Python
Design Patterns GoF Patterns Complete Guide 2026
Implement all 23 GoF design patterns — creational, structural, behavioral — with Python and Java examples.
Python
Implement all 23 GoF design patterns — creational, structural, behavioral — with Python and Java examples.
Python
Understand how compilers work — lexical analysis, parsing, AST construction, and build a mini-interpreter.
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Learn networking fundamentals — TCP/IP stack, DNS resolution, HTTP protocol, sockets, and Wireshark analysis.
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Understand OS concepts every developer needs — processes, threads, virtual memory, file systems, and syscalls.
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Integrate security into development — threat modeling, SAST, DAST, dependency scanning, and DevSecOps.
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Understand cryptography for software — hashing (SHA-256), symmetric (AES), asymmetric (RSA/ECC), and PKI.
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Harden containers — non-root images, read-only filesystems, K8s NetworkPolicies, OPA Gatekeeper, Trivy scanning.
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Secure JWTs properly — algorithm confusion, none algorithm attack, weak secrets, and security best practices.
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Scan web apps for vulnerabilities automatically with OWASP ZAP — active scan, passive scan, and reports.
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Test web application security with Burp Suite — proxy, scanner, Repeater, Intruder, and finding vulnerabilities.
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Build and ship mobile apps fast with Expo — managed workflow, EAS Build, OTA updates, and native modules.
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Manage Flutter state at scale — Riverpod providers, BLoC pattern, Cubit, and choosing between them.
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Go deeper with SwiftUI — matched geometry animations, NavigationStack, Combine publishers, and async/await.
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Build production Android apps — MVVM with ViewModel, Room database, Hilt DI, Coroutines, and Flow.
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Build Android UIs declaratively with Jetpack Compose — Composables, state, theming, navigation, and animations.
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Run open-source LLMs locally with Ollama — setup, models, API, Python integration, and hardware requirements.
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Build multi-agent AI systems with AutoGen — AssistantAgent, UserProxyAgent, group chat, and tool calling.
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Index and query your data with LlamaIndex — document loaders, node parsers, query engines, and agents.
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Build custom MCP servers to extend AI assistants — tools, resources, prompts, and connecting to Claude.
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Reduce LLM API costs — prompt caching, token counting, model selection, batching, and caching strategies.
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Work with multimodal LLMs — vision, audio transcription, image generation, and combining modalities.
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Evaluate LLM applications — RAGAS metrics, hallucination detection, relevance scoring, and regression testing.
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Build a complete RAG chatbot — ingest docs, embed, store in vector DB, query, and deploy as a web app.
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Extract structured data from LLMs — function calling, JSON mode, response_format, and tool use patterns.
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Use Python Protocols for duck typing with static checks — Protocol, runtime_checkable, and structural subtyping.