# Tech News Monitor - Hacker News Brand & Keyword Tracker (`listless_adzuki/tech-news-monitor`) Actor Monitor Hacker News for specific keywords to track brand mentions, competitor discussions, product feedback, and market trends. Uses the official Algolia API for reliable data. Returns structured results with sentiment analysis, engagement metrics, and match details. - **URL**: https://apify.com/listless\_adzuki/tech-news-monitor.md - **Developed by:** [Andres Rodriguez](https://apify.com/listless_adzuki) (community) - **Categories:** News, Developer tools, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Tech News Monitor - Hacker News Brand & Keyword Tracker Track brand mentions, competitor discussions, and market trends on Hacker News. Get structured data with sentiment analysis, engagement metrics, and match details — delivered as a clean dataset you can integrate into any workflow. ### What It Does This Actor monitors Hacker News via the official Algolia API for posts matching your configured keywords. It returns: - **Matched posts** with title, author, URL, upvotes, and comments - **Keyword mentions** with context snippets showing exactly where your keywords appear - **Sentiment analysis** — positive, neutral, or negative - **Engagement scoring** — low/medium/high based on upvotes - **Post categorization** — announcement, review, problem report, recommendation request, comparison, tutorial, Ask HN, hiring, or discussion - **Monitoring summary** — aggregate stats across all matches ### Use Cases - **Brand monitoring**: Track mentions of your product/company - **Competitor tracking**: Monitor discussions about competitors - **Product feedback**: Find what people are saying about your industry - **Market research**: Identify trends and sentiment shifts - **Lead generation**: Discover people asking for recommendations in your space - **Crisis detection**: Spot negative sentiment spikes early ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **keywords** | array of strings | `["saas", "startup"]` | Keywords to search for. Posts matching ANY keyword are returned. | | **excludeKeywords** | array of strings | `[]` | Posts containing ANY of these keywords are excluded. | | **timeFilter** | string | `"week"` | Only search posts from this period: `hour`, `day`, `week`, `month`, `year`, `all` | | **maxResults** | integer | `50` | Max posts to fetch from HN (1–100) | | **minPoints** | integer | `1` | Minimum upvotes required. Set higher to filter low-engagement posts. | ### Example Input ```json { "keywords": ["open source", "LLM"], "excludeKeywords": ["nsfw"], "timeFilter": "week", "maxResults": 50, "minPoints": 10 } ```` ### Output Example ```json { "match_type": "hn_post", "post_id": "48745445", "title": "Box3D, an open source 3D physics engine", "author": "makepanic", "external_url": "https://box2d.org/posts/2026/06/announcing-box3d/", "hn_url": "https://news.ycombinator.com/item?id=48745445", "points": 518, "num_comments": 128, "created_at": "2026-07-01T12:12:17+00:00", "matched_keywords": ["open source"], "mentions": [ { "keyword": "open source", "matched_text": "open source", "position": 10, "context_snippet": "Box3D, an open source 3D physics engine" } ], "sentiment": { "label": "neutral", "score": 0.0, "positive_count": 0, "negative_count": 0 }, "category": "discussion", "engagement_level": "high" } ``` ### Summary Output The final record is a summary with aggregate statistics: ```json { "type": "monitoring_summary", "total_posts_fetched": 50, "total_matches": 4, "keyword_match_counts": {"open source": 4}, "sentiment_breakdown": {"neutral": 4}, "category_breakdown": {"discussion": 3, "announcement": 1}, "engagement_breakdown": {"high": 1, "medium": 1, "low": 2}, "average_points": 153.2, "top_post": { "title": "Box3D, an open source 3D physics engine", "hn_url": "https://news.ycombinator.com/item?id=48745445", "points": 518 } } ``` ### Pricing **Pay Per Event (PPE)** — $0.003 per matched result. You only pay for posts that actually match your keywords. No results = no charge. ### Why This Actor? 1. **Reliable API**: Uses the official HN Algolia API — no scraping, no blocking, no rate limit issues 2. **Structured data**: Clean JSON output with sentiment, categories, and engagement scores 3. **Context snippets**: See exactly where and how your keyword appears 4. **Smart filtering**: Exclude keywords, set minimum engagement thresholds 5. **Zero maintenance**: No proxy rotation, no anti-bot measures needed ### Tips for Best Results - Use **specific keywords** (e.g., `"Stripe Atlas"` instead of just `"Stripe"`) to reduce noise - Set **minPoints** to 20+ to focus on high-engagement discussions - Use **excludeKeywords** to filter out irrelevant topics - Combine with **timeFilter: "day"** for daily brand monitoring automation ### Integration Ideas - Connect to **Slack/Discord** via webhook for real-time alerts - Pipe to **Airtable/Notion** for a brand mention database - Feed into **n8n/Zapier** workflows for automated responses - Use with **Apify Dataset** export for CSV/Excel reports ### Support Open an issue on GitHub or message me on Apify Community Discord. ### Changelog **v0.1** — Initial release with keyword matching, sentiment analysis, and engagement scoring. # Actor input Schema ## `keywords` (type: `array`): Keywords to search for in Hacker News post titles and content. Posts matching ANY keyword will be returned. ## `excludeKeywords` (type: `array`): Posts containing ANY of these keywords will be excluded from results. ## `timeFilter` (type: `string`): Only search posts from this time period. ## `maxResults` (type: `integer`): Maximum number of posts to fetch from HN (1-100). ## `minPoints` (type: `integer`): Only include posts with at least this many upvotes. Set to 1 to include all posts. ## Actor input object example ```json { "keywords": [ "saas", "startup" ], "excludeKeywords": [], "timeFilter": "week", "maxResults": 50, "minPoints": 1 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("listless_adzuki/tech-news-monitor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("listless_adzuki/tech-news-monitor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call listless_adzuki/tech-news-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=listless_adzuki/tech-news-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tech News Monitor - Hacker News Brand & Keyword Tracker", "description": "Monitor Hacker News for specific keywords to track brand mentions, competitor discussions, product feedback, and market trends. Uses the official Algolia API for reliable data. Returns structured results with sentiment analysis, engagement metrics, and match details.", "version": "0.1", "x-build-id": "msCI3I9wS9ibtizt6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/listless_adzuki~tech-news-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-listless_adzuki-tech-news-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/listless_adzuki~tech-news-monitor/runs": { "post": { "operationId": "runs-sync-listless_adzuki-tech-news-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/listless_adzuki~tech-news-monitor/run-sync": { "post": { "operationId": "run-sync-listless_adzuki-tech-news-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "keywords" ], "properties": { "keywords": { "title": "Keywords to track", "type": "array", "description": "Keywords to search for in Hacker News post titles and content. Posts matching ANY keyword will be returned.", "items": { "type": "string" }, "default": [ "saas", "startup" ] }, "excludeKeywords": { "title": "Keywords to exclude", "type": "array", "description": "Posts containing ANY of these keywords will be excluded from results.", "items": { "type": "string" }, "default": [] }, "timeFilter": { "title": "Time period", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Only search posts from this time period.", "default": "week" }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of posts to fetch from HN (1-100).", "default": 50 }, "minPoints": { "title": "Minimum upvotes", "minimum": 1, "type": "integer", "description": "Only include posts with at least this many upvotes. Set to 1 to include all posts.", "default": 1 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```