I’m making more and more of my content agent-consumable, i.e. easier for ChatGPT, Claude Code, etc. to read, in three ways.
One, I export content in an agent-friendly way.
- Google email, calendar, chat. I use
gwsto back up into scannable one-line entries. - Meet recordings. I back up transcripts and videos (with a compact audio copy).
- WhatsApp chats that I back up into similar one-liners.
- Browsing history by exporting my Edge history SQLite database.
- Daily activities by integrating the above with my command line and commit history.
- AI conversations by exporting them manually or via bookmarklets.
- Social media records like LinkedIn invites/conversations, Twitter, Hacker News, Discourse, etc via bookmarklets or scripts.
- Financial records like bank statements, receipts, payslips, tax filings, utility payments, rentals, property records, investments, insurance, pensions, invoices, credit scores, etc. by exporting them manually.
- Medical records like tests, prescriptions, doctor visits, etc. by exporting them manually.
- Personal records like certificates, educational records, CV, passport / visa applications, etc. by exporting them manually.
Two, I log / generate more content. For example:
- Things I learnt and blog posts I write.
- Prompts I use frequently.
- Trending GitHub repos I want to evaluate.
- Talks I deliver and data stories I write.
- Demos I build and code I write.
- Weight and other fitness data.
- Teaching material, assessments and evaluations and analysis.
- Coding agent logs.
- Sensors: Location, mostly.
- Daily journals: Food, sleep, deeds, pains, …
- Media journals: Books, movies, TV series, …
- Notes (of various kinds): TODOs, app / tool ideas, people I know / meet, questions I’m asked, my beliefs, …
(Notably missing are photos / videos, which I’ve fallen out of the habit of.)
Three, I summarize the content for agents. For example:
- Adding blog frontmatter by summarizing my blog posts
- Adding transcript frontmatter by summarizing my meeting transcripts.
- Identifying actions extracted from transcripts.
- Summarizing my code as podcasts.
- Summarizing prompts as SKILL.md files.
- Summarizing conversations into advice for AI, time management, etc.
- Summarizing technical choices into a technology radar
- Extracting transcripts elements, like insights, experiments to run, actions, what I missed, what they missed, etc.
On my list is Karpathy’s LLM wiki, summarizing my photos, and more.
Just writing this post took me an hour! It also convinced me that I have lots of content and there’s a lot of under-leverage in unleashing agents on what I already have.