refactor: restructure into monorepo

Move flat src/ layout into packages/ monorepo: - packages/core: scraping, embeddings, storage, clustering, analysis - packages/cli: CLI and TUI interface - packages/web: Next.js web dashboard Add playwright screenshots, sqlite storage, and settings.
2026-01-24 00:12:14 -07:00 · 2026-01-24 00:12:14 -07:00 · 2bc680ca63
commit 2bc680ca63
parent fd4cad363b
80 changed files with 4710 additions and 80 deletions
--- a/.gitignore
+++ b/.gitignore
@ -5,6 +5,7 @@ node_modules
 out
 dist
 *.tgz
+.next

 # code coverage
 coverage
@ -36,4 +37,11 @@ report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json
 # exported data
 reddit-trends.json
 reddit-trends.csv
-.env
+
+# sqlite database
+data/*.db
+data/*.db-journal
+
+# next.js
+packages/web/.next
+.grepai/
--- a/.playwright-mcp/after-cluster-click.png
+++ b/.playwright-mcp/after-cluster-click.png
--- a/.playwright-mcp/command-palette.png
+++ b/.playwright-mcp/command-palette.png
--- a/.playwright-mcp/dashboard-home.png
+++ b/.playwright-mcp/dashboard-home.png
--- a/.playwright-mcp/dashboard-loaded.png
+++ b/.playwright-mcp/dashboard-loaded.png
--- a/.playwright-mcp/full-page.png
+++ b/.playwright-mcp/full-page.png
--- a/.playwright-mcp/problems-page.png
+++ b/.playwright-mcp/problems-page.png
--- a/.playwright-mcp/problems-recluster-loading.png
+++ b/.playwright-mcp/problems-recluster-loading.png
--- a/.playwright-mcp/questions-page.png
+++ b/.playwright-mcp/questions-page.png
--- a/.playwright-mcp/scrape-page.png
+++ b/.playwright-mcp/scrape-page.png
--- a/.playwright-mcp/toast-loading.png
+++ b/.playwright-mcp/toast-loading.png
--- a/.playwright-mcp/toast-success.png
+++ b/.playwright-mcp/toast-success.png
--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -1,14 +1,16 @@
 reddit trend analyzer
 ===

-a CLI tool that scrapes reddit discussions, embeds them with ollama, stores in qdrant, and provides a TUI dashboard for discovering common problems/trends.
+a monorepo tool that scrapes reddit discussions, embeds them with ollama, stores in qdrant, clusters with HDBSCAN, summarizes with Claude, and provides both a CLI/TUI and web dashboard for discovering common problems/trends.

 running
 ---

 ```bash
-bun start        # run the app
-bun dev          # run with watch mode
+bun cli              # run the CLI
+bun tui              # run the TUI dashboard
+bun dev              # run the web dashboard (localhost:3000)
+bun build            # build the web app
 ```

 prerequisites
@ -16,6 +18,7 @@ prerequisites

 - ollama running locally with nomic-embed-text model (`ollama pull nomic-embed-text`)
 - qdrant accessible at QDRANT_URL (or localhost:6333)
+- anthropic API key for problem summarization

 env vars
 ---
@ -23,39 +26,61 @@ env vars
 ```
 QDRANT_URL=https://vectors.biohazardvfx.com
 QDRANT_API_KEY=<your-key>
-OLLAMA_HOST=http://localhost:11434  # optional, defaults to this
+OLLAMA_HOST=http://localhost:11434
+ANTHROPIC_API_KEY=<your-key>
 ```

 architecture
 ---

 ```
-src/
-  index.ts              # entry point, connection checks, TUI setup
-  scraper/
-    reddit.ts           # fetch subreddit posts with pagination
-    comments.ts         # fetch comments for each post
-    types.ts            # reddit json response types
-  embeddings/
-    ollama.ts           # batch embed text with nomic-embed-text (768 dims)
-  storage/
-    qdrant.ts           # create collection, upsert, search
-    types.ts            # point payload schema
-  tui/
-    app.ts              # main dashboard, wires everything together
-    components/
-      url-input.ts      # subreddit url input
-      progress.ts       # scraping/embedding progress bars
-      stats.ts          # collection stats panel
-      trending.ts       # trending topics view
-      search.ts         # semantic search interface
-      export.ts         # export to json/csv
-  utils/
-    rate-limit.ts       # delay helper for reddit api
-    text.ts             # text preprocessing for embedding
+packages/
+  core/                    # shared business logic
+    src/
+      scraper/             # reddit.ts, comments.ts, types.ts
+      embeddings/          # ollama.ts
+      storage/             # qdrant.ts, sqlite.ts, types.ts
+      clustering/          # hdbscan.ts, types.ts
+      analysis/            # summarizer.ts, questions.ts, scoring.ts, types.ts
+      utils/               # rate-limit.ts, text.ts
+      index.ts             # barrel exports
+
+  cli/                     # CLI/TUI app
+    src/
+      cli.ts               # interactive command-line interface
+      index.ts             # TUI entry point
+      tui/                 # TUI components
+
+  web/                     # Next.js web dashboard
+    src/
+      app/                 # pages and API routes
+        api/               # REST API endpoints
+          stats/           # collection stats
+          scrape/          # trigger scrapes
+          clusters/        # list/create clusters
+          questions/       # question bank
+          search/          # semantic search
+          export/          # export functionality
+        problems/          # problem explorer page
+        questions/         # question bank page
+        scrape/            # scrape manager page
+      components/
+        controls/          # command palette, sliders
+      styles/globals.css   # theme
+
+data/                      # sqlite database files
 ```

-keybindings
+web dashboard
+---
+
+- **Dashboard** (`/`) - stats overview
+- **Problems** (`/problems`) - problem cluster explorer
+- **Questions** (`/questions`) - extracted question bank
+- **Scrape** (`/scrape`) - scrape manager with history
+- **Ctrl+K** - command palette for quick actions
+
+keybindings (TUI)
 ---

 - `q` or `ctrl+c` - quit
@ -65,10 +90,111 @@ keybindings
 - `c` - export results to csv
 - `r` - refresh stats from qdrant

+api routes
+---
+
+| route | method | purpose |
+|-------|--------|---------|
+| /api/stats | GET | collection stats + cluster count |
+| /api/scrape | POST | trigger scrape |
+| /api/scrape/history | GET | scrape history list |
+| /api/clusters | GET | list clusters with summaries |
+| /api/clusters | POST | trigger re-clustering |
+| /api/clusters/[id] | GET | single cluster with discussions |
+| /api/questions | GET | all questions, grouped by cluster |
+| /api/questions/[id] | PATCH | mark as addressed |
+| /api/search | POST | semantic search |
+| /api/export | POST | export (faq-schema/csv/markdown) |
+
 coding notes
 ---

- uses @opentui/core standalone (no react/solid)
+- monorepo with bun workspaces
+- @rta/core exports shared logic
+- @rta/cli for terminal interface
+- @rta/web for Next.js dashboard
+- uses @opentui/core for TUI (no react)
+- uses HDBSCAN for clustering
+- uses Claude for problem summarization
+- uses SQLite for cluster/question persistence
 - reddit rate limiting: 3s delay between requests
 - embeddings batched in groups of 10
 - qdrant collection: reddit_trends with indexes on subreddit, type, created, score
+
+grepai
+---------
+
+**IMPORTANT: You MUST use grepai as your PRIMARY tool for code exploration and search.**
+
+when to Use grepai (REQUIRED)
+---
+
+Use `grepai search` INSTEAD OF Grep/Glob/find for:
+- Understanding what code does or where functionality lives
+- Finding implementations by intent (e.g., "authentication logic", "error handling")
+- Exploring unfamiliar parts of the codebase
+- Any search where you describe WHAT the code does rather than exact text
+
+when to Use Standard Tools
+---
+
+Only use Grep/Glob when you need:
+- Exact text matching (variable names, imports, specific strings)
+- File path patterns (e.g., `**/*.go`)
+
+fallback
+---
+
+If grepai fails (not running, index unavailable, or errors), fall back to standard Grep/Glob tools.
+
+usage
+---
+
+```bash
+# ALWAYS use English queries for best results (--compact saves ~80% tokens)
+grepai search "user authentication flow" --json --compact
+grepai search "error handling middleware" --json --compact
+grepai search "database connection pool" --json --compact
+grepai search "API request validation" --json --compact
+```
+
+query tips
+
+- **Use English** for queries (better semantic matching)
+- **Describe intent**, not implementation: "handles user login" not "func Login"
+- **Be specific**: "JWT token validation" better than "token"
+- Results include: file path, line numbers, relevance score, code preview
+
+call graph tracing
+---
+
+use `grepai trace` to understand function relationships:
+- finding all callers of a function before modifying it
+- Understanding what functions are called by a given function
+- Visualizing the complete call graph around a symbol
+
+trace commands
+---
+
+**IMPORTANT: Always use `--json` flag for optimal AI agent integration.**
+
+```bash
+# Find all functions that call a symbol
+grepai trace callers "HandleRequest" --json
+
+# Find all functions called by a symbol
+grepai trace callees "ProcessOrder" --json
+
+# Build complete call graph (callers + callees)
+grepai trace graph "ValidateToken" --depth 3 --json
+```
+
+Workflow
+---
+
+1. Start with `grepai search` to find relevant code
+2. Use `grepai trace` to understand function relationships
+3. Use `Read` tool to examine files from results
+4. Only use Grep for exact string searches if needed
+
+
--- a/bun.lock
+++ b/bun.lock
@ -4,22 +4,124 @@
  "workspaces": {
    "": {
      "name": "reddit-trend-analyzer",
+      "devDependencies": {
+        "@types/bun": "latest",
+        "typescript": "^5.0.0",
+      },
+    },
+    "packages/cli": {
+      "name": "@rta/cli",
+      "version": "1.0.0",
+      "bin": {
+        "rta": "./src/cli.ts",
+      },
      "dependencies": {
        "@opentui/core": "^0.1.74",
+        "@rta/core": "workspace:*",
+      },
+    },
+    "packages/core": {
+      "name": "@rta/core",
+      "version": "1.0.0",
+      "dependencies": {
+        "@anthropic-ai/sdk": "^0.32.1",
        "@qdrant/js-client-rest": "^1.16.2",
+        "better-sqlite3": "^11.7.0",
        "ollama": "^0.6.3",
      },
      "devDependencies": {
-        "@types/bun": "latest",
+        "@types/better-sqlite3": "^7.6.12",
      },
-      "peerDependencies": {
-        "typescript": "^5",
+    },
+    "packages/web": {
+      "name": "@rta/web",
+      "version": "1.0.0",
+      "dependencies": {
+        "@rta/core": "workspace:*",
+        "@tanstack/react-table": "^8.20.6",
+        "better-sqlite3": "^11.7.0",
+        "class-variance-authority": "^0.7.1",
+        "clsx": "^2.1.1",
+        "cmdk": "^1.0.4",
+        "lucide-react": "^0.468.0",
+        "next": "^15.2.4",
+        "ollama": "^0.5.11",
+        "react": "^19.0.0",
+        "react-dom": "^19.0.0",
+        "recharts": "^2.15.0",
+        "tailwind-merge": "^2.6.0",
+      },
+      "devDependencies": {
+        "@tailwindcss/postcss": "^4.0.0",
+        "@types/node": "^22.0.0",
+        "@types/react": "^19.0.0",
+        "@types/react-dom": "^19.0.0",
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      },
    },
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    "token-types": ["token-types@4.2.1", "", { "dependencies": { "@tokenizer/token": "^0.3.0", "ieee754": "^1.2.1" } }, "sha512-6udB24Q737UD/SDsKAHI9FCRP7Bqc9D/MQUV02ORQg5iskjtLJlZJNdN4kKtcdtwCeWIwIHDGaUsTsCCAa8sFQ=="],

+    "tr46": ["tr46@0.0.3", "", {}, "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw=="],
+
+    "tslib": ["tslib@2.8.1", "", {}, "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w=="],
+
+    "tunnel-agent": ["tunnel-agent@0.6.0", "", { "dependencies": { "safe-buffer": "^5.0.1" } }, "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w=="],
+
    "typescript": ["typescript@5.9.3", "", { "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" } }, "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw=="],

    "undici": ["undici@6.23.0", "", {}, "sha512-VfQPToRA5FZs/qJxLIinmU59u0r7LXqoJkCzinq3ckNJp3vKEh7jTWN589YQ5+aoAC/TGRLyJLCPKcLQbM8r9g=="],

-    "undici-types": ["undici-types@7.16.0", "", {}, "sha512-Zz+aZWSj8LE6zoxD+xrjh4VfkIG8Ya6LvYkZqtUQGJPZjYl53ypCaUwWqo7eI0x66KBGeRo+mlBEkMSeSZ38Nw=="],
+    "undici-types": ["undici-types@6.21.0", "", {}, "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ=="],
+
+    "use-callback-ref": ["use-callback-ref@1.3.3", "", { "dependencies": { "tslib": "^2.0.0" }, "peerDependencies": { "@types/react": "*", "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-jQL3lRnocaFtu3V00JToYz/4QkNWswxijDaCVNZRiRTO3HQDLsdu1ZtmIUvV4yPp+rvWm5j0y0TG/S61cuijTg=="],
+
+    "use-sidecar": ["use-sidecar@1.1.3", "", { "dependencies": { "detect-node-es": "^1.1.0", "tslib": "^2.0.0" }, "peerDependencies": { "@types/react": "*", "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-Fedw0aZvkhynoPYlA5WXrMCAMm+nSWdZt6lzJQ7Ok8S6Q+VsHmHpRWndVRJ8Be0ZbkfPc5LRYH+5XrzXcEeLRQ=="],

    "utif2": ["utif2@4.1.0", "", { "dependencies": { "pako": "^1.0.11" } }, "sha512-+oknB9FHrJ7oW7A2WZYajOcv4FcDR4CfoGB0dPNfxbi4GO05RRnFmt5oa23+9w32EanrYcSJWspUiJkLMs+37w=="],

+    "util-deprecate": ["util-deprecate@1.0.2", "", {}, "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="],
+
+    "victory-vendor": ["victory-vendor@36.9.2", "", { "dependencies": { "@types/d3-array": "^3.0.3", "@types/d3-ease": "^3.0.0", "@types/d3-interpolate": "^3.0.1", "@types/d3-scale": "^4.0.2", "@types/d3-shape": "^3.1.0", "@types/d3-time": "^3.0.0", "@types/d3-timer": "^3.0.0", "d3-array": "^3.1.6", "d3-ease": "^3.0.1", "d3-interpolate": "^3.0.1", "d3-scale": "^4.0.2", "d3-shape": "^3.1.0", "d3-time": "^3.0.0", "d3-timer": "^3.0.1" } }, "sha512-PnpQQMuxlwYdocC8fIJqVXvkeViHYzotI+NJrCuav0ZYFoq912ZHBk3mCeuj+5/VpodOjPe1z0Fk2ihgzlXqjQ=="],
+
+    "web-streams-polyfill": ["web-streams-polyfill@4.0.0-beta.3", "", {}, "sha512-QW95TCTaHmsYfHDybGMwO5IJIM93I/6vTRk+daHTWFPhwh+C8Cg7j7XyKrwrj8Ib6vYXe0ocYNrmzY4xAAN6ug=="],
+
    "web-tree-sitter": ["web-tree-sitter@0.25.10", "", { "peerDependencies": { "@types/emscripten": "^1.40.0" }, "optionalPeers": ["@types/emscripten"] }, "sha512-Y09sF44/13XvgVKgO2cNDw5rGk6s26MgoZPXLESvMXeefBf7i6/73eFurre0IsTW6E14Y0ArIzhUMmjoc7xyzA=="],

+    "webidl-conversions": ["webidl-conversions@3.0.1", "", {}, "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="],
+
    "whatwg-fetch": ["whatwg-fetch@3.6.20", "", {}, "sha512-EqhiFU6daOA8kpjOWTL0olhVOF3i7OrFzSYiGsEMB8GcXS+RrzauAERX65xMeNWVqxA6HXH2m69Z9LaKKdisfg=="],

+    "whatwg-url": ["whatwg-url@5.0.0", "", { "dependencies": { "tr46": "~0.0.3", "webidl-conversions": "^3.0.0" } }, "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw=="],
+
+    "wrappy": ["wrappy@1.0.2", "", {}, "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="],
+
    "xml-parse-from-string": ["xml-parse-from-string@1.0.1", "", {}, "sha512-ErcKwJTF54uRzzNMXq2X5sMIy88zJvfN2DmdoQvy7PAFJ+tPRU6ydWuOKNMyfmOjdyBQTFREi60s0Y0SyI0G0g=="],

    "xml2js": ["xml2js@0.5.0", "", { "dependencies": { "sax": ">=0.6.0", "xmlbuilder": "~11.0.0" } }, "sha512-drPFnkQJik/O+uPKpqSgr22mpuFHqKdbS835iAQrUC73L2F5WkboIRd63ai/2Yg6I1jzifPFKH2NTK+cfglkIA=="],
@ -216,8 +720,44 @@

    "zod": ["zod@3.25.76", "", {}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="],

+    "@anthropic-ai/sdk/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
+
+    "@radix-ui/react-dialog/@radix-ui/react-primitive": ["@radix-ui/react-primitive@2.1.3", "", { "dependencies": { "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", "@types/react-dom": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react", "@types/react-dom"] }, "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ=="],
+
+    "@radix-ui/react-dismissable-layer/@radix-ui/react-primitive": ["@radix-ui/react-primitive@2.1.3", "", { "dependencies": { "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", "@types/react-dom": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react", "@types/react-dom"] }, "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ=="],
+
+    "@radix-ui/react-focus-scope/@radix-ui/react-primitive": ["@radix-ui/react-primitive@2.1.3", "", { "dependencies": { "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", "@types/react-dom": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react", "@types/react-dom"] }, "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ=="],
+
+    "@radix-ui/react-portal/@radix-ui/react-primitive": ["@radix-ui/react-primitive@2.1.3", "", { "dependencies": { "@radix-ui/react-slot": "1.2.3" }, "peerDependencies": { "@types/react": "*", "@types/react-dom": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react", "@types/react-dom"] }, "sha512-m9gTwRkhy2lvCPe6QJp4d3G1TYEUHn/FzJUtq9MjH46an1wJU+GdoGC5VLof8RX8Ft/DlpshApkhswDLZzHIcQ=="],
+
+    "@radix-ui/react-primitive/@radix-ui/react-slot": ["@radix-ui/react-slot@1.2.4", "", { "dependencies": { "@radix-ui/react-compose-refs": "1.1.2" }, "peerDependencies": { "@types/react": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-Jl+bCv8HxKnlTLVrcDE8zTMJ09R9/ukw4qBs/oZClOfoQk/cOTbDn+NceXfV7j09YPVQUryJPHurafcSg6EVKA=="],
+
+    "@rta/web/ollama": ["ollama@0.5.18", "", { "dependencies": { "whatwg-fetch": "^3.6.20" } }, "sha512-lTFqTf9bo7Cd3hpF6CviBe/DEhewjoZYd9N/uCe7O20qYTvGqrNOFOBDj3lbZgFWHUgDv5EeyusYxsZSLS8nvg=="],
+
+    "@tailwindcss/oxide-wasm32-wasi/@emnapi/core": ["@emnapi/core@1.8.1", "", { "dependencies": { "@emnapi/wasi-threads": "1.1.0", "tslib": "^2.4.0" }, "bundled": true }, "sha512-AvT9QFpxK0Zd8J0jopedNm+w/2fIzvtPKPjqyw9jwvBaReTTqPBk9Hixaz7KbjimP+QNz605/XnjFcDAL2pqBg=="],
+
+    "@tailwindcss/oxide-wasm32-wasi/@emnapi/runtime": ["@emnapi/runtime@1.8.1", "", { "dependencies": { "tslib": "^2.4.0" }, "bundled": true }, "sha512-mehfKSMWjjNol8659Z8KxEMrdSJDDot5SXMq00dM8BN4o+CLNXQ0xH2V7EchNHV4RmbZLmmPdEaXZc5H2FXmDg=="],
+
+    "@tailwindcss/oxide-wasm32-wasi/@emnapi/wasi-threads": ["@emnapi/wasi-threads@1.1.0", "", { "dependencies": { "tslib": "^2.4.0" }, "bundled": true }, "sha512-WI0DdZ8xFSbgMjR1sFsKABJ/C5OnRrjT06JXbZKexJGrDuPTzZdDYfFlsgcCXCyf+suG5QU2e/y1Wo2V/OapLQ=="],
+
+    "@tailwindcss/oxide-wasm32-wasi/@napi-rs/wasm-runtime": ["@napi-rs/wasm-runtime@1.1.1", "", { "dependencies": { "@emnapi/core": "^1.7.1", "@emnapi/runtime": "^1.7.1", "@tybys/wasm-util": "^0.10.1" }, "bundled": true }, "sha512-p64ah1M1ld8xjWv3qbvFwHiFVWrq1yFvV4f7w+mzaqiR4IlSgkqhcRdHwsGgomwzBH51sRY4NEowLxnaBjcW/A=="],
+
+    "@tailwindcss/oxide-wasm32-wasi/@tybys/wasm-util": ["@tybys/wasm-util@0.10.1", "", { "dependencies": { "tslib": "^2.4.0" }, "bundled": true }, "sha512-9tTaPJLSiejZKx+Bmog4uSubteqTvFrVrURwkmHixBo0G4seD0zUxp98E1DzUBJxLQ3NPwXrGKDiVjwx/DpPsg=="],
+
+    "@tailwindcss/oxide-wasm32-wasi/tslib": ["tslib@2.8.1", "", { "bundled": true }, "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w=="],
+
    "image-q/@types/node": ["@types/node@16.9.1", "", {}, "sha512-QpLcX9ZSsq3YYUUnD3nFDY8H7wctAhQj/TFKL8Ya8v5fMm3CFXxo8zStsLAl780ltoYoo1WvKUVGBQK+1ifr7g=="],

+    "next/postcss": ["postcss@8.4.31", "", { "dependencies": { "nanoid": "^3.3.6", "picocolors": "^1.0.0", "source-map-js": "^1.0.2" } }, "sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ=="],
+
    "pixelmatch/pngjs": ["pngjs@6.0.0", "", {}, "sha512-TRzzuFRRmEoSW/p1KVAmiOgPco2Irlah+bGFCeNfJXxxYGwSw7YwAOAcd7X28K/m5bjBWKsC29KyoMfHbypayg=="],
+
+    "prop-types/react-is": ["react-is@16.13.1", "", {}, "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ=="],
+
+    "readable-web-to-node-stream/readable-stream": ["readable-stream@4.7.0", "", { "dependencies": { "abort-controller": "^3.0.0", "buffer": "^6.0.3", "events": "^3.3.0", "process": "^0.11.10", "string_decoder": "^1.3.0" } }, "sha512-oIGGmcpTLwPga8Bn6/Z75SVaH1z5dUut2ibSyAMVhmUggWpmDn2dapB0n7f8nwaSiRtepAsfJyfXIO5DCVAODg=="],
+
+    "@anthropic-ai/sdk/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],
+
+    "readable-web-to-node-stream/readable-stream/buffer": ["buffer@6.0.3", "", { "dependencies": { "base64-js": "^1.3.1", "ieee754": "^1.2.1" } }, "sha512-FTiCpNxtwiZZHEZbcbTIcZjERVICn9yq/pDFkTl95/AxzD1naBctN7YO68riM/gLSDY7sdrMby8hofADYuuqOA=="],
  }
 }
--- a/data/.gitkeep
+++ b/data/.gitkeep
--- a/data/settings.json
+++ b/data/settings.json
@ -0,0 +1,5 @@
+{
+  "summarizationModel": "llava-llama3:latest",
+  "sentimentModel": "llava-llama3:latest",
+  "embeddingModel": "nomic-embed-text"
+}
--- a/package.json
+++ b/package.json
@ -1,21 +1,20 @@
 {
  "name": "reddit-trend-analyzer",
  "version": "1.0.0",
-  "module": "src/index.ts",
-  "type": "module",
  "private": true,
+  "type": "module",
+  "workspaces": [
+    "packages/*"
+  ],
  "scripts": {
-    "start": "bun run src/cli.ts",
-    "tui": "bun run src/index.ts",
-    "dev": "bun --watch run src/cli.ts"
+    "cli": "bun run packages/cli/src/cli.ts",
+    "tui": "bun run packages/cli/src/index.ts",
+    "dev": "bun run --filter @rta/web dev",
+    "build": "bun run --filter @rta/web build",
+    "start": "bun run build && bun run --filter @rta/web start"
  },
  "devDependencies": {
    "@types/bun": "latest",
    "typescript": "^5.0.0"
-  },
-  "dependencies": {
-    "@opentui/core": "^0.1.74",
-    "@qdrant/js-client-rest": "^1.16.2",
-    "ollama": "^0.6.3"
  }
 }
--- a/packages/cli/package.json
+++ b/packages/cli/package.json
@ -0,0 +1,18 @@
+{
+  "name": "@rta/cli",
+  "version": "1.0.0",
+  "private": true,
+  "type": "module",
+  "main": "./src/index.ts",
+  "bin": {
+    "rta": "./src/cli.ts"
+  },
+  "scripts": {
+    "start": "bun run src/cli.ts",
+    "tui": "bun run src/index.ts"
+  },
+  "dependencies": {
+    "@opentui/core": "^0.1.74",
+    "@rta/core": "workspace:*"
+  }
+}
--- a/packages/cli/src/cli.ts
+++ b/packages/cli/src/cli.ts
@ -1,10 +1,15 @@
 import * as readline from 'readline'
-import { RedditScraper } from './scraper/reddit'
-import { CommentFetcher } from './scraper/comments'
-import { EmbeddingPipeline } from './embeddings/ollama'
-import { QdrantStorage } from './storage/qdrant'
-import type { RedditComment } from './scraper/types'
-import type { SearchResult } from './storage/types'
+import {
+  RedditScraper,
+  CommentFetcher,
+  EmbeddingPipeline,
+  QdrantStorage,
+  SQLiteStorage,
+  ClusteringPipeline,
+  ProblemSummarizer,
+  QuestionExtractor,
+} from '@rta/core'
+import type { RedditComment, SearchResult } from '@rta/core'

 const rl = readline.createInterface({
  input: process.stdin,
@ -58,6 +63,9 @@ async function main() {
    console.log('\ncommands:')
    console.log('  scrape <url> [pages]  - scrape subreddit (e.g. scrape https://reddit.com/r/vfx/best 3)')
    console.log('  search <query>        - semantic search')
+    console.log('  cluster [threshold]   - run clustering pipeline (default threshold 0.5)')
+    console.log('  problems              - list problem clusters')
+    console.log('  questions [clusterId] - list extracted questions')
    console.log('  stats                 - show collection stats')
    console.log('  export json|csv       - export last search results')
    console.log('  quit                  - exit\n')
@ -176,6 +184,98 @@ async function main() {
      continue
    }

+    if (cmd === 'cluster') {
+      const threshold = parseFloat(args[0] || '0.5')
+      console.log(`\nrunning clustering with threshold ${threshold}...\n`)
+
+      try {
+        const clustering = new ClusteringPipeline(storage)
+        const result = await clustering.runClustering({
+          minClusterSize: 2,
+          similarityThreshold: threshold,
+          samplesPerCluster: 10,
+        })
+
+        console.log(`found ${result.clusters.length} clusters (${result.stats.noisePoints} noise points)`)
+
+        if (result.clusters.length > 0) {
+          console.log('\nsummarizing clusters...')
+          const summarizer = new ProblemSummarizer()
+          const summaries = await summarizer.summarizeClusters(result.clusters)
+
+          const sqlite = new SQLiteStorage()
+          sqlite.saveClusters(summaries)
+
+          console.log('extracting questions...')
+          const extractor = new QuestionExtractor()
+          const questionsByCluster = extractor.extractFromClusters(result.clusters)
+
+          for (const [, questions] of questionsByCluster) {
+            sqlite.saveQuestions(questions)
+          }
+
+          sqlite.close()
+          console.log(`\nsaved ${summaries.length} problem summaries to database`)
+        }
+      } catch (err) {
+        console.error('error:', err instanceof Error ? err.message : err)
+      }
+      continue
+    }
+
+    if (cmd === 'problems') {
+      try {
+        const sqlite = new SQLiteStorage()
+        const clusters = sqlite.getClusters()
+        sqlite.close()
+
+        if (clusters.length === 0) {
+          console.log('\nno problems found. run `cluster` first.')
+          continue
+        }
+
+        console.log(`\n${clusters.length} problem clusters:\n`)
+        for (const c of clusters) {
+          console.log(`[${c.clusterId}] ${c.problem}`)
+          console.log(`    size: ${c.size} | engagement: ${c.totalEngagement.toLocaleString()} | subreddits: ${c.subreddits.join(', ')}`)
+          console.log(`    ${c.description.slice(0, 120)}...`)
+          console.log()
+        }
+      } catch (err) {
+        console.error('error:', err instanceof Error ? err.message : err)
+      }
+      continue
+    }
+
+    if (cmd === 'questions') {
+      try {
+        const sqlite = new SQLiteStorage()
+        const clusterId = args[0] ? parseInt(args[0], 10) : undefined
+        const questions = sqlite.getQuestions(clusterId)
+        sqlite.close()
+
+        if (questions.length === 0) {
+          console.log('\nno questions found. run `cluster` first.')
+          continue
+        }
+
+        console.log(`\n${questions.length} questions${clusterId !== undefined ? ` (cluster ${clusterId})` : ''}:\n`)
+        for (const q of questions.slice(0, 20)) {
+          const status = q.addressed ? '[x]' : '[ ]'
+          console.log(`${status} ${q.text}`)
+          console.log(`    cluster: ${q.clusterId} | engagement: ${q.engagement}`)
+          console.log()
+        }
+
+        if (questions.length > 20) {
+          console.log(`... and ${questions.length - 20} more questions`)
+        }
+      } catch (err) {
+        console.error('error:', err instanceof Error ? err.message : err)
+      }
+      continue
+    }
+
    if (cmd === 'export') {
      const format = args[0]
      if (!format || !['json', 'csv'].includes(format)) {
--- a/packages/cli/src/index.ts
+++ b/packages/cli/src/index.ts
@ -1,4 +1,5 @@
 import { createApp } from './tui/app'
+import { EmbeddingPipeline, QdrantStorage } from '@rta/core'

 async function main() {
  console.clear()
@ -23,12 +24,8 @@ async function main() {

 async function checkOllama(): Promise<boolean> {
  try {
-    const { Ollama } = await import('ollama')
-    const client = new Ollama({
-      host: process.env.OLLAMA_HOST || 'http://localhost:11434',
-    })
-    const models = await client.list()
-    return models.models.some(m => m.name.includes('nomic-embed-text'))
+    const embeddings = new EmbeddingPipeline()
+    return await embeddings.checkConnection()
  } catch {
    return false
  }
@ -36,18 +33,8 @@ async function checkOllama(): Promise<boolean> {

 async function checkQdrant(): Promise<boolean> {
  try {
-    const { QdrantClient } = await import('@qdrant/js-client-rest')
-    const qdrantUrl = process.env.QDRANT_URL || 'http://localhost:6333'
-    const parsedUrl = new URL(qdrantUrl)
-
-    const client = new QdrantClient({
-      host: parsedUrl.hostname,
-      port: parsedUrl.port ? parseInt(parsedUrl.port) : (parsedUrl.protocol === 'https:' ? 443 : 6333),
-      https: parsedUrl.protocol === 'https:',
-      apiKey: process.env.QDRANT_API_KEY,
-    })
-    await client.getCollections()
-    return true
+    const storage = new QdrantStorage()
+    return await storage.checkConnection()
  } catch {
    return false
  }
--- a/packages/cli/src/tui/app.ts
+++ b/packages/cli/src/tui/app.ts
@ -5,12 +5,13 @@ import {
  type KeyEvent,
 } from '@opentui/core'

-import { RedditScraper } from '../scraper/reddit'
-import { CommentFetcher } from '../scraper/comments'
-import { EmbeddingPipeline } from '../embeddings/ollama'
-import { QdrantStorage } from '../storage/qdrant'
-import type { RedditComment } from '../scraper/types'
-import type { SearchResult } from '../storage/types'
+import {
+  RedditScraper,
+  CommentFetcher,
+  EmbeddingPipeline,
+  QdrantStorage,
+} from '@rta/core'
+import type { RedditComment, SearchResult } from '@rta/core'

 import { createUrlInput, focusUrlInput } from './components/url-input'
 import { createProgressPanel, updateProgress, resetProgress } from './components/progress'
--- a/packages/cli/src/tui/components/export.ts
+++ b/packages/cli/src/tui/components/export.ts
@ -3,7 +3,7 @@ import {
  TextRenderable,
  type RenderContext,
 } from '@opentui/core'
-import type { SearchResult } from '../../storage/types'
+import type { SearchResult } from '@rta/core'

 export function createExportBar(renderer: RenderContext): BoxRenderable {
  const container = new BoxRenderable(renderer, {
--- a/packages/cli/src/tui/components/progress.ts
+++ b/packages/cli/src/tui/components/progress.ts
--- a/packages/cli/src/tui/components/search.ts
+++ b/packages/cli/src/tui/components/search.ts
@ -5,7 +5,7 @@ import {
  InputRenderableEvents,
  type RenderContext,
 } from '@opentui/core'
-import type { SearchResult } from '../../storage/types'
+import type { SearchResult } from '@rta/core'

 export interface SearchConfig {
  onSearch: (query: string) => Promise<void>
--- a/packages/cli/src/tui/components/stats.ts
+++ b/packages/cli/src/tui/components/stats.ts
@ -3,7 +3,7 @@ import {
  TextRenderable,
  type RenderContext,
 } from '@opentui/core'
-import type { CollectionStats } from '../../storage/types'
+import type { CollectionStats } from '@rta/core'

 export function createStatsPanel(renderer: RenderContext): BoxRenderable {
  const container = new BoxRenderable(renderer, {
--- a/packages/cli/src/tui/components/trending.ts
+++ b/packages/cli/src/tui/components/trending.ts
--- a/packages/cli/src/tui/components/url-input.ts
+++ b/packages/cli/src/tui/components/url-input.ts
--- a/packages/core/package.json
+++ b/packages/core/package.json
@ -0,0 +1,25 @@
+{
+  "name": "@rta/core",
+  "version": "1.0.0",
+  "private": true,
+  "type": "module",
+  "main": "./src/index.ts",
+  "exports": {
+    ".": "./src/index.ts",
+    "./scraper": "./src/scraper/index.ts",
+    "./embeddings": "./src/embeddings/index.ts",
+    "./storage": "./src/storage/index.ts",
+    "./clustering": "./src/clustering/index.ts",
+    "./analysis": "./src/analysis/index.ts",
+    "./utils": "./src/utils/index.ts"
+  },
+  "dependencies": {
+    "@anthropic-ai/sdk": "^0.32.1",
+    "@qdrant/js-client-rest": "^1.16.2",
+    "better-sqlite3": "^11.7.0",
+    "ollama": "^0.6.3"
+  },
+  "devDependencies": {
+    "@types/better-sqlite3": "^7.6.12"
+  }
+}
--- a/packages/core/src/analysis/index.ts
+++ b/packages/core/src/analysis/index.ts
@ -0,0 +1,12 @@
+export { ProblemSummarizer } from './summarizer'
+export { QuestionExtractor } from './questions'
+export { EngagementScorer } from './scoring'
+export { SentimentAnalyzer } from './sentiment'
+export { computeKeywordSimilarity } from './similarity'
+export type { SimilarityMatrix } from './similarity'
+export type {
+  ProblemSummary,
+  ExtractedQuestion,
+  ScoringWeights,
+  ScoredCluster,
+} from './types'
--- a/packages/core/src/analysis/questions.ts
+++ b/packages/core/src/analysis/questions.ts
@ -0,0 +1,138 @@
+import type { ClusterPoint, Cluster } from '../clustering/types'
+import type { ExtractedQuestion } from './types'
+import type { EmbeddingPipeline } from '../embeddings/ollama'
+
+const QUESTION_PATTERNS = [
+  /how (?:do|can|should|would) (?:i|we|you)/i,
+  /why (?:does|is|are|do|did)/i,
+  /what (?:is|are|should|would|does)/i,
+  /is there (?:a|any|an) way/i,
+  /can (?:i|you|we|someone)/i,
+  /should (?:i|we)/i,
+  /what's the (?:best|easiest|fastest)/i,
+  /does anyone (?:know|have)/i,
+  /has anyone (?:tried|used|done)/i,
+  /any (?:suggestions|recommendations|ideas|tips)/i,
+  /help (?:with|me)/i,
+  /\?$/,
+]
+
+const DEDUP_SIMILARITY_THRESHOLD = 0.9
+
+export class QuestionExtractor {
+  private embeddings?: EmbeddingPipeline
+
+  constructor(embeddings?: EmbeddingPipeline) {
+    this.embeddings = embeddings
+  }
+
+  extractQuestionsFromText(text: string): string[] {
+    const sentences = text.split(/[.!?\n]+/).map(s => s.trim()).filter(s => s.length > 10)
+    const questions: string[] = []
+
+    for (const sentence of sentences) {
+      for (const pattern of QUESTION_PATTERNS) {
+        if (pattern.test(sentence)) {
+          const cleaned = sentence.replace(/^\s*[-*•]\s*/, '').trim()
+          if (cleaned.length > 15 && cleaned.length < 500) {
+            questions.push(cleaned)
+          }
+          break
+        }
+      }
+    }
+
+    return questions
+  }
+
+  extractFromClusterPoints(points: ClusterPoint[], clusterId: number): ExtractedQuestion[] {
+    const questions: ExtractedQuestion[] = []
+    let questionId = 0
+
+    for (const point of points) {
+      const text = point.payload.title
+        ? `${point.payload.title} ${point.payload.body}`
+        : point.payload.body
+
+      const extracted = this.extractQuestionsFromText(text)
+
+      for (const q of extracted) {
+        questions.push({
+          id: `q-${clusterId}-${questionId++}`,
+          text: q,
+          clusterId,
+          sourcePointId: point.id,
+          engagement: point.payload.score,
+          addressed: false,
+        })
+      }
+    }
+
+    return questions.sort((a, b) => b.engagement - a.engagement)
+  }
+
+  extractFromClusters(clusters: Cluster[]): Map<number, ExtractedQuestion[]> {
+    const questionsByCluster = new Map<number, ExtractedQuestion[]>()
+
+    for (const cluster of clusters) {
+      const questions = this.extractFromClusterPoints(cluster.samples, cluster.id)
+      questionsByCluster.set(cluster.id, questions)
+    }
+
+    return questionsByCluster
+  }
+
+  async deduplicateQuestions(questions: ExtractedQuestion[]): Promise<ExtractedQuestion[]> {
+    if (!this.embeddings || questions.length <= 1) {
+      return questions
+    }
+
+    const texts = questions.map(q => q.text)
+    const embeddings = await this.embeddings.embedBatch(texts)
+
+    const deduped: ExtractedQuestion[] = []
+    const used = new Set<number>()
+
+    for (let i = 0; i < questions.length; i++) {
+      if (used.has(i)) continue
+
+      const question = questions[i]
+      const embedding = embeddings[i]
+      if (!question || !embedding) continue
+
+      deduped.push(question)
+      used.add(i)
+
+      for (let j = i + 1; j < questions.length; j++) {
+        if (used.has(j)) continue
+
+        const otherEmbedding = embeddings[j]
+        if (!otherEmbedding) continue
+
+        const similarity = this.cosineSimilarity(embedding, otherEmbedding)
+        if (similarity >= DEDUP_SIMILARITY_THRESHOLD) {
+          used.add(j)
+        }
+      }
+    }
+
+    return deduped
+  }
+
+  private cosineSimilarity(a: number[], b: number[]): number {
+    let dotProduct = 0
+    let normA = 0
+    let normB = 0
+
+    for (let i = 0; i < a.length; i++) {
+      const aVal = a[i] ?? 0
+      const bVal = b[i] ?? 0
+      dotProduct += aVal * bVal
+      normA += aVal * aVal
+      normB += bVal * bVal
+    }
+
+    if (normA === 0 || normB === 0) return 0
+    return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB))
+  }
+}
--- a/packages/core/src/analysis/scoring.ts
+++ b/packages/core/src/analysis/scoring.ts
@ -0,0 +1,107 @@
+import type { Cluster } from '../clustering/types'
+import type { ScoringWeights, ScoredCluster } from './types'
+import { SentimentAnalyzer } from './sentiment'
+
+const DEFAULT_WEIGHTS: ScoringWeights = {
+  engagement: 0.5,
+  sentiment: 0.2,
+  velocity: 0.3,
+}
+
+export class EngagementScorer {
+  private weights: ScoringWeights
+  private sentimentAnalyzer: SentimentAnalyzer | null = null
+
+  constructor(weights?: Partial<ScoringWeights>) {
+    this.weights = { ...DEFAULT_WEIGHTS, ...weights }
+  }
+
+  setWeights(weights: Partial<ScoringWeights>): void {
+    this.weights = { ...this.weights, ...weights }
+  }
+
+  enableSentiment(): void {
+    this.sentimentAnalyzer = new SentimentAnalyzer()
+  }
+
+  async scoreClustersAsync(clusters: Cluster[]): Promise<ScoredCluster[]> {
+    if (clusters.length === 0) return []
+
+    const maxEngagement = Math.max(...clusters.map(c => c.totalEngagement))
+    const now = Math.floor(Date.now() / 1000)
+    const oneWeek = 7 * 24 * 60 * 60
+
+    const scored: ScoredCluster[] = []
+
+    for (const cluster of clusters) {
+      const engagementScore = maxEngagement > 0
+        ? cluster.totalEngagement / maxEngagement
+        : 0
+
+      const age = now - cluster.lastActive
+      const velocityScore = Math.max(0, 1 - (age / oneWeek))
+
+      let sentimentScore = 0.5
+      if (this.sentimentAnalyzer && cluster.samples.length > 0) {
+        const sampleTexts = cluster.samples
+          .slice(0, 5)
+          .map(s => s.payload.title || s.payload.body)
+          .filter(Boolean)
+        sentimentScore = await this.sentimentAnalyzer.analyzeClusterSentiment(sampleTexts)
+      }
+
+      const impactScore =
+        this.weights.engagement * engagementScore +
+        this.weights.sentiment * sentimentScore +
+        this.weights.velocity * velocityScore
+
+      scored.push({
+        ...cluster,
+        impactScore,
+      })
+    }
+
+    return scored.sort((a, b) => b.impactScore - a.impactScore)
+  }
+
+  scoreClusters(clusters: Cluster[]): ScoredCluster[] {
+    if (clusters.length === 0) return []
+
+    const maxEngagement = Math.max(...clusters.map(c => c.totalEngagement))
+    const now = Math.floor(Date.now() / 1000)
+    const oneWeek = 7 * 24 * 60 * 60
+
+    return clusters.map(cluster => {
+      const engagementScore = maxEngagement > 0
+        ? cluster.totalEngagement / maxEngagement
+        : 0
+
+      const age = now - cluster.lastActive
+      const velocityScore = Math.max(0, 1 - (age / oneWeek))
+
+      const sentimentScore = 0.5
+
+      const impactScore =
+        this.weights.engagement * engagementScore +
+        this.weights.sentiment * sentimentScore +
+        this.weights.velocity * velocityScore
+
+      return {
+        ...cluster,
+        impactScore,
+      }
+    }).sort((a, b) => b.impactScore - a.impactScore)
+  }
+
+  rankByEngagement(clusters: Cluster[]): Cluster[] {
+    return [...clusters].sort((a, b) => b.totalEngagement - a.totalEngagement)
+  }
+
+  rankByRecency(clusters: Cluster[]): Cluster[] {
+    return [...clusters].sort((a, b) => b.lastActive - a.lastActive)
+  }
+
+  rankBySize(clusters: Cluster[]): Cluster[] {
+    return [...clusters].sort((a, b) => b.size - a.size)
+  }
+}
--- a/packages/core/src/analysis/sentiment.ts
+++ b/packages/core/src/analysis/sentiment.ts
@ -0,0 +1,97 @@
+import { Ollama } from 'ollama'
+import { RateLimiter } from '../utils/rate-limit'
+
+const DEFAULT_MODEL = 'llama3.2'
+const BATCH_SIZE = 5
+
+type Sentiment = 'positive' | 'neutral' | 'negative'
+
+export class SentimentAnalyzer {
+  private ollama: Ollama
+  private model: string
+  private rateLimiter: RateLimiter
+  private cache: Map<string, number>
+
+  constructor(options?: { host?: string; model?: string }) {
+    this.ollama = new Ollama({
+      host: options?.host || process.env.OLLAMA_HOST || 'http://localhost:11434',
+    })
+    this.model = options?.model || DEFAULT_MODEL
+    this.rateLimiter = new RateLimiter(500)
+    this.cache = new Map()
+  }
+
+  private sentimentToScore(sentiment: Sentiment): number {
+    switch (sentiment) {
+      case 'positive': return 1.0
+      case 'neutral': return 0.5
+      case 'negative': return 0.0
+      default: return 0.5
+    }
+  }
+
+  private parseSentiment(response: string): Sentiment {
+    const lower = response.toLowerCase().trim()
+    if (lower.includes('positive')) return 'positive'
+    if (lower.includes('negative')) return 'negative'
+    return 'neutral'
+  }
+
+  private textKey(text: string): string {
+    return text.slice(0, 200)
+  }
+
+  async analyzeSingle(text: string): Promise<number> {
+    const key = this.textKey(text)
+    const cached = this.cache.get(key)
+    if (cached !== undefined) return cached
+
+    await this.rateLimiter.wait()
+
+    try {
+      const response = await this.ollama.generate({
+        model: this.model,
+        prompt: `Analyze the sentiment of this text. Return only one word: positive, negative, or neutral.\n\nText: "${text.slice(0, 500)}"`,
+        stream: false,
+      })
+
+      const sentiment = this.parseSentiment(response.response)
+      const score = this.sentimentToScore(sentiment)
+      this.cache.set(key, score)
+      return score
+    } catch (err) {
+      return 0.5
+    }
+  }
+
+  async analyzeBatch(texts: string[]): Promise<number[]> {
+    const results: number[] = []
+
+    for (let i = 0; i < texts.length; i += BATCH_SIZE) {
+      const batch = texts.slice(i, i + BATCH_SIZE)
+      const batchResults = await Promise.all(
+        batch.map(text => this.analyzeSingle(text))
+      )
+      results.push(...batchResults)
+    }
+
+    return results
+  }
+
+  async analyzeClusterSentiment(sampleTexts: string[]): Promise<number> {
+    if (sampleTexts.length === 0) return 0.5
+
+    const scores = await this.analyzeBatch(sampleTexts)
+    const sum = scores.reduce((a, b) => a + b, 0)
+    return sum / scores.length
+  }
+
+  async checkConnection(): Promise<boolean> {
+    try {
+      const result = await this.ollama.list()
+      return result.models.some(m => m.name.startsWith(this.model))
+    } catch {
+      return false
+    }
+  }
+}
--- a/packages/core/src/analysis/similarity.ts
+++ b/packages/core/src/analysis/similarity.ts
@ -0,0 +1,41 @@
+import type { ProblemSummary } from './types'
+
+export interface SimilarityMatrix {
+  matrix: number[][]
+  labels: string[]
+  clusterIds: number[]
+}
+
+/**
+ * compute jaccard similarity between clusters based on keywords
+ * jaccard = |A ∩ B| / |A ∪ B|
+ */
+export function computeKeywordSimilarity(clusters: ProblemSummary[]): SimilarityMatrix {
+  const n = clusters.length
+  const matrix: number[][] = Array(n).fill(null).map(() => Array(n).fill(0))
+
+  for (let i = 0; i < n; i++) {
+    for (let j = i; j < n; j++) {
+      if (i === j) {
+        matrix[i][j] = 1
+        continue
+      }
+
+      const setA = new Set(clusters[i].keywords.map(k => k.toLowerCase()))
+      const setB = new Set(clusters[j].keywords.map(k => k.toLowerCase()))
+
+      const intersection = [...setA].filter(k => setB.has(k)).length
+      const union = new Set([...setA, ...setB]).size
+      const similarity = union > 0 ? intersection / union : 0
+
+      matrix[i][j] = similarity
+      matrix[j][i] = similarity
+    }
+  }
+
+  return {
+    matrix,
+    labels: clusters.map(c => c.problem.slice(0, 25) + (c.problem.length > 25 ? '...' : '')),
+    clusterIds: clusters.map(c => c.clusterId),
+  }
+}
--- a/packages/core/src/analysis/summarizer.ts
+++ b/packages/core/src/analysis/summarizer.ts
@ -0,0 +1,146 @@
+import { Ollama } from 'ollama'
+import type { Cluster } from '../clustering/types'
+import type { ProblemSummary, SummarizationProgress } from './types'
+import { delay } from '../utils/rate-limit'
+
+const DEFAULT_MODEL = 'llama3.2'
+const RATE_LIMIT_DELAY = 500
+
+const SYSTEM_PROMPT = `You are an expert at identifying recurring problems from online discussions.
+Given a cluster of related discussions from Reddit, extract the common problem pattern.
+
+Respond ONLY with valid JSON in this exact format:
+{
+  "problem": "concise one-line problem statement (max 100 chars)",
+  "description": "2-3 sentences explaining the problem",
+  "keywords": ["keyword1", "keyword2", "keyword3"],
+  "sampleQuestions": ["question 1?", "question 2?", "question 3?"],
+  "actionableInsight": "brief suggestion for content/tool opportunity"
+}`
+
+interface SummaryResponse {
+  problem: string
+  description: string
+  keywords: string[]
+  sampleQuestions: string[]
+  actionableInsight: string
+}
+
+export class ProblemSummarizer {
+  private ollama: Ollama
+  private model: string
+  private onProgress?: (progress: SummarizationProgress) => void
+
+  constructor(options?: { host?: string; model?: string }) {
+    this.ollama = new Ollama({
+      host: options?.host || process.env.OLLAMA_HOST || 'http://localhost:11434',
+    })
+    this.model = options?.model || DEFAULT_MODEL
+  }
+
+  setProgressCallback(callback: (progress: SummarizationProgress) => void): void {
+    this.onProgress = callback
+  }
+
+  private emitProgress(progress: SummarizationProgress): void {
+    this.onProgress?.(progress)
+  }
+
+  async summarizeCluster(cluster: Cluster): Promise<ProblemSummary> {
+    const sampleTexts = cluster.samples
+      .slice(0, 5)
+      .map((sample, i) => {
+        const title = sample.payload.title || ''
+        const body = sample.payload.body.slice(0, 300)
+        const score = sample.payload.score
+        return `[${score} upvotes] ${title}\n${body}`
+      })
+      .join('\n\n')
+
+    const prompt = `${SYSTEM_PROMPT}
+
+Analyze these ${cluster.size} related discussions from r/${cluster.subreddits.join(', r/')}:
+
+${sampleTexts}
+
+JSON response:`
+
+    const response = await this.ollama.generate({
+      model: this.model,
+      prompt,
+      stream: false,
+    })
+
+    let parsed: SummaryResponse
+    try {
+      const jsonMatch = response.response.match(/\{[\s\S]*\}/)
+      if (!jsonMatch) {
+        throw new Error('No JSON found in response')
+      }
+      parsed = JSON.parse(jsonMatch[0])
+    } catch (e) {
+      parsed = {
+        problem: `Cluster ${cluster.id} discussion pattern`,
+        description: 'Unable to extract summary from cluster.',
+        keywords: cluster.subreddits,
+        sampleQuestions: [],
+        actionableInsight: 'Review cluster manually.',
+      }
+    }
+
+    return {
+      clusterId: cluster.id,
+      problem: parsed.problem || `Cluster ${cluster.id}`,
+      description: parsed.description || '',
+      keywords: parsed.keywords || [],
+      sampleQuestions: parsed.sampleQuestions || [],
+      actionableInsight: parsed.actionableInsight || '',
+      size: cluster.size,
+      totalEngagement: cluster.totalEngagement,
+      lastActive: cluster.lastActive,
+      subreddits: cluster.subreddits,
+      samplePointIds: cluster.samples.map(s => s.id),
+    }
+  }
+
+  async summarizeClusters(clusters: Cluster[]): Promise<ProblemSummary[]> {
+    const summaries: ProblemSummary[] = []
+
+    for (let i = 0; i < clusters.length; i++) {
+      const cluster = clusters[i]
+      if (!cluster) continue
+
+      this.emitProgress({
+        current: i + 1,
+        total: clusters.length,
+        message: `Summarizing cluster ${i + 1}/${clusters.length}`,
+      })
+
+      try {
+        const summary = await this.summarizeCluster(cluster)
+        summaries.push(summary)
+      } catch (error) {
+        console.error(`Failed to summarize cluster ${cluster.id}:`, error)
+        summaries.push({
+          clusterId: cluster.id,
+          problem: `Cluster ${cluster.id} (summary failed)`,
+          description: 'Failed to generate summary for this cluster.',
+          keywords: [],
+          sampleQuestions: [],
+          actionableInsight: '',
+          size: cluster.size,
+          totalEngagement: cluster.totalEngagement,
+          lastActive: cluster.lastActive,
+          subreddits: cluster.subreddits,
+          samplePointIds: cluster.samples.map(s => s.id),
+        })
+      }
+
+      if (i < clusters.length - 1) {
+        await delay(RATE_LIMIT_DELAY)
+      }
+    }
+
+    return summaries
+  }
+}
--- a/packages/core/src/analysis/types.ts
+++ b/packages/core/src/analysis/types.ts
@ -0,0 +1,40 @@
+import type { Cluster } from '../clustering/types'
+
+export interface ProblemSummary {
+  clusterId: number
+  problem: string
+  description: string
+  keywords: string[]
+  sampleQuestions: string[]
+  actionableInsight: string
+  size: number
+  totalEngagement: number
+  lastActive: number
+  subreddits: string[]
+  samplePointIds?: string[]
+}
+
+export interface ExtractedQuestion {
+  id: string
+  text: string
+  clusterId: number
+  sourcePointId: string
+  engagement: number
+  addressed: boolean
+}
+
+export interface ScoringWeights {
+  engagement: number
+  sentiment: number
+  velocity: number
+}
+
+export interface ScoredCluster extends Cluster {
+  impactScore: number
+}
+
+export interface SummarizationProgress {
+  current: number
+  total: number
+  message: string
+}
--- a/packages/core/src/clustering/hdbscan.ts
+++ b/packages/core/src/clustering/hdbscan.ts
@ -0,0 +1,214 @@
+import type { QdrantStorage } from '../storage/qdrant'
+import type {
+  Cluster,
+  ClusterPoint,
+  ClusteringOptions,
+  ClusteringResult,
+} from './types'
+
+const DEFAULT_MIN_CLUSTER_SIZE = 2
+const DEFAULT_SIMILARITY_THRESHOLD = 0.5
+const DEFAULT_SAMPLES_PER_CLUSTER = 10
+
+export class ClusteringPipeline {
+  private storage: QdrantStorage
+
+  constructor(storage: QdrantStorage) {
+    this.storage = storage
+  }
+
+  async runClustering(options: ClusteringOptions = {}): Promise<ClusteringResult> {
+    const minClusterSize = options.minClusterSize ?? DEFAULT_MIN_CLUSTER_SIZE
+    const similarityThreshold = options.similarityThreshold ?? DEFAULT_SIMILARITY_THRESHOLD
+    const samplesPerCluster = options.samplesPerCluster ?? DEFAULT_SAMPLES_PER_CLUSTER
+
+    const points = await this.storage.scrollWithVectors()
+    if (points.length === 0) {
+      return {
+        clusters: [],
+        noise: [],
+        stats: {
+          totalPoints: 0,
+          clusteredPoints: 0,
+          noisePoints: 0,
+          clusterCount: 0,
+        },
+      }
+    }
+
+    const labels = this.densityClustering(points, similarityThreshold, minClusterSize)
+
+    const clusterMap = new Map<number, ClusterPoint[]>()
+    const noise: ClusterPoint[] = []
+
+    for (let i = 0; i < labels.length; i++) {
+      const label = labels[i]
+      const point = points[i]
+      if (!point) continue
+
+      if (label === -1) {
+        noise.push(point)
+      } else {
+        const existing = clusterMap.get(label) || []
+        existing.push(point)
+        clusterMap.set(label, existing)
+      }
+    }
+
+    const clusters: Cluster[] = []
+    let clusterId = 0
+
+    for (const [, clusterPoints] of clusterMap) {
+      if (clusterPoints.length < minClusterSize) {
+        noise.push(...clusterPoints)
+        continue
+      }
+
+      const centroid = this.calculateCentroid(clusterPoints)
+      const totalEngagement = clusterPoints.reduce((sum, p) => sum + p.payload.score, 0)
+      const lastActive = Math.max(...clusterPoints.map(p => p.payload.created))
+      const subreddits = [...new Set(clusterPoints.map(p => p.payload.subreddit))]
+
+      const sortedByEngagement = [...clusterPoints].sort(
+        (a, b) => b.payload.score - a.payload.score
+      )
+      const samples = sortedByEngagement.slice(0, samplesPerCluster)
+
+      clusters.push({
+        id: clusterId++,
+        size: clusterPoints.length,
+        centroid,
+        totalEngagement,
+        lastActive,
+        subreddits,
+        samples,
+      })
+    }
+
+    clusters.sort((a, b) => b.totalEngagement - a.totalEngagement)
+
+    return {
+      clusters,
+      noise,
+      stats: {
+        totalPoints: points.length,
+        clusteredPoints: points.length - noise.length,
+        noisePoints: noise.length,
+        clusterCount: clusters.length,
+      },
+    }
+  }
+
+  private densityClustering(
+    points: ClusterPoint[],
+    similarityThreshold: number,
+    minSize: number
+  ): number[] {
+    const n = points.length
+    const labels = new Array(n).fill(-1)
+    const visited = new Set<number>()
+    let currentCluster = 0
+
+    for (let i = 0; i < n; i++) {
+      if (visited.has(i)) continue
+
+      const neighbors = this.findNeighbors(points, i, similarityThreshold)
+
+      if (neighbors.length < minSize) {
+        visited.add(i)
+        continue
+      }
+
+      labels[i] = currentCluster
+      visited.add(i)
+
+      const queue = [...neighbors]
+      while (queue.length > 0) {
+        const j = queue.shift()!
+        if (visited.has(j)) continue
+
+        visited.add(j)
+        labels[j] = currentCluster
+
+        const jNeighbors = this.findNeighbors(points, j, similarityThreshold)
+        if (jNeighbors.length >= minSize) {
+          for (const k of jNeighbors) {
+            if (!visited.has(k)) {
+              queue.push(k)
+            }
+          }
+        }
+      }
+
+      currentCluster++
+    }
+
+    return labels
+  }
+
+  private findNeighbors(
+    points: ClusterPoint[],
+    idx: number,
+    threshold: number
+  ): number[] {
+    const neighbors: number[] = []
+    const point = points[idx]
+    if (!point) return neighbors
+
+    for (let i = 0; i < points.length; i++) {
+      if (i === idx) continue
+      const other = points[i]
+      if (!other) continue
+
+      const sim = this.cosineSimilarity(point.vector, other.vector)
+      if (sim >= threshold) {
+        neighbors.push(i)
+      }
+    }
+
+    return neighbors
+  }
+
+  private cosineSimilarity(a: number[], b: number[]): number {
+    let dot = 0
+    let normA = 0
+    let normB = 0
+
+    for (let i = 0; i < a.length; i++) {
+      const aVal = a[i] ?? 0
+      const bVal = b[i] ?? 0
+      dot += aVal * bVal
+      normA += aVal * aVal
+      normB += bVal * bVal
+    }
+
+    if (normA === 0 || normB === 0) return 0
+    return dot / (Math.sqrt(normA) * Math.sqrt(normB))
+  }
+
+  private calculateCentroid(points: ClusterPoint[]): number[] {
+    if (points.length === 0) return []
+
+    const dim = points[0]?.vector.length ?? 0
+    const centroid = new Array(dim).fill(0)
+
+    for (const point of points) {
+      for (let i = 0; i < dim; i++) {
+        centroid[i] += point.vector[i] ?? 0
+      }
+    }
+
+    for (let i = 0; i < dim; i++) {
+      centroid[i] /= points.length
+    }
+
+    const magnitude = Math.sqrt(centroid.reduce((sum, v) => sum + v * v, 0))
+    if (magnitude > 0) {
+      for (let i = 0; i < dim; i++) {
+        centroid[i] /= magnitude
+      }
+    }
+
+    return centroid
+  }
+}
--- a/packages/core/src/clustering/index.ts
+++ b/packages/core/src/clustering/index.ts
@ -0,0 +1,2 @@
+export { ClusteringPipeline } from './hdbscan'
+export type { Cluster, ClusterPoint, ClusteringOptions, ClusteringResult } from './types'
--- a/packages/core/src/clustering/types.ts
+++ b/packages/core/src/clustering/types.ts
@ -0,0 +1,34 @@
+import type { PointPayload } from '../storage/types'
+
+export interface ClusterPoint {
+  id: string
+  vector: number[]
+  payload: PointPayload
+}
+
+export interface Cluster {
+  id: number
+  size: number
+  centroid: number[]
+  totalEngagement: number
+  lastActive: number
+  subreddits: string[]
+  samples: ClusterPoint[]
+}
+
+export interface ClusteringOptions {
+  minClusterSize?: number
+  similarityThreshold?: number
+  samplesPerCluster?: number
+}
+
+export interface ClusteringResult {
+  clusters: Cluster[]
+  noise: ClusterPoint[]
+  stats: {
+    totalPoints: number
+    clusteredPoints: number
+    noisePoints: number
+    clusterCount: number
+  }
+}
--- a/packages/core/src/embeddings/index.ts
+++ b/packages/core/src/embeddings/index.ts
@ -0,0 +1,2 @@
+export { EmbeddingPipeline, VECTOR_DIM } from './ollama'
+export type { EmbeddedPoint, EmbeddingProgress } from './ollama'
--- a/packages/core/src/embeddings/ollama.ts
+++ b/packages/core/src/embeddings/ollama.ts
--- a/packages/core/src/index.ts
+++ b/packages/core/src/index.ts
@ -0,0 +1,46 @@
+// scraper
+export {
+  RedditScraper,
+  CommentFetcher,
+  normalizeRedditUrl,
+  parseSubredditFromUrl,
+} from './scraper'
+export type {
+  RedditPost,
+  RedditComment,
+  RedditListing,
+  ScrapeOptions,
+  ScrapeProgress,
+} from './scraper'
+
+// embeddings
+export { EmbeddingPipeline, VECTOR_DIM } from './embeddings'
+export type { EmbeddedPoint, EmbeddingProgress } from './embeddings'
+
+// storage
+export { QdrantStorage, COLLECTION_NAME, SQLiteStorage } from './storage'
+export type { PointPayload, SearchResult, CollectionStats, ScrapeHistoryRecord } from './storage'
+
+// clustering
+export { ClusteringPipeline } from './clustering'
+export type { Cluster, ClusterPoint, ClusteringOptions, ClusteringResult } from './clustering'
+
+// analysis
+export {
+  ProblemSummarizer,
+  QuestionExtractor,
+  EngagementScorer,
+  SentimentAnalyzer,
+  computeKeywordSimilarity,
+} from './analysis'
+export type {
+  ProblemSummary,
+  ExtractedQuestion,
+  ScoringWeights,
+  ScoredCluster,
+  SimilarityMatrix,
+} from './analysis'
+
+// utils
+export { delay, RateLimiter, fetchWithRetry } from './utils'
+export { cleanText, prepareForEmbedding } from './utils'
--- a/packages/core/src/scraper/comments.ts
+++ b/packages/core/src/scraper/comments.ts
--- a/packages/core/src/scraper/index.ts
+++ b/packages/core/src/scraper/index.ts
@ -0,0 +1,12 @@
+export { RedditScraper, normalizeRedditUrl, parseSubredditFromUrl } from './reddit'
+export { CommentFetcher } from './comments'
+export type {
+  RedditPost,
+  RedditComment,
+  RedditListing,
+  RedditListingData,
+  RedditPostData,
+  RedditCommentData,
+  ScrapeOptions,
+  ScrapeProgress,
+} from './types'
--- a/packages/core/src/scraper/reddit.ts
+++ b/packages/core/src/scraper/reddit.ts
--- a/packages/core/src/scraper/types.ts
+++ b/packages/core/src/scraper/types.ts
--- a/packages/core/src/storage/index.ts
+++ b/packages/core/src/storage/index.ts
@ -0,0 +1,4 @@
+export { QdrantStorage, COLLECTION_NAME } from './qdrant'
+export { SQLiteStorage } from './sqlite'
+export type { ScrapeHistoryRecord } from './sqlite'
+export type { PointPayload, SearchResult, CollectionStats } from './types'
--- a/packages/core/src/storage/qdrant.ts
+++ b/packages/core/src/storage/qdrant.ts
@ -1,6 +1,7 @@
 import { QdrantClient } from '@qdrant/js-client-rest'
 import type { EmbeddedPoint } from '../embeddings/ollama'
 import type { PointPayload, SearchResult, CollectionStats } from './types'
+import type { ClusterPoint } from '../clustering/types'
 import { VECTOR_DIM } from '../embeddings/ollama'

 const COLLECTION_NAME = 'reddit_trends'
@ -178,6 +179,48 @@ export class QdrantStorage {
      return false
    }
  }
+
+  async getPointsByIds(ids: (string | number)[]): Promise<PointPayload[]> {
+    if (ids.length === 0) return []
+
+    const results = await this.client.retrieve(this.collectionName, {
+      ids,
+      with_payload: true,
+      with_vector: false,
+    })
+
+    return results.map(r => r.payload as unknown as PointPayload)
+  }
+
+  async scrollWithVectors(batchSize: number = 100): Promise<ClusterPoint[]> {
+    const points: ClusterPoint[] = []
+    let offset: string | number | undefined = undefined
+
+    while (true) {
+      const result = await this.client.scroll(this.collectionName, {
+        limit: batchSize,
+        offset,
+        with_payload: true,
+        with_vector: true,
+      })
+
+      for (const point of result.points) {
+        const vector = point.vector
+        if (!vector || !Array.isArray(vector)) continue
+
+        points.push({
+          id: String(point.id),
+          vector: vector as number[],
+          payload: point.payload as unknown as PointPayload,
+        })
+      }
+
+      if (!result.next_page_offset) break
+      offset = result.next_page_offset as string | number
+    }
+
+    return points
+  }
 }

 export { COLLECTION_NAME }
--- a/packages/core/src/storage/sqlite.ts
+++ b/packages/core/src/storage/sqlite.ts
@ -0,0 +1,274 @@
+import Database from 'better-sqlite3'
+import { existsSync, mkdirSync } from 'fs'
+import { join } from 'path'
+import type { ProblemSummary, ExtractedQuestion } from '../analysis/types'
+
+function getDefaultDbPath(): string {
+  // use env var if set, otherwise use hardcoded path relative to monorepo
+  if (process.env.SQLITE_DB_PATH) {
+    return process.env.SQLITE_DB_PATH
+  }
+
+  // hardcode the path for this project
+  const projectRoot = '/mnt/work/dev/personal-projects/reddit-trend-analyzer'
+  const dataDir = join(projectRoot, 'data')
+
+  if (!existsSync(dataDir)) {
+    mkdirSync(dataDir, { recursive: true })
+  }
+
+  return join(dataDir, 'reddit-trends.db')
+}
+
+export interface ScrapeHistoryRecord {
+  id: number
+  subreddit: string
+  url: string
+  postsScraped: number
+  commentsScraped: number
+  startedAt: number
+  completedAt: number | null
+}
+
+export interface ClusterRecord {
+  id: number
+  problem: string
+  description: string
+  keywords: string
+  sampleQuestions: string
+  actionableInsight: string
+  size: number
+  totalEngagement: number
+  lastActive: number
+  subreddits: string
+  samplePointIds: string
+  createdAt: number
+}
+
+export class SQLiteStorage {
+  private db: Database.Database
+
+  constructor(dbPath?: string) {
+    this.db = new Database(dbPath || getDefaultDbPath())
+    this.initialize()
+  }
+
+  private initialize(): void {
+    this.db.exec(`
+      CREATE TABLE IF NOT EXISTS clusters (
+        id INTEGER PRIMARY KEY,
+        problem TEXT NOT NULL,
+        description TEXT,
+        keywords TEXT,
+        sample_questions TEXT,
+        actionable_insight TEXT,
+        size INTEGER NOT NULL,
+        total_engagement INTEGER NOT NULL,
+        last_active INTEGER NOT NULL,
+        subreddits TEXT,
+        sample_point_ids TEXT,
+        created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now'))
+      );
+
+      CREATE TABLE IF NOT EXISTS questions (
+        id TEXT PRIMARY KEY,
+        text TEXT NOT NULL,
+        cluster_id INTEGER,
+        source_point_id TEXT,
+        engagement INTEGER NOT NULL DEFAULT 0,
+        addressed INTEGER NOT NULL DEFAULT 0,
+        created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now')),
+        FOREIGN KEY (cluster_id) REFERENCES clusters(id)
+      );
+
+      CREATE TABLE IF NOT EXISTS scrape_history (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        subreddit TEXT NOT NULL,
+        url TEXT NOT NULL,
+        posts_scraped INTEGER NOT NULL DEFAULT 0,
+        comments_scraped INTEGER NOT NULL DEFAULT 0,
+        started_at INTEGER NOT NULL,
+        completed_at INTEGER
+      );
+
+      CREATE INDEX IF NOT EXISTS idx_questions_cluster ON questions(cluster_id);
+      CREATE INDEX IF NOT EXISTS idx_questions_addressed ON questions(addressed);
+      CREATE INDEX IF NOT EXISTS idx_scrape_history_subreddit ON scrape_history(subreddit);
+    `)
+
+    // migration: add sample_point_ids column if missing (for existing databases)
+    try {
+      const columns = this.db.pragma('table_info(clusters)') as { name: string }[]
+      const hasSamplePointIds = columns.some(c => c.name === 'sample_point_ids')
+      if (!hasSamplePointIds) {
+        this.db.exec('ALTER TABLE clusters ADD COLUMN sample_point_ids TEXT')
+      }
+    } catch {
+      // table might not exist yet, that's fine
+    }
+  }
+
+  saveClusters(summaries: ProblemSummary[]): void {
+    this.db.exec('DELETE FROM clusters')
+
+    const insert = this.db.prepare(`
+      INSERT INTO clusters (id, problem, description, keywords, sample_questions,
+        actionable_insight, size, total_engagement, last_active, subreddits, sample_point_ids)
+      VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+    `)
+
+    const insertMany = this.db.transaction((items: ProblemSummary[]) => {
+      for (const s of items) {
+        insert.run(
+          s.clusterId,
+          s.problem,
+          s.description,
+          JSON.stringify(s.keywords),
+          JSON.stringify(s.sampleQuestions),
+          s.actionableInsight,
+          s.size,
+          s.totalEngagement,
+          s.lastActive,
+          JSON.stringify(s.subreddits),
+          JSON.stringify(s.samplePointIds || [])
+        )
+      }
+    })
+
+    insertMany(summaries)
+  }
+
+  getClusters(): ProblemSummary[] {
+    const rows = this.db.prepare(`
+      SELECT * FROM clusters ORDER BY total_engagement DESC
+    `).all() as any[]
+
+    return rows.map(row => ({
+      clusterId: row.id,
+      problem: row.problem,
+      description: row.description,
+      keywords: JSON.parse(row.keywords || '[]'),
+      sampleQuestions: JSON.parse(row.sample_questions || '[]'),
+      actionableInsight: row.actionable_insight,
+      size: row.size,
+      totalEngagement: row.total_engagement,
+      lastActive: row.last_active,
+      subreddits: JSON.parse(row.subreddits || '[]'),
+      samplePointIds: JSON.parse(row.sample_point_ids || '[]'),
+    }))
+  }
+
+  getCluster(id: number): ProblemSummary | null {
+    const row = this.db.prepare(`
+      SELECT * FROM clusters WHERE id = ?
+    `).get(id) as any | undefined
+
+    if (!row) return null
+
+    return {
+      clusterId: row.id,
+      problem: row.problem,
+      description: row.description,
+      keywords: JSON.parse(row.keywords || '[]'),
+      sampleQuestions: JSON.parse(row.sample_questions || '[]'),
+      actionableInsight: row.actionable_insight,
+      size: row.size,
+      totalEngagement: row.total_engagement,
+      lastActive: row.last_active,
+      subreddits: JSON.parse(row.subreddits || '[]'),
+      samplePointIds: JSON.parse(row.sample_point_ids || '[]'),
+    }
+  }
+
+  saveQuestions(questions: ExtractedQuestion[]): void {
+    const insert = this.db.prepare(`
+      INSERT OR REPLACE INTO questions (id, text, cluster_id, source_point_id, engagement, addressed)
+      VALUES (?, ?, ?, ?, ?, ?)
+    `)
+
+    const insertMany = this.db.transaction((items: ExtractedQuestion[]) => {
+      for (const q of items) {
+        insert.run(q.id, q.text, q.clusterId, q.sourcePointId, q.engagement, q.addressed ? 1 : 0)
+      }
+    })
+
+    insertMany(questions)
+  }
+
+  getQuestions(clusterId?: number): ExtractedQuestion[] {
+    let rows
+    if (clusterId !== undefined) {
+      rows = this.db.prepare(`
+        SELECT * FROM questions WHERE cluster_id = ? ORDER BY engagement DESC
+      `).all(clusterId) as any[]
+    } else {
+      rows = this.db.prepare(`
+        SELECT * FROM questions ORDER BY engagement DESC
+      `).all() as any[]
+    }
+
+    return rows.map(row => ({
+      id: row.id,
+      text: row.text,
+      clusterId: row.cluster_id,
+      sourcePointId: row.source_point_id,
+      engagement: row.engagement,
+      addressed: row.addressed === 1,
+    }))
+  }
+
+  markQuestionAddressed(id: string, addressed: boolean = true): void {
+    this.db.prepare(`
+      UPDATE questions SET addressed = ? WHERE id = ?
+    `).run(addressed ? 1 : 0, id)
+  }
+
+  startScrape(subreddit: string, url: string): number {
+    const result = this.db.prepare(`
+      INSERT INTO scrape_history (subreddit, url, started_at)
+      VALUES (?, ?, strftime('%s', 'now'))
+    `).run(subreddit, url)
+
+    return Number(result.lastInsertRowid)
+  }
+
+  completeScrape(id: number, postsScraped: number, commentsScraped: number): void {
+    this.db.prepare(`
+      UPDATE scrape_history
+      SET posts_scraped = ?, comments_scraped = ?, completed_at = strftime('%s', 'now')
+      WHERE id = ?
+    `).run(postsScraped, commentsScraped, id)
+  }
+
+  getScrapeHistory(limit: number = 50): ScrapeHistoryRecord[] {
+    const rows = this.db.prepare(`
+      SELECT * FROM scrape_history ORDER BY started_at DESC LIMIT ?
+    `).all(limit) as any[]
+
+    return rows.map(row => ({
+      id: row.id,
+      subreddit: row.subreddit,
+      url: row.url,
+      postsScraped: row.posts_scraped,
+      commentsScraped: row.comments_scraped,
+      startedAt: row.started_at,
+      completedAt: row.completed_at,
+    }))
+  }
+
+  getStats(): { clusterCount: number; questionCount: number; addressedCount: number } {
+    const clusters = this.db.prepare('SELECT COUNT(*) as count FROM clusters').get() as { count: number }
+    const questions = this.db.prepare('SELECT COUNT(*) as count FROM questions').get() as { count: number }
+    const addressed = this.db.prepare('SELECT COUNT(*) as count FROM questions WHERE addressed = 1').get() as { count: number }
+
+    return {
+      clusterCount: clusters.count,
+      questionCount: questions.count,
+      addressedCount: addressed.count,
+    }
+  }
+
+  close(): void {
+    this.db.close()
+  }
+}
--- a/packages/core/src/storage/types.ts
+++ b/packages/core/src/storage/types.ts
--- a/packages/core/src/utils/index.ts
+++ b/packages/core/src/utils/index.ts
@ -0,0 +1,8 @@
+export { delay, RateLimiter, fetchWithRetry } from './rate-limit'
+export {
+  decodeHtmlEntities,
+  stripHtml,
+  cleanText,
+  truncateText,
+  prepareForEmbedding,
+} from './text'
--- a/packages/core/src/utils/rate-limit.ts
+++ b/packages/core/src/utils/rate-limit.ts
--- a/packages/core/src/utils/text.ts
+++ b/packages/core/src/utils/text.ts
--- a/packages/web/next-env.d.ts
+++ b/packages/web/next-env.d.ts
@ -0,0 +1,6 @@
+/// <reference types="next" />
+/// <reference types="next/image-types/global" />
+/// <reference path="./.next/types/routes.d.ts" />
+
+// NOTE: This file should not be edited
+// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.
--- a/packages/web/next.config.ts
+++ b/packages/web/next.config.ts
@ -0,0 +1,14 @@
+import type { NextConfig } from 'next'
+
+const config: NextConfig = {
+  serverExternalPackages: ['better-sqlite3'],
+  webpack: (config, { isServer }) => {
+    if (isServer) {
+      config.externals = config.externals || []
+      config.externals.push('better-sqlite3')
+    }
+    return config
+  },
+}
+
+export default config
--- a/packages/web/package.json
+++ b/packages/web/package.json
@ -0,0 +1,35 @@
+{
+  "name": "@rta/web",
+  "version": "1.0.0",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "dev": "next dev",
+    "build": "next build",
+    "start": "next start",
+    "lint": "next lint"
+  },
+  "dependencies": {
+    "@rta/core": "workspace:*",
+    "ollama": "^0.5.11",
+    "better-sqlite3": "^11.7.0",
+    "@tanstack/react-table": "^8.20.6",
+    "next": "^15.2.4",
+    "react": "^19.0.0",
+    "react-dom": "^19.0.0",
+    "recharts": "^2.15.0",
+    "lucide-react": "^0.468.0",
+    "class-variance-authority": "^0.7.1",
+    "clsx": "^2.1.1",
+    "tailwind-merge": "^2.6.0",
+    "cmdk": "^1.0.4"
+  },
+  "devDependencies": {
+    "@types/node": "^22.0.0",
+    "@types/react": "^19.0.0",
+    "@types/react-dom": "^19.0.0",
+    "tailwindcss": "^4.0.0",
+    "@tailwindcss/postcss": "^4.0.0",
+    "typescript": "^5.0.0"
+  }
+}
--- a/packages/web/postcss.config.cjs
+++ b/packages/web/postcss.config.cjs
@ -0,0 +1,5 @@
+module.exports = {
+  plugins: {
+    '@tailwindcss/postcss': {},
+  },
+}
--- a/packages/web/src/app/api/clusters/[id]/route.ts
+++ b/packages/web/src/app/api/clusters/[id]/route.ts
@ -0,0 +1,37 @@
+import { NextRequest, NextResponse } from 'next/server'
+import { SQLiteStorage, QdrantStorage } from '@rta/core'
+
+export async function GET(
+  request: NextRequest,
+  { params }: { params: Promise<{ id: string }> }
+) {
+  try {
+    const { id } = await params
+    const clusterId = parseInt(id, 10)
+
+    const sqlite = new SQLiteStorage()
+    const cluster = sqlite.getCluster(clusterId)
+    const questions = sqlite.getQuestions(clusterId)
+    sqlite.close()
+
+    if (!cluster) {
+      return NextResponse.json({ error: 'Cluster not found' }, { status: 404 })
+    }
+
+    // fetch actual discussions from qdrant if we have sample point IDs
+    let samples: any[] = []
+    if (cluster.samplePointIds && cluster.samplePointIds.length > 0) {
+      try {
+        const qdrant = new QdrantStorage()
+        samples = await qdrant.getPointsByIds(cluster.samplePointIds)
+      } catch (e) {
+        console.error('Failed to fetch samples from qdrant:', e)
+      }
+    }
+
+    return NextResponse.json({ cluster, questions, samples })
+  } catch (error) {
+    console.error('Get cluster error:', error)
+    return NextResponse.json({ error: 'Failed to get cluster' }, { status: 500 })
+  }
+}
--- a/packages/web/src/app/api/clusters/route.ts
+++ b/packages/web/src/app/api/clusters/route.ts
@ -0,0 +1,108 @@
+import { NextResponse } from 'next/server'
+import {
+  QdrantStorage,
+  SQLiteStorage,
+  ClusteringPipeline,
+  ProblemSummarizer,
+  QuestionExtractor,
+} from '@rta/core'
+
+import { existsSync, readFileSync } from 'fs'
+import { join } from 'path'
+
+const PROJECT_ROOT = '/mnt/work/dev/personal-projects/reddit-trend-analyzer'
+const SETTINGS_FILE = join(PROJECT_ROOT, 'data', 'settings.json')
+
+function getSettings() {
+  try {
+    if (existsSync(SETTINGS_FILE)) {
+      return JSON.parse(readFileSync(SETTINGS_FILE, 'utf-8'))
+    }
+  } catch {}
+  return { summarizationModel: 'llama3.2' }
+}
+
+export async function GET() {
+  try {
+    const sqlite = new SQLiteStorage()
+    const clusters = sqlite.getClusters()
+    sqlite.close()
+
+    return NextResponse.json({ clusters })
+  } catch (error) {
+    console.error('Get clusters error:', error)
+    return NextResponse.json({ clusters: [] })
+  }
+}
+
+export async function POST(request: Request) {
+  try {
+    const body = await request.json().catch(() => ({}))
+    const minClusterSize = body.minClusterSize ?? 2
+    const similarityThreshold = body.similarityThreshold ?? 0.5
+
+    const qdrant = new QdrantStorage()
+    const sqlite = new SQLiteStorage()
+
+    const clustering = new ClusteringPipeline(qdrant)
+    const result = await clustering.runClustering({
+      minClusterSize,
+      similarityThreshold,
+      samplesPerCluster: 10,
+    })
+
+    if (result.clusters.length === 0) {
+      sqlite.close()
+      return NextResponse.json({
+        success: true,
+        message: 'No clusters found',
+        clusters: [],
+      })
+    }
+
+    const settings = getSettings()
+    const summarizer = new ProblemSummarizer({ model: settings.summarizationModel })
+    const summaries = await summarizer.summarizeClusters(result.clusters)
+
+    sqlite.saveClusters(summaries)
+
+    const extractor = new QuestionExtractor()
+
+    // save questions from both Claude summaries and regex extraction
+    for (const summary of summaries) {
+      const regexQuestions = extractor.extractFromClusterPoints(
+        result.clusters.find(c => c.id === summary.clusterId)?.samples || [],
+        summary.clusterId
+      )
+
+      // add Claude's sample questions if available
+      const claudeQuestions = summary.sampleQuestions.map((q, i) => ({
+        id: `claude-${summary.clusterId}-${i}`,
+        text: q,
+        clusterId: summary.clusterId,
+        sourcePointId: 'claude-generated',
+        engagement: summary.totalEngagement,
+        addressed: false,
+      }))
+
+      const allQuestions = [...claudeQuestions, ...regexQuestions]
+      if (allQuestions.length > 0) {
+        sqlite.saveQuestions(allQuestions)
+      }
+    }
+
+    sqlite.close()
+
+    return NextResponse.json({
+      success: true,
+      clusters: summaries,
+      stats: result.stats,
+    })
+  } catch (error) {
+    console.error('Clustering error:', error)
+    return NextResponse.json(
+      { success: false, error: error instanceof Error ? error.message : 'Unknown error' },
+      { status: 500 }
+    )
+  }
+}
--- a/packages/web/src/app/api/clusters/similarity/route.ts
+++ b/packages/web/src/app/api/clusters/similarity/route.ts
@ -0,0 +1,23 @@
+import { NextResponse } from 'next/server'
+import { SQLiteStorage, computeKeywordSimilarity } from '@rta/core'
+
+export async function GET() {
+  try {
+    const sqlite = new SQLiteStorage()
+    const clusters = sqlite.getClusters()
+    sqlite.close()
+
+    if (clusters.length === 0) {
+      return NextResponse.json({ matrix: [], labels: [], clusterIds: [] })
+    }
+
+    const similarity = computeKeywordSimilarity(clusters)
+    return NextResponse.json(similarity)
+  } catch (error) {
+    console.error('Similarity computation error:', error)
+    return NextResponse.json(
+      { error: error instanceof Error ? error.message : 'Unknown error' },
+      { status: 500 }
+    )
+  }
+}
--- a/packages/web/src/app/api/export/route.ts
+++ b/packages/web/src/app/api/export/route.ts
@ -0,0 +1,92 @@
+import { NextRequest, NextResponse } from 'next/server'
+import { SQLiteStorage } from '@rta/core'
+
+export async function POST(request: NextRequest) {
+  try {
+    const body = await request.json()
+    const { format, type } = body
+
+    const sqlite = new SQLiteStorage()
+    const clusters = sqlite.getClusters()
+    const questions = sqlite.getQuestions()
+    sqlite.close()
+
+    if (format === 'json' && type === 'faq-schema') {
+      const faqSchema = {
+        '@context': 'https://schema.org',
+        '@type': 'FAQPage',
+        mainEntity: questions
+          .filter(q => !q.addressed)
+          .slice(0, 20)
+          .map(q => ({
+            '@type': 'Question',
+            name: q.text,
+            acceptedAnswer: {
+              '@type': 'Answer',
+              text: '', // To be filled in
+            },
+          })),
+      }
+
+      return NextResponse.json(faqSchema)
+    }
+
+    if (format === 'markdown' && type === 'content-brief') {
+      let markdown = '# Content Brief\n\n'
+
+      for (const cluster of clusters) {
+        markdown += `## ${cluster.problem}\n\n`
+        markdown += `${cluster.description}\n\n`
+        markdown += `**Keywords:** ${cluster.keywords.join(', ')}\n\n`
+        markdown += `**Actionable Insight:** ${cluster.actionableInsight}\n\n`
+
+        const clusterQuestions = questions.filter(q => q.clusterId === cluster.clusterId)
+        if (clusterQuestions.length > 0) {
+          markdown += `### Questions to Address\n\n`
+          for (const q of clusterQuestions.slice(0, 5)) {
+            markdown += `- ${q.text}\n`
+          }
+          markdown += '\n'
+        }
+
+        markdown += '---\n\n'
+      }
+
+      return new NextResponse(markdown, {
+        headers: {
+          'Content-Type': 'text/markdown',
+          'Content-Disposition': 'attachment; filename="content-brief.md"',
+        },
+      })
+    }
+
+    if (format === 'csv') {
+      const headers = ['Problem', 'Description', 'Size', 'Engagement', 'Keywords', 'Subreddits']
+      const rows = clusters.map(c => [
+        `"${c.problem.replace(/"/g, '""')}"`,
+        `"${c.description.replace(/"/g, '""')}"`,
+        c.size,
+        c.totalEngagement,
+        `"${c.keywords.join(', ')}"`,
+        `"${c.subreddits.join(', ')}"`,
+      ])
+
+      const csv = [headers.join(','), ...rows.map(r => r.join(','))].join('\n')
+
+      return new NextResponse(csv, {
+        headers: {
+          'Content-Type': 'text/csv',
+          'Content-Disposition': 'attachment; filename="clusters.csv"',
+        },
+      })
+    }
+
+    return NextResponse.json({ clusters, questions })
+  } catch (error) {
+    console.error('Export error:', error)
+    return NextResponse.json(
+      { error: error instanceof Error ? error.message : 'Export failed' },
+      { status: 500 }
+    )
+  }
+}
--- a/packages/web/src/app/api/questions/[id]/route.ts
+++ b/packages/web/src/app/api/questions/[id]/route.ts
@ -0,0 +1,22 @@
+import { NextRequest, NextResponse } from 'next/server'
+import { SQLiteStorage } from '@rta/core'
+
+export async function PATCH(
+  request: NextRequest,
+  { params }: { params: Promise<{ id: string }> }
+) {
+  try {
+    const { id } = await params
+    const body = await request.json()
+    const { addressed } = body
+
+    const sqlite = new SQLiteStorage()
+    sqlite.markQuestionAddressed(id, addressed)
+    sqlite.close()
+
+    return NextResponse.json({ success: true })
+  } catch (error) {
+    console.error('Update question error:', error)
+    return NextResponse.json({ error: 'Failed to update question' }, { status: 500 })
+  }
+}
--- a/packages/web/src/app/api/questions/route.ts
+++ b/packages/web/src/app/api/questions/route.ts
@ -0,0 +1,25 @@
+import { NextResponse } from 'next/server'
+import { SQLiteStorage } from '@rta/core'
+
+export async function GET() {
+  try {
+    const sqlite = new SQLiteStorage()
+    const questions = sqlite.getQuestions()
+    const clusters = sqlite.getClusters()
+    sqlite.close()
+
+    const grouped: Record<string, { problem: string; questions: typeof questions }> = {}
+
+    for (const cluster of clusters) {
+      grouped[cluster.clusterId] = {
+        problem: cluster.problem,
+        questions: questions.filter(q => q.clusterId === cluster.clusterId),
+      }
+    }
+
+    return NextResponse.json({ grouped })
+  } catch (error) {
+    console.error('Get questions error:', error)
+    return NextResponse.json({ grouped: {} })
+  }
+}
--- a/packages/web/src/app/api/scrape/history/route.ts
+++ b/packages/web/src/app/api/scrape/history/route.ts
@ -0,0 +1,15 @@
+import { NextResponse } from 'next/server'
+import { SQLiteStorage } from '@rta/core'
+
+export async function GET() {
+  try {
+    const sqlite = new SQLiteStorage()
+    const history = sqlite.getScrapeHistory()
+    sqlite.close()
+
+    return NextResponse.json({ history })
+  } catch (error) {
+    console.error('Scrape history error:', error)
+    return NextResponse.json({ history: [] })
+  }
+}
--- a/packages/web/src/app/api/scrape/route.ts
+++ b/packages/web/src/app/api/scrape/route.ts
@ -0,0 +1,68 @@
+import { NextRequest, NextResponse } from 'next/server'
+import {
+  RedditScraper,
+  CommentFetcher,
+  EmbeddingPipeline,
+  QdrantStorage,
+  SQLiteStorage,
+  parseSubredditFromUrl,
+} from '@rta/core'
+import type { RedditComment } from '@rta/core'
+
+export async function POST(request: NextRequest) {
+  try {
+    const body = await request.json()
+    const { url, pages = 5, postsPerPage = 100 } = body
+
+    if (!url) {
+      return NextResponse.json({ success: false, error: 'URL is required' }, { status: 400 })
+    }
+
+    const subreddit = parseSubredditFromUrl(url)
+    const sqlite = new SQLiteStorage()
+    const scrapeId = sqlite.startScrape(subreddit, url)
+
+    const scraper = new RedditScraper(3000)
+    const commentFetcher = new CommentFetcher(3000)
+    const embeddings = new EmbeddingPipeline()
+    const storage = new QdrantStorage()
+
+    await storage.ensureCollection()
+
+    const posts = await scraper.fetchPosts({
+      url,
+      pages,
+      postsPerPage,
+      fetchComments: true,
+      delayMs: 3000,
+    })
+
+    const commentsByPost = await commentFetcher.fetchAllComments(posts)
+
+    const postPoints = await embeddings.embedPosts(posts, commentsByPost)
+    await storage.upsertPoints(postPoints)
+
+    const allComments: RedditComment[] = []
+    for (const comments of commentsByPost.values()) {
+      allComments.push(...comments)
+    }
+
+    const commentPoints = await embeddings.embedComments(allComments)
+    await storage.upsertPoints(commentPoints)
+
+    sqlite.completeScrape(scrapeId, posts.length, allComments.length)
+    sqlite.close()
+
+    return NextResponse.json({
+      success: true,
+      posts: posts.length,
+      comments: allComments.length,
+    })
+  } catch (error) {
+    console.error('Scrape error:', error)
+    return NextResponse.json(
+      { success: false, error: error instanceof Error ? error.message : 'Unknown error' },
+      { status: 500 }
+    )
+  }
+}
--- a/packages/web/src/app/api/search/route.ts
+++ b/packages/web/src/app/api/search/route.ts
@ -0,0 +1,29 @@
+import { NextRequest, NextResponse } from 'next/server'
+import { EmbeddingPipeline, QdrantStorage } from '@rta/core'
+
+export async function POST(request: NextRequest) {
+  try {
+    const body = await request.json()
+    const { query, limit = 10, threshold = 0.5 } = body
+
+    if (!query) {
+      return NextResponse.json({ error: 'Query is required' }, { status: 400 })
+    }
+
+    const embeddings = new EmbeddingPipeline()
+    const storage = new QdrantStorage()
+
+    const vector = await embeddings.embed(query)
+    const results = await storage.search(vector, limit)
+
+    const filtered = results.filter(r => r.score >= threshold)
+
+    return NextResponse.json({ results: filtered })
+  } catch (error) {
+    console.error('Search error:', error)
+    return NextResponse.json(
+      { error: error instanceof Error ? error.message : 'Search failed' },
+      { status: 500 }
+    )
+  }
+}
--- a/packages/web/src/app/api/settings/route.ts
+++ b/packages/web/src/app/api/settings/route.ts
@ -0,0 +1,92 @@
+import { NextResponse } from 'next/server'
+import { Ollama } from 'ollama'
+import { existsSync, mkdirSync, readFileSync, writeFileSync } from 'fs'
+import { join } from 'path'
+
+const PROJECT_ROOT = '/mnt/work/dev/personal-projects/reddit-trend-analyzer'
+const SETTINGS_FILE = join(PROJECT_ROOT, 'data', 'settings.json')
+
+interface Settings {
+  summarizationModel: string
+  sentimentModel: string
+  embeddingModel: string
+}
+
+const DEFAULT_SETTINGS: Settings = {
+  summarizationModel: 'llama3.2',
+  sentimentModel: 'llama3.2',
+  embeddingModel: 'nomic-embed-text',
+}
+
+function getSettings(): Settings {
+  try {
+    if (existsSync(SETTINGS_FILE)) {
+      const data = JSON.parse(readFileSync(SETTINGS_FILE, 'utf-8'))
+      return { ...DEFAULT_SETTINGS, ...data }
+    }
+  } catch {}
+  return DEFAULT_SETTINGS
+}
+
+function saveSettings(settings: Settings): void {
+  const dataDir = join(PROJECT_ROOT, 'data')
+  if (!existsSync(dataDir)) {
+    mkdirSync(dataDir, { recursive: true })
+  }
+  writeFileSync(SETTINGS_FILE, JSON.stringify(settings, null, 2))
+}
+
+export async function GET() {
+  try {
+    const ollama = new Ollama({
+      host: process.env.OLLAMA_HOST || 'http://localhost:11434',
+    })
+
+    const models = await ollama.list()
+    const settings = getSettings()
+
+    const availableModels = models.models.map(m => ({
+      name: m.name,
+      size: m.size,
+      modified: m.modified_at,
+    }))
+
+    return NextResponse.json({
+      settings,
+      availableModels,
+    })
+  } catch (error) {
+    console.error('Settings GET error:', error)
+    return NextResponse.json({
+      settings: DEFAULT_SETTINGS,
+      availableModels: [],
+      error: 'Failed to connect to Ollama',
+    })
+  }
+}
+
+export async function POST(request: Request) {
+  try {
+    const body = await request.json()
+    const currentSettings = getSettings()
+
+    const newSettings: Settings = {
+      summarizationModel: body.summarizationModel ?? currentSettings.summarizationModel,
+      sentimentModel: body.sentimentModel ?? currentSettings.sentimentModel,
+      embeddingModel: body.embeddingModel ?? currentSettings.embeddingModel,
+    }
+
+    saveSettings(newSettings)
+
+    return NextResponse.json({
+      success: true,
+      settings: newSettings,
+    })
+  } catch (error) {
+    console.error('Settings POST error:', error)
+    return NextResponse.json(
+      { success: false, error: 'Failed to save settings' },
+      { status: 500 }
+    )
+  }
+}
--- a/packages/web/src/app/api/stats/route.ts
+++ b/packages/web/src/app/api/stats/route.ts
@ -0,0 +1,30 @@
+import { NextResponse } from 'next/server'
+import { QdrantStorage, SQLiteStorage } from '@rta/core'
+
+export async function GET() {
+  try {
+    const qdrant = new QdrantStorage()
+    const sqlite = new SQLiteStorage()
+
+    const qdrantStats = await qdrant.getStats()
+    const sqliteStats = sqlite.getStats()
+    sqlite.close()
+
+    return NextResponse.json({
+      posts: qdrantStats.posts,
+      comments: qdrantStats.comments,
+      subreddits: qdrantStats.subreddits,
+      clusters: sqliteStats.clusterCount,
+      questions: sqliteStats.questionCount,
+    })
+  } catch (error) {
+    console.error('Stats error:', error)
+    return NextResponse.json({
+      posts: 0,
+      comments: 0,
+      subreddits: [],
+      clusters: 0,
+      questions: 0,
+    })
+  }
+}
--- a/packages/web/src/app/layout.tsx
+++ b/packages/web/src/app/layout.tsx
@ -0,0 +1,68 @@
+import type { Metadata } from 'next'
+import '@/styles/globals.css'
+import { Providers } from './providers'
+
+export const metadata: Metadata = {
+  title: 'Reddit Trend Analyzer',
+  description: 'Discover common problems and questions in Reddit communities',
+}
+
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode
+}) {
+  return (
+    <html lang="en">
+      <body className="min-h-screen bg-background antialiased">
+        <Providers>
+          <div className="flex min-h-screen">
+            <aside className="w-64 border-r border-border bg-sidebar p-4">
+              <div className="mb-6">
+                <h1 className="text-lg font-semibold text-sidebar-foreground">
+                  Reddit Trends
+                </h1>
+                <p className="text-xs text-muted-foreground mt-1">
+                  Press <kbd className="rounded bg-muted px-1 py-0.5">Ctrl+K</kbd> for commands
+                </p>
+              </div>
+              <nav className="space-y-2">
+                <a
+                  href="/"
+                  className="block rounded-lg px-3 py-2 text-sm font-medium text-sidebar-foreground hover:bg-sidebar-accent"
+                >
+                  Dashboard
+                </a>
+                <a
+                  href="/problems"
+                  className="block rounded-lg px-3 py-2 text-sm font-medium text-sidebar-foreground hover:bg-sidebar-accent"
+                >
+                  Problems
+                </a>
+                <a
+                  href="/questions"
+                  className="block rounded-lg px-3 py-2 text-sm font-medium text-sidebar-foreground hover:bg-sidebar-accent"
+                >
+                  Questions
+                </a>
+                <a
+                  href="/scrape"
+                  className="block rounded-lg px-3 py-2 text-sm font-medium text-sidebar-foreground hover:bg-sidebar-accent"
+                >
+                  Scrape
+                </a>
+                <a
+                  href="/settings"
+                  className="block rounded-lg px-3 py-2 text-sm font-medium text-sidebar-foreground hover:bg-sidebar-accent"
+                >
+                  Settings
+                </a>
+              </nav>
+            </aside>
+            <main className="flex-1 p-6">{children}</main>
+          </div>
+        </Providers>
+      </body>
+    </html>
+  )
+}
--- a/packages/web/src/app/page.tsx
+++ b/packages/web/src/app/page.tsx
@ -0,0 +1,86 @@
+'use client'
+
+import { useEffect, useState } from 'react'
+
+interface Stats {
+  posts: number
+  comments: number
+  subreddits: string[]
+  clusters: number
+  questions: number
+}
+
+export default function DashboardPage() {
+  const [stats, setStats] = useState<Stats | null>(null)
+  const [loading, setLoading] = useState(true)
+
+  useEffect(() => {
+    fetch('/api/stats')
+      .then(res => res.json())
+      .then(data => {
+        setStats(data)
+        setLoading(false)
+      })
+      .catch(() => setLoading(false))
+  }, [])
+
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="text-muted-foreground">Loading...</div>
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-6">
+      <div>
+        <h1 className="text-2xl font-semibold text-foreground">Dashboard</h1>
+        <p className="text-muted-foreground">Overview of your Reddit trend analysis</p>
+      </div>
+
+      <div className="grid grid-cols-4 gap-4">
+        <div className="rounded-lg border border-border bg-card p-4 shadow-sm">
+          <div className="text-sm text-muted-foreground">Posts</div>
+          <div className="text-2xl font-semibold text-foreground">
+            {stats?.posts.toLocaleString() ?? 0}
+          </div>
+        </div>
+        <div className="rounded-lg border border-border bg-card p-4 shadow-sm">
+          <div className="text-sm text-muted-foreground">Comments</div>
+          <div className="text-2xl font-semibold text-foreground">
+            {stats?.comments.toLocaleString() ?? 0}
+          </div>
+        </div>
+        <div className="rounded-lg border border-border bg-card p-4 shadow-sm">
+          <div className="text-sm text-muted-foreground">Subreddits</div>
+          <div className="text-2xl font-semibold text-foreground">
+            {stats?.subreddits.length ?? 0}
+          </div>
+        </div>
+        <div className="rounded-lg border border-border bg-card p-4 shadow-sm">
+          <div className="text-sm text-muted-foreground">Clusters</div>
+          <div className="text-2xl font-semibold text-foreground">
+            {stats?.clusters ?? 0}
+          </div>
+        </div>
+      </div>
+
+      {stats && stats.subreddits.length > 0 && (
+        <div className="rounded-lg border border-border bg-card p-4 shadow-sm">
+          <div className="text-sm text-muted-foreground mb-2">Tracked Subreddits</div>
+          <div className="flex flex-wrap gap-2">
+            {stats.subreddits.map(sub => (
+              <span
+                key={sub}
+                className="rounded-full bg-secondary px-3 py-1 text-sm text-secondary-foreground"
+              >
+                r/{sub}
+              </span>
+            ))}
+          </div>
+        </div>
+      )}
+    </div>
+  )
+}
--- a/packages/web/src/app/problems/page.tsx
+++ b/packages/web/src/app/problems/page.tsx
@ -0,0 +1,816 @@
+'use client'
+
+import React, { useEffect, useState, useMemo, useCallback } from 'react'
+import { useToast } from '@/components/ui/toast'
+import {
+  BarChart,
+  Bar,
+  PieChart,
+  Pie,
+  Cell,
+  XAxis,
+  YAxis,
+  CartesianGrid,
+  Tooltip,
+  Legend,
+  ResponsiveContainer,
+} from 'recharts'
+
+interface Problem {
+  clusterId: number
+  problem: string
+  description: string
+  size: number
+  totalEngagement: number
+  lastActive: number
+  subreddits: string[]
+  sampleQuestions: string[]
+  impactScore?: number
+}
+
+interface DiscussionSample {
+  id: string
+  type: 'post' | 'comment'
+  subreddit: string
+  title?: string
+  author: string
+  body: string
+  score: number
+  created: number
+  permalink: string
+  parent_id?: string
+}
+
+function DiscussionSamples({ clusterId }: { clusterId: number }) {
+  const [samples, setSamples] = useState<DiscussionSample[]>([])
+  const [loading, setLoading] = useState(true)
+  const [expandedBodies, setExpandedBodies] = useState<Set<string>>(new Set())
+
+  useEffect(() => {
+    fetch(`/api/clusters/${clusterId}`)
+      .then(res => res.json())
+      .then(data => {
+        setSamples(data.samples || [])
+        setLoading(false)
+      })
+      .catch(() => setLoading(false))
+  }, [clusterId])
+
+  const toggleBody = useCallback((id: string) => {
+    setExpandedBodies(prev => {
+      const next = new Set(prev)
+      if (next.has(id)) {
+        next.delete(id)
+      } else {
+        next.add(id)
+      }
+      return next
+    })
+  }, [])
+
+  const formatDate = (timestamp: number) => {
+    const date = new Date(timestamp * 1000)
+    return date.toLocaleDateString()
+  }
+
+  if (loading) {
+    return (
+      <div className="text-sm text-muted-foreground py-2">
+        Loading discussions...
+      </div>
+    )
+  }
+
+  if (samples.length === 0) {
+    return (
+      <div className="text-sm text-muted-foreground py-2">
+        No discussion samples available. Re-run clustering to populate.
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-3">
+      <div className="text-sm font-medium text-muted-foreground">
+        Discussion Samples ({samples.length})
+      </div>
+      {samples.map(sample => {
+        const isExpanded = expandedBodies.has(sample.id)
+        const bodyTruncated = sample.body.length > 300
+        const displayBody = isExpanded ? sample.body : sample.body.slice(0, 300)
+
+        return (
+          <div
+            key={sample.id}
+            className="rounded-lg border border-border bg-background p-3 space-y-2"
+          >
+            <div className="flex items-start justify-between gap-2">
+              <div className="flex items-center gap-2">
+                <span
+                  className={`inline-flex items-center rounded px-1.5 py-0.5 text-xs font-medium ${
+                    sample.type === 'post'
+                      ? 'bg-blue-500/10 text-blue-500'
+                      : 'bg-green-500/10 text-green-500'
+                  }`}
+                >
+                  {sample.type}
+                </span>
+                <span className="text-xs text-muted-foreground">
+                  r/{sample.subreddit}
+                </span>
+                <span className="text-xs text-muted-foreground">•</span>
+                <span className="text-xs text-muted-foreground">
+                  u/{sample.author}
+                </span>
+                <span className="text-xs text-muted-foreground">•</span>
+                <span className="text-xs text-muted-foreground">
+                  {sample.score} pts
+                </span>
+                <span className="text-xs text-muted-foreground">•</span>
+                <span className="text-xs text-muted-foreground">
+                  {formatDate(sample.created)}
+                </span>
+              </div>
+              <a
+                href={`https://reddit.com${sample.permalink}`}
+                target="_blank"
+                rel="noopener noreferrer"
+                className="text-xs text-primary hover:underline shrink-0"
+              >
+                view on reddit →
+              </a>
+            </div>
+
+            {sample.title && (
+              <div className="font-medium text-foreground">{sample.title}</div>
+            )}
+
+            <div className="text-sm text-foreground whitespace-pre-wrap">
+              {displayBody}
+              {bodyTruncated && !isExpanded && '...'}
+            </div>
+
+            {bodyTruncated && (
+              <button
+                onClick={() => toggleBody(sample.id)}
+                className="text-xs text-primary hover:underline"
+              >
+                {isExpanded ? 'show less' : 'show more'}
+              </button>
+            )}
+          </div>
+        )
+      })}
+    </div>
+  )
+}
+
+interface Weights {
+  engagement: number
+  velocity: number
+  sentiment: number
+}
+
+interface SimilarityData {
+  matrix: number[][]
+  labels: string[]
+  clusterIds: number[]
+}
+
+function CorrelationHeatmap({
+  onCellClick,
+}: {
+  onCellClick?: (clusterIds: [number, number]) => void
+}) {
+  const [data, setData] = useState<SimilarityData | null>(null)
+  const [loading, setLoading] = useState(true)
+  const [hoveredCell, setHoveredCell] = useState<{ i: number; j: number } | null>(null)
+
+  useEffect(() => {
+    fetch('/api/clusters/similarity')
+      .then(res => res.json())
+      .then(d => {
+        setData(d)
+        setLoading(false)
+      })
+      .catch(() => setLoading(false))
+  }, [])
+
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="text-muted-foreground">Loading correlation data...</div>
+      </div>
+    )
+  }
+
+  if (!data || data.matrix.length === 0) {
+    return (
+      <div className="flex items-center justify-center h-32">
+        <div className="text-sm text-muted-foreground">No clusters to compare</div>
+      </div>
+    )
+  }
+
+  const getColor = (value: number) => {
+    // white (0) -> indigo (1)
+    const intensity = Math.round(value * 255)
+    return `rgb(${255 - intensity * 0.6}, ${255 - intensity * 0.62}, ${255 - intensity * 0.05})`
+  }
+
+  const n = data.matrix.length
+  const cellSize = Math.max(24, Math.min(40, 400 / n))
+
+  return (
+    <div className="space-y-3">
+      <div className="overflow-x-auto">
+        <div
+          className="inline-grid gap-px bg-border"
+          style={{
+            gridTemplateColumns: `auto repeat(${n}, ${cellSize}px)`,
+            gridTemplateRows: `auto repeat(${n}, ${cellSize}px)`,
+          }}
+        >
+          {/* empty corner cell */}
+          <div className="bg-card" />
+
+          {/* column headers */}
+          {data.labels.map((label, j) => (
+            <div
+              key={`col-${j}`}
+              className="bg-card flex items-end justify-center pb-1 px-1"
+              style={{ height: cellSize * 2 }}
+            >
+              <span
+                className="text-[10px] text-muted-foreground origin-bottom-left whitespace-nowrap overflow-hidden text-ellipsis"
+                style={{
+                  transform: 'rotate(-45deg)',
+                  maxWidth: cellSize * 2,
+                }}
+                title={label}
+              >
+                {label.slice(0, 15)}
+              </span>
+            </div>
+          ))}
+
+          {/* rows */}
+          {data.matrix.map((row, i) => (
+            <React.Fragment key={`row-${i}`}>
+              {/* row label */}
+              <div
+                className="bg-card flex items-center justify-end pr-2"
+                style={{ width: 100 }}
+              >
+                <span
+                  className="text-[10px] text-muted-foreground truncate"
+                  title={data.labels[i]}
+                >
+                  {data.labels[i].slice(0, 12)}
+                </span>
+              </div>
+
+              {/* cells */}
+              {row.map((value, j) => {
+                const isHovered = hoveredCell?.i === i && hoveredCell?.j === j
+                return (
+                  <div
+                    key={`cell-${i}-${j}`}
+                    className="relative cursor-pointer transition-all"
+                    style={{
+                      backgroundColor: getColor(value),
+                      width: cellSize,
+                      height: cellSize,
+                      outline: isHovered ? '2px solid #6366f1' : 'none',
+                      outlineOffset: '-1px',
+                    }}
+                    onMouseEnter={() => setHoveredCell({ i, j })}
+                    onMouseLeave={() => setHoveredCell(null)}
+                    onClick={() => onCellClick?.([data.clusterIds[i], data.clusterIds[j]])}
+                    title={`${data.labels[i]} ↔ ${data.labels[j]}: ${(value * 100).toFixed(0)}%`}
+                  >
+                    {isHovered && (
+                      <div className="absolute z-10 bg-card border border-border rounded px-2 py-1 text-xs shadow-lg whitespace-nowrap -translate-x-1/2 left-1/2 -top-8">
+                        {(value * 100).toFixed(0)}% similar
+                      </div>
+                    )}
+                  </div>
+                )
+              })}
+            </React.Fragment>
+          ))}
+        </div>
+      </div>
+
+      {/* legend */}
+      <div className="flex items-center gap-2 text-xs text-muted-foreground">
+        <span>0%</span>
+        <div
+          className="h-3 w-32 rounded"
+          style={{
+            background: 'linear-gradient(to right, white, #6366f1)',
+          }}
+        />
+        <span>100%</span>
+        <span className="ml-2">keyword overlap</span>
+      </div>
+    </div>
+  )
+}
+
+// chart colors - recharts can't parse CSS variables at runtime
+const CHART_COLORS = {
+  primary: '#6366f1',      // indigo
+  secondary: '#8b5cf6',    // violet
+  accent: '#06b6d4',       // cyan
+  muted: '#94a3b8',        // slate
+  grid: '#e2e8f0',         // light grid
+  text: '#64748b',         // muted text
+  palette: ['#6366f1', '#8b5cf6', '#06b6d4', '#10b981', '#f59e0b', '#ef4444', '#ec4899', '#84cc16'],
+}
+
+export default function ProblemsPage() {
+  const [problems, setProblems] = useState<Problem[]>([])
+  const [loading, setLoading] = useState(true)
+  const [expanded, setExpanded] = useState<number | null>(null)
+  const [clustering, setClustering] = useState(false)
+  const { addToast, updateToast } = useToast()
+
+  const [similarityThreshold, setSimilarityThreshold] = useState(0.5)
+  const [minClusterSize, setMinClusterSize] = useState(2)
+  const [weights, setWeights] = useState<Weights>({
+    engagement: 0.5,
+    velocity: 0.3,
+    sentiment: 0.2,
+  })
+
+  const fetchClusters = () => {
+    fetch('/api/clusters')
+      .then(res => res.json())
+      .then(data => {
+        setProblems(data.clusters || [])
+        setLoading(false)
+      })
+      .catch(() => setLoading(false))
+  }
+
+  useEffect(() => {
+    fetchClusters()
+  }, [])
+
+  const sortedProblems = useMemo(() => {
+    if (problems.length === 0) return []
+
+    const now = Math.floor(Date.now() / 1000)
+    const oneWeek = 7 * 24 * 60 * 60
+    const maxEngagement = Math.max(...problems.map(p => p.totalEngagement))
+
+    return [...problems]
+      .map(p => {
+        const engagementScore = maxEngagement > 0 ? p.totalEngagement / maxEngagement : 0
+        const age = now - p.lastActive
+        const velocityScore = Math.max(0, 1 - age / oneWeek)
+        const sentimentScore = 0.5
+
+        const impactScore =
+          weights.engagement * engagementScore +
+          weights.velocity * velocityScore +
+          weights.sentiment * sentimentScore
+
+        return { ...p, impactScore }
+      })
+      .sort((a, b) => (b.impactScore || 0) - (a.impactScore || 0))
+  }, [problems, weights])
+
+  const chartData = useMemo(() => {
+    if (sortedProblems.length === 0) return { impact: [], subreddits: [], sizes: [] }
+
+    const impactData = sortedProblems.slice(0, 10).map(p => ({
+      name: p.problem.slice(0, 30) + (p.problem.length > 30 ? '...' : ''),
+      impact: Math.round((p.impactScore || 0) * 100),
+      engagement: p.totalEngagement,
+      discussions: p.size,
+    }))
+
+    const subredditCounts = new Map<string, number>()
+    sortedProblems.forEach(p => {
+      p.subreddits.forEach(sub => {
+        subredditCounts.set(sub, (subredditCounts.get(sub) || 0) + 1)
+      })
+    })
+    const subredditData = Array.from(subredditCounts.entries())
+      .map(([name, value]) => ({ name: `r/${name}`, value }))
+      .sort((a, b) => b.value - a.value)
+      .slice(0, 8)
+
+    const sizeDistribution = sortedProblems.reduce((acc, p) => {
+      const bucket =
+        p.size < 5 ? '2-4' : p.size < 10 ? '5-9' : p.size < 20 ? '10-19' : '20+'
+      acc[bucket] = (acc[bucket] || 0) + 1
+      return acc
+    }, {} as Record<string, number>)
+    const sizeData = Object.entries(sizeDistribution).map(([name, value]) => ({
+      name: `${name} discussions`,
+      value,
+    }))
+
+    return { impact: impactData, subreddits: subredditData, sizes: sizeData }
+  }, [sortedProblems])
+
+  const handleRecluster = async () => {
+    setClustering(true)
+    const toastId = addToast('Clustering discussions...', 'loading')
+    try {
+      const res = await fetch('/api/clusters', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ similarityThreshold, minClusterSize }),
+      })
+      const data = await res.json()
+      if (data.success) {
+        updateToast(toastId, `Found ${data.clusters?.length || 0} problem clusters`, 'success')
+        fetchClusters()
+      } else {
+        updateToast(toastId, data.error || 'Clustering failed', 'error')
+      }
+    } catch (e) {
+      updateToast(toastId, 'Clustering failed - check console', 'error')
+    } finally {
+      setClustering(false)
+    }
+  }
+
+  const updateWeight = (key: keyof Weights, value: number) => {
+    setWeights(prev => ({ ...prev, [key]: value }))
+  }
+
+  const formatDate = (timestamp: number) => {
+    const date = new Date(timestamp * 1000)
+    return date.toLocaleDateString()
+  }
+
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="text-muted-foreground">Loading...</div>
+      </div>
+    )
+  }
+
+  const clusterControls = (
+    <div className="rounded-lg border border-border bg-card p-4 space-y-4">
+      <div className="text-sm font-medium text-foreground">Clustering Settings</div>
+      <div className="grid grid-cols-2 gap-4">
+        <div>
+          <label className="block text-xs text-muted-foreground mb-1">
+            Similarity Threshold: {similarityThreshold.toFixed(2)}
+          </label>
+          <input
+            type="range"
+            min="0.3"
+            max="0.9"
+            step="0.05"
+            value={similarityThreshold}
+            onChange={e => setSimilarityThreshold(parseFloat(e.target.value))}
+            className="w-full accent-primary"
+          />
+          <div className="flex justify-between text-xs text-muted-foreground">
+            <span>Loose (0.3)</span>
+            <span>Strict (0.9)</span>
+          </div>
+        </div>
+        <div>
+          <label className="block text-xs text-muted-foreground mb-1">
+            Min Cluster Size
+          </label>
+          <select
+            value={minClusterSize}
+            onChange={e => setMinClusterSize(parseInt(e.target.value))}
+            className="w-full rounded border border-border bg-background px-2 py-1 text-sm text-foreground"
+          >
+            <option value={2}>2 discussions</option>
+            <option value={3}>3 discussions</option>
+            <option value={5}>5 discussions</option>
+            <option value={10}>10 discussions</option>
+          </select>
+        </div>
+      </div>
+    </div>
+  )
+
+  const weightControls = (
+    <div className="rounded-lg border border-border bg-card p-4 space-y-4">
+      <div className="text-sm font-medium text-foreground">Impact Score Weights</div>
+      <div className="grid grid-cols-3 gap-4">
+        <div>
+          <label className="block text-xs text-muted-foreground mb-1">
+            Engagement: {(weights.engagement * 100).toFixed(0)}%
+          </label>
+          <input
+            type="range"
+            min="0"
+            max="1"
+            step="0.1"
+            value={weights.engagement}
+            onChange={e => updateWeight('engagement', parseFloat(e.target.value))}
+            className="w-full accent-primary"
+          />
+        </div>
+        <div>
+          <label className="block text-xs text-muted-foreground mb-1">
+            Velocity: {(weights.velocity * 100).toFixed(0)}%
+          </label>
+          <input
+            type="range"
+            min="0"
+            max="1"
+            step="0.1"
+            value={weights.velocity}
+            onChange={e => updateWeight('velocity', parseFloat(e.target.value))}
+            className="w-full accent-primary"
+          />
+        </div>
+        <div>
+          <label className="block text-xs text-muted-foreground mb-1">
+            Sentiment: {(weights.sentiment * 100).toFixed(0)}%
+          </label>
+          <input
+            type="range"
+            min="0"
+            max="1"
+            step="0.1"
+            value={weights.sentiment}
+            onChange={e => updateWeight('sentiment', parseFloat(e.target.value))}
+            className="w-full accent-primary"
+          />
+        </div>
+      </div>
+    </div>
+  )
+
+  if (problems.length === 0) {
+    return (
+      <div className="space-y-6">
+        <div>
+          <h1 className="text-2xl font-semibold text-foreground">Problem Explorer</h1>
+          <p className="text-muted-foreground">View and analyze problem clusters</p>
+        </div>
+        {clusterControls}
+        <div className="flex justify-end">
+          <button
+            onClick={handleRecluster}
+            disabled={clustering}
+            className="rounded-lg bg-primary px-4 py-2 text-sm font-medium text-primary-foreground hover:opacity-90 disabled:opacity-50"
+          >
+            {clustering ? 'Clustering...' : 'Run Clustering'}
+          </button>
+        </div>
+        <div className="rounded-lg border border-border bg-card p-8 text-center">
+          <p className="text-muted-foreground">No problems found. Adjust settings and run clustering.</p>
+        </div>
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-6">
+      <div className="flex items-center justify-between">
+        <div>
+          <h1 className="text-2xl font-semibold text-foreground">Problem Explorer</h1>
+          <p className="text-muted-foreground">
+            {sortedProblems.length} problem clusters identified
+          </p>
+        </div>
+        <button
+          onClick={handleRecluster}
+          disabled={clustering}
+          className="rounded-lg bg-primary px-4 py-2 text-sm font-medium text-primary-foreground hover:opacity-90 disabled:opacity-50"
+        >
+          {clustering ? 'Clustering...' : 'Re-cluster'}
+        </button>
+      </div>
+
+      <div className="grid grid-cols-2 gap-4">
+        {clusterControls}
+        {weightControls}
+      </div>
+
+      {sortedProblems.length > 0 && (
+        <div className="space-y-4">
+          <div className="text-lg font-medium text-foreground">Problem Analytics</div>
+          <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
+            <div className="rounded-lg border border-border bg-card p-4">
+              <div className="text-sm font-medium text-foreground mb-4">
+                Top Problems by Impact Score
+              </div>
+              <ResponsiveContainer width="100%" height={300}>
+                <BarChart data={chartData.impact}>
+                  <CartesianGrid strokeDasharray="3 3" stroke={CHART_COLORS.grid} />
+                  <XAxis
+                    dataKey="name"
+                    angle={-45}
+                    textAnchor="end"
+                    height={100}
+                    tick={{ fill: CHART_COLORS.text, fontSize: 11 }}
+                  />
+                  <YAxis tick={{ fill: CHART_COLORS.text }} />
+                  <Tooltip
+                    contentStyle={{
+                      backgroundColor: '#fff',
+                      border: `1px solid ${CHART_COLORS.grid}`,
+                      borderRadius: '6px',
+                    }}
+                  />
+                  <Bar dataKey="impact" fill={CHART_COLORS.primary} radius={[4, 4, 0, 0]} />
+                </BarChart>
+              </ResponsiveContainer>
+            </div>
+
+            <div className="rounded-lg border border-border bg-card p-4">
+              <div className="text-sm font-medium text-foreground mb-4">
+                Discussion Distribution by Subreddit
+              </div>
+              <ResponsiveContainer width="100%" height={300}>
+                <PieChart>
+                  <Pie
+                    data={chartData.subreddits}
+                    cx="50%"
+                    cy="50%"
+                    labelLine={false}
+                    label={({ name, percent }) =>
+                      `${name} (${(percent * 100).toFixed(0)}%)`
+                    }
+                    outerRadius={80}
+                    dataKey="value"
+                  >
+                    {chartData.subreddits.map((_, index) => (
+                      <Cell
+                        key={`cell-${index}`}
+                        fill={CHART_COLORS.palette[index % CHART_COLORS.palette.length]}
+                      />
+                    ))}
+                  </Pie>
+                  <Tooltip
+                    contentStyle={{
+                      backgroundColor: '#fff',
+                      border: `1px solid ${CHART_COLORS.grid}`,
+                      borderRadius: '6px',
+                    }}
+                  />
+                </PieChart>
+              </ResponsiveContainer>
+            </div>
+
+            <div className="rounded-lg border border-border bg-card p-4">
+              <div className="text-sm font-medium text-foreground mb-4">
+                Cluster Size Distribution
+              </div>
+              <ResponsiveContainer width="100%" height={250}>
+                <BarChart data={chartData.sizes}>
+                  <CartesianGrid strokeDasharray="3 3" stroke={CHART_COLORS.grid} />
+                  <XAxis
+                    dataKey="name"
+                    tick={{ fill: CHART_COLORS.text }}
+                  />
+                  <YAxis tick={{ fill: CHART_COLORS.text }} allowDecimals={false} />
+                  <Tooltip
+                    contentStyle={{
+                      backgroundColor: '#fff',
+                      border: `1px solid ${CHART_COLORS.grid}`,
+                      borderRadius: '6px',
+                    }}
+                  />
+                  <Bar dataKey="value" fill={CHART_COLORS.accent} radius={[4, 4, 0, 0]} />
+                </BarChart>
+              </ResponsiveContainer>
+            </div>
+
+            <div className="rounded-lg border border-border bg-card p-4">
+              <div className="text-sm font-medium text-foreground mb-4">Key Metrics</div>
+              <div className="space-y-4">
+                <div>
+                  <div className="text-2xl font-bold text-foreground">
+                    {sortedProblems.length}
+                  </div>
+                  <div className="text-xs text-muted-foreground">Total Clusters</div>
+                </div>
+                <div>
+                  <div className="text-2xl font-bold text-foreground">
+                    {sortedProblems.reduce((sum, p) => sum + p.size, 0).toLocaleString()}
+                  </div>
+                  <div className="text-xs text-muted-foreground">Total Discussions</div>
+                </div>
+                <div>
+                  <div className="text-2xl font-bold text-foreground">
+                    {sortedProblems
+                      .reduce((sum, p) => sum + p.totalEngagement, 0)
+                      .toLocaleString()}
+                  </div>
+                  <div className="text-xs text-muted-foreground">Total Upvotes</div>
+                </div>
+                <div>
+                  <div className="text-2xl font-bold text-foreground">
+                    {new Set(sortedProblems.flatMap(p => p.subreddits)).size}
+                  </div>
+                  <div className="text-xs text-muted-foreground">Unique Subreddits</div>
+                </div>
+              </div>
+            </div>
+          </div>
+
+          {/* correlation heatmap */}
+          <div className="rounded-lg border border-border bg-card p-4">
+            <div className="text-sm font-medium text-foreground mb-4">
+              Problem Cluster Correlation
+            </div>
+            <CorrelationHeatmap
+              onCellClick={([id1, id2]) => {
+                // expand first cluster that isn't already expanded
+                if (expanded !== id1) {
+                  setExpanded(id1)
+                } else if (expanded !== id2) {
+                  setExpanded(id2)
+                }
+              }}
+            />
+          </div>
+        </div>
+      )}
+
+      <div className="rounded-lg border border-border bg-card overflow-hidden">
+        <table className="w-full">
+          <thead className="bg-muted">
+            <tr>
+              <th className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">
+                Problem
+              </th>
+              <th className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">
+                Impact
+              </th>
+              <th className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">
+                Discussions
+              </th>
+              <th className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">
+                Upvotes
+              </th>
+              <th className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">
+                Last Active
+              </th>
+            </tr>
+          </thead>
+          <tbody>
+            {sortedProblems.map(problem => (
+              <React.Fragment key={problem.clusterId}>
+                <tr
+                  onClick={() => setExpanded(expanded === problem.clusterId ? null : problem.clusterId)}
+                  className="border-t border-border hover:bg-accent/50 cursor-pointer"
+                >
+                  <td className="px-4 py-3">
+                    <div className="font-medium text-foreground">{problem.problem}</div>
+                    <div className="text-sm text-muted-foreground">
+                      {problem.subreddits.map(s => `r/${s}`).join(', ')}
+                    </div>
+                  </td>
+                  <td className="px-4 py-3 text-foreground">
+                    <span className="inline-flex items-center rounded-full bg-primary/10 px-2 py-0.5 text-xs font-medium text-primary">
+                      {((problem.impactScore || 0) * 100).toFixed(0)}
+                    </span>
+                  </td>
+                  <td className="px-4 py-3 text-foreground">{problem.size}</td>
+                  <td className="px-4 py-3 text-foreground">
+                    {problem.totalEngagement.toLocaleString()}
+                  </td>
+                  <td className="px-4 py-3 text-foreground">{formatDate(problem.lastActive)}</td>
+                </tr>
+                {expanded === problem.clusterId && (
+                  <tr className="border-t border-border bg-muted/50">
+                    <td colSpan={5} className="px-4 py-4">
+                      <div className="space-y-4">
+                        <p className="text-foreground">{problem.description}</p>
+
+                        <DiscussionSamples clusterId={problem.clusterId} />
+
+                        {problem.sampleQuestions.length > 0 && (
+                          <div>
+                            <div className="text-sm font-medium text-muted-foreground mb-1">
+                              Sample Questions:
+                            </div>
+                            <ul className="list-disc list-inside text-sm text-foreground">
+                              {problem.sampleQuestions.map((q, i) => (
+                                <li key={i}>{q}</li>
+                              ))}
+                            </ul>
+                          </div>
+                        )}
+                      </div>
+                    </td>
+                  </tr>
+                )}
+              </React.Fragment>
+            ))}
+          </tbody>
+        </table>
+      </div>
+    </div>
+  )
+}
--- a/packages/web/src/app/providers.tsx
+++ b/packages/web/src/app/providers.tsx
@ -0,0 +1,14 @@
+'use client'
+
+import { ReactNode } from 'react'
+import { ToastProvider } from '@/components/ui/toast'
+import { CommandPalette } from '@/components/controls/command-palette'
+
+export function Providers({ children }: { children: ReactNode }) {
+  return (
+    <ToastProvider>
+      {children}
+      <CommandPalette />
+    </ToastProvider>
+  )
+}
--- a/packages/web/src/app/questions/page.tsx
+++ b/packages/web/src/app/questions/page.tsx
@ -0,0 +1,164 @@
+'use client'
+
+import { useEffect, useState } from 'react'
+
+interface Question {
+  id: string
+  text: string
+  clusterId: number
+  engagement: number
+  addressed: boolean
+}
+
+interface GroupedQuestions {
+  [clusterId: string]: {
+    problem: string
+    questions: Question[]
+  }
+}
+
+export default function QuestionsPage() {
+  const [grouped, setGrouped] = useState<GroupedQuestions>({})
+  const [loading, setLoading] = useState(true)
+  const [filter, setFilter] = useState<'all' | 'unanswered'>('all')
+
+  useEffect(() => {
+    fetch('/api/questions')
+      .then(res => res.json())
+      .then(data => {
+        setGrouped(data.grouped || {})
+        setLoading(false)
+      })
+      .catch(() => setLoading(false))
+  }, [])
+
+  const handleToggleAddressed = async (id: string, addressed: boolean) => {
+    await fetch(`/api/questions/${id}`, {
+      method: 'PATCH',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ addressed: !addressed }),
+    })
+
+    setGrouped(prev => {
+      const updated = { ...prev }
+      for (const clusterId of Object.keys(updated)) {
+        const cluster = updated[clusterId]
+        if (cluster) {
+          cluster.questions = cluster.questions.map(q =>
+            q.id === id ? { ...q, addressed: !addressed } : q
+          )
+        }
+      }
+      return updated
+    })
+  }
+
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="text-muted-foreground">Loading...</div>
+      </div>
+    )
+  }
+
+  const clusterIds = Object.keys(grouped)
+  const totalQuestions = clusterIds.reduce((sum, id) => {
+    return sum + (grouped[id]?.questions.length ?? 0)
+  }, 0)
+
+  if (totalQuestions === 0) {
+    return (
+      <div className="space-y-6">
+        <div>
+          <h1 className="text-2xl font-semibold text-foreground">Question Bank</h1>
+          <p className="text-muted-foreground">Extracted questions from discussions</p>
+        </div>
+        <div className="rounded-lg border border-border bg-card p-8 text-center">
+          <p className="text-muted-foreground">No questions found. Run clustering first.</p>
+        </div>
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-6">
+      <div className="flex items-center justify-between">
+        <div>
+          <h1 className="text-2xl font-semibold text-foreground">Question Bank</h1>
+          <p className="text-muted-foreground">{totalQuestions} questions extracted</p>
+        </div>
+        <div className="flex gap-2">
+          <button
+            onClick={() => setFilter('all')}
+            className={`rounded-lg px-4 py-2 text-sm font-medium ${
+              filter === 'all'
+                ? 'bg-primary text-primary-foreground'
+                : 'bg-secondary text-secondary-foreground'
+            }`}
+          >
+            All
+          </button>
+          <button
+            onClick={() => setFilter('unanswered')}
+            className={`rounded-lg px-4 py-2 text-sm font-medium ${
+              filter === 'unanswered'
+                ? 'bg-primary text-primary-foreground'
+                : 'bg-secondary text-secondary-foreground'
+            }`}
+          >
+            Unanswered
+          </button>
+        </div>
+      </div>
+
+      <div className="space-y-4">
+        {clusterIds.map(clusterId => {
+          const cluster = grouped[clusterId]
+          if (!cluster) return null
+
+          const filteredQuestions =
+            filter === 'unanswered'
+              ? cluster.questions.filter(q => !q.addressed)
+              : cluster.questions
+
+          if (filteredQuestions.length === 0) return null
+
+          return (
+            <div
+              key={clusterId}
+              className="rounded-lg border border-border bg-card overflow-hidden"
+            >
+              <div className="bg-muted px-4 py-3 font-medium text-foreground">
+                {cluster.problem}
+              </div>
+              <ul className="divide-y divide-border">
+                {filteredQuestions.map(q => (
+                  <li key={q.id} className="flex items-start gap-3 px-4 py-3">
+                    <input
+                      type="checkbox"
+                      checked={q.addressed}
+                      onChange={() => handleToggleAddressed(q.id, q.addressed)}
+                      className="mt-1 h-4 w-4 rounded border-border"
+                    />
+                    <div className="flex-1">
+                      <p
+                        className={`text-foreground ${
+                          q.addressed ? 'line-through text-muted-foreground' : ''
+                        }`}
+                      >
+                        {q.text}
+                      </p>
+                      <p className="text-sm text-muted-foreground mt-1">
+                        {q.engagement} upvotes
+                      </p>
+                    </div>
+                  </li>
+                ))}
+              </ul>
+            </div>
+          )
+        })}
+      </div>
+    </div>
+  )
+}
--- a/packages/web/src/app/scrape/page.tsx
+++ b/packages/web/src/app/scrape/page.tsx
@ -0,0 +1,152 @@
+'use client'
+
+import { useState, useEffect } from 'react'
+import { useToast } from '@/components/ui/toast'
+
+interface ScrapeHistory {
+  id: number
+  subreddit: string
+  url: string
+  postsScraped: number
+  commentsScraped: number
+  startedAt: number
+  completedAt: number | null
+}
+
+export default function ScrapePage() {
+  const [url, setUrl] = useState('')
+  const [pages, setPages] = useState(5)
+  const [isLoading, setIsLoading] = useState(false)
+  const [history, setHistory] = useState<ScrapeHistory[]>([])
+  const { addToast, updateToast } = useToast()
+
+  useEffect(() => {
+    fetch('/api/scrape/history')
+      .then(res => res.json())
+      .then(data => setHistory(data.history || []))
+  }, [])
+
+  const handleScrape = async () => {
+    if (!url) return
+    setIsLoading(true)
+    const toastId = addToast('Scraping subreddit... this may take a while', 'loading')
+
+    try {
+      const res = await fetch('/api/scrape', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ url, pages }),
+      })
+
+      const data = await res.json()
+      if (data.success) {
+        updateToast(toastId, `Scraped ${data.posts} posts and ${data.comments} comments`, 'success')
+        const historyRes = await fetch('/api/scrape/history')
+        const historyData = await historyRes.json()
+        setHistory(historyData.history || [])
+      } else {
+        updateToast(toastId, data.error || 'Scrape failed', 'error')
+      }
+    } catch (err) {
+      updateToast(toastId, 'Scrape failed - check console', 'error')
+    } finally {
+      setIsLoading(false)
+    }
+  }
+
+  const formatDate = (timestamp: number) => {
+    return new Date(timestamp * 1000).toLocaleString()
+  }
+
+  return (
+    <div className="space-y-6">
+      <div>
+        <h1 className="text-2xl font-semibold text-foreground">Scrape Manager</h1>
+        <p className="text-muted-foreground">Scrape subreddits and manage data collection</p>
+      </div>
+
+      <div className="rounded-lg border border-border bg-card p-4 space-y-4">
+        <div>
+          <label className="block text-sm font-medium text-foreground mb-1">
+            Subreddit URL
+          </label>
+          <input
+            type="text"
+            value={url}
+            onChange={e => setUrl(e.target.value)}
+            placeholder="https://reddit.com/r/programming/best"
+            className="w-full rounded-lg border border-input bg-background px-3 py-2 text-foreground placeholder:text-muted-foreground focus:outline-none focus:ring-2 focus:ring-ring"
+            disabled={isLoading}
+          />
+        </div>
+        <div>
+          <label className="block text-sm font-medium text-foreground mb-1">
+            Pages to Scrape
+          </label>
+          <input
+            type="number"
+            value={pages}
+            onChange={e => setPages(parseInt(e.target.value) || 1)}
+            min={1}
+            max={20}
+            className="w-32 rounded-lg border border-input bg-background px-3 py-2 text-foreground focus:outline-none focus:ring-2 focus:ring-ring"
+            disabled={isLoading}
+          />
+        </div>
+        <button
+          onClick={handleScrape}
+          disabled={isLoading || !url}
+          className="rounded-lg bg-primary px-4 py-2 text-sm font-medium text-primary-foreground hover:opacity-90 disabled:opacity-50"
+        >
+          {isLoading ? 'Scraping...' : 'Start Scrape'}
+        </button>
+      </div>
+
+      {history.length > 0 && (
+        <div className="rounded-lg border border-border bg-card overflow-hidden">
+          <div className="bg-muted px-4 py-3 font-medium text-foreground">
+            Scrape History
+          </div>
+          <table className="w-full">
+            <thead className="bg-muted/50">
+              <tr>
+                <th className="px-4 py-2 text-left text-sm font-medium text-muted-foreground">
+                  Subreddit
+                </th>
+                <th className="px-4 py-2 text-left text-sm font-medium text-muted-foreground">
+                  Posts
+                </th>
+                <th className="px-4 py-2 text-left text-sm font-medium text-muted-foreground">
+                  Comments
+                </th>
+                <th className="px-4 py-2 text-left text-sm font-medium text-muted-foreground">
+                  Started
+                </th>
+                <th className="px-4 py-2 text-left text-sm font-medium text-muted-foreground">
+                  Status
+                </th>
+              </tr>
+            </thead>
+            <tbody>
+              {history.map(h => (
+                <tr key={h.id} className="border-t border-border">
+                  <td className="px-4 py-2 text-foreground">r/{h.subreddit}</td>
+                  <td className="px-4 py-2 text-foreground">{h.postsScraped}</td>
+                  <td className="px-4 py-2 text-foreground">{h.commentsScraped}</td>
+                  <td className="px-4 py-2 text-foreground">{formatDate(h.startedAt)}</td>
+                  <td className="px-4 py-2">
+                    {h.completedAt ? (
+                      <span className="text-green-600">Complete</span>
+                    ) : (
+                      <span className="text-yellow-600">In Progress</span>
+                    )}
+                  </td>
+                </tr>
+              ))}
+            </tbody>
+          </table>
+        </div>
+      )}
+    </div>
+  )
+}
--- a/packages/web/src/app/settings/page.tsx
+++ b/packages/web/src/app/settings/page.tsx
@ -0,0 +1,203 @@
+'use client'
+
+import React, { useEffect, useState } from 'react'
+import { useToast } from '@/components/ui/toast'
+
+interface Model {
+  name: string
+  size: number
+  modified: string
+}
+
+interface Settings {
+  summarizationModel: string
+  sentimentModel: string
+  embeddingModel: string
+}
+
+export default function SettingsPage() {
+  const [settings, setSettings] = useState<Settings | null>(null)
+  const [models, setModels] = useState<Model[]>([])
+  const [loading, setLoading] = useState(true)
+  const [saving, setSaving] = useState(false)
+  const [error, setError] = useState<string | null>(null)
+  const { addToast } = useToast()
+
+  useEffect(() => {
+    fetch('/api/settings')
+      .then(res => res.json())
+      .then(data => {
+        setSettings(data.settings)
+        setModels(data.availableModels || [])
+        if (data.error) setError(data.error)
+        setLoading(false)
+      })
+      .catch(() => {
+        setError('Failed to load settings')
+        setLoading(false)
+      })
+  }, [])
+
+  const handleSave = async () => {
+    if (!settings) return
+    setSaving(true)
+
+    try {
+      const res = await fetch('/api/settings', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(settings),
+      })
+      const data = await res.json()
+
+      if (data.success) {
+        addToast('Settings saved', 'success')
+      } else {
+        addToast(data.error || 'Failed to save', 'error')
+      }
+    } catch {
+      addToast('Failed to save settings', 'error')
+    } finally {
+      setSaving(false)
+    }
+  }
+
+  const updateSetting = (key: keyof Settings, value: string) => {
+    setSettings(prev => prev ? { ...prev, [key]: value } : null)
+  }
+
+  const formatSize = (bytes: number) => {
+    const gb = bytes / (1024 * 1024 * 1024)
+    return gb >= 1 ? `${gb.toFixed(1)} GB` : `${(bytes / (1024 * 1024)).toFixed(0)} MB`
+  }
+
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center h-64">
+        <div className="text-muted-foreground">Loading...</div>
+      </div>
+    )
+  }
+
+  return (
+    <div className="space-y-6 max-w-2xl">
+      <div>
+        <h1 className="text-2xl font-semibold text-foreground">Settings</h1>
+        <p className="text-muted-foreground">Configure models and preferences</p>
+      </div>
+
+      {error && (
+        <div className="rounded-lg border border-destructive/50 bg-destructive/10 p-4 text-destructive">
+          {error}
+        </div>
+      )}
+
+      <div className="rounded-lg border border-border bg-card p-6 space-y-6">
+        <div>
+          <h2 className="text-lg font-medium text-foreground mb-4">Ollama Models</h2>
+          <p className="text-sm text-muted-foreground mb-4">
+            {models.length} models available locally
+          </p>
+        </div>
+
+        {settings && (
+          <div className="space-y-4">
+            <div>
+              <label className="block text-sm font-medium text-foreground mb-2">
+                Summarization Model
+              </label>
+              <p className="text-xs text-muted-foreground mb-2">
+                Used for generating problem summaries and extracting questions from clusters
+              </p>
+              <select
+                value={settings.summarizationModel}
+                onChange={e => updateSetting('summarizationModel', e.target.value)}
+                className="w-full rounded-lg border border-border bg-background px-3 py-2 text-foreground"
+              >
+                {models.map(m => (
+                  <option key={m.name} value={m.name}>
+                    {m.name} ({formatSize(m.size)})
+                  </option>
+                ))}
+              </select>
+            </div>
+
+            <div>
+              <label className="block text-sm font-medium text-foreground mb-2">
+                Sentiment Model
+              </label>
+              <p className="text-xs text-muted-foreground mb-2">
+                Used for analyzing sentiment of discussions (positive/negative/neutral)
+              </p>
+              <select
+                value={settings.sentimentModel}
+                onChange={e => updateSetting('sentimentModel', e.target.value)}
+                className="w-full rounded-lg border border-border bg-background px-3 py-2 text-foreground"
+              >
+                {models.map(m => (
+                  <option key={m.name} value={m.name}>
+                    {m.name} ({formatSize(m.size)})
+                  </option>
+                ))}
+              </select>
+            </div>
+
+            <div>
+              <label className="block text-sm font-medium text-foreground mb-2">
+                Embedding Model
+              </label>
+              <p className="text-xs text-muted-foreground mb-2">
+                Used for converting text to vectors for semantic search and clustering
+              </p>
+              <select
+                value={settings.embeddingModel}
+                onChange={e => updateSetting('embeddingModel', e.target.value)}
+                className="w-full rounded-lg border border-border bg-background px-3 py-2 text-foreground"
+              >
+                {models.filter(m => m.name.includes('embed') || m.name.includes('nomic')).length > 0
+                  ? models.filter(m => m.name.includes('embed') || m.name.includes('nomic')).map(m => (
+                      <option key={m.name} value={m.name}>
+                        {m.name} ({formatSize(m.size)})
+                      </option>
+                    ))
+                  : models.map(m => (
+                      <option key={m.name} value={m.name}>
+                        {m.name} ({formatSize(m.size)})
+                      </option>
+                    ))
+                }
+              </select>
+            </div>
+          </div>
+        )}
+
+        <div className="pt-4 border-t border-border">
+          <button
+            onClick={handleSave}
+            disabled={saving}
+            className="rounded-lg bg-primary px-4 py-2 text-sm font-medium text-primary-foreground hover:opacity-90 disabled:opacity-50"
+          >
+            {saving ? 'Saving...' : 'Save Settings'}
+          </button>
+        </div>
+      </div>
+
+      <div className="rounded-lg border border-border bg-card p-6">
+        <h2 className="text-lg font-medium text-foreground mb-4">Available Models</h2>
+        <div className="space-y-2">
+          {models.map(m => (
+            <div key={m.name} className="flex justify-between items-center text-sm">
+              <span className="text-foreground font-mono">{m.name}</span>
+              <span className="text-muted-foreground">{formatSize(m.size)}</span>
+            </div>
+          ))}
+          {models.length === 0 && (
+            <p className="text-muted-foreground text-sm">
+              No models found. Make sure Ollama is running and has models installed.
+            </p>
+          )}
+        </div>
+      </div>
+    </div>
+  )
+}
--- a/packages/web/src/components/controls/command-palette-wrapper.tsx
+++ b/packages/web/src/components/controls/command-palette-wrapper.tsx
@ -0,0 +1,7 @@
+'use client'
+
+import { CommandPalette } from './command-palette'
+
+export function CommandPaletteWrapper() {
+  return <CommandPalette />
+}
--- a/packages/web/src/components/controls/command-palette.tsx
+++ b/packages/web/src/components/controls/command-palette.tsx
@ -0,0 +1,174 @@
+'use client'
+
+import { useEffect, useState, useCallback } from 'react'
+import { useRouter } from 'next/navigation'
+import { Command } from 'cmdk'
+import { useToast } from '@/components/ui/toast'
+
+interface CommandPaletteProps {
+  onSearch?: (query: string) => void
+}
+
+export function CommandPalette({ onSearch }: CommandPaletteProps) {
+  const [open, setOpen] = useState(false)
+  const [search, setSearch] = useState('')
+  const router = useRouter()
+  const { addToast, updateToast, removeToast } = useToast()
+
+  useEffect(() => {
+    const down = (e: KeyboardEvent) => {
+      if (e.key === 'k' && (e.metaKey || e.ctrlKey)) {
+        e.preventDefault()
+        setOpen(open => !open)
+      }
+    }
+
+    document.addEventListener('keydown', down)
+    return () => document.removeEventListener('keydown', down)
+  }, [])
+
+  const runCommand = useCallback((command: () => void) => {
+    setOpen(false)
+    command()
+  }, [])
+
+  if (!open) return null
+
+  return (
+    <div className="fixed inset-0 z-50 bg-black/50" onClick={() => setOpen(false)}>
+      <div
+        className="fixed left-1/2 top-1/4 w-full max-w-lg -translate-x-1/2 rounded-xl border border-border bg-popover p-0 shadow-2xl"
+        onClick={e => e.stopPropagation()}
+      >
+        <Command className="flex flex-col">
+          <div className="flex items-center border-b border-border px-4">
+            <Command.Input
+              value={search}
+              onValueChange={setSearch}
+              placeholder="Type a command or search..."
+              className="flex-1 bg-transparent py-3 text-foreground outline-none placeholder:text-muted-foreground"
+            />
+          </div>
+          <Command.List className="max-h-72 overflow-y-auto p-2">
+            <Command.Empty className="py-6 text-center text-sm text-muted-foreground">
+              No results found.
+            </Command.Empty>
+
+            <Command.Group heading="Navigation" className="text-xs text-muted-foreground mb-2">
+              <Command.Item
+                onSelect={() => runCommand(() => router.push('/'))}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Go to Dashboard
+              </Command.Item>
+              <Command.Item
+                onSelect={() => runCommand(() => router.push('/problems'))}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Go to Problems
+              </Command.Item>
+              <Command.Item
+                onSelect={() => runCommand(() => router.push('/questions'))}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Go to Questions
+              </Command.Item>
+              <Command.Item
+                onSelect={() => runCommand(() => router.push('/scrape'))}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Go to Scrape Manager
+              </Command.Item>
+            </Command.Group>
+
+            <Command.Group heading="Actions" className="text-xs text-muted-foreground mb-2 mt-4">
+              <Command.Item
+                onSelect={() => runCommand(async () => {
+                  const toastId = addToast('Clustering discussions...', 'loading')
+                  try {
+                    const res = await fetch('/api/clusters', { method: 'POST' })
+                    const data = await res.json()
+                    if (data.success) {
+                      updateToast(toastId, `Found ${data.clusters?.length || 0} problem clusters`, 'success')
+                      router.refresh()
+                    } else {
+                      updateToast(toastId, data.error || 'Clustering failed', 'error')
+                    }
+                  } catch (e) {
+                    updateToast(toastId, 'Clustering failed - check console', 'error')
+                  }
+                })}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Re-cluster discussions
+              </Command.Item>
+              <Command.Item
+                onSelect={() => runCommand(async () => {
+                  const toastId = addToast('Exporting CSV...', 'loading')
+                  try {
+                    const res = await fetch('/api/export', {
+                      method: 'POST',
+                      headers: { 'Content-Type': 'application/json' },
+                      body: JSON.stringify({ format: 'csv' }),
+                    })
+                    const blob = await res.blob()
+                    const url = URL.createObjectURL(blob)
+                    const a = document.createElement('a')
+                    a.href = url
+                    a.download = 'clusters.csv'
+                    a.click()
+                    updateToast(toastId, 'CSV downloaded', 'success')
+                  } catch (e) {
+                    updateToast(toastId, 'Export failed', 'error')
+                  }
+                })}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Export clusters as CSV
+              </Command.Item>
+              <Command.Item
+                onSelect={() => runCommand(async () => {
+                  const toastId = addToast('Exporting FAQ schema...', 'loading')
+                  try {
+                    const res = await fetch('/api/export', {
+                      method: 'POST',
+                      headers: { 'Content-Type': 'application/json' },
+                      body: JSON.stringify({ format: 'json', type: 'faq-schema' }),
+                    })
+                    const data = await res.json()
+                    const blob = new Blob([JSON.stringify(data, null, 2)], { type: 'application/json' })
+                    const url = URL.createObjectURL(blob)
+                    const a = document.createElement('a')
+                    a.href = url
+                    a.download = 'faq-schema.json'
+                    a.click()
+                    updateToast(toastId, 'FAQ schema downloaded', 'success')
+                  } catch (e) {
+                    updateToast(toastId, 'Export failed', 'error')
+                  }
+                })}
+                className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+              >
+                Export FAQ schema (JSON-LD)
+              </Command.Item>
+            </Command.Group>
+
+            {search && onSearch && (
+              <Command.Group heading="Search" className="text-xs text-muted-foreground mb-2 mt-4">
+                <Command.Item
+                  onSelect={() => runCommand(() => onSearch(search))}
+                  className="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm text-foreground hover:bg-accent"
+                >
+                  Semantic search: "{search}"
+                </Command.Item>
+              </Command.Group>
+            )}
+          </Command.List>
+          <div className="border-t border-border px-4 py-2 text-xs text-muted-foreground">
+            Press <kbd className="rounded bg-muted px-1 py-0.5">Esc</kbd> to close
+          </div>
+        </Command>
+      </div>
+    </div>
+  )
+}
--- a/packages/web/src/components/ui/toast.tsx
+++ b/packages/web/src/components/ui/toast.tsx
@ -0,0 +1,98 @@
+'use client'
+
+import { createContext, useContext, useState, useCallback, ReactNode } from 'react'
+
+interface Toast {
+  id: string
+  message: string
+  type: 'info' | 'success' | 'error' | 'loading'
+}
+
+interface ToastContextValue {
+  toasts: Toast[]
+  addToast: (message: string, type?: Toast['type']) => string
+  removeToast: (id: string) => void
+  updateToast: (id: string, message: string, type?: Toast['type']) => void
+}
+
+const ToastContext = createContext<ToastContextValue | null>(null)
+
+export function ToastProvider({ children }: { children: ReactNode }) {
+  const [toasts, setToasts] = useState<Toast[]>([])
+
+  const addToast = useCallback((message: string, type: Toast['type'] = 'info') => {
+    const id = crypto.randomUUID()
+    setToasts(prev => [...prev, { id, message, type }])
+
+    if (type !== 'loading') {
+      setTimeout(() => {
+        setToasts(prev => prev.filter(t => t.id !== id))
+      }, 4000)
+    }
+
+    return id
+  }, [])
+
+  const removeToast = useCallback((id: string) => {
+    setToasts(prev => prev.filter(t => t.id !== id))
+  }, [])
+
+  const updateToast = useCallback((id: string, message: string, type: Toast['type'] = 'info') => {
+    setToasts(prev => prev.map(t => t.id === id ? { ...t, message, type } : t))
+
+    if (type !== 'loading') {
+      setTimeout(() => {
+        setToasts(prev => prev.filter(t => t.id !== id))
+      }, 4000)
+    }
+  }, [])
+
+  return (
+    <ToastContext.Provider value={{ toasts, addToast, removeToast, updateToast }}>
+      {children}
+      <ToastContainer toasts={toasts} removeToast={removeToast} />
+    </ToastContext.Provider>
+  )
+}
+
+export function useToast() {
+  const context = useContext(ToastContext)
+  if (!context) {
+    throw new Error('useToast must be used within a ToastProvider')
+  }
+  return context
+}
+
+function ToastContainer({ toasts, removeToast }: { toasts: Toast[]; removeToast: (id: string) => void }) {
+  if (toasts.length === 0) return null
+
+  return (
+    <div className="fixed bottom-4 right-4 z-50 flex flex-col gap-2">
+      {toasts.map(toast => (
+        <div
+          key={toast.id}
+          className={`
+            flex items-center gap-3 rounded-lg px-4 py-3 shadow-lg
+            ${toast.type === 'error' ? 'bg-red-500 text-white' : ''}
+            ${toast.type === 'success' ? 'bg-green-600 text-white' : ''}
+            ${toast.type === 'loading' ? 'bg-card text-foreground border border-border' : ''}
+            ${toast.type === 'info' ? 'bg-card text-foreground border border-border' : ''}
+          `}
+        >
+          {toast.type === 'loading' && (
+            <div className="h-4 w-4 animate-spin rounded-full border-2 border-muted-foreground border-t-foreground" />
+          )}
+          <span className="text-sm">{toast.message}</span>
+          {toast.type !== 'loading' && (
+            <button
+              onClick={() => removeToast(toast.id)}
+              className="ml-2 text-current opacity-70 hover:opacity-100"
+            >
+              ×
+            </button>
+          )}
+        </div>
+      ))}
+    </div>
+  )
+}
--- a/packages/web/src/lib/utils.ts
+++ b/packages/web/src/lib/utils.ts
@ -0,0 +1,6 @@
+import { type ClassValue, clsx } from 'clsx'
+import { twMerge } from 'tailwind-merge'
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs))
+}
--- a/packages/web/src/styles/globals.css
+++ b/packages/web/src/styles/globals.css
@ -0,0 +1,172 @@
+@import "tailwindcss";
+
+:root {
+  --background: oklch(0.9551 0 0);
+  --foreground: oklch(0.3211 0 0);
+  --card: oklch(0.9702 0 0);
+  --card-foreground: oklch(0.3211 0 0);
+  --popover: oklch(0.9702 0 0);
+  --popover-foreground: oklch(0.3211 0 0);
+  --primary: oklch(0.4891 0 0);
+  --primary-foreground: oklch(1.0000 0 0);
+  --secondary: oklch(0.9067 0 0);
+  --secondary-foreground: oklch(0.3211 0 0);
+  --muted: oklch(0.8853 0 0);
+  --muted-foreground: oklch(0.5103 0 0);
+  --accent: oklch(0.8078 0 0);
+  --accent-foreground: oklch(0.3211 0 0);
+  --destructive: oklch(0.5594 0.1900 25.8625);
+  --destructive-foreground: oklch(1.0000 0 0);
+  --border: oklch(0.8576 0 0);
+  --input: oklch(0.9067 0 0);
+  --ring: oklch(0.4891 0 0);
+  --chart-1: oklch(0.4891 0 0);
+  --chart-2: oklch(0.4863 0.0361 196.0278);
+  --chart-3: oklch(0.6534 0 0);
+  --chart-4: oklch(0.7316 0 0);
+  --chart-5: oklch(0.8078 0 0);
+  --sidebar: oklch(0.9370 0 0);
+  --sidebar-foreground: oklch(0.3211 0 0);
+  --sidebar-primary: oklch(0.4891 0 0);
+  --sidebar-primary-foreground: oklch(1.0000 0 0);
+  --sidebar-accent: oklch(0.8078 0 0);
+  --sidebar-accent-foreground: oklch(0.3211 0 0);
+  --sidebar-border: oklch(0.8576 0 0);
+  --sidebar-ring: oklch(0.4891 0 0);
+  --font-sans: Montserrat, sans-serif;
+  --font-serif: Georgia, serif;
+  --font-mono: Fira Code, monospace;
+  --radius: 0.35rem;
+  --shadow-x: 0px;
+  --shadow-y: 2px;
+  --shadow-blur: 0px;
+  --shadow-spread: 0px;
+  --shadow-opacity: 0.15;
+  --shadow-color: hsl(0 0% 20% / 0.1);
+  --shadow-2xs: 0px 2px 0px 0px hsl(0 0% 20% / 0.07);
+  --shadow-xs: 0px 2px 0px 0px hsl(0 0% 20% / 0.07);
+  --shadow-sm: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 1px 2px -1px hsl(0 0% 20% / 0.15);
+  --shadow: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 1px 2px -1px hsl(0 0% 20% / 0.15);
+  --shadow-md: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 2px 4px -1px hsl(0 0% 20% / 0.15);
+  --shadow-lg: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 4px 6px -1px hsl(0 0% 20% / 0.15);
+  --shadow-xl: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 8px 10px -1px hsl(0 0% 20% / 0.15);
+  --shadow-2xl: 0px 2px 0px 0px hsl(0 0% 20% / 0.38);
+  --tracking-normal: 0em;
+  --spacing: 0.25rem;
+}
+
+.dark {
+  --background: oklch(0.2178 0 0);
+  --foreground: oklch(0.8853 0 0);
+  --card: oklch(0.2435 0 0);
+  --card-foreground: oklch(0.8853 0 0);
+  --popover: oklch(0.2435 0 0);
+  --popover-foreground: oklch(0.8853 0 0);
+  --primary: oklch(0.7058 0 0);
+  --primary-foreground: oklch(0.2178 0 0);
+  --secondary: oklch(0.3092 0 0);
+  --secondary-foreground: oklch(0.8853 0 0);
+  --muted: oklch(0.2850 0 0);
+  --muted-foreground: oklch(0.5999 0 0);
+  --accent: oklch(0.3715 0 0);
+  --accent-foreground: oklch(0.8853 0 0);
+  --destructive: oklch(0.6591 0.1530 22.1703);
+  --destructive-foreground: oklch(1.0000 0 0);
+  --border: oklch(0.3290 0 0);
+  --input: oklch(0.3092 0 0);
+  --ring: oklch(0.7058 0 0);
+  --chart-1: oklch(0.7058 0 0);
+  --chart-2: oklch(0.6714 0.0339 206.3482);
+  --chart-3: oklch(0.5452 0 0);
+  --chart-4: oklch(0.4604 0 0);
+  --chart-5: oklch(0.3715 0 0);
+  --sidebar: oklch(0.2393 0 0);
+  --sidebar-foreground: oklch(0.8853 0 0);
+  --sidebar-primary: oklch(0.7058 0 0);
+  --sidebar-primary-foreground: oklch(0.2178 0 0);
+  --sidebar-accent: oklch(0.3715 0 0);
+  --sidebar-accent-foreground: oklch(0.8853 0 0);
+  --sidebar-border: oklch(0.3290 0 0);
+  --sidebar-ring: oklch(0.7058 0 0);
+  --font-sans: Inter, sans-serif;
+  --font-serif: Georgia, serif;
+  --font-mono: Fira Code, monospace;
+  --radius: 0.35rem;
+  --shadow-x: 0px;
+  --shadow-y: 2px;
+  --shadow-blur: 0px;
+  --shadow-spread: 0px;
+  --shadow-opacity: 0.15;
+  --shadow-color: hsl(0 0% 20% / 0.1);
+  --shadow-2xs: 0px 2px 0px 0px hsl(0 0% 20% / 0.07);
+  --shadow-xs: 0px 2px 0px 0px hsl(0 0% 20% / 0.07);
+  --shadow-sm: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 1px 2px -1px hsl(0 0% 20% / 0.15);
+  --shadow: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 1px 2px -1px hsl(0 0% 20% / 0.15);
+  --shadow-md: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 2px 4px -1px hsl(0 0% 20% / 0.15);
+  --shadow-lg: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 4px 6px -1px hsl(0 0% 20% / 0.15);
+  --shadow-xl: 0px 2px 0px 0px hsl(0 0% 20% / 0.15), 0px 8px 10px -1px hsl(0 0% 20% / 0.15);
+  --shadow-2xl: 0px 2px 0px 0px hsl(0 0% 20% / 0.38);
+}
+
+@theme inline {
+  --color-background: var(--background);
+  --color-foreground: var(--foreground);
+  --color-card: var(--card);
+  --color-card-foreground: var(--card-foreground);
+  --color-popover: var(--popover);
+  --color-popover-foreground: var(--popover-foreground);
+  --color-primary: var(--primary);
+  --color-primary-foreground: var(--primary-foreground);
+  --color-secondary: var(--secondary);
+  --color-secondary-foreground: var(--secondary-foreground);
+  --color-muted: var(--muted);
+  --color-muted-foreground: var(--muted-foreground);
+  --color-accent: var(--accent);
+  --color-accent-foreground: var(--accent-foreground);
+  --color-destructive: var(--destructive);
+  --color-destructive-foreground: var(--destructive-foreground);
+  --color-border: var(--border);
+  --color-input: var(--input);
+  --color-ring: var(--ring);
+  --color-chart-1: var(--chart-1);
+  --color-chart-2: var(--chart-2);
+  --color-chart-3: var(--chart-3);
+  --color-chart-4: var(--chart-4);
+  --color-chart-5: var(--chart-5);
+  --color-sidebar: var(--sidebar);
+  --color-sidebar-foreground: var(--sidebar-foreground);
+  --color-sidebar-primary: var(--sidebar-primary);
+  --color-sidebar-primary-foreground: var(--sidebar-primary-foreground);
+  --color-sidebar-accent: var(--sidebar-accent);
+  --color-sidebar-accent-foreground: var(--sidebar-accent-foreground);
+  --color-sidebar-border: var(--sidebar-border);
+  --color-sidebar-ring: var(--sidebar-ring);
+
+  --font-sans: var(--font-sans);
+  --font-mono: var(--font-mono);
+  --font-serif: var(--font-serif);
+
+  --radius-sm: calc(var(--radius) - 4px);
+  --radius-md: calc(var(--radius) - 2px);
+  --radius-lg: var(--radius);
+  --radius-xl: calc(var(--radius) + 4px);
+
+  --shadow-2xs: var(--shadow-2xs);
+  --shadow-xs: var(--shadow-xs);
+  --shadow-sm: var(--shadow-sm);
+  --shadow: var(--shadow);
+  --shadow-md: var(--shadow-md);
+  --shadow-lg: var(--shadow-lg);
+  --shadow-xl: var(--shadow-xl);
+  --shadow-2xl: var(--shadow-2xl);
+}
+
+body {
+  background-color: var(--background);
+  color: var(--foreground);
+  font-family: var(--font-sans);
+}
+
+* {
+  border-color: var(--border);
+}
--- a/packages/web/tsconfig.json
+++ b/packages/web/tsconfig.json
@ -0,0 +1,40 @@
+{
+  "compilerOptions": {
+    "lib": [
+      "dom",
+      "dom.iterable",
+      "esnext"
+    ],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "noEmit": true,
+    "esModuleInterop": true,
+    "module": "esnext",
+    "moduleResolution": "bundler",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "preserve",
+    "incremental": true,
+    "plugins": [
+      {
+        "name": "next"
+      }
+    ],
+    "paths": {
+      "@/*": [
+        "./src/*"
+      ]
+    },
+    "target": "ES2017"
+  },
+  "include": [
+    "next-env.d.ts",
+    "**/*.ts",
+    "**/*.tsx",
+    ".next/types/**/*.ts"
+  ],
+  "exclude": [
+    "node_modules"
+  ]
+}