SWE Prep / Learning OS

Prepare for software-engineering interviews by building understanding you can prove

SWE Interview Prep is a personal learning OS for engineers preparing for interviews or strengthening practical technical judgment. It turns study into retrieval, practice, code or diagrams, an explain-back, and scheduled review.

Learn through evidence, not passive reading

Every concept is designed around the same loop: Concept → Drill → Build → Review → Apply. Start with a concise mental model and primary source, test it with an executable exercise, build a measurable artifact, explain the mechanism back, and let FSRS schedule the next review.

Start without an account; guest progress stays in the current browser. Google sign-in keeps learning state across sessions. The product is currently maintenance-only and has no paid tier, subscription, or checkout.

Explore the curriculum

Search & IR — Lexical retrieval beyond embeddings: tokenization, inverted indexes, BM25, ranking, hybrid search, and search evaluation.
Mathematics — Active math only: solve, derive, implement, simulate — never aesthetic consumption. Stack: probability & statistics → linear algebra → optimization → quant bridge. No artifact, no learning.
Vector DB & ANN — Vector search engines: similarity, top-k, brute force, HNSW, IVF, quantization, metadata filtering, and recall/latency tradeoffs.
AI Systems — Practical AI engineering: LLM apps, RAG, chunking, tool calling, agents, evals, and model/transformer foundations.
Backend — Production backend strength: HTTP, API design, auth, rate limiting, idempotency, queues, jobs, caching, and observability.
Databases & Storage — Storage foundations for Turbopuffer-class systems: B-trees, LSM trees, WAL, compaction, partitioning, replication, object storage.
System Design — Architecture-level thinking: low-level design, scalability, distributed systems, event-driven design, and end-to-end case studies.
DSA & Implementation — Fast, clean implementation ability: arrays, graphs, trees, dynamic programming, and the core algorithmic patterns.
Behavioral & Communication — The interview round that is not about code: influence, conflict, ownership, prioritisation, and learning from failure.
Go-to-Market — Getting a built thing in front of people: positioning, landing pages, SEO, and product analytics.
Systems Foundations — Operating systems, networks, concurrency, hardware, runtimes, performance, security, and isolation.
Infrastructure & Platforms — Cloud infrastructure, containers, CI/CD, orchestration, reliability, observability, sandboxes, and infrastructure automation.
Distributed Systems — Coordination, replication, partitioning, event systems, caching, durable workflows, consistency, and recovery.
Inference & Serving — Inference engines, batching, KV caches, attention kernels, decoding, routing, hardware utilization, and serving economics.
Agent Systems — Agent loops, tools, memory, MCP, coordination, durable execution, permissions, computer use, and long-running work.
Evaluation & AI Reliability — LLM and agent evaluations, regression gates, failure detection, tracing, verification, human review, and quality economics.
Developer Tools & Code Intelligence — Code review, analysis, testing infrastructure, repository graphs, coding agents, supply-chain health, and remediation.
Application Engineering — Backend, web, mobile, product analytics, UX, real-time applications, interactive systems, and distribution loops.
Multimodal & Spatial Computing — Vision, pose, voice, generation, on-device intelligence, robotics, spatial interfaces, and human-computer interaction.

Browse the public curriculum, practice system-design interview cases, or continue as a guest for the interactive learning workspace.