Study Mode signals a shift: build composable AI platforms that let users design, evaluate, and customize their own interaction modes. Lowering friction produces fit-for-purpose experiences and a durable moat.
Redefine real work around outcomes and human strengths, and use AI for real growth by automating routine tasks while elevating judgment and creativity. This four-part method helps you turn task completion into compounding impact.
Codify architecture rationale, product goals, constraints, and personal priorities so AI stops guessing and starts aligning with how you really decide. A simple, reusable context brief makes recommendations reliably tradeoff-aware.
Simplify LLM reasoning by making intent explicit and trimming steps. Ground outputs with external checks to ship reliable, engineerable features.
Balance focus and creativity by treating research as a tunable system: manage friction, diversify inputs, and turn exploration into concrete builds.
Coordination beats verbosity. Learn how to design modular LLM workflows by centralizing must-have rules, splitting single-purpose steps, and enforcing schemas for cleaner, more reliable outputs.
Agreeable assistants can be risky. This piece shows how to prevent AI sycophancy by making challenge-first the default—counterarguments, risk flags, and refusal built into prompts, policy, and evaluation.
Structured prompts beat open boxes. Learn how to constrain AI prompts with smart defaults, validation, and limited controls to keep outputs consistent at scale.
Treat inputs and model behavior as untrusted and install system-level guardrails—rate limits, quotas, cheap checks, and structured interfaces—to keep LLM features reliable and affordable.
Stop waiting for the next model. Learn how to integrate AI into workflows by nailing intent, context, and fit so you can ship a reliable v1 and iterate fast.
