Download cheat sheets, guides, and templates to accelerate your learning
46 resources
Complete OSCP exam preparation checklist. Covers enumeration, exploitation, privilege escalation, Active Directory attacks, buffer overflows, and exam strategy tips.
Complete checklist for building IDPs. Covers Backstage developer portals, Crossplane infrastructure provisioning, ArgoCD GitOps, and golden path implementation for 60% faster onboarding.
System prompt patterns analyzed from Cursor, Claude Code, Devin, Windsurf, v0, and 30+ production AI tools. The exact patterns used by industry leaders for reliable outputs.
Hands-on guide to building 6 production projects with AI. Covers SaaS starters, mobile apps, CLI tools, AI-powered features, real-time applications, and monetization strategies.
Essential Python syntax, data structures, API patterns, and environment setup for AI development. Quick reference for lists, dicts, async/await, HTTP requests, and project organization.
Step-by-step guide for building production-ready RAG systems. Covers embedding models, vector databases (Pinecone, Weaviate, Chroma), hybrid search, reranking, and evaluation frameworks.
Professional guide to offensive AI security testing. Covers vulnerability discovery with DeepTeam, PyRIT, and Garak, multi-turn attack execution, and actionable security report templates.
Interview prep for security engineering roles. Covers application security, cloud security, OWASP Top 10 (2025), Zero Trust, threat modeling, and incident response questions.
Beginner's guide to AI-assisted development with Cursor, Claude Code, Windsurf, and GitHub Copilot. Covers tool selection, prompting techniques, debugging AI code, and production workflows.
Essential Ollama commands, model selection guide, LangChain integration patterns, and performance optimization tips. Run LLMs locally with privacy and control.
Complete guide to building AI integrations with Model Context Protocol. Covers MCP server creation, tool and resource exposure, Claude integration, and production deployment patterns.
Complete career guide for breaking into ML/AI. Includes resume templates, portfolio project ideas, LinkedIn optimization tips, and networking strategies for landing your first role.
Comprehensive prep for ML/AI engineering interviews. Covers Python coding challenges, ML algorithms, evaluation metrics, system trade-offs, STAR method for behavioral rounds, and salary negotiation.
Interview prep for MLOps roles covering Kubernetes orchestration, ML pipeline design, model serving, monitoring systems, and CI/CD for ML. Real questions from FAANG and ML-first companies.
Complete MLOps implementation checklist covering DVC data versioning, Kubeflow orchestration, Feast feature stores, BentoML model serving, and Evidently monitoring.
Ready-to-use workflow templates connecting ChatGPT and Claude to your business tools. Includes email automation, content generation, lead processing, and data sync patterns.
Production checklist for LLM guardrails covering multi-layer architectures, LlamaGuard 3, ShieldGemma, NeMo Guardrails, Guardrails AI, and Presidio PII detection.
Quick reference for LLM inference optimization. Covers vLLM, TensorRT-LLM, KV cache management, speculative decoding, LLM gateways (LiteLLM, Helicone), and cost optimization strategies.
Ready-to-use prompt templates for marketing, sales, HR, and operations. Includes system prompts from real AI tools like Cursor, Claude Code, and Devin. Copy-paste patterns that work.
Comprehensive checklist for production LLM operations and evaluation. Covers LangSmith, MLflow, W&B Weave observability, evaluation pipelines, and quality metrics for reliable AI systems.
Complete security checklist for CI/CD pipelines. Covers SAST, DAST, SCA, container security, secrets management, and compliance automation with Snyk, Trivy, Vault, and GitHub Advanced Security.
Quick reference for parameter-efficient fine-tuning. Covers LoRA configurations, QLoRA quantization, DPO alignment, hyperparameter selection, and deployment with Unsloth and Ollama.
Quick reference for Kubernetes ML workloads. Covers GPU scheduling, Kubeflow pipelines, KServe model serving, Istio service mesh, and GitOps with ArgoCD for production ML.
Quick reference for LangGraph development. Covers state management, checkpointing, human-in-the-loop workflows, LangSmith monitoring, and enterprise deployment patterns.
Interview prep for LLM engineering roles at OpenAI, Anthropic, Meta. Covers transformer architecture, prompt engineering, fine-tuning decisions, evaluation metrics, and production systems for L5-L6 roles.
Interview prep for cloud architect roles at $150K-$350K+. Covers multi-cloud design patterns (AWS, GCP, Azure), Well-Architected Frameworks, cost optimization, and executive communication.
Multi-cloud security checklist covering IAM best practices, storage security, network controls, container security, and cloud pentesting methodology for AWS, Azure, and GCP.
Interview prep for data engineering roles at $150K-$300K+. Covers deep SQL, data modeling, ETL pipelines, Spark, streaming systems, and behavioral preparation.
Essential data skills guide for business professionals. Learn to read Power BI, Tableau, and Looker dashboards, assess data quality, verify AI outputs, and communicate with data teams.
Interview prep for data science roles with 50+ practice problems. Covers statistics, SQL, A/B testing, business case frameworks used at Meta, Google, Netflix, and top tech companies.
Interview prep for DevOps and SRE roles. Covers Linux, networking, CI/CD, Infrastructure as Code, Kubernetes, monitoring, observability, incident response, and salary negotiation for L3-L6 roles.
Executive templates for AI strategy including AI readiness assessment, ROI calculator, change management plan, risk mitigation framework, and team building checklist.
Comprehensive interview prep for AI system design rounds. Covers LLM application architecture, multi-agent systems, RAG patterns, scaling strategies, and sample answers for top AI companies.
Complete bug bounty hunting checklist. Covers reconnaissance techniques, OWASP Top 10:2025 vulnerabilities, API security testing, and professional report writing templates.
Complete CI/CD checklist for ML systems. Covers GitHub Actions/GitLab CI, data validation with Great Expectations and Pandera, experiment automation with DVC and CML, and GitOps with ArgoCD.
Master Claude's Computer Use API for building autonomous desktop and browser agents. Covers Agent SDK, safety patterns, production deployment, and Claude Sonnet 4.5 best practices.
Ready-to-use templates for AI product management. Includes AI-specific PRD format, vendor evaluation scorecards, ML team communication frameworks, and AI feature success metrics.
Security checklist for LLM applications covering extraction prevention, injection defenses, hardening strategies, and incident response. Based on analysis of 36+ leaked AI system prompts.
Security checklist for LLM applications covering OWASP LLM Top 10 vulnerabilities, prompt injection defenses, guardrails implementation, and production security best practices.
Teaching-ready AI templates including MagicSchool lesson plan prompts, grading automation workflows, AI detection policy templates, and classroom usage guidelines.
Beginner-friendly guide to understanding AI, large language models, and effective prompting. Learn to choose the right LLM for your needs and master basic prompting techniques.
Ready-to-use AI templates for marketing and sales teams. Includes content creation prompts, email personalization frameworks, lead scoring criteria, and compliance checklists.
Advanced AI-assisted development reference covering multi-agent orchestration, large codebase management, complex project architecture, and production deployment workflows.
Developer's guide to building AI agents with LangChain and CrewAI. Covers agentic workflows, memory systems, tool integration, error handling, and production deployment patterns.
Quick reference for AI agent architectures, memory systems, error handling patterns, and multi-agent orchestration. Covers LangChain, CrewAI, AutoGen, and production deployment best practices.
Beginner's guide to understanding AI agents. Covers what agents are, how they work, when to use them, and practical applications for business. Perfect for non-technical professionals.
