initial commit of CLI

2026-01-21 05:35:27 -07:00 · 2026-01-21 05:35:27 -07:00 · fd4cad363b
commit fd4cad363b
25 changed files with 2637 additions and 0 deletions
--- a/.env.example
+++ b/.env.example
@ -0,0 +1,3 @@
+QDRANT_URL=https://vectors.biohazardvfx.com
+QDRANT_API_KEY=your-api-key-here
+OLLAMA_HOST=http://localhost:11434
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,39 @@
+# dependencies (bun install)
+node_modules
+
+# output
+out
+dist
+*.tgz
+
+# code coverage
+coverage
+*.lcov
+
+# logs
+logs
+_.log
+report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json
+
+# dotenv environment variable files
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# caches
+.eslintcache
+.cache
+*.tsbuildinfo
+
+# IntelliJ based IDEs
+.idea
+
+# Finder (MacOS) folder config
+.DS_Store
+
+# exported data
+reddit-trends.json
+reddit-trends.csv
+.env
--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -0,0 +1,74 @@
+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.
+
+running
+---
+
+```bash
+bun start        # run the app
+bun dev          # run with watch mode
+```
+
+prerequisites
+---
+
+- ollama running locally with nomic-embed-text model (`ollama pull nomic-embed-text`)
+- qdrant accessible at QDRANT_URL (or localhost:6333)
+
+env vars
+---
+
+```
+QDRANT_URL=https://vectors.biohazardvfx.com
+QDRANT_API_KEY=<your-key>
+OLLAMA_HOST=http://localhost:11434  # optional, defaults to this
+```
+
+architecture
+---
+
+```
+src/
+  index.ts              # entry point, connection checks, TUI setup
+  scraper/
+    reddit.ts           # fetch subreddit posts with pagination
+    comments.ts         # fetch comments for each post
+    types.ts            # reddit json response types
+  embeddings/
+    ollama.ts           # batch embed text with nomic-embed-text (768 dims)
+  storage/
+    qdrant.ts           # create collection, upsert, search
+    types.ts            # point payload schema
+  tui/
+    app.ts              # main dashboard, wires everything together
+    components/
+      url-input.ts      # subreddit url input
+      progress.ts       # scraping/embedding progress bars
+      stats.ts          # collection stats panel
+      trending.ts       # trending topics view
+      search.ts         # semantic search interface
+      export.ts         # export to json/csv
+  utils/
+    rate-limit.ts       # delay helper for reddit api
+    text.ts             # text preprocessing for embedding
+```
+
+keybindings
+---
+
+- `q` or `ctrl+c` - quit
+- `enter` - start scrape (when url is entered)
+- `tab` - switch between url and search inputs
+- `e` - export results to json
+- `c` - export results to csv
+- `r` - refresh stats from qdrant
+
+coding notes
+---
+
+- uses @opentui/core standalone (no react/solid)
+- reddit rate limiting: 3s delay between requests
+- embeddings batched in groups of 10
+- qdrant collection: reddit_trends with indexes on subreddit, type, created, score
--- a/README.md
+++ b/README.md
@ -0,0 +1,15 @@
+# reddit-trend-analyzer
+
+To install dependencies:
+
+```bash
+bun install
+```
+
+To run:
+
+```bash
+bun run index.ts
+```
+
+This project was created using `bun init` in bun v1.3.5. [Bun](https://bun.com) is a fast all-in-one JavaScript runtime.
--- a/brief.md
+++ b/brief.md
@ -0,0 +1,346 @@
+reddit trend analyzer
+===
+
+a tool for discovering common problems and questions in reddit communities to inform content strategy and tool development.
+
+core goal
+---
+
+find what people struggle with most -> create content/tools that solve those problems -> organic SEO growth
+
+tech stack
+---
+
+- vector database: qdrant
+- embeddings: nomic-embed-text (ollama)
+- framework: next.js
+- components: shadcn
+- charts: recharts (simple, shadcn-compatible)
+- theme: shadcn tokens from globals.css inline theme ONLY
+
+data pipeline
+---
+
+```
+reddit scrape -> text cleaning -> embedding -> qdrant storage
+                                      |
+                              clustering (HDBSCAN)
+                                      |
+                              problem extraction (LLM)
+                                      |
+                              frequency + engagement scoring
+```
+
+core features
+---
+
+**1. data ingestion**
+
+existing CLI handles this well:
+- scrape subreddit posts + comments
+- embed with nomic-embed-text
+- store in qdrant with metadata (score, created, subreddit, type)
+
+**2. problem clustering**
+
+the key feature. group similar discussions to surface recurring themes.
+
+- cluster embeddings using HDBSCAN (density-based, handles noise well)
+- extract cluster centroids as topic anchors
+- LLM pass to generate human-readable problem statements from each cluster
+- rank clusters by:
+  - size (discussion count)
+  - total engagement (sum of upvotes)
+  - recency (still being talked about?)
+
+output example:
+```
+| problem                                      | discussions | upvotes | last seen |
+|----------------------------------------------|-------------|---------|-----------|
+| users struggle with X when doing Y           | 47          | 2.3k    | 2d ago    |
+| confusion about how to configure Z           | 31          | 890     | 1w ago    |
+| no good free alternative to [competitor]     | 28          | 1.1k    | 3d ago    |
+```
+
+**3. question extraction**
+
+pull out actual questions people ask.
+
+- pattern matching: "how do I", "why does", "is there a way to", "what's the best", etc.
+- deduplicate semantically similar questions (vector similarity > 0.9)
+- rank by engagement
+- group under parent problem clusters
+
+output: FAQ-ready list for blog posts, docs, or schema markup
+
+**4. search + explore**
+
+- semantic search across all scraped content
+- filter by: subreddit, date range, min upvotes, type (post/comment)
+- click through to original reddit discussions
+
+**5. export**
+
+- problem clusters as markdown content briefs
+- questions as FAQ schema (json-ld ready)
+- csv for spreadsheet analysis
+- raw json for custom processing
+
+dashboard views
+---
+
+**home / stats**
+
+simple overview:
+- total posts/comments in db
+- subreddits being tracked
+- problem clusters identified
+- recent scrape activity
+
+**problem explorer** (main view)
+
+sortable/filterable table of problem clusters:
+- columns: problem summary, discussion count, total upvotes, avg sentiment, last active
+- expand row -> sample discussions + extracted questions
+- select multiple -> bulk export as content briefs
+- search within problems
+
+**question bank**
+
+all extracted questions:
+- grouped by parent problem cluster (collapsible)
+- search/filter
+- copy as json-ld FAQ schema
+- mark as "addressed" when content exists
+
+**scrape manager**
+
+- list of tracked subreddits
+- manual scrape trigger
+- scrape history with stats
+- add/remove subreddits
+
+
+To give the user "Ultimate Control," the dashboard should include:
+
+1. **Similarity Sensitivity Slider:** A global control that adjusts how strict the vector database is. Lower similarity = more broad, creative connections. Higher similarity = more specific, literal results.
+2. **The "Impact Score" Weighting:** Allow users to toggle what "Importance" means to them. Is it **Upvote Count**? **Sentiment Extremity**? Or **Topic Velocity**? Adjusting these weights should re-order the "Competitor Hijack" table in real-time.
+3. **Command Palette:** Instead of clicking through menus, a "Ctrl + K" command bar allows the user to type "Find gaps in comparison intent" to instantly update the visualizations.
+
+implementation phases
+---
+
+**phase 1: clustering + extraction (backend)**
+
+- [ ] add HDBSCAN clustering to pipeline
+- [ ] LLM integration for problem summarization (claude or local)
+- [ ] question extraction with pattern matching + dedup
+- [ ] store clusters in qdrant (or sqlite sidecar)
+- [ ] CLI commands: `cluster`, `problems`, `questions`
+
+**phase 2: web UI**
+
+- [ ] next.js app with shadcn
+- [ ] problem explorer table (tanstack table)
+- [ ] question bank view
+- [ ] semantic search
+- [ ] export functionality
+- [ ] basic stats dashboard
+
+**phase 3: polish**
+
+- [ ] scheduled/recurring scrapes
+- [ ] better semantic deduplication
+- [ ] sentiment scoring (optional)
+- [ ] "addressed" tracking (link to published content)
+
+env vars
+---
+
+```
+QDRANT_URL=https://vectors.biohazardvfx.com
+QDRANT_API_KEY=<key>
+OLLAMA_HOST=http://localhost:11434
+ANTHROPIC_API_KEY=<key>  # for problem summarization
+```
+
+success criteria
+---
+
+tool is working if:
+- we can identify 10+ distinct problems from a subreddit scrape
+- problem summaries are actionable (could write a blog post about it)
+- question extraction gives us real FAQs people are asking
+- export format is immediately usable for content planning
+
+everything else is nice-to-have.
+
+---
+
+theme (globals.css)
+---
+
+```css
+: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);
+}
+```
+
+
--- a/bun.lock
+++ b/bun.lock
@ -0,0 +1,223 @@
+{
+  "lockfileVersion": 1,
+  "configVersion": 1,
+  "workspaces": {
+    "": {
+      "name": "reddit-trend-analyzer",
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+        "@qdrant/js-client-rest": "^1.16.2",
+        "ollama": "^0.6.3",
+      },
+      "devDependencies": {
+        "@types/bun": "latest",
+      },
+      "peerDependencies": {
+        "typescript": "^5",
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+    "omggif": ["omggif@1.0.10", "", {}, "sha512-LMJTtvgc/nugXj0Vcrrs68Mn2D1r0zf630VNtqtpI1FEO7e+O9FP4gqs9AcnBaSEeoHIPm28u6qgPR0oyEpGSw=="],
+
+    "pako": ["pako@1.0.11", "", {}, "sha512-4hLB8Py4zZce5s4yd9XzopqwVv/yGNhV1Bl8NTmCq1763HeK2+EwVTv+leGeL13Dnh2wfbqowVPXCIO0z4taYw=="],
+
+    "parse-bmfont-ascii": ["parse-bmfont-ascii@1.0.6", "", {}, "sha512-U4RrVsUFCleIOBsIGYOMKjn9PavsGOXxbvYGtMOEfnId0SVNsgehXh1DxUdVPLoxd5mvcEtvmKs2Mmf0Mpa1ZA=="],
+
+    "parse-bmfont-binary": ["parse-bmfont-binary@1.0.6", "", {}, "sha512-GxmsRea0wdGdYthjuUeWTMWPqm2+FAd4GI8vCvhgJsFnoGhTrLhXDDupwTo7rXVAgaLIGoVHDZS9p/5XbSqeWA=="],
+
+    "parse-bmfont-xml": ["parse-bmfont-xml@1.1.6", "", { "dependencies": { "xml-parse-from-string": "^1.0.0", "xml2js": "^0.5.0" } }, "sha512-0cEliVMZEhrFDwMh4SxIyVJpqYoOWDJ9P895tFuS+XuNzI5UBmBk5U5O4KuJdTnZpSBI4LFA2+ZiJaiwfSwlMA=="],
+
+    "peek-readable": ["peek-readable@4.1.0", "", {}, "sha512-ZI3LnwUv5nOGbQzD9c2iDG6toheuXSZP5esSHBjopsXH4dg19soufvpUGA3uohi5anFtGb2lhAVdHzH6R/Evvg=="],
+
+    "pixelmatch": ["pixelmatch@5.3.0", "", { "dependencies": { "pngjs": "^6.0.0" }, "bin": { "pixelmatch": "bin/pixelmatch" } }, "sha512-o8mkY4E/+LNUf6LzX96ht6k6CEDi65k9G2rjMtBe9Oo+VPKSvl+0GKHuH/AlG+GA5LPG/i5hrekkxUc3s2HU+Q=="],
+
+    "planck": ["planck@1.4.2", "", { "peerDependencies": { "stage-js": "^1.0.0-alpha.12" } }, "sha512-mNbhnV3g8X2rwGxzcesjmN8BDA6qfXgQxXVMkWau9MCRlQY0RLNEkyHlVp6yFy/X6qrzAXyNONCnZ1cGDLrNew=="],
+
+    "pngjs": ["pngjs@7.0.0", "", {}, "sha512-LKWqWJRhstyYo9pGvgor/ivk2w94eSjE3RGVuzLGlr3NmD8bf7RcYGze1mNdEHRP6TRP6rMuDHk5t44hnTRyow=="],
+
+    "process": ["process@0.11.10", "", {}, "sha512-cdGef/drWFoydD1JsMzuFf8100nZl+GT+yacc2bEced5f9Rjk4z+WtFUTBu9PhOi9j/jfmBPu0mMEY4wIdAF8A=="],
+
+    "readable-stream": ["readable-stream@4.7.0", "", { "dependencies": { "abort-controller": "^3.0.0", "buffer": "^6.0.3", "events": "^3.3.0", "process": "^0.11.10", "string_decoder": "^1.3.0" } }, "sha512-oIGGmcpTLwPga8Bn6/Z75SVaH1z5dUut2ibSyAMVhmUggWpmDn2dapB0n7f8nwaSiRtepAsfJyfXIO5DCVAODg=="],
+
+    "readable-web-to-node-stream": ["readable-web-to-node-stream@3.0.4", "", { "dependencies": { "readable-stream": "^4.7.0" } }, "sha512-9nX56alTf5bwXQ3ZDipHJhusu9NTQJ/CVPtb/XHAJCXihZeitfJvIRS4GqQ/mfIoOE3IelHMrpayVrosdHBuLw=="],
+
+    "safe-buffer": ["safe-buffer@5.2.1", "", {}, "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ=="],
+
+    "sax": ["sax@1.4.4", "", {}, "sha512-1n3r/tGXO6b6VXMdFT54SHzT9ytu9yr7TaELowdYpMqY/Ao7EnlQGmAQ1+RatX7Tkkdm6hONI2owqNx2aZj5Sw=="],
+
+    "simple-xml-to-json": ["simple-xml-to-json@1.2.3", "", {}, "sha512-kWJDCr9EWtZ+/EYYM5MareWj2cRnZGF93YDNpH4jQiHB+hBIZnfPFSQiVMzZOdk+zXWqTZ/9fTeQNu2DqeiudA=="],
+
+    "stage-js": ["stage-js@1.0.0-alpha.17", "", {}, "sha512-AzlMO+t51v6cFvKZ+Oe9DJnL1OXEH5s9bEy6di5aOrUpcP7PCzI/wIeXF0u3zg0L89gwnceoKxrLId0ZpYnNXw=="],
+
+    "string_decoder": ["string_decoder@1.3.0", "", { "dependencies": { "safe-buffer": "~5.2.0" } }, "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA=="],
+
+    "strtok3": ["strtok3@6.3.0", "", { "dependencies": { "@tokenizer/token": "^0.3.0", "peek-readable": "^4.1.0" } }, "sha512-fZtbhtvI9I48xDSywd/somNqgUHl2L2cstmXCCif0itOf96jeW18MBSyrLuNicYQVkvpOxkZtkzujiTJ9LW5Jw=="],
+
+    "three": ["three@0.177.0", "", {}, "sha512-EiXv5/qWAaGI+Vz2A+JfavwYCMdGjxVsrn3oBwllUoqYeaBO75J63ZfyaQKoiLrqNHoTlUc6PFgMXnS0kI45zg=="],
+
+    "tinycolor2": ["tinycolor2@1.6.0", "", {}, "sha512-XPaBkWQJdsf3pLKJV9p4qN/S+fm2Oj8AIPo1BTUhg5oxkvm9+SVEGFdhyOz7tTdUTfvxMiAs4sp6/eZO2Ew+pw=="],
+
+    "token-types": ["token-types@4.2.1", "", { "dependencies": { "@tokenizer/token": "^0.3.0", "ieee754": "^1.2.1" } }, "sha512-6udB24Q737UD/SDsKAHI9FCRP7Bqc9D/MQUV02ORQg5iskjtLJlZJNdN4kKtcdtwCeWIwIHDGaUsTsCCAa8sFQ=="],
+
+    "typescript": ["typescript@5.9.3", "", { "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" } }, "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw=="],
+
+    "undici": ["undici@6.23.0", "", {}, "sha512-VfQPToRA5FZs/qJxLIinmU59u0r7LXqoJkCzinq3ckNJp3vKEh7jTWN589YQ5+aoAC/TGRLyJLCPKcLQbM8r9g=="],
+
+    "undici-types": ["undici-types@7.16.0", "", {}, "sha512-Zz+aZWSj8LE6zoxD+xrjh4VfkIG8Ya6LvYkZqtUQGJPZjYl53ypCaUwWqo7eI0x66KBGeRo+mlBEkMSeSZ38Nw=="],
+
+    "utif2": ["utif2@4.1.0", "", { "dependencies": { "pako": "^1.0.11" } }, "sha512-+oknB9FHrJ7oW7A2WZYajOcv4FcDR4CfoGB0dPNfxbi4GO05RRnFmt5oa23+9w32EanrYcSJWspUiJkLMs+37w=="],
+
+    "web-tree-sitter": ["web-tree-sitter@0.25.10", "", { "peerDependencies": { "@types/emscripten": "^1.40.0" }, "optionalPeers": ["@types/emscripten"] }, "sha512-Y09sF44/13XvgVKgO2cNDw5rGk6s26MgoZPXLESvMXeefBf7i6/73eFurre0IsTW6E14Y0ArIzhUMmjoc7xyzA=="],
+
+    "whatwg-fetch": ["whatwg-fetch@3.6.20", "", {}, "sha512-EqhiFU6daOA8kpjOWTL0olhVOF3i7OrFzSYiGsEMB8GcXS+RrzauAERX65xMeNWVqxA6HXH2m69Z9LaKKdisfg=="],
+
+    "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=="],
+
+    "xmlbuilder": ["xmlbuilder@11.0.1", "", {}, "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA=="],
+
+    "yoga-layout": ["yoga-layout@3.2.1", "", {}, "sha512-0LPOt3AxKqMdFBZA3HBAt/t/8vIKq7VaQYbuA8WxCgung+p9TVyKRYdpvCb80HcdTN2NkbIKbhNwKUfm3tQywQ=="],
+
+    "zod": ["zod@3.25.76", "", {}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="],
+
+    "image-q/@types/node": ["@types/node@16.9.1", "", {}, "sha512-QpLcX9ZSsq3YYUUnD3nFDY8H7wctAhQj/TFKL8Ya8v5fMm3CFXxo8zStsLAl780ltoYoo1WvKUVGBQK+1ifr7g=="],
+
+    "pixelmatch/pngjs": ["pngjs@6.0.0", "", {}, "sha512-TRzzuFRRmEoSW/p1KVAmiOgPco2Irlah+bGFCeNfJXxxYGwSw7YwAOAcd7X28K/m5bjBWKsC29KyoMfHbypayg=="],
+  }
+}
--- a/package.json
+++ b/package.json
@ -0,0 +1,21 @@
+{
+  "name": "reddit-trend-analyzer",
+  "version": "1.0.0",
+  "module": "src/index.ts",
+  "type": "module",
+  "private": true,
+  "scripts": {
+    "start": "bun run src/cli.ts",
+    "tui": "bun run src/index.ts",
+    "dev": "bun --watch run src/cli.ts"
+  },
+  "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/src/cli.ts
+++ b/src/cli.ts
@ -0,0 +1,238 @@
+import * as readline from 'readline'
+import { RedditScraper } from './scraper/reddit'
+import { CommentFetcher } from './scraper/comments'
+import { EmbeddingPipeline } from './embeddings/ollama'
+import { QdrantStorage } from './storage/qdrant'
+import type { RedditComment } from './scraper/types'
+import type { SearchResult } from './storage/types'
+
+const rl = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+})
+
+function prompt(question: string): Promise<string> {
+  return new Promise(resolve => rl.question(question, resolve))
+}
+
+function progressBar(current: number, total: number, width: number = 30): string {
+  const percent = total > 0 ? current / total : 0
+  const filled = Math.round(percent * width)
+  const empty = width - filled
+  return `[${'█'.repeat(filled)}${'░'.repeat(empty)}] ${Math.round(percent * 100)}%`
+}
+
+async function main() {
+  console.log('\n=== reddit trend analyzer ===\n')
+
+  const scraper = new RedditScraper(5000)
+  const commentFetcher = new CommentFetcher(5000)
+  const embeddings = new EmbeddingPipeline()
+  const storage = new QdrantStorage()
+
+  // check connections
+  console.log('checking connections...')
+
+  const ollamaOk = await embeddings.checkConnection()
+  if (!ollamaOk) {
+    console.error('error: cannot connect to ollama')
+    console.error('run: ollama pull nomic-embed-text')
+    process.exit(1)
+  }
+  console.log('  ollama: ok')
+
+  const qdrantOk = await storage.checkConnection()
+  if (!qdrantOk) {
+    console.error('error: cannot connect to qdrant')
+    process.exit(1)
+  }
+  console.log('  qdrant: ok')
+
+  await storage.ensureCollection()
+  const stats = await storage.getStats()
+  console.log(`\ncurrent stats: ${stats.posts} posts, ${stats.comments} comments, ${stats.subreddits.length} subreddits\n`)
+
+  let lastResults: SearchResult[] = []
+
+  while (true) {
+    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('  stats                 - show collection stats')
+    console.log('  export json|csv       - export last search results')
+    console.log('  quit                  - exit\n')
+
+    const input = await prompt('> ')
+    const [cmd, ...args] = input.trim().split(' ')
+
+    if (cmd === 'quit' || cmd === 'q' || cmd === 'exit') {
+      console.log('bye!')
+      rl.close()
+      process.exit(0)
+    }
+
+    if (cmd === 'stats') {
+      const s = await storage.getStats()
+      console.log(`\nposts: ${s.posts.toLocaleString()}`)
+      console.log(`comments: ${s.comments.toLocaleString()}`)
+      console.log(`subreddits: ${s.subreddits.join(', ') || 'none'}`)
+      continue
+    }
+
+    if (cmd === 'scrape') {
+      const url = args[0]
+      const pages = parseInt(args[1] || '3', 10)
+
+      if (!url) {
+        console.log('usage: scrape <url> [pages]')
+        console.log('example: scrape https://reddit.com/r/vfx/best 5')
+        continue
+      }
+
+      console.log(`\nscraping ${url} (${pages} pages)...\n`)
+
+      try {
+        // fetch posts
+        scraper.setProgressCallback((p) => {
+          process.stdout.write(`\rfetching posts: ${progressBar(p.current, p.total)} ${p.current}/${p.total}  `)
+        })
+
+        const posts = await scraper.fetchPosts({
+          url,
+          pages,
+          postsPerPage: 100,
+          fetchComments: true,
+          delayMs: 3000,
+        })
+        console.log(`\nfetched ${posts.length} posts`)
+
+        // fetch comments
+        commentFetcher.setProgressCallback((p) => {
+          process.stdout.write(`\rfetching comments: ${progressBar(p.current, p.total)} ${p.current}/${p.total}  `)
+        })
+
+        const commentsByPost = await commentFetcher.fetchAllComments(posts)
+        const totalComments = Array.from(commentsByPost.values()).reduce((acc, c) => acc + c.length, 0)
+        console.log(`\nfetched ${totalComments} comments`)
+
+        // embed posts
+        embeddings.setProgressCallback((p) => {
+          process.stdout.write(`\rembedding posts: ${progressBar(p.current, p.total)} ${p.current}/${p.total}  `)
+        })
+
+        const postPoints = await embeddings.embedPosts(posts, commentsByPost)
+        await storage.upsertPoints(postPoints)
+        console.log(`\nembedded ${postPoints.length} posts`)
+
+        // embed comments
+        const allComments: RedditComment[] = []
+        for (const comments of commentsByPost.values()) {
+          allComments.push(...comments)
+        }
+
+        embeddings.setProgressCallback((p) => {
+          process.stdout.write(`\rembedding comments: ${progressBar(p.current, p.total)} ${p.current}/${p.total}  `)
+        })
+
+        const commentPoints = await embeddings.embedComments(allComments)
+        await storage.upsertPoints(commentPoints)
+        console.log(`\nembedded ${commentPoints.length} comments`)
+
+        console.log('\ndone!')
+
+      } catch (err) {
+        console.error('\nerror:', err instanceof Error ? err.message : err)
+      }
+      continue
+    }
+
+    if (cmd === 'search') {
+      const query = args.join(' ')
+      if (!query) {
+        console.log('usage: search <query>')
+        continue
+      }
+
+      try {
+        console.log(`\nsearching for "${query}"...\n`)
+        const vector = await embeddings.embed(query)
+        const results = await storage.search(vector, 10)
+        lastResults = results
+
+        if (results.length === 0) {
+          console.log('no results found')
+          continue
+        }
+
+        for (const r of results) {
+          const preview = (r.payload.title || r.payload.body || '').slice(0, 80).replace(/\n/g, ' ')
+          const type = r.payload.type === 'post' ? '[post]' : '[comment]'
+          console.log(`${type} (${r.score.toFixed(3)}) ${preview}...`)
+          console.log(`  -> ${r.payload.permalink}\n`)
+        }
+      } catch (err) {
+        console.error('error:', err instanceof Error ? err.message : err)
+      }
+      continue
+    }
+
+    if (cmd === 'export') {
+      const format = args[0]
+      if (!format || !['json', 'csv'].includes(format)) {
+        console.log('usage: export json|csv')
+        continue
+      }
+
+      if (lastResults.length === 0) {
+        console.log('no results to export (run a search first)')
+        continue
+      }
+
+      const filename = `reddit-trends.${format}`
+
+      if (format === 'json') {
+        const data = lastResults.map(r => ({
+          id: r.payload.id,
+          type: r.payload.type,
+          subreddit: r.payload.subreddit,
+          title: r.payload.title,
+          body: r.payload.body,
+          author: r.payload.author,
+          score: r.payload.score,
+          created: new Date(r.payload.created * 1000).toISOString(),
+          permalink: r.payload.permalink,
+          similarity: r.score,
+        }))
+        await Bun.write(filename, JSON.stringify(data, null, 2))
+      } else {
+        const headers = ['id', 'type', 'subreddit', 'title', 'body', 'author', 'score', 'created', 'permalink', 'similarity']
+        const escape = (val: string | number | undefined): string => {
+          if (val === undefined) return ''
+          const str = String(val)
+          if (str.includes(',') || str.includes('"') || str.includes('\n')) {
+            return `"${str.replace(/"/g, '""')}"`
+          }
+          return str
+        }
+        const rows = lastResults.map(r => [
+          r.payload.id, r.payload.type, r.payload.subreddit, r.payload.title || '',
+          r.payload.body, r.payload.author, r.payload.score,
+          new Date(r.payload.created * 1000).toISOString(), r.payload.permalink, r.score.toFixed(4),
+        ].map(escape).join(','))
+        await Bun.write(filename, [headers.join(','), ...rows].join('\n'))
+      }
+
+      console.log(`exported to ${filename}`)
+      continue
+    }
+
+    if (cmd) {
+      console.log(`unknown command: ${cmd}`)
+    }
+  }
+}
+
+main().catch(err => {
+  console.error('fatal error:', err)
+  process.exit(1)
+})
--- a/src/embeddings/ollama.ts
+++ b/src/embeddings/ollama.ts
@ -0,0 +1,175 @@
+import { Ollama } from 'ollama'
+import { prepareForEmbedding, cleanText } from '../utils/text'
+import type { RedditPost, RedditComment } from '../scraper/types'
+import type { PointPayload } from '../storage/types'
+
+const MODEL = 'nomic-embed-text'
+const VECTOR_DIM = 768
+const BATCH_SIZE = 10
+
+export interface EmbeddedPoint {
+  id: string
+  vector: number[]
+  payload: PointPayload
+}
+
+export interface EmbeddingProgress {
+  current: number
+  total: number
+  message: string
+}
+
+export class EmbeddingPipeline {
+  private ollama: Ollama
+  private onProgress?: (progress: EmbeddingProgress) => void
+
+  constructor(host?: string) {
+    this.ollama = new Ollama({
+      host: host || process.env.OLLAMA_HOST || 'http://localhost:11434',
+    })
+  }
+
+  setProgressCallback(callback: (progress: EmbeddingProgress) => void): void {
+    this.onProgress = callback
+  }
+
+  private emitProgress(progress: EmbeddingProgress): void {
+    this.onProgress?.(progress)
+  }
+
+  async embed(text: string): Promise<number[]> {
+    const response = await this.ollama.embed({
+      model: MODEL,
+      input: text,
+    })
+    return response.embeddings[0] ?? []
+  }
+
+  async embedBatch(texts: string[]): Promise<number[][]> {
+    const response = await this.ollama.embed({
+      model: MODEL,
+      input: texts,
+    })
+    return response.embeddings
+  }
+
+  async embedPosts(
+    posts: RedditPost[],
+    commentsByPost: Map<string, RedditComment[]>
+  ): Promise<EmbeddedPoint[]> {
+    const points: EmbeddedPoint[] = []
+    const batches: Array<{ text: string; payload: PointPayload }> = []
+
+    for (const post of posts) {
+      const comments = commentsByPost.get(post.id) || []
+      const topComments = comments
+        .sort((a, b) => b.score - a.score)
+        .slice(0, 5)
+        .map(c => c.body)
+
+      const text = prepareForEmbedding(post.title, post.selftext, topComments)
+
+      batches.push({
+        text,
+        payload: {
+          id: post.id,
+          type: 'post',
+          subreddit: post.subreddit,
+          title: post.title,
+          author: post.author,
+          body: post.selftext || '',
+          score: post.score,
+          created: post.created_utc,
+          permalink: `https://reddit.com${post.permalink}`,
+        },
+      })
+    }
+
+    for (let i = 0; i < batches.length; i += BATCH_SIZE) {
+      const batch = batches.slice(i, i + BATCH_SIZE)
+
+      this.emitProgress({
+        current: Math.min(i + BATCH_SIZE, batches.length),
+        total: batches.length,
+        message: `Embedding posts ${i + 1}-${Math.min(i + BATCH_SIZE, batches.length)}`,
+      })
+
+      const texts = batch.map(b => b.text)
+      const embeddings = await this.embedBatch(texts)
+
+      for (let j = 0; j < batch.length; j++) {
+        const item = batch[j]
+        const embedding = embeddings[j]
+        if (item && embedding) {
+          points.push({
+            id: item.payload.id,
+            vector: embedding,
+            payload: item.payload,
+          })
+        }
+      }
+    }
+
+    return points
+  }
+
+  async embedComments(comments: RedditComment[]): Promise<EmbeddedPoint[]> {
+    const points: EmbeddedPoint[] = []
+
+    const validComments = comments.filter(
+      c => c.body && c.body.length > 20 && c.author !== '[deleted]'
+    )
+
+    for (let i = 0; i < validComments.length; i += BATCH_SIZE) {
+      const batch = validComments.slice(i, i + BATCH_SIZE)
+
+      this.emitProgress({
+        current: Math.min(i + BATCH_SIZE, validComments.length),
+        total: validComments.length,
+        message: `Embedding comments ${i + 1}-${Math.min(i + BATCH_SIZE, validComments.length)}`,
+      })
+
+      const texts = batch.map(c => cleanText(c.body))
+      const embeddings = await this.embedBatch(texts)
+
+      for (let j = 0; j < batch.length; j++) {
+        const comment = batch[j]
+        const embedding = embeddings[j]
+        if (comment && embedding) {
+          points.push({
+            id: comment.id,
+            vector: embedding,
+            payload: {
+              id: comment.id,
+              type: 'comment',
+              subreddit: comment.subreddit,
+              author: comment.author,
+              body: comment.body,
+              score: comment.score,
+              created: comment.created_utc,
+              permalink: `https://reddit.com${comment.permalink}`,
+              parent_id: comment.parent_id,
+            },
+          })
+        }
+      }
+    }
+
+    return points
+  }
+
+  async checkConnection(): Promise<boolean> {
+    try {
+      const result = await this.ollama.list()
+      const hasModel = result.models.some(m => m.name.startsWith(MODEL))
+      if (!hasModel) {
+        console.warn(`Model ${MODEL} not found. Available models:`, result.models.map(m => m.name))
+      }
+      return true
+    } catch {
+      return false
+    }
+  }
+}
+
+export { VECTOR_DIM }
--- a/src/index.ts
+++ b/src/index.ts
@ -0,0 +1,59 @@
+import { createApp } from './tui/app'
+
+async function main() {
+  console.clear()
+
+  const ollamaOk = await checkOllama()
+  if (!ollamaOk) {
+    console.error('error: cannot connect to ollama at', process.env.OLLAMA_HOST || 'http://localhost:11434')
+    console.error('make sure ollama is running and nomic-embed-text model is available')
+    console.error('run: ollama pull nomic-embed-text')
+    process.exit(1)
+  }
+
+  const qdrantOk = await checkQdrant()
+  if (!qdrantOk) {
+    console.error('error: cannot connect to qdrant at', process.env.QDRANT_URL || 'http://localhost:6333')
+    console.error('make sure qdrant is running and QDRANT_API_KEY is set if required')
+    process.exit(1)
+  }
+
+  await createApp()
+}
+
+async function checkOllama(): Promise<boolean> {
+  try {
+    const { Ollama } = await import('ollama')
+    const client = new Ollama({
+      host: process.env.OLLAMA_HOST || 'http://localhost:11434',
+    })
+    const models = await client.list()
+    return models.models.some(m => m.name.includes('nomic-embed-text'))
+  } catch {
+    return false
+  }
+}
+
+async function checkQdrant(): Promise<boolean> {
+  try {
+    const { QdrantClient } = await import('@qdrant/js-client-rest')
+    const qdrantUrl = process.env.QDRANT_URL || 'http://localhost:6333'
+    const parsedUrl = new URL(qdrantUrl)
+
+    const client = new QdrantClient({
+      host: parsedUrl.hostname,
+      port: parsedUrl.port ? parseInt(parsedUrl.port) : (parsedUrl.protocol === 'https:' ? 443 : 6333),
+      https: parsedUrl.protocol === 'https:',
+      apiKey: process.env.QDRANT_API_KEY,
+    })
+    await client.getCollections()
+    return true
+  } catch {
+    return false
+  }
+}
+
+main().catch(err => {
+  console.error('fatal error:', err)
+  process.exit(1)
+})
--- a/src/scraper/comments.ts
+++ b/src/scraper/comments.ts
@ -0,0 +1,120 @@
+import { RateLimiter, fetchWithRetry } from '../utils/rate-limit'
+import type {
+  RedditComment,
+  RedditPost,
+  RedditListing,
+  RedditCommentData,
+  ScrapeProgress,
+} from './types'
+
+const USER_AGENT = 'reddit-trend-analyzer/1.0 (by /u/trend-analyzer-bot)'
+
+export class CommentFetcher {
+  private rateLimiter: RateLimiter
+  private onProgress?: (progress: ScrapeProgress) => void
+
+  constructor(delayMs: number = 5000) {
+    this.rateLimiter = new RateLimiter(delayMs)
+  }
+
+  setProgressCallback(callback: (progress: ScrapeProgress) => void): void {
+    this.onProgress = callback
+  }
+
+  private emitProgress(progress: ScrapeProgress): void {
+    this.onProgress?.(progress)
+  }
+
+  private extractComments(
+    listing: RedditListing<RedditCommentData>,
+    subreddit: string,
+    depth: number = 0
+  ): RedditComment[] {
+    const comments: RedditComment[] = []
+
+    for (const child of listing.data.children) {
+      if (child.kind !== 't1') continue
+      const data = child.data
+
+      if (data.author === '[deleted]' || !data.body) continue
+
+      comments.push({
+        id: data.id,
+        name: data.name,
+        body: data.body,
+        author: data.author,
+        score: data.score,
+        created_utc: data.created_utc,
+        permalink: data.permalink,
+        parent_id: data.parent_id,
+        subreddit: subreddit,
+        depth: depth,
+      })
+
+      if (data.replies && typeof data.replies === 'object') {
+        comments.push(
+          ...this.extractComments(data.replies, subreddit, depth + 1)
+        )
+      }
+    }
+
+    return comments
+  }
+
+  async fetchCommentsForPost(post: RedditPost): Promise<RedditComment[]> {
+    const url = `https://www.reddit.com${post.permalink}.json?limit=100&depth=3`
+
+    try {
+      const response = await fetchWithRetry<
+        [RedditListing<RedditCommentData>, RedditListing<RedditCommentData>]
+      >(
+        url,
+        {
+          headers: {
+            'User-Agent': USER_AGENT,
+            'Accept': 'application/json',
+          },
+        },
+        this.rateLimiter,
+        3
+      )
+
+      if (!response[1]?.data?.children) {
+        return []
+      }
+
+      return this.extractComments(response[1], post.subreddit)
+    } catch (error) {
+      // silently skip failed posts
+      return []
+    }
+  }
+
+  async fetchAllComments(posts: RedditPost[]): Promise<Map<string, RedditComment[]>> {
+    const commentsByPost = new Map<string, RedditComment[]>()
+
+    for (let i = 0; i < posts.length; i++) {
+      const post = posts[i]
+      if (!post) continue
+
+      this.emitProgress({
+        phase: 'comments',
+        current: i + 1,
+        total: posts.length,
+        message: `Fetching comments for post ${i + 1}/${posts.length}`,
+      })
+
+      const comments = await this.fetchCommentsForPost(post)
+      commentsByPost.set(post.id, comments)
+    }
+
+    this.emitProgress({
+      phase: 'comments',
+      current: posts.length,
+      total: posts.length,
+      message: 'Done fetching comments',
+    })
+
+    return commentsByPost
+  }
+}
--- a/src/scraper/reddit.ts
+++ b/src/scraper/reddit.ts
@ -0,0 +1,120 @@
+import { RateLimiter, fetchWithRetry } from '../utils/rate-limit'
+import type {
+  RedditPost,
+  RedditListing,
+  RedditPostData,
+  ScrapeOptions,
+  ScrapeProgress,
+} from './types'
+
+const USER_AGENT = 'reddit-trend-analyzer/1.0 (by /u/trend-analyzer-bot)'
+
+export function normalizeRedditUrl(url: string): string {
+  let normalized = url.trim()
+
+  if (!normalized.startsWith('http')) {
+    normalized = `https://www.reddit.com${normalized.startsWith('/') ? '' : '/'}${normalized}`
+  }
+
+  normalized = normalized
+    .replace('old.reddit.com', 'www.reddit.com')
+    .replace('new.reddit.com', 'www.reddit.com')
+
+  if (!normalized.endsWith('.json') && !normalized.includes('.json?')) {
+    const hasQuery = normalized.includes('?')
+    normalized = hasQuery
+      ? normalized.replace('?', '.json?')
+      : normalized + '.json'
+  }
+
+  return normalized
+}
+
+export function parseSubredditFromUrl(url: string): string {
+  const match = url.match(/\/r\/([^/?]+)/)
+  return match?.[1] ?? 'unknown'
+}
+
+export class RedditScraper {
+  private rateLimiter: RateLimiter
+  private onProgress?: (progress: ScrapeProgress) => void
+
+  constructor(delayMs: number = 5000) {
+    this.rateLimiter = new RateLimiter(delayMs)
+  }
+
+  setProgressCallback(callback: (progress: ScrapeProgress) => void): void {
+    this.onProgress = callback
+  }
+
+  private emitProgress(progress: ScrapeProgress): void {
+    this.onProgress?.(progress)
+  }
+
+  async fetchPosts(options: ScrapeOptions): Promise<RedditPost[]> {
+    const baseUrl = normalizeRedditUrl(options.url)
+    const subreddit = parseSubredditFromUrl(options.url)
+    const posts: RedditPost[] = []
+    let after: string | null = null
+
+    for (let page = 0; page < options.pages; page++) {
+      const url = new URL(baseUrl)
+      url.searchParams.set('limit', String(options.postsPerPage))
+      if (after) {
+        url.searchParams.set('after', after)
+      }
+
+      this.emitProgress({
+        phase: 'posts',
+        current: posts.length,
+        total: options.pages * options.postsPerPage,
+        message: `Fetching page ${page + 1}/${options.pages}...`,
+      })
+
+      const listing = await fetchWithRetry<RedditListing<RedditPostData>>(
+        url.toString(),
+        {
+          headers: {
+            'User-Agent': USER_AGENT,
+            'Accept': 'application/json',
+          },
+        },
+        this.rateLimiter,
+        3
+      )
+
+      for (const child of listing.data.children) {
+        if (child.kind !== 't3') continue
+        const data = child.data
+
+        posts.push({
+          id: data.id,
+          name: data.name,
+          title: data.title,
+          selftext: data.selftext || '',
+          author: data.author,
+          score: data.score,
+          upvote_ratio: data.upvote_ratio,
+          num_comments: data.num_comments,
+          created_utc: data.created_utc,
+          permalink: data.permalink,
+          subreddit: subreddit,
+          url: data.url,
+          is_self: data.is_self,
+        })
+      }
+
+      after = listing.data.after
+      if (!after) break
+    }
+
+    this.emitProgress({
+      phase: 'posts',
+      current: posts.length,
+      total: posts.length,
+      message: `Fetched ${posts.length} posts`,
+    })
+
+    return posts
+  }
+}
--- a/src/scraper/types.ts
+++ b/src/scraper/types.ts
@ -0,0 +1,87 @@
+export interface RedditPost {
+  id: string
+  name: string
+  title: string
+  selftext: string
+  author: string
+  score: number
+  upvote_ratio: number
+  num_comments: number
+  created_utc: number
+  permalink: string
+  subreddit: string
+  url: string
+  is_self: boolean
+}
+
+export interface RedditComment {
+  id: string
+  name: string
+  body: string
+  author: string
+  score: number
+  created_utc: number
+  permalink: string
+  parent_id: string
+  subreddit: string
+  depth: number
+}
+
+export interface RedditListingData<T> {
+  after: string | null
+  before: string | null
+  children: Array<{ kind: string; data: T }>
+  dist: number
+}
+
+export interface RedditListing<T> {
+  kind: string
+  data: RedditListingData<T>
+}
+
+export interface RedditPostData {
+  id: string
+  name: string
+  title: string
+  selftext: string
+  selftext_html: string | null
+  author: string
+  score: number
+  upvote_ratio: number
+  num_comments: number
+  created_utc: number
+  permalink: string
+  subreddit: string
+  url: string
+  is_self: boolean
+}
+
+export interface RedditCommentData {
+  id: string
+  name: string
+  body: string
+  body_html: string
+  author: string
+  score: number
+  created_utc: number
+  permalink: string
+  parent_id: string
+  subreddit: string
+  depth: number
+  replies?: RedditListing<RedditCommentData> | ''
+}
+
+export interface ScrapeOptions {
+  url: string
+  pages: number
+  postsPerPage: number
+  fetchComments: boolean
+  delayMs: number
+}
+
+export interface ScrapeProgress {
+  phase: 'posts' | 'comments' | 'done'
+  current: number
+  total: number
+  message: string
+}
--- a/src/storage/qdrant.ts
+++ b/src/storage/qdrant.ts
@ -0,0 +1,183 @@
+import { QdrantClient } from '@qdrant/js-client-rest'
+import type { EmbeddedPoint } from '../embeddings/ollama'
+import type { PointPayload, SearchResult, CollectionStats } from './types'
+import { VECTOR_DIM } from '../embeddings/ollama'
+
+const COLLECTION_NAME = 'reddit_trends'
+const BATCH_SIZE = 100
+
+export class QdrantStorage {
+  private client: QdrantClient
+  private collectionName: string
+
+  constructor(url?: string, apiKey?: string, collectionName?: string) {
+    const qdrantUrl = url || process.env.QDRANT_URL || 'http://localhost:6333'
+    const parsedUrl = new URL(qdrantUrl)
+
+    this.client = new QdrantClient({
+      host: parsedUrl.hostname,
+      port: parsedUrl.port ? parseInt(parsedUrl.port) : (parsedUrl.protocol === 'https:' ? 443 : 6333),
+      https: parsedUrl.protocol === 'https:',
+      apiKey: apiKey || process.env.QDRANT_API_KEY,
+    })
+    this.collectionName = collectionName || COLLECTION_NAME
+  }
+
+  async ensureCollection(): Promise<void> {
+    const collections = await this.client.getCollections()
+    const exists = collections.collections.some(c => c.name === this.collectionName)
+
+    if (!exists) {
+      await this.client.createCollection(this.collectionName, {
+        vectors: {
+          size: VECTOR_DIM,
+          distance: 'Cosine',
+        },
+      })
+
+      await this.client.createPayloadIndex(this.collectionName, {
+        field_name: 'subreddit',
+        field_schema: 'keyword',
+      })
+
+      await this.client.createPayloadIndex(this.collectionName, {
+        field_name: 'type',
+        field_schema: 'keyword',
+      })
+
+      await this.client.createPayloadIndex(this.collectionName, {
+        field_name: 'created',
+        field_schema: 'integer',
+      })
+
+      await this.client.createPayloadIndex(this.collectionName, {
+        field_name: 'score',
+        field_schema: 'integer',
+      })
+    }
+  }
+
+  async upsertPoints(points: EmbeddedPoint[]): Promise<void> {
+    for (let i = 0; i < points.length; i += BATCH_SIZE) {
+      const batch = points.slice(i, i + BATCH_SIZE)
+
+      await this.client.upsert(this.collectionName, {
+        wait: true,
+        points: batch.map((point, idx) => ({
+          id: i + idx,
+          vector: point.vector,
+          payload: point.payload as unknown as Record<string, unknown>,
+        })),
+      })
+    }
+  }
+
+  async search(
+    vector: number[],
+    limit: number = 10,
+    filters?: {
+      subreddit?: string
+      type?: 'post' | 'comment'
+      minScore?: number
+      afterDate?: number
+    }
+  ): Promise<SearchResult[]> {
+    const must: any[] = []
+
+    if (filters?.subreddit) {
+      must.push({
+        key: 'subreddit',
+        match: { value: filters.subreddit },
+      })
+    }
+
+    if (filters?.type) {
+      must.push({
+        key: 'type',
+        match: { value: filters.type },
+      })
+    }
+
+    if (filters?.minScore !== undefined) {
+      must.push({
+        key: 'score',
+        range: { gte: filters.minScore },
+      })
+    }
+
+    if (filters?.afterDate !== undefined) {
+      must.push({
+        key: 'created',
+        range: { gte: filters.afterDate },
+      })
+    }
+
+    const results = await this.client.search(this.collectionName, {
+      vector,
+      limit,
+      with_payload: true,
+      filter: must.length > 0 ? { must } : undefined,
+    })
+
+    return results.map(r => ({
+      id: String(r.id),
+      score: r.score,
+      payload: r.payload as unknown as PointPayload,
+    }))
+  }
+
+  async getStats(): Promise<CollectionStats> {
+    try {
+      const info = await this.client.getCollection(this.collectionName)
+      const pointCount = info.points_count || 0
+
+      const postCount = await this.client.count(this.collectionName, {
+        filter: { must: [{ key: 'type', match: { value: 'post' } }] },
+      })
+
+      const commentCount = await this.client.count(this.collectionName, {
+        filter: { must: [{ key: 'type', match: { value: 'comment' } }] },
+      })
+
+      const scroll = await this.client.scroll(this.collectionName, {
+        limit: 1000,
+        with_payload: { include: ['subreddit'] },
+      })
+
+      const subreddits = new Set<string>()
+      for (const point of scroll.points) {
+        const payload = point.payload as { subreddit?: string }
+        if (payload?.subreddit) {
+          subreddits.add(payload.subreddit)
+        }
+      }
+
+      return {
+        posts: postCount.count,
+        comments: commentCount.count,
+        subreddits: Array.from(subreddits),
+      }
+    } catch {
+      return { posts: 0, comments: 0, subreddits: [] }
+    }
+  }
+
+  async deleteCollection(): Promise<void> {
+    try {
+      await this.client.deleteCollection(this.collectionName)
+    } catch {
+      // collection might not exist
+    }
+  }
+
+  async checkConnection(): Promise<boolean> {
+    try {
+      await this.client.getCollections()
+      return true
+    } catch {
+      return false
+    }
+  }
+}
+
+export { COLLECTION_NAME }
--- a/src/storage/types.ts
+++ b/src/storage/types.ts
@ -0,0 +1,24 @@
+export interface PointPayload {
+  id: string
+  type: 'post' | 'comment'
+  subreddit: string
+  title?: string
+  author: string
+  body: string
+  score: number
+  created: number
+  permalink: string
+  parent_id?: string
+}
+
+export interface SearchResult {
+  id: string
+  score: number
+  payload: PointPayload
+}
+
+export interface CollectionStats {
+  posts: number
+  comments: number
+  subreddits: string[]
+}
--- a/src/tui/app.ts
+++ b/src/tui/app.ts
@ -0,0 +1,268 @@
+import {
+  createCliRenderer,
+  BoxRenderable,
+  TextRenderable,
+  type KeyEvent,
+} from '@opentui/core'
+
+import { RedditScraper } from '../scraper/reddit'
+import { CommentFetcher } from '../scraper/comments'
+import { EmbeddingPipeline } from '../embeddings/ollama'
+import { QdrantStorage } from '../storage/qdrant'
+import type { RedditComment } from '../scraper/types'
+import type { SearchResult } from '../storage/types'
+
+import { createUrlInput, focusUrlInput } from './components/url-input'
+import { createProgressPanel, updateProgress, resetProgress } from './components/progress'
+import { createStatsPanel, updateStats } from './components/stats'
+import { createTrendingPanel, updateTrending } from './components/trending'
+import { createSearchPanel, updateSearchResults, focusSearch } from './components/search'
+import { createExportBar, exportToJson, exportToCsv } from './components/export'
+
+export interface AppState {
+  url: string
+  pages: number
+  postsPerPage: number
+  isRunning: boolean
+  lastResults: SearchResult[]
+}
+
+export async function createApp() {
+  const renderer = await createCliRenderer({ exitOnCtrlC: false })
+
+  const state: AppState = {
+    url: '',
+    pages: 5,
+    postsPerPage: 100,
+    isRunning: false,
+    lastResults: [],
+  }
+
+  const scraper = new RedditScraper(3000)
+  const commentFetcher = new CommentFetcher(3000)
+  const embeddings = new EmbeddingPipeline()
+  const storage = new QdrantStorage()
+
+  const root = new BoxRenderable(renderer, {
+    id: 'root',
+    flexDirection: 'column',
+    padding: 1,
+  })
+
+  const header = new BoxRenderable(renderer, {
+    id: 'header',
+    flexDirection: 'row',
+    justifyContent: 'space-between',
+    paddingBottom: 1,
+  })
+
+  header.add(new TextRenderable(renderer, {
+    id: 'title',
+    content: '  reddit trend analyzer',
+  }))
+
+  header.add(new TextRenderable(renderer, {
+    id: 'quit-hint-header',
+    content: '[q]uit  ',
+  }))
+
+  root.add(header)
+
+  let progressPanel: BoxRenderable
+  let statsPanel: BoxRenderable
+  let trendingPanel: BoxRenderable
+  let searchPanel: BoxRenderable
+
+  const urlInput = createUrlInput(renderer, {
+    onUrlSubmit: (url) => { state.url = url },
+    onPagesChange: (pages) => { state.pages = pages },
+    onPostsPerPageChange: (count) => { state.postsPerPage = count },
+    onStartScrape: () => runScrape(),
+  })
+  root.add(urlInput)
+
+  progressPanel = createProgressPanel(renderer)
+  root.add(progressPanel)
+
+  const middleRow = new BoxRenderable(renderer, {
+    id: 'middle-row',
+    flexDirection: 'row',
+    gap: 2,
+  })
+
+  statsPanel = createStatsPanel(renderer)
+  middleRow.add(statsPanel)
+
+  trendingPanel = createTrendingPanel(renderer)
+  middleRow.add(trendingPanel)
+
+  root.add(middleRow)
+
+  searchPanel = createSearchPanel(renderer, {
+    onSearch: async (query) => {
+      if (state.isRunning) return
+      try {
+        const vector = await embeddings.embed(query)
+        const results = await storage.search(vector, 10)
+        state.lastResults = results
+        updateSearchResults(searchPanel, results)
+      } catch (err) {
+        console.error('Search error:', err)
+      }
+    },
+  })
+  root.add(searchPanel)
+
+  const exportBar = createExportBar(renderer)
+  root.add(exportBar)
+
+  renderer.root.add(root)
+
+  async function runScrape() {
+    if (state.isRunning || !state.url) return
+    state.isRunning = true
+    resetProgress(progressPanel)
+
+    try {
+      await storage.ensureCollection()
+
+      scraper.setProgressCallback((p) => {
+        updateProgress(progressPanel, {
+          phase: p.phase,
+          current: p.current,
+          total: p.total,
+          message: p.message,
+        })
+      })
+
+      const posts = await scraper.fetchPosts({
+        url: state.url,
+        pages: state.pages,
+        postsPerPage: state.postsPerPage,
+        fetchComments: true,
+        delayMs: 3000,
+      })
+
+      commentFetcher.setProgressCallback((p) => {
+        updateProgress(progressPanel, {
+          phase: 'comments',
+          current: p.current,
+          total: p.total,
+          message: p.message,
+        })
+      })
+
+      const commentsByPost = await commentFetcher.fetchAllComments(posts)
+
+      embeddings.setProgressCallback((p) => {
+        updateProgress(progressPanel, {
+          phase: 'embedding',
+          current: p.current,
+          total: p.total,
+          message: p.message,
+        })
+      })
+
+      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)
+
+      const stats = await storage.getStats()
+      updateStats(statsPanel, stats)
+
+      updateTrending(trendingPanel, [
+        { title: 'scrape complete', count: postPoints.length, avgScore: 0 },
+      ])
+
+    } catch (err) {
+      console.error('Scrape error:', err)
+      updateTrending(trendingPanel, [
+        { title: `error: ${err instanceof Error ? err.message : 'unknown'}`, count: 0, avgScore: 0 },
+      ])
+    } finally {
+      state.isRunning = false
+    }
+  }
+
+  async function refreshStats() {
+    try {
+      const stats = await storage.getStats()
+      updateStats(statsPanel, stats)
+    } catch (err) {
+      console.error('Stats refresh error:', err)
+    }
+  }
+
+  renderer.keyInput.on('keypress', async (key: KeyEvent) => {
+    const urlInputEl = urlInput.getRenderable('url-input')
+    const searchInputEl = searchPanel.getRenderable('search-input')
+    const inputFocused = urlInputEl?.focused || searchInputEl?.focused
+
+    // always allow quit
+    if (key.ctrl && key.name === 'c') {
+      renderer.destroy()
+      process.exit(0)
+    }
+
+    // tab always switches focus
+    if (key.name === 'tab') {
+      if (urlInputEl?.focused) {
+        searchInputEl?.focus()
+      } else {
+        urlInputEl?.focus()
+      }
+      return
+    }
+
+    // escape unfocuses inputs
+    if (key.name === 'escape' && inputFocused) {
+      urlInputEl?.blur?.()
+      searchInputEl?.blur?.()
+      return
+    }
+
+    // only handle hotkeys when no input is focused
+    if (!inputFocused) {
+      if (key.name === 'q') {
+        renderer.destroy()
+        process.exit(0)
+      }
+
+      if (key.name === 'e' && !state.isRunning && state.lastResults.length > 0) {
+        await exportToJson(state.lastResults)
+        updateTrending(trendingPanel, [
+          { title: 'exported to reddit-trends.json', count: 0, avgScore: 0 },
+        ])
+      }
+
+      if (key.name === 'c' && !state.isRunning && state.lastResults.length > 0) {
+        await exportToCsv(state.lastResults)
+        updateTrending(trendingPanel, [
+          { title: 'exported to reddit-trends.csv', count: 0, avgScore: 0 },
+        ])
+      }
+
+      if (key.name === 'r' && !state.isRunning) {
+        await refreshStats()
+      }
+    }
+
+    // enter starts scrape (works even with input focused if url is set)
+    if (key.name === 'return' && !state.isRunning && state.url) {
+      await runScrape()
+    }
+  })
+
+  focusUrlInput(urlInput)
+
+  await refreshStats()
+
+  return { renderer, state }
+}
--- a/src/tui/components/export.ts
+++ b/src/tui/components/export.ts
@ -0,0 +1,92 @@
+import {
+  BoxRenderable,
+  TextRenderable,
+  type RenderContext,
+} from '@opentui/core'
+import type { SearchResult } from '../../storage/types'
+
+export function createExportBar(renderer: RenderContext): BoxRenderable {
+  const container = new BoxRenderable(renderer, {
+    id: 'export-bar',
+    flexDirection: 'row',
+    gap: 2,
+    padding: 1,
+  })
+
+  container.add(new TextRenderable(renderer, {
+    id: 'export-json',
+    content: '[e]xport json',
+  }))
+
+  container.add(new TextRenderable(renderer, {
+    id: 'export-csv',
+    content: '[c]sv',
+  }))
+
+  container.add(new TextRenderable(renderer, {
+    id: 'refresh-stats',
+    content: '[r]efresh stats',
+  }))
+
+  container.add(new TextRenderable(renderer, {
+    id: 'quit-hint',
+    content: '[q]uit',
+  }))
+
+  return container
+}
+
+export async function exportToJson(
+  results: SearchResult[],
+  filename: string = 'reddit-trends.json'
+): Promise<void> {
+  const data = results.map(r => ({
+    id: r.payload.id,
+    type: r.payload.type,
+    subreddit: r.payload.subreddit,
+    title: r.payload.title,
+    body: r.payload.body,
+    author: r.payload.author,
+    score: r.payload.score,
+    created: new Date(r.payload.created * 1000).toISOString(),
+    permalink: r.payload.permalink,
+    similarity: r.score,
+  }))
+
+  await Bun.write(filename, JSON.stringify(data, null, 2))
+}
+
+export async function exportToCsv(
+  results: SearchResult[],
+  filename: string = 'reddit-trends.csv'
+): Promise<void> {
+  const headers = [
+    'id', 'type', 'subreddit', 'title', 'body', 'author',
+    'score', 'created', 'permalink', 'similarity'
+  ]
+
+  const escape = (val: string | number | undefined): string => {
+    if (val === undefined) return ''
+    const str = String(val)
+    if (str.includes(',') || str.includes('"') || str.includes('\n')) {
+      return `"${str.replace(/"/g, '""')}"`
+    }
+    return str
+  }
+
+  const rows = results.map(r => [
+    r.payload.id,
+    r.payload.type,
+    r.payload.subreddit,
+    r.payload.title || '',
+    r.payload.body,
+    r.payload.author,
+    r.payload.score,
+    new Date(r.payload.created * 1000).toISOString(),
+    r.payload.permalink,
+    r.score.toFixed(4),
+  ].map(escape).join(','))
+
+  const csv = [headers.join(','), ...rows].join('\n')
+  await Bun.write(filename, csv)
+}
--- a/src/tui/components/progress.ts
+++ b/src/tui/components/progress.ts
@ -0,0 +1,77 @@
+import {
+  BoxRenderable,
+  TextRenderable,
+  type RenderContext,
+} from '@opentui/core'
+
+export interface ProgressState {
+  phase: string
+  current: number
+  total: number
+  message: string
+}
+
+function createProgressBar(percent: number, width: number = 20): string {
+  const filled = Math.round((percent / 100) * width)
+  const empty = width - filled
+  return '█'.repeat(filled) + '░'.repeat(empty)
+}
+
+export function createProgressPanel(renderer: RenderContext): BoxRenderable {
+  const container = new BoxRenderable(renderer, {
+    id: 'progress-panel',
+    border: true,
+    title: ' progress ',
+    flexDirection: 'column',
+    padding: 1,
+    gap: 0,
+    height: 5,
+  })
+
+  const fetchLine = new TextRenderable(renderer, {
+    id: 'fetch-progress',
+    content: 'waiting...',
+  })
+  container.add(fetchLine)
+
+  const embedLine = new TextRenderable(renderer, {
+    id: 'embed-progress',
+    content: '',
+  })
+  container.add(embedLine)
+
+  return container
+}
+
+export function updateProgress(
+  container: BoxRenderable,
+  state: ProgressState
+): void {
+  const percent = state.total > 0 ? Math.round((state.current / state.total) * 100) : 0
+  const bar = createProgressBar(percent)
+  const text = `${state.message} ${state.current}/${state.total} ${bar} ${percent}%`
+
+  if (state.phase === 'posts' || state.phase === 'comments') {
+    const fetchText = container.getRenderable('fetch-progress') as TextRenderable
+    if (fetchText) {
+      fetchText.content = text
+    }
+  } else if (state.phase === 'embedding') {
+    const embedText = container.getRenderable('embed-progress') as TextRenderable
+    if (embedText) {
+      embedText.content = text
+    }
+  }
+}
+
+export function resetProgress(container: BoxRenderable): void {
+  const fetchText = container.getRenderable('fetch-progress') as TextRenderable
+  const embedText = container.getRenderable('embed-progress') as TextRenderable
+
+  if (fetchText) {
+    fetchText.content = 'waiting...'
+  }
+  if (embedText) {
+    embedText.content = ''
+  }
+}
--- a/src/tui/components/search.ts
+++ b/src/tui/components/search.ts
@ -0,0 +1,94 @@
+import {
+  BoxRenderable,
+  TextRenderable,
+  InputRenderable,
+  InputRenderableEvents,
+  type RenderContext,
+} from '@opentui/core'
+import type { SearchResult } from '../../storage/types'
+
+export interface SearchConfig {
+  onSearch: (query: string) => Promise<void>
+}
+
+export function createSearchPanel(
+  renderer: RenderContext,
+  config: SearchConfig
+): BoxRenderable {
+  const container = new BoxRenderable(renderer, {
+    id: 'search-panel',
+    border: true,
+    title: ' search ',
+    flexDirection: 'column',
+    padding: 1,
+    gap: 1,
+    height: 12,
+  })
+
+  const queryRow = new BoxRenderable(renderer, {
+    id: 'query-row',
+    flexDirection: 'row',
+    gap: 1,
+  })
+
+  queryRow.add(new TextRenderable(renderer, {
+    id: 'query-label',
+    content: 'query:',
+    width: 7,
+  }))
+
+  const queryInput = new InputRenderable(renderer, {
+    id: 'search-input',
+    width: 45,
+    placeholder: 'semantic search...',
+    cursorColor: '#00FF00',
+    focusedBackgroundColor: '#1a1a1a',
+  })
+
+  queryInput.on(InputRenderableEvents.CHANGE, async (value: string) => {
+    if (value.trim()) {
+      await config.onSearch(value)
+    }
+  })
+
+  queryRow.add(queryInput)
+  container.add(queryRow)
+
+  container.add(new TextRenderable(renderer, {
+    id: 'results-label',
+    content: 'results:',
+  }))
+
+  container.add(new TextRenderable(renderer, {
+    id: 'results-content',
+    content: '',
+  }))
+
+  return container
+}
+
+export function updateSearchResults(
+  container: BoxRenderable,
+  results: SearchResult[]
+): void {
+  const resultsContent = container.getRenderable('results-content') as TextRenderable
+  if (!resultsContent) return
+
+  if (results.length === 0) {
+    resultsContent.content = 'no results'
+    return
+  }
+
+  const lines = results.slice(0, 5).map(result => {
+    const preview = (result.payload.title || result.payload.body || '')
+      .slice(0, 50)
+      .replace(/\n/g, ' ')
+    return `> "${preview}..." (${result.score.toFixed(2)})`
+  }).join('\n')
+
+  resultsContent.content = lines
+}
+
+export function focusSearch(container: BoxRenderable): void {
+  container.getRenderable('search-input')?.focus()
+}
--- a/src/tui/components/stats.ts
+++ b/src/tui/components/stats.ts
@ -0,0 +1,51 @@
+import {
+  BoxRenderable,
+  TextRenderable,
+  type RenderContext,
+} from '@opentui/core'
+import type { CollectionStats } from '../../storage/types'
+
+export function createStatsPanel(renderer: RenderContext): BoxRenderable {
+  const container = new BoxRenderable(renderer, {
+    id: 'stats-panel',
+    border: true,
+    title: ' stats ',
+    flexDirection: 'column',
+    padding: 1,
+    width: 20,
+    height: 7,
+  })
+
+  container.add(new TextRenderable(renderer, {
+    id: 'posts-count',
+    content: 'posts: 0',
+  }))
+
+  container.add(new TextRenderable(renderer, {
+    id: 'comments-count',
+    content: 'comments: 0',
+  }))
+
+  container.add(new TextRenderable(renderer, {
+    id: 'subreddits-count',
+    content: 'subreddits: 0',
+  }))
+
+  return container
+}
+
+export function updateStats(container: BoxRenderable, stats: CollectionStats): void {
+  const posts = container.getRenderable('posts-count') as TextRenderable
+  const comments = container.getRenderable('comments-count') as TextRenderable
+  const subreddits = container.getRenderable('subreddits-count') as TextRenderable
+
+  if (posts) {
+    posts.content = `posts: ${stats.posts.toLocaleString()}`
+  }
+  if (comments) {
+    comments.content = `comments: ${stats.comments.toLocaleString()}`
+  }
+  if (subreddits) {
+    subreddits.content = `subreddits: ${stats.subreddits.length}`
+  }
+}
--- a/src/tui/components/trending.ts
+++ b/src/tui/components/trending.ts
@ -0,0 +1,49 @@
+import {
+  BoxRenderable,
+  TextRenderable,
+  type RenderContext,
+} from '@opentui/core'
+
+export interface TrendingTopic {
+  title: string
+  count: number
+  avgScore: number
+}
+
+export function createTrendingPanel(renderer: RenderContext): BoxRenderable {
+  const container = new BoxRenderable(renderer, {
+    id: 'trending-panel',
+    border: true,
+    title: ' trending / status ',
+    flexDirection: 'column',
+    padding: 1,
+    height: 10,
+  })
+
+  container.add(new TextRenderable(renderer, {
+    id: 'trending-content',
+    content: 'scrape data to see trends',
+  }))
+
+  return container
+}
+
+export function updateTrending(
+  container: BoxRenderable,
+  topics: TrendingTopic[]
+): void {
+  const contentText = container.getRenderable('trending-content') as TextRenderable
+  if (!contentText) return
+
+  if (topics.length === 0) {
+    contentText.content = 'no trends found'
+    return
+  }
+
+  const lines = topics
+    .slice(0, 8)
+    .map((topic, i) => `${i + 1}. ${topic.title}`)
+    .join('\n')
+
+  contentText.content = lines
+}
--- a/src/tui/components/url-input.ts
+++ b/src/tui/components/url-input.ts
@ -0,0 +1,115 @@
+import {
+  BoxRenderable,
+  TextRenderable,
+  InputRenderable,
+  InputRenderableEvents,
+  type RenderContext,
+} from '@opentui/core'
+
+export interface UrlInputConfig {
+  onUrlSubmit: (url: string) => void
+  onPagesChange: (pages: number) => void
+  onPostsPerPageChange: (count: number) => void
+  onStartScrape: () => void
+}
+
+export function createUrlInput(renderer: RenderContext, config: UrlInputConfig): BoxRenderable {
+  const container = new BoxRenderable(renderer, {
+    id: 'scrape-panel',
+    border: true,
+    title: ' scrape ',
+    flexDirection: 'column',
+    padding: 1,
+    gap: 1,
+  })
+
+  const urlRow = new BoxRenderable(renderer, {
+    id: 'url-row',
+    flexDirection: 'row',
+    gap: 1,
+  })
+
+  urlRow.add(new TextRenderable(renderer, {
+    id: 'url-label',
+    content: 'url:',
+    width: 5,
+  }))
+
+  const urlInput = new InputRenderable(renderer, {
+    id: 'url-input',
+    width: 50,
+    placeholder: 'https://reddit.com/r/________/best',
+    cursorColor: '#00FF00',
+    focusedBackgroundColor: '#1a1a1a',
+  })
+
+  urlInput.on(InputRenderableEvents.CHANGE, (value: string) => {
+    config.onUrlSubmit(value)
+  })
+
+  urlRow.add(urlInput)
+  container.add(urlRow)
+
+  const optionsRow = new BoxRenderable(renderer, {
+    id: 'options-row',
+    flexDirection: 'row',
+    gap: 2,
+  })
+
+  optionsRow.add(new TextRenderable(renderer, {
+    id: 'pages-label',
+    content: 'pages:',
+  }))
+
+  const pagesInput = new InputRenderable(renderer, {
+    id: 'pages-input',
+    width: 5,
+    placeholder: '5',
+    cursorColor: '#00FF00',
+    focusedBackgroundColor: '#1a1a1a',
+  })
+
+  pagesInput.on(InputRenderableEvents.CHANGE, (value: string) => {
+    const num = parseInt(value, 10)
+    if (!isNaN(num) && num > 0) {
+      config.onPagesChange(num)
+    }
+  })
+
+  optionsRow.add(pagesInput)
+
+  optionsRow.add(new TextRenderable(renderer, {
+    id: 'posts-label',
+    content: 'posts/page:',
+  }))
+
+  const postsInput = new InputRenderable(renderer, {
+    id: 'posts-input',
+    width: 5,
+    placeholder: '100',
+    cursorColor: '#00FF00',
+    focusedBackgroundColor: '#1a1a1a',
+  })
+
+  postsInput.on(InputRenderableEvents.CHANGE, (value: string) => {
+    const num = parseInt(value, 10)
+    if (!isNaN(num) && num > 0) {
+      config.onPostsPerPageChange(num)
+    }
+  })
+
+  optionsRow.add(postsInput)
+
+  optionsRow.add(new TextRenderable(renderer, {
+    id: 'start-hint',
+    content: '[enter to start]',
+  }))
+
+  container.add(optionsRow)
+
+  return container
+}
+
+export function focusUrlInput(container: BoxRenderable): void {
+  container.getRenderable('url-input')?.focus()
+}
--- a/src/utils/rate-limit.ts
+++ b/src/utils/rate-limit.ts
@ -0,0 +1,66 @@
+export function delay(ms: number): Promise<void> {
+  return new Promise(resolve => setTimeout(resolve, ms))
+}
+
+export class RateLimiter {
+  private lastRequest = 0
+  private minDelay: number
+  private backoffMultiplier = 1
+
+  constructor(minDelayMs: number = 5000) {
+    this.minDelay = minDelayMs
+  }
+
+  async wait(): Promise<void> {
+    const now = Date.now()
+    const effectiveDelay = this.minDelay * this.backoffMultiplier
+    const elapsed = now - this.lastRequest
+    if (elapsed < effectiveDelay) {
+      await delay(effectiveDelay - elapsed)
+    }
+    this.lastRequest = Date.now()
+  }
+
+  backoff(): void {
+    this.backoffMultiplier = Math.min(this.backoffMultiplier * 2, 10)
+    console.log(`  rate limited, backing off to ${this.minDelay * this.backoffMultiplier}ms`)
+  }
+
+  reset(): void {
+    this.backoffMultiplier = 1
+  }
+
+  setDelay(ms: number): void {
+    this.minDelay = ms
+  }
+}
+
+export async function fetchWithRetry<T>(
+  url: string,
+  options: RequestInit,
+  rateLimiter: RateLimiter,
+  maxRetries: number = 3
+): Promise<T> {
+  for (let attempt = 0; attempt <= maxRetries; attempt++) {
+    await rateLimiter.wait()
+
+    const response = await fetch(url, options)
+
+    if (response.status === 429) {
+      rateLimiter.backoff()
+      if (attempt < maxRetries) {
+        continue
+      }
+      throw new Error('Rate limited after max retries')
+    }
+
+    if (!response.ok) {
+      throw new Error(`HTTP error: ${response.status} ${response.statusText}`)
+    }
+
+    rateLimiter.reset()
+    return response.json() as Promise<T>
+  }
+
+  throw new Error('Max retries exceeded')
+}
--- a/src/utils/text.ts
+++ b/src/utils/text.ts
@ -0,0 +1,69 @@
+const HTML_ENTITIES: Record<string, string> = {
+  '&amp;': '&',
+  '&lt;': '<',
+  '&gt;': '>',
+  '&quot;': '"',
+  '&#39;': "'",
+  '&nbsp;': ' ',
+  '&#x200B;': '',
+}
+
+export function decodeHtmlEntities(text: string): string {
+  let result = text
+  for (const [entity, char] of Object.entries(HTML_ENTITIES)) {
+    result = result.replaceAll(entity, char)
+  }
+  return result
+}
+
+export function stripHtml(text: string): string {
+  return text
+    .replace(/<[^>]+>/g, '')
+    .replace(/\s+/g, ' ')
+    .trim()
+}
+
+export function cleanText(text: string): string {
+  return decodeHtmlEntities(stripHtml(text))
+    .replace(/\[deleted\]/gi, '')
+    .replace(/\[removed\]/gi, '')
+    .replace(/https?:\/\/\S+/g, '')
+    .replace(/\s+/g, ' ')
+    .trim()
+}
+
+export function truncateText(text: string, maxLength: number = 8000): string {
+  if (text.length <= maxLength) return text
+  return text.slice(0, maxLength) + '...'
+}
+
+export function prepareForEmbedding(
+  title: string,
+  body: string,
+  comments: string[] = []
+): string {
+  const parts: string[] = []
+
+  if (title) {
+    parts.push(`Title: ${cleanText(title)}`)
+  }
+
+  if (body) {
+    const cleanBody = cleanText(body)
+    if (cleanBody) {
+      parts.push(`Content: ${cleanBody}`)
+    }
+  }
+
+  if (comments.length > 0) {
+    const topComments = comments
+      .slice(0, 5)
+      .map(c => cleanText(c))
+      .filter(c => c.length > 10)
+    if (topComments.length > 0) {
+      parts.push(`Discussion: ${topComments.join(' | ')}`)
+    }
+  }
+
+  return truncateText(parts.join('\n\n'))
+}
--- a/tsconfig.json
+++ b/tsconfig.json
@ -0,0 +1,29 @@
+{
+  "compilerOptions": {
+    // Environment setup & latest features
+    "lib": ["ESNext"],
+    "target": "ESNext",
+    "module": "Preserve",
+    "moduleDetection": "force",
+    "jsx": "react-jsx",
+    "allowJs": true,
+
+    // Bundler mode
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "verbatimModuleSyntax": true,
+    "noEmit": true,
+
+    // Best practices
+    "strict": true,
+    "skipLibCheck": true,
+    "noFallthroughCasesInSwitch": true,
+    "noUncheckedIndexedAccess": true,
+    "noImplicitOverride": true,
+
+    // Some stricter flags (disabled by default)
+    "noUnusedLocals": false,
+    "noUnusedParameters": false,
+    "noPropertyAccessFromIndexSignature": false
+  }
+}