blog

To write in Anand's style for blog posts, emails, talk summaries, interview questions, conversations, ...

Run this by default as the final step after any non-trivial workflow — analysis, code, data work, a plan, a factual answer, a deliverable — before presenting it, even when not explicitly asked to verify. Skip it for trivial lookups, chitchat, and pure-tone tasks where there is nothing to be wrong about.

The user's question may be a DRAFT of their real need. For substantive requests, check if a better question changes what's solved and serves them better. If so, reframe, explain why (in 1 line), THEN answer. Most questions need no reframe; skip precise, mechanical, tightly specified, or already-sharp requests and simply answer.

Apply to any substantive answer for Anand - strategy, design, build, analysis, teaching, writing, research - to steer it toward his goals (compounding assets, durable skills, the surprising read). Skip for trivial lookups, mechanical edits, focused tasks. Shapes HOW to answer, never overrides WHAT was asked.

Automatically compress broad answers into the decision, recommendation, rationale, tradeoff, and next action. Use for recommendations, prioritization, research synthesis, strategy, planning, reviews, rankings, and large-context requests.

Brainstorm creative, novel, non-obvious ideas or options for an open-ended problem and narrow them to a recommendation. Not for writing prose, research, explanation, summaries, or questions with one right answer.

Apply relevant mental models for strategy, forecasts, recommendations, or advice. Skip for execution, lookups, and mechanical edits.

Use when creating REALISTIC synthetic data leading to actionable business hypotheses.

Use to intuitively, memorably explain abstract, complex, unfamiliar concepts. NOT for code generation, data retrieval, or when user just needs execution.

Use when value may hide in what's NOT said, asked, measured, tested, shown, or decided: strategy, advisory/sales calls, reviews, data analysis, design, demos, AI outputs, decisions, transcript post-mortems. Skip for lookups, mechanical edits, tightly scoped execution.

Automatically improve trust, auditability, and reuse by separating facts, assumptions, inferences, sources, confidence, and verification. Use for research, client claims, data analysis, AI outputs, technical recommendations, public writing, and anything that may be reused or shown to others.

Automatically stress-test plans, demos, claims, workflows, AI systems, code, data analyses, assessments, and recommendations by identifying likely failures, misuse, misunderstanding, edge cases, incentives, security/privacy risks, and lightweight mitigations.

Summarize into something I can just read out in a meeting: Minto pyramid structure, answer-first, bolded for scanning, conversational.