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AI for Professionals Who Don't Code - Site Overview

Author/Source: Chris Blattman (political economist, UChicago Harris), claudeblattman.com

Key Ideas

  • The site documents a complete AI workflow system built by a non-coder professor, covering chatbot prompting through advanced Claude Code automation
  • Core workflows include: executive assistant (email triage, briefings, meeting prep), project management (living dashboards from transcripts and documents), and continuous improvement (tips pipeline that evolves CLAUDE.md and skills over time)
  • A "Prompt, Plan, Review, Revise" loop is the foundational workflow pattern: brain-dump an idea, structure it, stress-test with fresh agents, capture learnings
  • The tax season case study demonstrates end-to-end document collection, compilation, and error-checking as a transferable pattern for any document-heavy workflow
  • Planned sections include data analysis co-pilot (Stata/R/Python code writing and auditing) and research writing (LaTeX/Overleaf workflows with referee response management)
  • The system includes 20+ downloadable skills with one-command install, config templates, and a GitHub repository
  • The author emphasizes that no prior coding experience is needed: "I've never coded in my life, so if I can do this you can"

Summary

This article is the homepage and mission statement for the Claude Blattman site, an open-source collection of AI workflow tools and guides built by Chris Blattman, a political economist at the University of Chicago. The site spans two tiers of AI usage: browser-based "Essentials" (chatbot prompting, voice dictation, meeting transcription, NotebookLM for literature reviews) that require no technical setup, and Claude Code workflows that automate recurring professional tasks through terminal-based AI with integrations to email, calendar, and documents.

The site organizes workflows around professional roles: an executive assistant system handles email triage, morning briefings, and meeting follow-up; a project management system maintains living dashboards across email, docs, and meetings; a continuous improvement pipeline captures tips and automatically proposes system upgrades. A tax season case study serves as the primary worked example, demonstrating how document collection, data compilation, and multi-year anomaly detection can be automated. The author explicitly targets non-coders and positions the system as accessible to anyone willing to use a terminal. Planned future sections on data analysis and research writing suggest the site is evolving toward a comprehensive academic productivity platform.

Relevance to Economics Research

This site is one of the most directly relevant resources for economists adopting AI tools, as it is built by an active economics researcher whose workflows mirror common academic tasks: managing co-author communications, tracking complex multi-country research projects, drafting grant proposals, and handling administrative overhead. The planned data analysis co-pilot (covering Stata, R, and Python) and research writing sections (LaTeX, referee reports) would address the core technical workflows in empirical economics. The "Prompt, Plan, Review, Revise" loop maps well onto the iterative nature of research paper development.