Interview prep
Personal study notes for 2026-2027 interviews. Primary focus: backend Python and AI/ML. Frontend and DevOps/platform are also covered.
File counts are as of 2026-08-10. Modernization progress is tracked in PLAN.md; current versions and facts live in STACK_BASELINE.md; per-folder review state is in LEDGER.md.
Domains
| Folder | Files | What’s inside |
|---|---|---|
| backend/ | 650 | Python internals, OOP, async, web frameworks, databases, queues, caching, protocols, architecture, infra, cloud, DSA, security, data engineering, quant/fintech, healthcare/regulated |
| frontend/ | 222 | HTML, CSS, JS, TypeScript, React, Vue, state, build tools, testing, performance, a11y, security, browser internals, rendering modes |
| ai_ml/ | 124 | Rebuilt. Math and ML foundations, classical ML, features, evaluation, deep learning, transformers/LLM, training, serving, RAG, agents, MCP, context, evals, guardrails, MLOps, ML system design, speech/realtime |
| system_design/ | 30 | Integration patterns, resilience, async, secrets, observability, design framework, worked designs, scaling building blocks |
| behavioral/ | 16 | STAR stories and interview-prep answers |
| methodologies/ | 9 | Agile, Scrum, Kanban, Waterfall, XP/Lean, estimation, AI-assisted development |
| claude_certification/ | 8 | Agentic architecture, tool design + MCP, Claude Code workflows, prompt engineering, context management |
| practical_cases/ | 3 | End-to-end “how would you build this?” scenarios with worked answers |
| techcheck/ | 4 | Real interview debriefs and theme navigation — the best signal for what to prioritize |
Backend
| # | Folder | Files | Topics |
|---|---|---|---|
| 01 | theory_foundations/ | 7 | SOLID, Big-O, paradigms, DRY/KISS, GoF patterns |
| 02 | python_core/ | 108 | semantics, generators, decorators, memory, internals + typing/, stdlib/, performance/, packaging/, tricky_questions/ |
| 03 | python_oop/ | 10 | classes, polymorphism, descriptors, protocols, metaclasses |
| 04 | async_concurrency/ | 18 | asyncio, threads, processes, GIL, TaskGroup, anyio/trio |
| 05 | testing/ | 18 | pytest, fixtures, mocking, hypothesis, strategy |
| 06 | web_frameworks/ | 68 | FastAPI, Django + DRF + ORM, Flask, Litestar, Streamlit, Pydantic, aiohttp, WSGI/ASGI |
| 07 | rest_apis/ | 9 | REST principles, status codes, idempotency, versioning, HATEOAS |
| 08 | databases/ | 55 | SQL, indexes, transactions, SQLAlchemy, MongoDB, Elasticsearch, Cassandra |
| 09 | caching/ | 5 | Redis, cache stampede, Redlock, Valkey |
| 10 | message_queues/ | 26 | Kafka, RabbitMQ, Celery, Temporal, RQ, stream processing |
| 11 | authentication/ | 25 | JWT, SSO, SAML, OIDC, SCIM, MFA |
| 12 | protocols/ | 49 | HTTP, gRPC, GraphQL, WebSockets, SSE, SOAP, nginx |
| 13 | architecture_design/ | 20 | DI, clean/hexagonal, DDD, CQRS, saga, outbox, 12-factor |
| 14 | microservices/ | 10 | discovery, tracing, mesh, gateway, resilience |
| 15 | observability/ | 13 | Sentry, Grafana, Prometheus, OpenTelemetry, SLO/SLI, structured logging |
| 16 | docker/ | 9 | images, layers, compose, networks, multi-stage |
| 17 | kubernetes/ | 9 | pods, services, probes, HPA, ingress, Helm |
| 18 | iac/ | 9 | Terraform, CloudFormation, Ansible |
| 19 | cloud_aws/ | 61 | Lambda, EC2, API Gateway, DynamoDB, S3, SQS/SNS, EventBridge, Step Functions, ECS/EKS/Fargate, RDS, VPC, IAM |
| 20 | cloud_azure/ | 1 | AWS-to-Azure mapping and where the model differs |
| 21 | cloud_gcp/ | 3 | AWS-to-GCP mapping; GKE in practice — Autopilot, Workload Identity, Gateway API, autoscalers, cross-cloud |
| 22 | git/ | 15 | rebase, bisect, reflog, worktrees, hooks, internals |
| 23 | linux_bash/ | 15 | processes, signals, fds, permissions, systemd, one-liners |
| 24 | dsa/ | 19 | arrays, hashmaps, lists, trees + BST/tries, graphs, DP + 2D DP, recursion, binary search, monotonic stack, intervals, greedy/bits |
| 25 | security/ | 10 | OWASP, injection, hashing, OAuth2/OIDC, SSRF, deserialization |
| 26 | code_quality/ | 6 | formatters, linters, type checking, refactoring, tech debt |
| 27 | cicd/ | 8 | GitHub Actions, GitLab CI, runners, deployment, supply chain |
| 28 | networking/ | 17 | OSI/TCP-IP, DNS, TLS, load balancers, proxies, CDN, VPC |
| 29 | data_engineering/ | 12 | Spark/Databricks, pandas, Polars, Parquet, Delta Lake, data mesh/contracts, ETL+dbt+Airflow, catalogue/lineage |
| 30 | quant_fintech/ | 9 | market data, backtesting (+vectorbt/Backtrader/QuantConnect), portfolio optimization, ML for finance, FIX protocol, trading risk |
| 31 | healthcare_regulated/ | 5 | HL7/FHIR/OMOP, PHI & HIPAA, GxP + 21 CFR Part 11 + CSV/CSA, clinical document intelligence |
Frontend
| # | Folder | Files | # | Folder | Files |
|---|---|---|---|---|---|
| 01 | html/ | 4 | 11 | apis_data_fetching/ | 10 |
| 02 | css/ | 5 | 12 | project_structure/ | 7 |
| 03 | javascript_core/ | 17 | 13 | es_features/ | 9 |
| 04 | typescript/ | 11 | 14 | frontend_system_design/ | 10 |
| 05 | react/ | 30 | 15 | performance/ | 11 |
| 06 | vue/ | 25 | 16 | accessibility/ | 8 |
| 07 | state_managers/ | 17 | 17 | security/ | 9 |
| 08 | component_libraries/ | 10 | 18 | browser_internals/ | 10 |
| 09 | build_tools/ | 9 | 19 | rendering_modes/ | 8 |
| 10 | testing/ | 11 |
Frontend backlog: frontend/_TODO.md.
AI / ML
Rebuilt — foundations through advanced, 124 files. Full index: ai_ml/README.md.
| # | Folder | Topics |
|---|---|---|
| 00-01 | math_foundations/ · ml_foundations/ | linear algebra, probability, optimisation; problem types, splits, bias-variance, lifecycle |
| 02-04 | classical_ml/ · feature_engineering/ · model_evaluation/ | regression, trees, gradient boosting, clustering, PCA; leakage, imbalance; ROC-AUC vs PR-AUC, calibration |
| 05 | deep_learning/ | networks, CNN, RNN/LSTM, norms, transfer learning |
| 06-08 | transformers_llm/ · training_finetuning/ · inference_serving/ | GQA/MLA, KV cache, MoE, reasoning models; DPO/GRPO/RLVR, quantization; continuous batching, speculative decoding |
| 09-12 | rag_embeddings/ · agents_orchestration/ · mcp/ · context_engineering/ | hybrid search + reranking, agentic RAG; LangGraph 1.0, A2A; MCP 2026-07-28 stateless core; compaction, memory |
| 13-15 | evaluation/ · guardrails_safety/ · mlops_llmops/ | eval sets, LLM-as-judge biases; prompt injection, EU AI Act; MLflow, drift, OTel GenAI |
| 16-18 | ml_frameworks/ · ml_system_design/ · speech_and_realtime/ | PyTorch, scikit-learn, JAX, Optuna; RAG assistant, fraud detection; STT/TTS, realtime voice agents |
System design
| # | Folder | Files | Topics |
|---|---|---|---|
| 01 | api_integrations/ | 7 | integration design, schema and models, clean architecture walkthrough, follow-ups |
| 02 | resilience/ | 5 | timeouts, retries, backoff, circuit breakers, bulkheads, fallbacks, orchestration |
| 03 | async_patterns/ | 1 | async I/O and background work |
| 04 | secrets_config/ | 1 | secrets and configuration |
| 05 | observability/ | 1 | observability in practice |
| 06 | design_framework/ | 3 | interview framework, capacity estimation, latency numbers |
| 07 | worked_designs/ | 8 | URL shortener, rate limiter, fanout, feed, chat, cache, scheduler, payments |
| 08 | scaling_building_blocks/ | 3 | load balancing, CDN, replication, sharding, CAP/PACELC, consistency |
Other
- practical_cases/ — LLM file pipeline, webhook ingestion, bulk CSV import
- behavioral/ — 16 stories: challenges, AI workflow, disagreement, failure, ambiguity, tech debt, mentoring
- methodologies/ — Agile, Scrum, Kanban, Waterfall, XP/Lean, estimation, choosing, AI-assisted development and AI-DLC
- claude_certification/ — 7 exam domains
- techcheck/ — real interview debriefs (questions.md), the MINT prep map, plus theme navigation
Maintenance
python _tools/check_links.py # every relative link resolves
python _tools/gen_ledger.py # regenerate LEDGER.md from diskConventions and the writing standard: CLAUDE.md.