# Hacker News Lead & Trend Monitor (`techionik9993/hacker-news-lead-trend-monitor`) Actor lead generation, product research, founder research, trend monitoring, and competitive intelligence. It uses the public Hacker News Algolia API, so runs are fast, low-cost, and do not need a proxy by default. - **URL**: https://apify.com/techionik9993/hacker-news-lead-trend-monitor.md - **Developed by:** [Techionik](https://apify.com/techionik9993) (community) - **Categories:** Lead generation, SEO tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $1.20 / 1,000 hacker news rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Hacker News Lead & Trend Monitor Find Hacker News stories and comments that mention products, competitors, pain points, hiring needs, alternatives, startup ideas, and fresh market signals. This Actor is built for lead generation, product research, founder research, trend monitoring, and competitive intelligence. It uses the public Hacker News Algolia API, so runs are fast, low-cost, and do not need a proxy by default. ### What It Returns - Hacker News stories and comments - Original HN URL and external story URL when available - Author, points, comment count, and publish time - Source query for each row - Relevance score and matched query terms - Intent score and matched buyer/research keywords - Optional content/comment text - Monitoring mode for newly discovered rows ### Common Uses - Find people asking for alternatives to a tool - Track startup/product launches around a keyword - Monitor technical hiring and service demand - Watch discussions around competitors - Discover founder pain points and customer language - Build prospecting lists from fresh HN conversations ### Input Tips Use several focused queries instead of one generic query. Good examples: - `stripe alternative` - `looking for crm` - `need analytics` - `shopify app` - `open source billing` - `ai automation agency` For lead generation, set `minimumIntentScore` to `1` or higher. For broad market research, keep it at `0`. Use `maxResultsPerQuery` when you provide several keywords and want balanced output. For example, with `maxResults` set to 500 and 5 queries, set `maxResultsPerQuery` to 100 so one popular query does not fill the entire run. ### Result Limits `maxResults` is a maximum cap, not a guarantee. The Actor only writes rows that pass the selected relevance and intent filters. If you ask for 500 rows but the filtered search only finds 160 useful matches, the Actor returns 160 clean rows instead of padding the dataset with irrelevant results. The minimum `maxResults` is 50 so runs are commercially practical and users are charged for meaningful output. ### Monitoring Enable `monitorChanges` to compare the current run against the previous snapshot in the Actor key-value store. When `onlyChanges` is enabled, the dataset contains only newly detected HN rows. When it is disabled, the dataset contains the current full result set while the change snapshot is still stored for automation. ### Proxy Use No proxy is needed by default. This Actor uses a public API and does not run a browser, which keeps speed high and usage cost low. ### Output Each dataset row represents one Hacker News story or comment. The primary output event is one dataset row. ### Notes This Actor is not affiliated with Hacker News, Y Combinator, or Algolia. It reads public search API results and stores structured rows in your Apify dataset. # Actor input Schema ## `searchQueries` (type: `array`): Search terms to track on Hacker News. Use buyer-intent, competitor, product, hiring, or pain-point phrases. ## `resultTypes` (type: `string`): Choose whether to return stories, comments, or both. Comments often contain the strongest sales/research signals. ## `sort` (type: `string`): Newest is best for monitoring and fresh leads. Relevance is best for research. ## `maxResults` (type: `integer`): Maximum rows to write for the whole run. The minimum is 50 for better marketplace economics. ## `pagesPerQuery` (type: `integer`): How many Hacker News API pages to scan per query. Increase this when you need hundreds of results. ## `maxResultsPerQuery` (type: `integer`): Optional cap per search query so one broad keyword does not fill the entire run. Use 0 for no per-query cap. ## `daysBack` (type: `integer`): Only include results from the last N days. Use 0 to disable date filtering. ## `includeContent` (type: `boolean`): Include story title and comment text/content where available. ## `intentKeywords` (type: `array`): Keywords used to score buyer/research intent. ## `minimumRelevanceScore` (type: `integer`): Minimum number of meaningful query words that must appear in the row. Use 1 for clean output, 0 for broad research. ## `minimumIntentScore` (type: `integer`): Minimum number of intent keywords required. Use 0 for trends, 1+ for stronger lead-generation output. ## `monitorChanges` (type: `boolean`): Compare against the previous saved snapshot and detect newly found HN rows. ## `onlyChanges` (type: `boolean`): When monitoring is enabled, write only newly detected rows to the dataset. ## Actor input object example ```json { "searchQueries": [ "shopify app", "open source crm", "need analytics" ], "resultTypes": "both", "sort": "newest", "maxResults": 100, "pagesPerQuery": 3, "maxResultsPerQuery": 0, "daysBack": 365, "includeContent": true, "intentKeywords": [ "need", "looking for", "recommend", "alternative" ], "minimumRelevanceScore": 1, "minimumIntentScore": 0, "monitorChanges": false, "onlyChanges": false } ```` # Actor output Schema ## `results` (type: `string`): Normal runs contain Hacker News story/comment rows. When output-only-changes is enabled, rows contain newly detected HN rows. # 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 = { "searchQueries": [ "stripe alternative", "looking for crm", "need ai automation" ] }; // Run the Actor and wait for it to finish const run = await client.actor("techionik9993/hacker-news-lead-trend-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 = { "searchQueries": [ "stripe alternative", "looking for crm", "need ai automation", ] } # Run the Actor and wait for it to finish run = client.actor("techionik9993/hacker-news-lead-trend-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 '{ "searchQueries": [ "stripe alternative", "looking for crm", "need ai automation" ] }' | apify call techionik9993/hacker-news-lead-trend-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=techionik9993/hacker-news-lead-trend-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hacker News Lead & Trend Monitor", "description": "lead generation, product research, founder research, trend monitoring, and competitive intelligence. It uses the public Hacker News Algolia API, so runs are fast, low-cost, and do not need a proxy by default.", "version": "0.0", "x-build-id": "hlWC0n78IMWx0e2VP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/techionik9993~hacker-news-lead-trend-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-techionik9993-hacker-news-lead-trend-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/techionik9993~hacker-news-lead-trend-monitor/runs": { "post": { "operationId": "runs-sync-techionik9993-hacker-news-lead-trend-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/techionik9993~hacker-news-lead-trend-monitor/run-sync": { "post": { "operationId": "run-sync-techionik9993-hacker-news-lead-trend-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "searchQueries": { "title": "Search queries", "type": "array", "description": "Search terms to track on Hacker News. Use buyer-intent, competitor, product, hiring, or pain-point phrases.", "default": [ "stripe alternative", "looking for crm", "need ai automation" ], "items": { "type": "string" } }, "resultTypes": { "title": "Result types", "enum": [ "both", "story", "comment" ], "type": "string", "description": "Choose whether to return stories, comments, or both. Comments often contain the strongest sales/research signals.", "default": "both" }, "sort": { "title": "Sort", "enum": [ "newest", "relevance" ], "type": "string", "description": "Newest is best for monitoring and fresh leads. Relevance is best for research.", "default": "newest" }, "maxResults": { "title": "Max results", "minimum": 50, "maximum": 2000, "type": "integer", "description": "Maximum rows to write for the whole run. The minimum is 50 for better marketplace economics.", "default": 100 }, "pagesPerQuery": { "title": "Pages per query", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many Hacker News API pages to scan per query. Increase this when you need hundreds of results.", "default": 3 }, "maxResultsPerQuery": { "title": "Max results per query", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Optional cap per search query so one broad keyword does not fill the entire run. Use 0 for no per-query cap.", "default": 0 }, "daysBack": { "title": "Days back", "minimum": 0, "maximum": 3650, "type": "integer", "description": "Only include results from the last N days. Use 0 to disable date filtering.", "default": 365 }, "includeContent": { "title": "Include content", "type": "boolean", "description": "Include story title and comment text/content where available.", "default": true }, "intentKeywords": { "title": "Intent keywords", "type": "array", "description": "Keywords used to score buyer/research intent.", "default": [ "need", "looking for", "recommend", "alternative", "hire", "problem", "pain", "switching", "building", "launched" ], "items": { "type": "string" } }, "minimumRelevanceScore": { "title": "Minimum relevance score", "minimum": 0, "maximum": 10, "type": "integer", "description": "Minimum number of meaningful query words that must appear in the row. Use 1 for clean output, 0 for broad research.", "default": 1 }, "minimumIntentScore": { "title": "Minimum intent score", "minimum": 0, "maximum": 10, "type": "integer", "description": "Minimum number of intent keywords required. Use 0 for trends, 1+ for stronger lead-generation output.", "default": 0 }, "monitorChanges": { "title": "Monitor changes since last run", "type": "boolean", "description": "Compare against the previous saved snapshot and detect newly found HN rows.", "default": false }, "onlyChanges": { "title": "Output only new rows", "type": "boolean", "description": "When monitoring is enabled, write only newly detected rows to the dataset.", "default": false } } }, "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 } } } } } } } } } } ```