From a04e9fb374d8de748f1a532e9e6faa252e1d61ce Mon Sep 17 00:00:00 2001
From: ilia <idobkin+github@gmail.com>
Date: Thu, 3 Jul 2025 21:43:45 -0400
Subject: [PATCH] update

---
 README.md            |  494 +++++++++----------
 ai-analyzer-local.js | 1080 +++++++++++++++++++++---------------------
 test/test.js         |   38 +-
 3 files changed, 806 insertions(+), 806 deletions(-)

diff --git a/README.md b/README.md
index 59a5651..5b29249 100644
--- a/README.md
+++ b/README.md
@@ -1,247 +1,247 @@
-# LinkedOut - LinkedIn Posts Scraper
-
-A Node.js application that automates LinkedIn login and scrapes posts containing specific keywords. The tool is designed to help track job market trends, layoffs, and open work opportunities by monitoring LinkedIn content.
-
-## Features
-
-- **Automated LinkedIn Login**: Uses Playwright to automate browser interactions
-- **Keyword-based Search**: Searches for posts containing keywords from CSV files or CLI
-- **Flexible Keyword Sources**: Supports multiple CSV files in `keywords/` or CLI-only mode
-- **Configurable Search Parameters**: Customizable date ranges, sorting options, city, and scroll behavior
-- **Duplicate Detection**: Prevents duplicate posts and profiles in results
-- **Clean Text Processing**: Removes hashtags, emojis, and URLs from post content
-- **Timestamped Results**: Saves results to JSON files with timestamps
-- **Command-line Overrides**: Support for runtime parameter adjustments
-- **Enhanced Geographic Location Validation**: Validates user locations against 200+ Canadian cities with smart matching
-- **Local AI Analysis (Ollama)**: Free, private, and fast post-processing with local LLMs
-- **Flexible Processing**: Disable features, run AI analysis immediately, or process results later
-
-## Prerequisites
-
-- Node.js (v14 or higher)
-- Valid LinkedIn account credentials
-- [Ollama](https://ollama.ai/) with a model (free, private, local AI)
-
-## Installation
-
-1. Clone the repository or download the files
-2. Install dependencies:
-
-   ```bash
-   npm install
-   ```
-
-3. Copy the configuration template and customize:
-
-   ```bash
-   cp env-config.example .env
-   ```
-
-4. Edit `.env` with your settings (see Configuration section below)
-
-## Configuration
-
-### Environment Variables (.env file)
-
-Create a `.env` file from `env-config.example`:
-
-```env
-# LinkedIn Credentials (Required)
-LINKEDIN_USERNAME=your_email@example.com
-LINKEDIN_PASSWORD=your_password
-
-# Basic Settings
-HEADLESS=true
-KEYWORDS=keywords-layoff.csv   # Just the filename; always looks in keywords/ unless path is given
-DATE_POSTED=past-week
-SORT_BY=date_posted
-CITY=Toronto
-WHEELS=5
-
-# Enhanced Location Filtering
-LOCATION_FILTER=Ontario,Manitoba
-ENABLE_LOCATION_CHECK=true
-
-# Local AI Analysis (Ollama)
-ENABLE_LOCAL_AI=true
-OLLAMA_MODEL=mistral
-OLLAMA_HOST=http://localhost:11434
-RUN_LOCAL_AI_AFTER_SCRAPING=false  # true = run after scraping, false = run manually
-AI_CONTEXT=job layoffs and workforce reduction
-AI_CONFIDENCE=0.7
-AI_BATCH_SIZE=3
-```
-
-### Configuration Options
-
-#### Required
-
-- `LINKEDIN_USERNAME`: Your LinkedIn email/username
-- `LINKEDIN_PASSWORD`: Your LinkedIn password
-
-#### Basic Settings
-
-- `HEADLESS`: Browser headless mode (`true`/`false`, default: `true`)
-- `KEYWORDS`: CSV file name (default: `keywords-layoff.csv` in `keywords/` folder)
-- `DATE_POSTED`: Filter by date (`past-24h`, `past-week`, `past-month`, or empty)
-- `SORT_BY`: Sort results (`relevance` or `date_posted`)
-- `CITY`: Search location (default: `Toronto`)
-- `WHEELS`: Number of scrolls to load posts (default: `5`)
-
-#### Enhanced Location Filtering
-
-- `LOCATION_FILTER`: Geographic filter - supports multiple provinces/cities:
-  - Single: `Ontario` or `Toronto`
-  - Multiple: `Ontario,Manitoba` or `Toronto,Vancouver`
-- `ENABLE_LOCATION_CHECK`: Enable location validation (`true`/`false`)
-
-#### Local AI Analysis (Ollama)
-
-- `ENABLE_LOCAL_AI=true`: Enable local AI analysis
-- `OLLAMA_MODEL`: Model to use (`mistral`, `llama2`, `codellama`)
-- `OLLAMA_HOST`: Ollama server URL (default: `http://localhost:11434`)
-- `RUN_LOCAL_AI_AFTER_SCRAPING`: Run AI immediately after scraping (`true`/`false`)
-- `AI_CONTEXT`: Context for analysis (e.g., `job layoffs`)
-- `AI_CONFIDENCE`: Minimum confidence threshold (0.0-1.0, default: 0.7)
-- `AI_BATCH_SIZE`: Posts per batch (default: 3)
-
-## Usage
-
-### Basic Commands
-
-```bash
-# Standard scraping with configured settings
-node linkedout.js
-
-# Visual mode (see browser)
-node linkedout.js --headless=false
-
-# Use only these keywords (ignore CSV)
-node linkedout.js --keyword="layoff,downsizing"
-
-# Add extra keywords to CSV/CLI list
-node linkedout.js --add-keyword="hiring freeze,open to work"
-
-# Override city and date
-node linkedout.js --city="Vancouver" --date_posted=past-month
-
-# Custom output file
-node linkedout.js --output=results/myfile.json
-
-# Skip location and AI filtering (fastest)
-node linkedout.js --no-location --no-ai
-
-# Run AI analysis immediately after scraping
-node linkedout.js --ai-after
-
-# Show help
-node linkedout.js --help
-```
-
-### All Command-line Options
-
-- `--headless=true|false`: Override browser headless mode
-- `--keyword="kw1,kw2"`: Use only these keywords (comma-separated, overrides CSV)
-- `--add-keyword="kw1,kw2"`: Add extra keywords to CSV/CLI list
-- `--city="CityName"`: Override city
-- `--date_posted=VALUE`: Override date posted (past-24h, past-week, past-month, or empty)
-- `--sort_by=VALUE`: Override sort by (date_posted or relevance)
-- `--location_filter=VALUE`: Override location filter
-- `--output=FILE`: Output file name
-- `--no-location`: Disable location filtering
-- `--no-ai`: Disable AI analysis
-- `--ai-after`: Run local AI analysis after scraping
-- `--help, -h`: Show help message
-
-### Keyword Files
-
-- Place all keyword CSVs in the `keywords/` folder
-- Example: `keywords/keywords-layoff.csv`, `keywords/keywords-open-work.csv`
-- Custom CSV format: header `keyword` with one keyword per line
-
-### Local AI Analysis Commands
-
-After scraping, you can run AI analysis on the results:
-
-```bash
-# Analyze latest results
-node ai-analyzer-local.js --context="job layoffs"
-
-# Analyze specific file
-node ai-analyzer-local.js --input=results/results-2024-01-15.json --context="hiring"
-
-# Use different model
-node ai-analyzer-local.js --model=llama2 --context="remote work"
-
-# Change confidence and batch size
-node ai-analyzer-local.js --context="job layoffs" --confidence=0.8 --batch-size=5
-```
-
-## Workflow Examples
-
-### 1. Quick Start (All Features)
-
-```bash
-node linkedout.js --ai-after
-```
-
-### 2. Fast Scraping Only
-
-```bash
-node linkedout.js --no-location --no-ai
-```
-
-### 3. Location-Only Filtering
-
-```bash
-node linkedout.js --no-ai
-```
-
-### 4. Test Different AI Contexts
-
-```bash
-node linkedout.js --no-ai
-node ai-analyzer-local.js --context="job layoffs"
-node ai-analyzer-local.js --context="hiring opportunities"
-node ai-analyzer-local.js --context="remote work"
-```
-
-## Project Structure
-
-```
-linkedout/
-├── .env                     # Your configuration (create from template)
-├── env-config.example       # Configuration template
-├── linkedout.js             # Main scraper
-├── ai-analyzer-local.js     # Free local AI analyzer (Ollama)
-├── location-utils.js        # Enhanced location utilities
-├── package.json             # Dependencies
-├── keywords/                # All keyword CSVs go here
-│   ├── keywords-layoff.csv
-│   └── keywords-open-work.csv
-├── results/                 # Output directory
-└── README.md                # This documentation
-```
-
-## Legal & Security
-
-- **Credentials**: Store securely in `.env`, add to `.gitignore`
-- **LinkedIn ToS**: Respect rate limits and usage guidelines
-- **Privacy**: Local AI keeps all data on your machine
-- **Usage**: Educational and research purposes only
-
-## Dependencies
-
-- `playwright`: Browser automation
-- `dotenv`: Environment variables
-- `csv-parser`: CSV file reading
-- Built-in: `fs`, `path`, `child_process`
-
-## Support
-
-For issues:
-
-1. Check this README
-2. Verify `.env` configuration
-3. Test with `--headless=false` for debugging
-4. Check Ollama status: `ollama list`
+# LinkedOut - LinkedIn Posts Scraper
+
+A Node.js application that automates LinkedIn login and scrapes posts containing specific keywords. The tool is designed to help track job market trends, layoffs, and open work opportunities by monitoring LinkedIn content.
+
+## Features
+
+- **Automated LinkedIn Login**: Uses Playwright to automate browser interactions
+- **Keyword-based Search**: Searches for posts containing keywords from CSV files or CLI
+- **Flexible Keyword Sources**: Supports multiple CSV files in `keywords/` or CLI-only mode
+- **Configurable Search Parameters**: Customizable date ranges, sorting options, city, and scroll behavior
+- **Duplicate Detection**: Prevents duplicate posts and profiles in results
+- **Clean Text Processing**: Removes hashtags, emojis, and URLs from post content
+- **Timestamped Results**: Saves results to JSON files with timestamps
+- **Command-line Overrides**: Support for runtime parameter adjustments
+- **Enhanced Geographic Location Validation**: Validates user locations against 200+ Canadian cities with smart matching
+- **Local AI Analysis (Ollama)**: Free, private, and fast post-processing with local LLMs
+- **Flexible Processing**: Disable features, run AI analysis immediately, or process results later
+
+## Prerequisites
+
+- Node.js (v14 or higher)
+- Valid LinkedIn account credentials
+- [Ollama](https://ollama.ai/) with a model (free, private, local AI)
+
+## Installation
+
+1. Clone the repository or download the files
+2. Install dependencies:
+
+   ```bash
+   npm install
+   ```
+
+3. Copy the configuration template and customize:
+
+   ```bash
+   cp env-config.example .env
+   ```
+
+4. Edit `.env` with your settings (see Configuration section below)
+
+## Configuration
+
+### Environment Variables (.env file)
+
+Create a `.env` file from `env-config.example`:
+
+```env
+# LinkedIn Credentials (Required)
+LINKEDIN_USERNAME=your_email@example.com
+LINKEDIN_PASSWORD=your_password
+
+# Basic Settings
+HEADLESS=true
+KEYWORDS=keywords-layoff.csv   # Just the filename; always looks in keywords/ unless path is given
+DATE_POSTED=past-week
+SORT_BY=date_posted
+CITY=Toronto
+WHEELS=5
+
+# Enhanced Location Filtering
+LOCATION_FILTER=Ontario,Manitoba
+ENABLE_LOCATION_CHECK=true
+
+# Local AI Analysis (Ollama)
+ENABLE_LOCAL_AI=true
+OLLAMA_MODEL=mistral
+OLLAMA_HOST=http://localhost:11434
+RUN_LOCAL_AI_AFTER_SCRAPING=false  # true = run after scraping, false = run manually
+AI_CONTEXT=job layoffs and workforce reduction
+AI_CONFIDENCE=0.7
+AI_BATCH_SIZE=3
+```
+
+### Configuration Options
+
+#### Required
+
+- `LINKEDIN_USERNAME`: Your LinkedIn email/username
+- `LINKEDIN_PASSWORD`: Your LinkedIn password
+
+#### Basic Settings
+
+- `HEADLESS`: Browser headless mode (`true`/`false`, default: `true`)
+- `KEYWORDS`: CSV file name (default: `keywords-layoff.csv` in `keywords/` folder)
+- `DATE_POSTED`: Filter by date (`past-24h`, `past-week`, `past-month`, or empty)
+- `SORT_BY`: Sort results (`relevance` or `date_posted`)
+- `CITY`: Search location (default: `Toronto`)
+- `WHEELS`: Number of scrolls to load posts (default: `5`)
+
+#### Enhanced Location Filtering
+
+- `LOCATION_FILTER`: Geographic filter - supports multiple provinces/cities:
+  - Single: `Ontario` or `Toronto`
+  - Multiple: `Ontario,Manitoba` or `Toronto,Vancouver`
+- `ENABLE_LOCATION_CHECK`: Enable location validation (`true`/`false`)
+
+#### Local AI Analysis (Ollama)
+
+- `ENABLE_LOCAL_AI=true`: Enable local AI analysis
+- `OLLAMA_MODEL`: Model to use (`mistral`, `llama2`, `codellama`)
+- `OLLAMA_HOST`: Ollama server URL (default: `http://localhost:11434`)
+- `RUN_LOCAL_AI_AFTER_SCRAPING`: Run AI immediately after scraping (`true`/`false`)
+- `AI_CONTEXT`: Context for analysis (e.g., `job layoffs`)
+- `AI_CONFIDENCE`: Minimum confidence threshold (0.0-1.0, default: 0.7)
+- `AI_BATCH_SIZE`: Posts per batch (default: 3)
+
+## Usage
+
+### Basic Commands
+
+```bash
+# Standard scraping with configured settings
+node linkedout.js
+
+# Visual mode (see browser)
+node linkedout.js --headless=false
+
+# Use only these keywords (ignore CSV)
+node linkedout.js --keyword="layoff,downsizing"
+
+# Add extra keywords to CSV/CLI list
+node linkedout.js --add-keyword="hiring freeze,open to work"
+
+# Override city and date
+node linkedout.js --city="Vancouver" --date_posted=past-month
+
+# Custom output file
+node linkedout.js --output=results/myfile.json
+
+# Skip location and AI filtering (fastest)
+node linkedout.js --no-location --no-ai
+
+# Run AI analysis immediately after scraping
+node linkedout.js --ai-after
+
+# Show help
+node linkedout.js --help
+```
+
+### All Command-line Options
+
+- `--headless=true|false`: Override browser headless mode
+- `--keyword="kw1,kw2"`: Use only these keywords (comma-separated, overrides CSV)
+- `--add-keyword="kw1,kw2"`: Add extra keywords to CSV/CLI list
+- `--city="CityName"`: Override city
+- `--date_posted=VALUE`: Override date posted (past-24h, past-week, past-month, or empty)
+- `--sort_by=VALUE`: Override sort by (date_posted or relevance)
+- `--location_filter=VALUE`: Override location filter
+- `--output=FILE`: Output file name
+- `--no-location`: Disable location filtering
+- `--no-ai`: Disable AI analysis
+- `--ai-after`: Run local AI analysis after scraping
+- `--help, -h`: Show help message
+
+### Keyword Files
+
+- Place all keyword CSVs in the `keywords/` folder
+- Example: `keywords/keywords-layoff.csv`, `keywords/keywords-open-work.csv`
+- Custom CSV format: header `keyword` with one keyword per line
+
+### Local AI Analysis Commands
+
+After scraping, you can run AI analysis on the results:
+
+```bash
+# Analyze latest results
+node ai-analyzer-local.js --context="job layoffs"
+
+# Analyze specific file
+node ai-analyzer-local.js --input=results/results-2024-01-15.json --context="hiring"
+
+# Use different model
+node ai-analyzer-local.js --model=llama2 --context="remote work"
+
+# Change confidence and batch size
+node ai-analyzer-local.js --context="job layoffs" --confidence=0.8 --batch-size=5
+```
+
+## Workflow Examples
+
+### 1. Quick Start (All Features)
+
+```bash
+node linkedout.js --ai-after
+```
+
+### 2. Fast Scraping Only
+
+```bash
+node linkedout.js --no-location --no-ai
+```
+
+### 3. Location-Only Filtering
+
+```bash
+node linkedout.js --no-ai
+```
+
+### 4. Test Different AI Contexts
+
+```bash
+node linkedout.js --no-ai
+node ai-analyzer-local.js --context="job layoffs"
+node ai-analyzer-local.js --context="hiring opportunities"
+node ai-analyzer-local.js --context="remote work"
+```
+
+## Project Structure
+
+```
+linkedout/
+├── .env                     # Your configuration (create from template)
+├── env-config.example       # Configuration template
+├── linkedout.js             # Main scraper
+├── ai-analyzer-local.js     # Free local AI analyzer (Ollama)
+├── location-utils.js        # Enhanced location utilities
+├── package.json             # Dependencies
+├── keywords/                # All keyword CSVs go here
+│   ├── keywords-layoff.csv
+│   └── keywords-open-work.csv
+├── results/                 # Output directory
+└── README.md                # This documentation
+```
+
+## Legal & Security
+
+- **Credentials**: Store securely in `.env`, add to `.gitignore`
+- **LinkedIn ToS**: Respect rate limits and usage guidelines
+- **Privacy**: Local AI keeps all data on your machine
+- **Usage**: Educational and research purposes only
+
+## Dependencies
+
+- `playwright`: Browser automation
+- `dotenv`: Environment variables
+- `csv-parser`: CSV file reading
+- Built-in: `fs`, `path`, `child_process`
+
+## Support
+
+For issues:
+
+1. Check this README
+2. Verify `.env` configuration
+3. Test with `--headless=false` for debugging
+4. Check Ollama status: `ollama list`
diff --git a/ai-analyzer-local.js b/ai-analyzer-local.js
index fea3a16..35576c5 100644
--- a/ai-analyzer-local.js
+++ b/ai-analyzer-local.js
@@ -1,540 +1,540 @@
-#!/usr/bin/env node
-
-/**
- * Local AI Post-Processing Analyzer for LinkedOut
- *
- * Uses Ollama for completely FREE local AI analysis.
- *
- * FEATURES:
- *   - Analyze LinkedOut results for context relevance (layoffs, hiring, etc.)
- *   - Works on latest or specified results file
- *   - Batch processing for speed
- *   - Configurable context, model, confidence, batch size
- *   - CLI and .env configuration
- *   - 100% local, private, and free
- *
- * USAGE:
- *   node ai-analyzer-local.js [options]
- *
- * COMMAND-LINE OPTIONS:
- *   --input=<file>       Input JSON file (default: latest in results/)
- *   --context=<text>     AI context to analyze against (required)
- *   --confidence=<num>   Minimum confidence threshold (0.0-1.0, default: 0.7)
- *   --model=<name>       Ollama model to use (default: llama2)
- *   --batch-size=<num>   Number of posts to process at once (default: 3)
- *   --output=<file>      Output file (default: adds -ai-local suffix)
- *   --help, -h           Show this help message
- *
- * EXAMPLES:
- *   node ai-analyzer-local.js --context="job layoffs"
- *   node ai-analyzer-local.js --input=results/results-2024-01-15.json --context="hiring"
- *   node ai-analyzer-local.js --model=mistral --context="remote work"
- *   node ai-analyzer-local.js --context="job layoffs" --confidence=0.8 --batch-size=5
- *
- * ENVIRONMENT VARIABLES (.env file):
- *   AI_CONTEXT, AI_CONFIDENCE, AI_BATCH_SIZE, OLLAMA_MODEL, OLLAMA_HOST
- *   See README for full list.
- *
- * OUTPUT:
- *   - Saves to results/ with -ai-local suffix unless --output is specified
- *
- * DEPENDENCIES:
- *   - Ollama (https://ollama.ai/)
- *   - Node.js built-ins: fs, path, fetch
- *
- * SECURITY & LEGAL:
- *   - All analysis is local, no data leaves your machine
- *   - Use responsibly for educational/research purposes
- */
-
-require("dotenv").config();
-const fs = require("fs");
-const path = require("path");
-
-// Configuration from environment and command line
-const DEFAULT_CONTEXT =
-  process.env.AI_CONTEXT || "job layoffs and workforce reduction";
-const DEFAULT_CONFIDENCE = parseFloat(process.env.AI_CONFIDENCE || "0.7");
-const DEFAULT_BATCH_SIZE = parseInt(process.env.AI_BATCH_SIZE || "3");
-const DEFAULT_MODEL = process.env.OLLAMA_MODEL || "llama2";
-const OLLAMA_HOST = process.env.OLLAMA_HOST || "http://localhost:11434";
-
-// Parse command line arguments
-const args = process.argv.slice(2);
-let inputFile = null;
-let context = DEFAULT_CONTEXT;
-let confidenceThreshold = DEFAULT_CONFIDENCE;
-let batchSize = DEFAULT_BATCH_SIZE;
-let model = DEFAULT_MODEL;
-let outputFile = null;
-
-for (const arg of args) {
-  if (arg.startsWith("--input=")) {
-    inputFile = arg.split("=")[1];
-  } else if (arg.startsWith("--context=")) {
-    context = arg.split("=")[1];
-  } else if (arg.startsWith("--confidence=")) {
-    confidenceThreshold = parseFloat(arg.split("=")[1]);
-  } else if (arg.startsWith("--batch-size=")) {
-    batchSize = parseInt(arg.split("=")[1]);
-  } else if (arg.startsWith("--model=")) {
-    model = arg.split("=")[1];
-  } else if (arg.startsWith("--output=")) {
-    outputFile = arg.split("=")[1];
-  }
-}
-
-if (!context) {
-  console.error("❌ Error: No AI context specified");
-  console.error('Use --context="your context" or set AI_CONTEXT in .env');
-  process.exit(1);
-}
-
-/**
- * Check if Ollama is running and the model is available
- */
-async function checkOllamaStatus() {
-  try {
-    // Check if Ollama is running
-    const response = await fetch(`${OLLAMA_HOST}/api/tags`);
-    if (!response.ok) {
-      throw new Error(`Ollama not running on ${OLLAMA_HOST}`);
-    }
-
-    const data = await response.json();
-    const availableModels = data.models.map((m) => m.name);
-
-    console.log(`🤖 Ollama is running`);
-    console.log(
-      `📦 Available models: ${availableModels
-        .map((m) => m.split(":")[0])
-        .join(", ")}`
-    );
-
-    // Check if requested model is available
-    const modelExists = availableModels.some((m) => m.startsWith(model));
-    if (!modelExists) {
-      console.error(`❌ Model "${model}" not found`);
-      console.error(`💡 Install it with: ollama pull ${model}`);
-      console.error(
-        `💡 Or choose from: ${availableModels
-          .map((m) => m.split(":")[0])
-          .join(", ")}`
-      );
-      process.exit(1);
-    }
-
-    console.log(`✅ Using model: ${model}`);
-    return true;
-  } catch (error) {
-    console.error("❌ Error connecting to Ollama:", error.message);
-    console.error("💡 Make sure Ollama is installed and running:");
-    console.error("   1. Install: https://ollama.ai/");
-    console.error("   2. Start: ollama serve");
-    console.error(`   3. Install model: ollama pull ${model}`);
-    process.exit(1);
-  }
-}
-
-/**
- * Find the most recent results file if none specified
- */
-function findLatestResultsFile() {
-  const resultsDir = "results";
-  if (!fs.existsSync(resultsDir)) {
-    throw new Error("Results directory not found. Run the scraper first.");
-  }
-
-  const files = fs
-    .readdirSync(resultsDir)
-    .filter(
-      (f) =>
-        f.startsWith("results-") && f.endsWith(".json") && !f.includes("-ai-")
-    )
-    .sort()
-    .reverse();
-
-  if (files.length === 0) {
-    throw new Error("No results files found. Run the scraper first.");
-  }
-
-  return path.join(resultsDir, files[0]);
-}
-
-/**
- * Analyze multiple posts using local Ollama
- */
-async function analyzeBatch(posts, context, model) {
-  console.log(`🤖 Analyzing batch of ${posts.length} posts with ${model}...`);
-
-  try {
-    const prompt = `You are an expert at analyzing LinkedIn posts for relevance to specific contexts.
-
-CONTEXT TO MATCH: "${context}"
-
-Analyze these ${
-      posts.length
-    } LinkedIn posts and determine if each relates to the context above.
-
-POSTS:
-${posts
-  .map(
-    (post, i) => `
-POST ${i + 1}:
-"${post.text.substring(0, 400)}${post.text.length > 400 ? "..." : ""}"
-`
-  )
-  .join("")}
-
-For each post, provide:
-- Is it relevant to "${context}"? (YES/NO)
-- Confidence level (0.0 to 1.0)
-- Brief reasoning
-
-Respond in this EXACT format for each post:
-POST 1: YES/NO | 0.X | brief reason
-POST 2: YES/NO | 0.X | brief reason
-POST 3: YES/NO | 0.X | brief reason
-
-Examples:
-- For layoff context: "laid off 50 employees" = YES | 0.9 | mentions layoffs
-- For hiring context: "we're hiring developers" = YES | 0.8 | job posting
-- Unrelated content = NO | 0.1 | not relevant to context`;
-
-    const response = await fetch(`${OLLAMA_HOST}/api/generate`, {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        model: model,
-        prompt: prompt,
-        stream: false,
-        options: {
-          temperature: 0.3,
-          top_p: 0.9,
-        },
-      }),
-    });
-
-    if (!response.ok) {
-      throw new Error(
-        `Ollama API error: ${response.status} ${response.statusText}`
-      );
-    }
-
-    const data = await response.json();
-    const aiResponse = data.response.trim();
-
-    // Parse the response
-    const analyses = [];
-    const lines = aiResponse.split("\n").filter((line) => line.trim());
-
-    for (let i = 0; i < posts.length; i++) {
-      let analysis = {
-        postIndex: i + 1,
-        isRelevant: false,
-        confidence: 0.5,
-        reasoning: "Could not parse AI response",
-      };
-
-      // Look for lines that match "POST X:" pattern
-      const postPattern = new RegExp(`POST\\s*${i + 1}:?\\s*(.+)`, "i");
-
-      for (const line of lines) {
-        const match = line.match(postPattern);
-        if (match) {
-          const content = match[1].trim();
-
-          // Parse: YES/NO | 0.X | reasoning
-          const parts = content.split("|").map((p) => p.trim());
-
-          if (parts.length >= 3) {
-            analysis.isRelevant = parts[0].toUpperCase().includes("YES");
-            analysis.confidence = Math.max(
-              0,
-              Math.min(1, parseFloat(parts[1]) || 0.5)
-            );
-            analysis.reasoning = parts[2] || "No reasoning provided";
-          } else {
-            // Fallback parsing
-            analysis.isRelevant =
-              content.toUpperCase().includes("YES") ||
-              content.toLowerCase().includes("relevant");
-            analysis.confidence = 0.6;
-            analysis.reasoning = content.substring(0, 100);
-          }
-          break;
-        }
-      }
-
-      analyses.push(analysis);
-    }
-
-    // If we didn't get enough analyses, fill in defaults
-    while (analyses.length < posts.length) {
-      analyses.push({
-        postIndex: analyses.length + 1,
-        isRelevant: false,
-        confidence: 0.3,
-        reasoning: "AI response parsing failed",
-      });
-    }
-
-    return analyses;
-  } catch (error) {
-    console.error(`❌ Error in batch AI analysis: ${error.message}`);
-
-    // Fallback: mark all as relevant with low confidence
-    return posts.map((_, i) => ({
-      postIndex: i + 1,
-      isRelevant: true,
-      confidence: 0.3,
-      reasoning: `Analysis failed: ${error.message}`,
-    }));
-  }
-}
-
-/**
- * Analyze a single post using local Ollama (fallback)
- */
-async function analyzeSinglePost(text, context, model) {
-  const prompt = `Analyze this LinkedIn post for relevance to: "${context}"
-
-Post: "${text}"
-
-Is this post relevant to "${context}"? Provide:
-1. YES or NO
-2. Confidence (0.0 to 1.0)  
-3. Brief reason
-
-Format: YES/NO | 0.X | reason`;
-
-  try {
-    const response = await fetch(`${OLLAMA_HOST}/api/generate`, {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        model: model,
-        prompt: prompt,
-        stream: false,
-        options: {
-          temperature: 0.3,
-        },
-      }),
-    });
-
-    if (!response.ok) {
-      throw new Error(`Ollama API error: ${response.status}`);
-    }
-
-    const data = await response.json();
-    const aiResponse = data.response.trim();
-
-    // Parse response
-    const parts = aiResponse.split("|").map((p) => p.trim());
-
-    if (parts.length >= 3) {
-      return {
-        isRelevant: parts[0].toUpperCase().includes("YES"),
-        confidence: Math.max(0, Math.min(1, parseFloat(parts[1]) || 0.5)),
-        reasoning: parts[2],
-      };
-    } else {
-      // Fallback parsing
-      return {
-        isRelevant:
-          aiResponse.toLowerCase().includes("yes") ||
-          aiResponse.toLowerCase().includes("relevant"),
-        confidence: 0.6,
-        reasoning: aiResponse.substring(0, 100),
-      };
-    }
-  } catch (error) {
-    return {
-      isRelevant: true, // Default to include on error
-      confidence: 0.3,
-      reasoning: `Analysis failed: ${error.message}`,
-    };
-  }
-}
-
-/**
- * Main processing function
- */
-async function main() {
-  try {
-    console.log("🚀 LinkedOut Local AI Analyzer Starting...");
-    console.log(`📊 Context: "${context}"`);
-    console.log(`🎯 Confidence Threshold: ${confidenceThreshold}`);
-    console.log(`📦 Batch Size: ${batchSize}`);
-    console.log(`🤖 Model: ${model}`);
-
-    // Check Ollama status
-    await checkOllamaStatus();
-
-    // Determine input file
-    if (!inputFile) {
-      inputFile = findLatestResultsFile();
-      console.log(`📂 Using latest results file: ${inputFile}`);
-    } else {
-      console.log(`📂 Using specified file: ${inputFile}`);
-    }
-
-    // Load results
-    if (!fs.existsSync(inputFile)) {
-      throw new Error(`Input file not found: ${inputFile}`);
-    }
-
-    const rawData = fs.readFileSync(inputFile, "utf-8");
-    const results = JSON.parse(rawData);
-
-    if (!Array.isArray(results) || results.length === 0) {
-      throw new Error("No posts found in input file");
-    }
-
-    console.log(`📋 Loaded ${results.length} posts for analysis`);
-
-    // Process in batches
-    const processedResults = [];
-    let totalRelevant = 0;
-    let totalProcessed = 0;
-
-    for (let i = 0; i < results.length; i += batchSize) {
-      const batch = results.slice(i, i + batchSize);
-      console.log(
-        `\n📦 Processing batch ${Math.floor(i / batchSize) + 1}/${Math.ceil(
-          results.length / batchSize
-        )} (${batch.length} posts)`
-      );
-
-      const analyses = await analyzeBatch(batch, context, model);
-
-      // Apply analyses to posts
-      for (let j = 0; j < batch.length; j++) {
-        const post = batch[j];
-        const analysis = analyses[j];
-
-        const enhancedPost = {
-          ...post,
-          aiRelevant: analysis.isRelevant,
-          aiConfidence: analysis.confidence,
-          aiReasoning: analysis.reasoning,
-          aiModel: model,
-          aiAnalyzedAt: new Date().toLocaleString("en-CA", {
-            year: "numeric",
-            month: "2-digit",
-            day: "2-digit",
-            hour: "2-digit",
-            minute: "2-digit",
-            second: "2-digit",
-            hour12: false,
-          }),
-          aiType: "local-ollama",
-          aiProcessed: true,
-        };
-
-        // Apply confidence threshold
-        if (analysis.confidence >= confidenceThreshold) {
-          if (analysis.isRelevant) {
-            processedResults.push(enhancedPost);
-            totalRelevant++;
-          }
-        } else {
-          // Include low-confidence posts but flag them
-          enhancedPost.lowConfidence = true;
-          processedResults.push(enhancedPost);
-        }
-
-        totalProcessed++;
-        console.log(
-          `   ${
-            analysis.isRelevant ? "✅" : "❌"
-          } Post ${totalProcessed}: ${analysis.confidence.toFixed(
-            2
-          )} confidence - ${analysis.reasoning.substring(0, 100)}...`
-        );
-      }
-
-      // Small delay between batches to be nice to the system
-      if (i + batchSize < results.length) {
-        console.log("⏳ Brief pause...");
-        await new Promise((resolve) => setTimeout(resolve, 500));
-      }
-    }
-
-    // Determine output file
-    if (!outputFile) {
-      const inputBasename = path.basename(inputFile, ".json");
-      const inputDir = path.dirname(inputFile);
-      outputFile = path.join(inputDir, `${inputBasename}-ai-local.json`);
-    }
-
-    // Save results
-    fs.writeFileSync(
-      outputFile,
-      JSON.stringify(processedResults, null, 2),
-      "utf-8"
-    );
-
-    console.log("\n🎉 Local AI Analysis Complete!");
-    console.log(`📊 Results:`);
-    console.log(`   Total posts processed: ${totalProcessed}`);
-    console.log(`   Relevant posts found: ${totalRelevant}`);
-    console.log(`   Final results saved: ${processedResults.length}`);
-    console.log(`📁 Output saved to: ${outputFile}`);
-    console.log(`💰 Cost: $0.00 (completely free!)`);
-  } catch (error) {
-    console.error("❌ Error:", error.message);
-    process.exit(1);
-  }
-}
-
-// Show help if requested
-if (args.includes("--help") || args.includes("-h")) {
-  console.log(`
-LinkedOut Local AI Analyzer (Ollama)
-
-🚀 FREE local AI analysis - No API costs, complete privacy!
-
-Usage: node ai-analyzer-local.js [options]
-
-Options:
-  --input=<file>       Input JSON file (default: latest in results/)
-  --context=<text>     AI context to analyze against (required)
-  --confidence=<num>   Minimum confidence threshold (0.0-1.0, default: 0.7)
-  --model=<name>       Ollama model to use (default: llama2)
-  --batch-size=<num>   Number of posts to process at once (default: 3)
-  --output=<file>      Output file (default: adds -ai-local suffix)
-  --help, -h           Show this help message
-
-Examples:
-  node ai-analyzer-local.js --context="job layoffs"
-  node ai-analyzer-local.js --model=mistral --context="hiring opportunities"
-  node ai-analyzer-local.js --context="remote work" --confidence=0.8
-
-Prerequisites:
-  1. Install Ollama: https://ollama.ai/
-  2. Install a model: ollama pull llama2
-  3. Start Ollama: ollama serve
-
-Popular Models:
-  - llama2 (good general purpose)
-  - mistral (fast and accurate)
-  - codellama (good for technical content)
-  - llama2:13b (more accurate, slower)
-
-Environment Variables:
-  AI_CONTEXT           Default context for analysis
-  AI_CONFIDENCE        Default confidence threshold
-  AI_BATCH_SIZE        Default batch size
-  OLLAMA_MODEL         Default model (llama2, mistral, etc.)
-  OLLAMA_HOST          Ollama host (default: http://localhost:11434)
-`);
-  process.exit(0);
-}
-
-// Run the analyzer
-main();
+#!/usr/bin/env node
+
+/**
+ * Local AI Post-Processing Analyzer for LinkedOut
+ *
+ * Uses Ollama for completely FREE local AI analysis.
+ *
+ * FEATURES:
+ *   - Analyze LinkedOut results for context relevance (layoffs, hiring, etc.)
+ *   - Works on latest or specified results file
+ *   - Batch processing for speed
+ *   - Configurable context, model, confidence, batch size
+ *   - CLI and .env configuration
+ *   - 100% local, private, and free
+ *
+ * USAGE:
+ *   node ai-analyzer-local.js [options]
+ *
+ * COMMAND-LINE OPTIONS:
+ *   --input=<file>       Input JSON file (default: latest in results/)
+ *   --context=<text>     AI context to analyze against (required)
+ *   --confidence=<num>   Minimum confidence threshold (0.0-1.0, default: 0.7)
+ *   --model=<name>       Ollama model to use (default: llama2)
+ *   --batch-size=<num>   Number of posts to process at once (default: 3)
+ *   --output=<file>      Output file (default: adds -ai-local suffix)
+ *   --help, -h           Show this help message
+ *
+ * EXAMPLES:
+ *   node ai-analyzer-local.js --context="job layoffs"
+ *   node ai-analyzer-local.js --input=results/results-2024-01-15.json --context="hiring"
+ *   node ai-analyzer-local.js --model=mistral --context="remote work"
+ *   node ai-analyzer-local.js --context="job layoffs" --confidence=0.8 --batch-size=5
+ *
+ * ENVIRONMENT VARIABLES (.env file):
+ *   AI_CONTEXT, AI_CONFIDENCE, AI_BATCH_SIZE, OLLAMA_MODEL, OLLAMA_HOST
+ *   See README for full list.
+ *
+ * OUTPUT:
+ *   - Saves to results/ with -ai-local suffix unless --output is specified
+ *
+ * DEPENDENCIES:
+ *   - Ollama (https://ollama.ai/)
+ *   - Node.js built-ins: fs, path, fetch
+ *
+ * SECURITY & LEGAL:
+ *   - All analysis is local, no data leaves your machine
+ *   - Use responsibly for educational/research purposes
+ */
+
+require("dotenv").config();
+const fs = require("fs");
+const path = require("path");
+
+// Configuration from environment and command line
+const DEFAULT_CONTEXT =
+  process.env.AI_CONTEXT || "job layoffs and workforce reduction";
+const DEFAULT_CONFIDENCE = parseFloat(process.env.AI_CONFIDENCE || "0.7");
+const DEFAULT_BATCH_SIZE = parseInt(process.env.AI_BATCH_SIZE || "3");
+const DEFAULT_MODEL = process.env.OLLAMA_MODEL || "llama2";
+const OLLAMA_HOST = process.env.OLLAMA_HOST || "http://localhost:11434";
+
+// Parse command line arguments
+const args = process.argv.slice(2);
+let inputFile = null;
+let context = DEFAULT_CONTEXT;
+let confidenceThreshold = DEFAULT_CONFIDENCE;
+let batchSize = DEFAULT_BATCH_SIZE;
+let model = DEFAULT_MODEL;
+let outputFile = null;
+
+for (const arg of args) {
+  if (arg.startsWith("--input=")) {
+    inputFile = arg.split("=")[1];
+  } else if (arg.startsWith("--context=")) {
+    context = arg.split("=")[1];
+  } else if (arg.startsWith("--confidence=")) {
+    confidenceThreshold = parseFloat(arg.split("=")[1]);
+  } else if (arg.startsWith("--batch-size=")) {
+    batchSize = parseInt(arg.split("=")[1]);
+  } else if (arg.startsWith("--model=")) {
+    model = arg.split("=")[1];
+  } else if (arg.startsWith("--output=")) {
+    outputFile = arg.split("=")[1];
+  }
+}
+
+if (!context) {
+  console.error("❌ Error: No AI context specified");
+  console.error('Use --context="your context" or set AI_CONTEXT in .env');
+  process.exit(1);
+}
+
+/**
+ * Check if Ollama is running and the model is available
+ */
+async function checkOllamaStatus() {
+  try {
+    // Check if Ollama is running
+    const response = await fetch(`${OLLAMA_HOST}/api/tags`);
+    if (!response.ok) {
+      throw new Error(`Ollama not running on ${OLLAMA_HOST}`);
+    }
+
+    const data = await response.json();
+    const availableModels = data.models.map((m) => m.name);
+
+    console.log(`🤖 Ollama is running`);
+    console.log(
+      `📦 Available models: ${availableModels
+        .map((m) => m.split(":")[0])
+        .join(", ")}`
+    );
+
+    // Check if requested model is available
+    const modelExists = availableModels.some((m) => m.startsWith(model));
+    if (!modelExists) {
+      console.error(`❌ Model "${model}" not found`);
+      console.error(`💡 Install it with: ollama pull ${model}`);
+      console.error(
+        `💡 Or choose from: ${availableModels
+          .map((m) => m.split(":")[0])
+          .join(", ")}`
+      );
+      process.exit(1);
+    }
+
+    console.log(`✅ Using model: ${model}`);
+    return true;
+  } catch (error) {
+    console.error("❌ Error connecting to Ollama:", error.message);
+    console.error("💡 Make sure Ollama is installed and running:");
+    console.error("   1. Install: https://ollama.ai/");
+    console.error("   2. Start: ollama serve");
+    console.error(`   3. Install model: ollama pull ${model}`);
+    process.exit(1);
+  }
+}
+
+/**
+ * Find the most recent results file if none specified
+ */
+function findLatestResultsFile() {
+  const resultsDir = "results";
+  if (!fs.existsSync(resultsDir)) {
+    throw new Error("Results directory not found. Run the scraper first.");
+  }
+
+  const files = fs
+    .readdirSync(resultsDir)
+    .filter(
+      (f) =>
+        f.startsWith("results-") && f.endsWith(".json") && !f.includes("-ai-")
+    )
+    .sort()
+    .reverse();
+
+  if (files.length === 0) {
+    throw new Error("No results files found. Run the scraper first.");
+  }
+
+  return path.join(resultsDir, files[0]);
+}
+
+/**
+ * Analyze multiple posts using local Ollama
+ */
+async function analyzeBatch(posts, context, model) {
+  console.log(`🤖 Analyzing batch of ${posts.length} posts with ${model}...`);
+
+  try {
+    const prompt = `You are an expert at analyzing LinkedIn posts for relevance to specific contexts.
+
+CONTEXT TO MATCH: "${context}"
+
+Analyze these ${
+      posts.length
+    } LinkedIn posts and determine if each relates to the context above.
+
+POSTS:
+${posts
+  .map(
+    (post, i) => `
+POST ${i + 1}:
+"${post.text.substring(0, 400)}${post.text.length > 400 ? "..." : ""}"
+`
+  )
+  .join("")}
+
+For each post, provide:
+- Is it relevant to "${context}"? (YES/NO)
+- Confidence level (0.0 to 1.0)
+- Brief reasoning
+
+Respond in this EXACT format for each post:
+POST 1: YES/NO | 0.X | brief reason
+POST 2: YES/NO | 0.X | brief reason
+POST 3: YES/NO | 0.X | brief reason
+
+Examples:
+- For layoff context: "laid off 50 employees" = YES | 0.9 | mentions layoffs
+- For hiring context: "we're hiring developers" = YES | 0.8 | job posting
+- Unrelated content = NO | 0.1 | not relevant to context`;
+
+    const response = await fetch(`${OLLAMA_HOST}/api/generate`, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        model: model,
+        prompt: prompt,
+        stream: false,
+        options: {
+          temperature: 0.3,
+          top_p: 0.9,
+        },
+      }),
+    });
+
+    if (!response.ok) {
+      throw new Error(
+        `Ollama API error: ${response.status} ${response.statusText}`
+      );
+    }
+
+    const data = await response.json();
+    const aiResponse = data.response.trim();
+
+    // Parse the response
+    const analyses = [];
+    const lines = aiResponse.split("\n").filter((line) => line.trim());
+
+    for (let i = 0; i < posts.length; i++) {
+      let analysis = {
+        postIndex: i + 1,
+        isRelevant: false,
+        confidence: 0.5,
+        reasoning: "Could not parse AI response",
+      };
+
+      // Look for lines that match "POST X:" pattern
+      const postPattern = new RegExp(`POST\\s*${i + 1}:?\\s*(.+)`, "i");
+
+      for (const line of lines) {
+        const match = line.match(postPattern);
+        if (match) {
+          const content = match[1].trim();
+
+          // Parse: YES/NO | 0.X | reasoning
+          const parts = content.split("|").map((p) => p.trim());
+
+          if (parts.length >= 3) {
+            analysis.isRelevant = parts[0].toUpperCase().includes("YES");
+            analysis.confidence = Math.max(
+              0,
+              Math.min(1, parseFloat(parts[1]) || 0.5)
+            );
+            analysis.reasoning = parts[2] || "No reasoning provided";
+          } else {
+            // Fallback parsing
+            analysis.isRelevant =
+              content.toUpperCase().includes("YES") ||
+              content.toLowerCase().includes("relevant");
+            analysis.confidence = 0.6;
+            analysis.reasoning = content.substring(0, 100);
+          }
+          break;
+        }
+      }
+
+      analyses.push(analysis);
+    }
+
+    // If we didn't get enough analyses, fill in defaults
+    while (analyses.length < posts.length) {
+      analyses.push({
+        postIndex: analyses.length + 1,
+        isRelevant: false,
+        confidence: 0.3,
+        reasoning: "AI response parsing failed",
+      });
+    }
+
+    return analyses;
+  } catch (error) {
+    console.error(`❌ Error in batch AI analysis: ${error.message}`);
+
+    // Fallback: mark all as relevant with low confidence
+    return posts.map((_, i) => ({
+      postIndex: i + 1,
+      isRelevant: true,
+      confidence: 0.3,
+      reasoning: `Analysis failed: ${error.message}`,
+    }));
+  }
+}
+
+/**
+ * Analyze a single post using local Ollama (fallback)
+ */
+async function analyzeSinglePost(text, context, model) {
+  const prompt = `Analyze this LinkedIn post for relevance to: "${context}"
+
+Post: "${text}"
+
+Is this post relevant to "${context}"? Provide:
+1. YES or NO
+2. Confidence (0.0 to 1.0)  
+3. Brief reason
+
+Format: YES/NO | 0.X | reason`;
+
+  try {
+    const response = await fetch(`${OLLAMA_HOST}/api/generate`, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        model: model,
+        prompt: prompt,
+        stream: false,
+        options: {
+          temperature: 0.3,
+        },
+      }),
+    });
+
+    if (!response.ok) {
+      throw new Error(`Ollama API error: ${response.status}`);
+    }
+
+    const data = await response.json();
+    const aiResponse = data.response.trim();
+
+    // Parse response
+    const parts = aiResponse.split("|").map((p) => p.trim());
+
+    if (parts.length >= 3) {
+      return {
+        isRelevant: parts[0].toUpperCase().includes("YES"),
+        confidence: Math.max(0, Math.min(1, parseFloat(parts[1]) || 0.5)),
+        reasoning: parts[2],
+      };
+    } else {
+      // Fallback parsing
+      return {
+        isRelevant:
+          aiResponse.toLowerCase().includes("yes") ||
+          aiResponse.toLowerCase().includes("relevant"),
+        confidence: 0.6,
+        reasoning: aiResponse.substring(0, 100),
+      };
+    }
+  } catch (error) {
+    return {
+      isRelevant: true, // Default to include on error
+      confidence: 0.3,
+      reasoning: `Analysis failed: ${error.message}`,
+    };
+  }
+}
+
+/**
+ * Main processing function
+ */
+async function main() {
+  try {
+    console.log("🚀 LinkedOut Local AI Analyzer Starting...");
+    console.log(`📊 Context: "${context}"`);
+    console.log(`🎯 Confidence Threshold: ${confidenceThreshold}`);
+    console.log(`📦 Batch Size: ${batchSize}`);
+    console.log(`🤖 Model: ${model}`);
+
+    // Check Ollama status
+    await checkOllamaStatus();
+
+    // Determine input file
+    if (!inputFile) {
+      inputFile = findLatestResultsFile();
+      console.log(`📂 Using latest results file: ${inputFile}`);
+    } else {
+      console.log(`📂 Using specified file: ${inputFile}`);
+    }
+
+    // Load results
+    if (!fs.existsSync(inputFile)) {
+      throw new Error(`Input file not found: ${inputFile}`);
+    }
+
+    const rawData = fs.readFileSync(inputFile, "utf-8");
+    const results = JSON.parse(rawData);
+
+    if (!Array.isArray(results) || results.length === 0) {
+      throw new Error("No posts found in input file");
+    }
+
+    console.log(`📋 Loaded ${results.length} posts for analysis`);
+
+    // Process in batches
+    const processedResults = [];
+    let totalRelevant = 0;
+    let totalProcessed = 0;
+
+    for (let i = 0; i < results.length; i += batchSize) {
+      const batch = results.slice(i, i + batchSize);
+      console.log(
+        `\n📦 Processing batch ${Math.floor(i / batchSize) + 1}/${Math.ceil(
+          results.length / batchSize
+        )} (${batch.length} posts)`
+      );
+
+      const analyses = await analyzeBatch(batch, context, model);
+
+      // Apply analyses to posts
+      for (let j = 0; j < batch.length; j++) {
+        const post = batch[j];
+        const analysis = analyses[j];
+
+        const enhancedPost = {
+          ...post,
+          aiRelevant: analysis.isRelevant,
+          aiConfidence: analysis.confidence,
+          aiReasoning: analysis.reasoning,
+          aiModel: model,
+          aiAnalyzedAt: new Date().toLocaleString("en-CA", {
+            year: "numeric",
+            month: "2-digit",
+            day: "2-digit",
+            hour: "2-digit",
+            minute: "2-digit",
+            second: "2-digit",
+            hour12: false,
+          }),
+          aiType: "local-ollama",
+          aiProcessed: true,
+        };
+
+        // Apply confidence threshold
+        if (analysis.confidence >= confidenceThreshold) {
+          if (analysis.isRelevant) {
+            processedResults.push(enhancedPost);
+            totalRelevant++;
+          }
+        } else {
+          // Include low-confidence posts but flag them
+          enhancedPost.lowConfidence = true;
+          processedResults.push(enhancedPost);
+        }
+
+        totalProcessed++;
+        console.log(
+          `   ${
+            analysis.isRelevant ? "✅" : "❌"
+          } Post ${totalProcessed}: ${analysis.confidence.toFixed(
+            2
+          )} confidence - ${analysis.reasoning.substring(0, 100)}...`
+        );
+      }
+
+      // Small delay between batches to be nice to the system
+      if (i + batchSize < results.length) {
+        console.log("⏳ Brief pause...");
+        await new Promise((resolve) => setTimeout(resolve, 500));
+      }
+    }
+
+    // Determine output file
+    if (!outputFile) {
+      const inputBasename = path.basename(inputFile, ".json");
+      const inputDir = path.dirname(inputFile);
+      outputFile = path.join(inputDir, `${inputBasename}-ai-local.json`);
+    }
+
+    // Save results
+    fs.writeFileSync(
+      outputFile,
+      JSON.stringify(processedResults, null, 2),
+      "utf-8"
+    );
+
+    console.log("\n🎉 Local AI Analysis Complete!");
+    console.log(`📊 Results:`);
+    console.log(`   Total posts processed: ${totalProcessed}`);
+    console.log(`   Relevant posts found: ${totalRelevant}`);
+    console.log(`   Final results saved: ${processedResults.length}`);
+    console.log(`📁 Output saved to: ${outputFile}`);
+    console.log(`💰 Cost: $0.00 (completely free!)`);
+  } catch (error) {
+    console.error("❌ Error:", error.message);
+    process.exit(1);
+  }
+}
+
+// Show help if requested
+if (args.includes("--help") || args.includes("-h")) {
+  console.log(`
+LinkedOut Local AI Analyzer (Ollama)
+
+🚀 FREE local AI analysis - No API costs, complete privacy!
+
+Usage: node ai-analyzer-local.js [options]
+
+Options:
+  --input=<file>       Input JSON file (default: latest in results/)
+  --context=<text>     AI context to analyze against (required)
+  --confidence=<num>   Minimum confidence threshold (0.0-1.0, default: 0.7)
+  --model=<name>       Ollama model to use (default: llama2)
+  --batch-size=<num>   Number of posts to process at once (default: 3)
+  --output=<file>      Output file (default: adds -ai-local suffix)
+  --help, -h           Show this help message
+
+Examples:
+  node ai-analyzer-local.js --context="job layoffs"
+  node ai-analyzer-local.js --model=mistral --context="hiring opportunities"
+  node ai-analyzer-local.js --context="remote work" --confidence=0.8
+
+Prerequisites:
+  1. Install Ollama: https://ollama.ai/
+  2. Install a model: ollama pull llama2
+  3. Start Ollama: ollama serve
+
+Popular Models:
+  - llama2 (good general purpose)
+  - mistral (fast and accurate)
+  - codellama (good for technical content)
+  - llama2:13b (more accurate, slower)
+
+Environment Variables:
+  AI_CONTEXT           Default context for analysis
+  AI_CONFIDENCE        Default confidence threshold
+  AI_BATCH_SIZE        Default batch size
+  OLLAMA_MODEL         Default model (llama2, mistral, etc.)
+  OLLAMA_HOST          Ollama host (default: http://localhost:11434)
+`);
+  process.exit(0);
+}
+
+// Run the analyzer
+main();
diff --git a/test/test.js b/test/test.js
index 86351f9..47d145e 100644
--- a/test/test.js
+++ b/test/test.js
@@ -1,19 +1,19 @@
-console.log("START!");
-
-const { chromium } = require("playwright");
-(async () => {
-  console.log("browser!");
-
-  const browser = await chromium.launch({
-    headless: true,
-    args: ["--no-sandbox", "--disable-setuid-sandbox"],
-  });
-  console.log("new page!");
-
-  const page = await browser.newPage();
-  console.log("GOTO!");
-
-  await page.goto("https://example.com");
-  console.log("Success!");
-  await browser.close();
-})();
+console.log("START!");
+
+const { chromium } = require("playwright");
+(async () => {
+  console.log("browser!");
+
+  const browser = await chromium.launch({
+    headless: true,
+    args: ["--no-sandbox", "--disable-setuid-sandbox"],
+  });
+  console.log("new page!");
+
+  const page = await browser.newPage();
+  console.log("GOTO!");
+
+  await page.goto("https://example.com");
+  console.log("Success!");
+  await browser.close();
+})();