I built a system that reads my AI newsletters every morning and turns them into a searchable catalog of AI tools, plus a chat website where I can ask questions about the catalog in plain English. The system has two parts: a workflow that collects the information and a website that answers questions about it. I subscribe to several AI newsletters, and each issue mentions five or ten interesting tools. I would read about one and think, “I should remember that.” Months later, I would vaguely remember it but have no quick way to find it. I wanted a repository of these AI tools that I could search easily. The collector is a scheduled Claude Code Routine that runs once a day. It searches Gmail for newsletters from the last 24 hours and reads each email in full. It extracts every AI tool’s name, description, category, official link if present, and source newsletter. It skips non-AI items and pure ads but keeps sponsors that are genuine tools. Before adding anything, it checks for duplicates. New tools are added under the appropriate category in alphabetical order. If an existing tool has fresh details, its description and “last updated” date are refreshed. Tools mentioned by three or more sources receive a “Highly Mentioned” tag, which is a useful signal for what is catching on. The workflow also creates categories when new tools do not fit into an existing one. The workflow commits and pushes the catalog to a private GitHub repository, then posts a summary to Slack. The catalog is a single Markdown file—plain text, human-readable, and versioned in Git, with no database. The chat website displays the tool count, category count, and last-updated date, along with clickable example questions. If I ask, “Is there anything for voice AI?” it returns a written answer listing every match with descriptions and working links. The site is a React single-page app on Netlify with two small backend functions: one returns the statistics, and the other handles chat. When I ask a question, the backend fetches the Markdown file from the private repository, sends it to Claude along with my question, and returns the answer. To recreate it, you’ll need a GitHub account, Netlify (the free tier is fine), an Anthropic API key, and Claude Code. The routine prompt is the most important part. It names the exact newsletters, lists the fields to extract, and explicitly says: never invent a URL, never delete an entry, and only add or update. Vague instructions produce files that degrade over time. I learned a few things the hard way. Newsletters deleted before the workflow runs may cause it to report “nothing new,” so the logic should also check Trash or Deleted items. Tokens expire, and mine quietly expired, which caused the search site to stop working. Choose a long expiration period and record the date. Also, explicitly say “never invent a URL,” or the workflow may produce plausible links that go nowhere. It now runs every morning without me. When I need something, I ask a question and get an answer in seconds instead of trying to remember which newsletter, and which month, mentioned the tool I’m thinking of. Step-by-step: 1. I created a private GitHub repository with a starter Markdown file containing “Last updated” and “Total tools” fields, category headings, and consistent fields for each tool. 2. I wrote a Claude Code Routine prompt that names the newsletters, specifies the fields to extract, and instructs the workflow never to invent a URL, never to delete an entry, and only to add or update tools. 3. I scheduled the routine to run daily with Gmail access. 4. Each day, the routine searches Gmail for newsletters from the previous 24 hours, reads them, extracts AI tools, skips non-AI items and pure ads, preserves genuine tool sponsors, and checks Trash or Deleted items when necessary. 5. The routine checks for duplicates, adds new tools alphabetically under the right category, updates existing tools with fresh details, creates categories when needed, and applies the “Highly Mentioned” tag to tools found in at least three sources. 6. The routine commits and pushes the Markdown catalog to the private GitHub repository and posts a summary to Slack. 7. I built the frontend with Vite and React, including a statistics header, a scrollable message list, an input box, and example questions. 8. I added two backend functions: one for statistics and one for chat, with the GitHub fetch handled by a shared helper with a short cache. 9. I created a fine-grained, read-only GitHub token limited to the single repository. 10. I deployed the site to Netlify with the required keys stored as environment variables rather than in the code. 11. When I ask a question, the backend fetches the Markdown catalog, sends it to Claude with my question, and returns the matching tools, descriptions, and links.