Hardware and Architecture: SMs, Tensor Cores, the memory hierarchy, peak FLOPS and the roofline, architecture generations from V100 to B200, and NVLink vs PCIe.
• GPU Programming Model, Simulated: Threads, warps, shuffles, coalescing, bank conflicts, occupancy and tiled GEMM, simulated in PyTorch.
• Online Softmax & FlashAttention: Merge partial softmax states, then build FlashAttention v1, v2 and Flash-Decoding block by block.
• Paged KV Cache: Slot mapping, block tables, paged and varlen attention, and a nano-vllm style BlockManager.
• Scheduling & Prefix Caching: Continuous batching, preemption, chunked prefill and radix-tree prefix caching.
• Speculative Decoding: Draft, verify and accept tokens without changing the target distribution.
• Quantization: INT8/INT4/FP8 schemes, SmoothQuant, GPTQ-style error compensation and KV cache quantization.
• Distributed Training, Simulated: Collectives, ring all-reduce, DDP, ZeRO, tensor and pipeline parallelism on a list of fake ranks.