24 prompts
Systematically explore a new dataset to understand its structure, quality, and what stories it might tell.
Plan a new software feature with the right architecture, edge cases, and implementation steps before writing a line of code.
Design a normalized, performant database schema for a new feature or application from scratch.
Build a working conceptual understanding of machine learning — the types of ML, how models learn from data, and the framework for deciding when ML is the right tool for a problem.
Understand the DevOps philosophy, culture, and practices — from the core principles of flow, feedback, and learning to the specific technical practices that enable high-performing technology organizations.
Debug any software issue methodically rather than randomly — find the root cause faster every time.
Review code changes with the thoroughness of a senior engineer — catching bugs, security issues, and architecture problems.
Write a README that developers immediately understand and can use to get started in under 5 minutes.
Make smarter build/buy/OSS decisions with a rigorous analysis of TCO, strategic value, and risk.
Understand the landscape of no-code and low-code tools — what is genuinely possible to build, where the limits are, and how to choose the right tool for your specific use case.
Write a PRD that gives engineering exactly what they need to build the right thing — no more, no less.
Select the technology stack for your startup — with the decision framework that balances speed of development, team skills, scalability requirements, and ecosystem maturity to make a choice you can live with for years.
Design prompts that force step-by-step reasoning to get more accurate and reliable answers from any AI model.
Build a reliable AI-powered research workflow that synthesizes information accurately and avoids hallucination.
Learn the core concepts of cybersecurity — the types of attacks, the defenders' framework, and the mental model that lets you make intelligent security decisions without becoming a full-time security professional.
Master the art of feature engineering — the data transformation techniques that convert raw inputs into the representation that enables machine learning models to find patterns effectively.
Design a scalable URL shortener from scratch — the classic system design interview question, with production depth.
Design the architecture for an MVP that ships fast, validates the idea, and does not require a full rewrite when you scale — the specific architectural decisions that balance speed now with maintainability later.
Craft high-quality examples that teach an AI model exactly what you want — the most reliable way to get consistent output.
Build automated workflows that connect your business tools — using Make or Zapier to trigger actions, transform data, and move information between systems without manual work.
Write a user interview script that uncovers real problems and jobs-to-be-done without leading the witness.
Use Docker to containerize your applications — from writing a production-quality Dockerfile through building a multi-service docker-compose environment to understanding the container security essentials.
Implement a personal cybersecurity practice — the specific actions on accounts, devices, and online behavior that protect against the most common individual-level attacks.
Build a structured AI writing workflow that produces high-quality first drafts with your voice, not generic AI text.