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Cognitive Revolution

Vibe-Coding an Attention Firewall, w/ Steve Newman, creator of The Curve

129 min episode · 3 min read
·

Episode

129 min

Read time

3 min

Topics

Software Development

AI-Generated Summary

Key Takeaways

  • Attention Firewall Architecture: Newman built a system pulling email, Slack, WhatsApp, Signal, and Twitter into a single classifier that applies a one-page rubric to flag only urgent messages. These surface on a dedicated second monitor alongside a three-hour calendar view. The result: he eliminated approximately 30 daily check-ins across five apps, recovering substantial focus time without missing time-sensitive communications. The rubric grew exception-by-exception over several months of iteration.
  • Universal Logging as Debugging Infrastructure: Every component of Newman's stack — backend services, JavaScript frontends, Android apps — logs to a single SQLite database hosted on Cloudflare. When something breaks, he runs a one-sentence "systematic debugging" prompt and Claude traces the failure through actual log evidence rather than guessing. This single infrastructure decision, combined with the Superpowers Claude plugin's systematic debugging skill, resolves nearly 100% of issues without manual investigation.
  • Anti-Token-Maxing Philosophy: Newman initially felt pressure to keep agents constantly fed with prompts to avoid idle compute time. He reversed this after breaking work into roughly 15 isolated microservice projects. Now he runs zero to five parallel Claude Code agents and prompts them when convenient, not when the agent is waiting. His framing: optimize the human's time, not the agent's. This shift reduced stress significantly while maintaining output.
  • WhatsApp Integration via Local SQLite: To achieve real-time WhatsApp message ingestion without risking account bans from unofficial API hacks, Newman reads directly from the unencrypted SQLite database that WhatsApp Desktop writes locally on macOS. A cron job polling approximately once per minute reads this file with no network calls to WhatsApp servers. This approach is undetectable to Meta and avoids the account-suspension risks associated with third-party WhatsApp API services.
  • RSS Summarization with Two-Level Summaries: Newman receives roughly 50 newsletters, blog posts, and podcast transcripts daily. His custom RSS reader pre-computes two summaries per item: a short version for triage decisions and a longer one-page version surfacing novel ideas and notable evidence. The prompt instructs Claude to flag ideas novel relative to its training data. He skims the short summary in about ten seconds per item, then decides whether to read, deep-dive, or archive.

What It Covers

Steve Newman, creator of what became Google Docs and founder of the Golden Gate Institute for AI, walks through 15 bespoke personal productivity applications he built using Claude Code. The conversation covers his attention firewall system, RSS summarization tool, agent status dashboard, Chrome extensions, and unified logging infrastructure, plus broader reflections on AI's trajectory and software engineering's future.

Key Questions Answered

  • Attention Firewall Architecture: Newman built a system pulling email, Slack, WhatsApp, Signal, and Twitter into a single classifier that applies a one-page rubric to flag only urgent messages. These surface on a dedicated second monitor alongside a three-hour calendar view. The result: he eliminated approximately 30 daily check-ins across five apps, recovering substantial focus time without missing time-sensitive communications. The rubric grew exception-by-exception over several months of iteration.
  • Universal Logging as Debugging Infrastructure: Every component of Newman's stack — backend services, JavaScript frontends, Android apps — logs to a single SQLite database hosted on Cloudflare. When something breaks, he runs a one-sentence "systematic debugging" prompt and Claude traces the failure through actual log evidence rather than guessing. This single infrastructure decision, combined with the Superpowers Claude plugin's systematic debugging skill, resolves nearly 100% of issues without manual investigation.
  • Anti-Token-Maxing Philosophy: Newman initially felt pressure to keep agents constantly fed with prompts to avoid idle compute time. He reversed this after breaking work into roughly 15 isolated microservice projects. Now he runs zero to five parallel Claude Code agents and prompts them when convenient, not when the agent is waiting. His framing: optimize the human's time, not the agent's. This shift reduced stress significantly while maintaining output.
  • WhatsApp Integration via Local SQLite: To achieve real-time WhatsApp message ingestion without risking account bans from unofficial API hacks, Newman reads directly from the unencrypted SQLite database that WhatsApp Desktop writes locally on macOS. A cron job polling approximately once per minute reads this file with no network calls to WhatsApp servers. This approach is undetectable to Meta and avoids the account-suspension risks associated with third-party WhatsApp API services.
  • RSS Summarization with Two-Level Summaries: Newman receives roughly 50 newsletters, blog posts, and podcast transcripts daily. His custom RSS reader pre-computes two summaries per item: a short version for triage decisions and a longer one-page version surfacing novel ideas and notable evidence. The prompt instructs Claude to flag ideas novel relative to its training data. He skims the short summary in about ten seconds per item, then decides whether to read, deep-dive, or archive.
  • Microservice Project Structure for Context Management: Newman deliberately splits his 15 projects into separate GitHub repositories, each with its own Cloudflare database and deployment. All repositories sit adjacent in one Docker container directory, allowing agents to read across project boundaries when needed. Claude Code runs in the container with dangerously-skip-permissions mode enabled, but all source data remains in its original location — Gmail, WhatsApp, Slack — so a catastrophic agent error loses only the custom interface, not the underlying data.
  • Voice-to-Prompt Brain Dump Workflow: For capturing ideas during walks, Newman dictates into the Gmail compose window using the iOS keyboard's built-in dictation button, addressed to himself. The raw brain dump then gets pasted into an LLM with a single instruction: organize this into a Claude Code prompt. He typically skips reviewing the cleaned output and lets the agent proceed, correcting misunderstandings after the fact rather than proofreading the prompt first — a deliberate shift away from his prior measure-twice engineering habits.

Notable Moment

Newman, a software engineer since 1985 who built Google Docs, admitted he went four decades without a second monitor — then purchased one specifically to display his attention firewall dashboard. The detail underscores how fundamentally these self-built tools are restructuring decades-old working habits, not merely adding convenience on top of existing workflows.

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