Stop Doomscrolling X: I Built a Chrome Extension That Turns Bookmarks into an AI Research Engine

infz — Tue, 17 Feb 2026 06:48:22 +0000

Most of us don't have an information problem anymore.
We have a signal problem.

I used to save dozens of "great" posts on X every week, then never use them again.
So I built Digg X to fix exactly that.

Try it here:

Chrome extension: Digg X - The Social Scout
Web app: diggx.nian.me

The problem: bookmarks are a graveyard

If you're a founder, marketer, researcher, or builder, your X feed is full of valuable ideas.

But default bookmarks are:

hard to search
hard to structure
hard to reuse

You save a lot. You retrieve almost nothing.

That gap is what Digg X is built for.

What Digg X does in one flow

You bookmark a post/thread on X
Digg X captures and structures the content
It syncs into your cloud inbox
You get AI-ready material for analysis, briefs, and reuse

The goal is simple:
turn passive saving into active intelligence.

Why this is different from "another AI tool"

Most AI tools start with a blank input box.
Digg X starts with your real browsing behavior.

Instead of asking you to create work, it upgrades work you're already doing:

saving posts
collecting references
tracking people worth following

This small UX shift massively reduces friction.

Product principles I optimized for

1) One-click capture or it doesn't matter

If saving takes too long, users stop.
So capture had to feel instant.

2) Resilience over perfect elegance

Social platforms change constantly.
I built fallback paths so extraction keeps working.

3) Signal density > feature count

I care more about helping users find one high-value insight quickly than shipping ten flashy features.

Stack (for builders)

Digg X is split into:

Chrome extension (Plasmo + React)
API backend (Cloudflare Worker + Hono + D1 + KV + R2 + Queue)
Web app (Vite + React)

This lets me move fast on product while keeping ingestion and sync stable.

Who it's for

Digg X is built for people who learn from social content professionally:

indie hackers
growth marketers
startup operators
content researchers
curious builders who want less noise, more leverage

What I'm improving next

Better semantic search across saved content
Stronger creator-level scoring
Automated weekly research briefs

If you're building in this space, I'd love feedback.

Install Digg X:
Chrome Web Store

I Built Digg X: A Chrome Extension That Turns X Bookmarks into a Searchable AI Research Inbox

infz — Tue, 17 Feb 2026 06:35:09 +0000

I built Digg X because I had a personal problem:

I was bookmarking great posts and threads on X/Twitter every day, but I couldn't reliably turn them into reusable knowledge.

So I built a workflow-first Chrome extension:

Capture what matters while you browse X
Sync it into a structured cloud inbox
Run AI analysis on top of clean, versioned content

Chrome Web Store:
Digg X - The Social Scout

What Digg X does

At a high level, Digg X is a social intelligence layer on top of X:

Extract content from posts/threads when you bookmark
Convert raw content into Markdown + metadata
Sync everything to a backend inbox (/api/v2/sync/inbox)
Build profiles/insights around creators and content

I wanted this to feel native to browsing, not like "yet another dashboard."

Product architecture

Digg X is a monorepo with three main apps:

digg-x-ext: Chrome extension (Plasmo + React)
digg-x: Cloudflare Worker backend (Hono + D1 + KV + R2 + Queue)
digg-x-web: Web app (Vite + React Router + Pages Functions)

This split worked well:

Extension is optimized for capture and browser context
Worker backend is optimized for ingestion, sync, and APIs
Web app is optimized for browsing, organization, and reports

Key implementation details

1) Bookmark interception with fallback extraction

When a user clicks X's bookmark button, the extension intercepts that action, tries to extract structured data from bridge/JSON first, then falls back to DOM parsing if needed.

This reduced breakage when X's UI changed.

// Simplified from bookmark scanner logic
if (tweetId) {
  const tweetResult = await getTweetDataFromBridge(tweetId)
  if (tweetResult) data = MarkdownExtractor.extractFromTweetResult(tweetResult)
}

if (!data) {
  data = MarkdownExtractor.extractFromPage(bookmarkButton as HTMLElement)
}

2) Structured sync API for ingestion

The extension sends normalized content to the backend sync endpoint:

PUT /api/v2/sync/inbox

On the backend, this lands in D1 and object storage, with queue-based async processing for heavier tasks.

3) Side panel AI profile analysis

Digg X includes an analyzer flow in the side panel. It attempts API-based analysis first, and falls back to UI automation if the API route fails.

try {
  const result = await executeViaAPI(handle, prompt, lang, pluginI18n, selectors)
  return mergeWithDomData(result, domData, interceptedData)
} catch {
  const result = await executeViaUI(handle, prompt, lang, pluginI18n, selectors)
  return mergeWithDomData(result, domData, interceptedData)
}

This "API-first, UI-fallback" strategy made the feature significantly more resilient.

4) Practical usage limits for free users

I added a lightweight local quota mechanism for free users (currently 3 analyses/day). It keeps costs controlled while still giving users a real experience.

Why Cloudflare for the backend

I chose Cloudflare because the stack maps well to this product shape:

Worker for fast API edge runtime
D1 for relational query patterns
KV for hot cache
R2 for content/media blobs
Queue for async enrichment

For a browser-first tool with many small sync operations, this setup has been cost-effective and operationally simple.

Lessons learned

1) "Capture quality" matters more than model quality

If extraction is noisy, downstream AI outputs are noisy. The biggest wins came from improving ingestion and normalization.

2) Build graceful degradation everywhere

Social platforms change often. Fallbacks (JSON -> DOM, API -> UI) are not optional in extension products.

3) Keep the first action incredibly fast

If saving content feels slow, users stop doing it. One-click capture with immediate feedback was critical.

4) Don't over-centralize too early

Keeping extension, API, and web responsibilities separate made iteration faster and safer.

What's next

I'm currently focused on:

Better semantic retrieval across saved items
Stronger creator-level insight scoring
More automation around weekly "research brief" generation

If you're building dev tools, research tools, or browser extensions, I'd love to compare notes.

Try Digg X:
Chrome extension

Web app

DEV Community: infz