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 cli              # run the CLI
-bun dev          # run with watch mode
+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
 ---
 ```
 packages/
  core/                    # shared business logic
    src/
-  index.ts              # entry point, connection checks, TUI setup
+      scraper/             # reddit.ts, comments.ts, types.ts
-  scraper/
+      embeddings/          # ollama.ts
-    reddit.ts           # fetch subreddit posts with pagination
+      storage/             # qdrant.ts, sqlite.ts, types.ts
-    comments.ts         # fetch comments for each post
+      clustering/          # hdbscan.ts, types.ts
-    types.ts            # reddit json response types
+      analysis/            # summarizer.ts, questions.ts, scoring.ts, types.ts
-  embeddings/
+      utils/               # rate-limit.ts, text.ts
-    ollama.ts           # batch embed text with nomic-embed-text (768 dims)
+      index.ts             # barrel exports
-  storage/
+
-    qdrant.ts           # create collection, upsert, search
+  cli/                     # CLI/TUI app
-    types.ts            # point payload schema
+    src/
-  tui/
+      cli.ts               # interactive command-line interface
-    app.ts              # main dashboard, wires everything together
+      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/
-      url-input.ts      # subreddit url input
+        controls/          # command palette, sliders
-      progress.ts       # scraping/embedding progress bars
+      styles/globals.css   # theme
-      stats.ts          # collection stats panel
+
-      trending.ts       # trending topics view
+data/                      # sqlite database files
      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
 ```
-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",
        "tailwindcss": "^4.0.0",
        "typescript": "^5.0.0",
      },
    },
  },
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    "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=="],
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    "@rta/web/ollama": ["ollama@0.5.18", "", { "dependencies": { "whatwg-fetch": "^3.6.20" } }, "sha512-lTFqTf9bo7Cd3hpF6CviBe/DEhewjoZYd9N/uCe7O20qYTvGqrNOFOBDj3lbZgFWHUgDv5EeyusYxsZSLS8nvg=="],
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  }
 }
--- 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",
+    "cli": "bun run packages/cli/src/cli.ts",
-    "tui": "bun run src/index.ts",
+    "tui": "bun run packages/cli/src/index.ts",
-    "dev": "bun --watch run src/cli.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 {
-import { CommentFetcher } from './scraper/comments'
+  RedditScraper,
-import { EmbeddingPipeline } from './embeddings/ollama'
+  CommentFetcher,
-import { QdrantStorage } from './storage/qdrant'
+  EmbeddingPipeline,
-import type { RedditComment } from './scraper/types'
+  QdrantStorage,
-import type { SearchResult } from './storage/types'
+  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 embeddings = new EmbeddingPipeline()
-    const client = new Ollama({
+    return await embeddings.checkConnection()
      host: process.env.OLLAMA_HOST || 'http://localhost:11434',
    })
    const models = await client.list()
    return models.models.some(m => m.name.includes('nomic-embed-text'))
  } 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 storage = new QdrantStorage()
-    const qdrantUrl = process.env.QDRANT_URL || 'http://localhost:6333'
+    return await storage.checkConnection()
    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
  } 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 {
-import { CommentFetcher } from '../scraper/comments'
+  RedditScraper,
-import { EmbeddingPipeline } from '../embeddings/ollama'
+  CommentFetcher,
-import { QdrantStorage } from '../storage/qdrant'
+  EmbeddingPipeline,
-import type { RedditComment } from '../scraper/types'
+  QdrantStorage,
-import type { SearchResult } from '../storage/types'
+} 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"
  ]
 }
		`@ -0,0 +1,2 @@`
							`export { ClusteringPipeline } from './hdbscan'`
							`export type { Cluster, ClusterPoint, ClusteringOptions, ClusteringResult } from './types'`
		`@ -0,0 +1,2 @@`
							`export { EmbeddingPipeline, VECTOR_DIM } from './ollama'`
							`export type { EmbeddedPoint, EmbeddingProgress } from './ollama'`