I had over 500 Twitter bookmarks. Saved with the best of intentions. Opened approximately never.
You know the pattern. See something interesting, hit bookmark, feel like you did something. Three months later you need that one thread about agentic UI patterns and you're scrolling through a graveyard of "I'll get to this later" content.
What I built
A local database that stores everything. A sync pipeline that pulls from Twitter's API. An AI categorizer that reads each bookmark and sorts it into categories I defined — things like "AI Agents," "Design," "Tools," plus private buckets for politics, personal stuff, and anything I wouldn't want on my public site.
The categorizer can use whatever AI model I have available — a local one running on my machine, or a cloud API for better results. The local model is slower but free, which matters when you're processing hundreds of bookmarks.
There's a public page on my portfolio that shows just the stuff I've marked as shareable. Search, category filters, the works.
The honest parts
Twitter's API is hostile to small developers. The free tier is extremely limited. I had to build rate limiting just to avoid burning through my quota in one sync.
The AI categorization is good but not perfect. It guesses wrong maybe fifteen percent of the time. Usually it's close — putting a design tool under "Tools" instead of "Design" — but sometimes it makes bizarre choices. I'm still tweaking the category descriptions to give the model better signal.
The public page is still a work in progress. It doesn't always show up reliably on the site, which kind of defeats the purpose. The backend works — the frontend needs attention.
What I'd do differently
I'd start with fewer categories. Fifteen is too many. The AI spreads things too thin, and some categories only have two or three entries.
But the core loop works. Save, categorize, decide what's worth sharing. My bookmarks actually mean something now instead of being a list I never open.
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