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Claude Code Mastery
A guided path through Anthropic's agentic coding CLI — from your first session to configuring it well for real projects (CLAUDE.md, memory, settings, permissions and hooks), working across its surfaces (IDE extensions, desktop, web, mobile, Slack), choosing models and managing cost and context, using its higher-autonomy planning and workflow modes, extending it with skills, slash commands and plugins, orchestrating MCP servers, subagents, dynamic workflows and agent teams, running it in git workflows, sandboxes, schedules, cloud routines and CI/headless mode, and the working practices that keep agentic sessions reliable and cheap — capped by an end-to-end case study.
Start this path →AI Engineer Interview
What it takes to design and ship production LLM applications — and to explain the reasoning in an interview. Starts with the model fundamentals (transformers, tokenization, the full pretraining-to-chatbot pipeline, prompting, fine-tuning) and reasoning models (inference-time scaling, training reasoning models with RLVR and reward models), then context engineering and multimodal models, retrieval-augmented generation end to end (architecture, chunking and embeddings, vector search, evaluation), agents and tool use (workflow design patterns, the agentic loop, memory, multi-agent orchestration, MCP, agent evaluation), and production concerns (guardrails and prompt-injection defense, cost and latency engineering, observability, local and open-weight deployment). Surveys the framework landscape, then finishes with five full worked designs: a customer-support assistant, a web-search agent, a deep-research agent, a Claude Code-shaped coding agent, and an RLHF data and training platform.
Start this path →Prompt & Context Engineering
Prompting treated as an engineering discipline, not typing nicely — from tokens and context windows, through few-shot/chain-of-thought and the advanced technique landscape, to engineering the full context window (selection, ordering, compaction, isolation), memory, injection defense, evaluation and versioning, cost/caching optimization, and context engineering for long-running agents. Closes with two case studies: designing the context pipeline for a long-context document assistant, and migrating a production prompt suite across a model deprecation.
Start this path →Top Subjects
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Claude Code Best Practices: Reliable, Cheap, High-Leverage Sessions
Synthesise the whole Claude Code track into working habits: manage the context window deliberately, always give Claude a check it can run, plan before touching code, prompt with acceptance criteria, scope permissions narrowly, control cost, and know how to recover when a session goes wrong.
Claude Code Fundamentals
Learn what makes Claude Code an agentic tool rather than an autocomplete engine: the read-act-observe loop, the built-in tools it uses to explore and change your codebase, the permission modes that govern its autonomy, and where its configuration lives. By the end you'll be able to trace exactly what happens, tool call by tool call, when you ask it to fix a bug.
System Design Basics
Learn the fundamental principles behind building large-scale, reliable systems: scalability, availability, latency, and the key trade-offs that drive real-world architecture decisions.
Consistent Hashing
Understand the hash ring, virtual nodes, and why consistent hashing is the foundation of distributed caches (Redis Cluster, Memcached), distributed databases (Cassandra, DynamoDB), and CDN request routing.
Concurrency vs Parallelism
Untangle four words engineers constantly confuse: concurrency, parallelism, threads, and processes. Learn what runs truly simultaneously versus what merely interleaves, why Python's async model is single-threaded, and how getting this wrong causes real production bugs like sharing one database session across coroutines.
Decision Trees
Learn how decision trees partition feature space using impurity measures, how recursive binary splitting works, which hyperparameters control overfitting, and how feature importance is calculated — the foundation for understanding gradient boosting models like LightGBM and XGBoost.
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