# HN Who Is Hiring Lead Extractor (`toronto_777/hn-who-is-hiring-leads`) Actor Extract structured company leads from Hacker News Who is hiring threads, including remote, visa, salary, tech stack, links, and email signals. - **URL**: https://apify.com/toronto\_777/hn-who-is-hiring-leads.md - **Developed by:** [Steven Feng](https://apify.com/toronto_777) (community) - **Categories:** Lead generation, Jobs, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## HN Who Is Hiring Lead Extractor Extract company leads from Hacker News "Who is hiring?" threads. The Actor finds monthly Ask HN hiring posts, parses top-level hiring comments, and returns structured leads with company names, locations, remote/visa/salary signals, tech keywords, application links, emails, and Hacker News evidence URLs. ### What It Does - Finds Hacker News "Ask HN: Who is hiring?" monthly posts through the Algolia HN Search API. - Reads the public comment tree for each selected month. - Converts each hiring comment into one lead record. - Extracts practical lead signals: company, remote, visa, salary mention, locations, tech keywords, links, and emails. - Produces a dataset view suitable for lead generation, job search, recruiting research, and startup hiring monitoring. ### Limits Comment formats vary. The Actor uses conservative text parsing and keeps the raw comment text plus evidence URLs so users can review each lead. It does not log in, contact companies, enrich people profiles, or scrape private data. # Actor input Schema ## `months` (type: `array`): Months to extract in YYYY-MM format. Leave empty to use the current month. ## `techKeywords` (type: `array`): Keywords to detect in each hiring comment. ## `locationKeywords` (type: `array`): Location or work-mode keywords to detect. ## `maxCommentsPerMonth` (type: `integer`): Maximum number of HN hiring comments to parse per month. ## `maxResults` (type: `integer`): Maximum number of lead records returned. ## `includeRawText` (type: `boolean`): Whether to include the full parsed HN comment text. ## Actor input object example ```json { "months": [], "techKeywords": [ "AI", "LLM", "TypeScript", "Python", "React", "Node", "Go", "Rust" ], "locationKeywords": [ "Remote", "San Francisco", "New York", "US", "Europe", "London" ], "maxCommentsPerMonth": 200, "maxResults": 100, "includeRawText": true } ```` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (type: `string`): No description # 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("toronto_777/hn-who-is-hiring-leads").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("toronto_777/hn-who-is-hiring-leads").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 toronto_777/hn-who-is-hiring-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=toronto_777/hn-who-is-hiring-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HN Who Is Hiring Lead Extractor", "description": "Extract structured company leads from Hacker News Who is hiring threads, including remote, visa, salary, tech stack, links, and email signals.", "version": "0.1", "x-build-id": "ZbKpcu3ga8MhZ0zzp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/toronto_777~hn-who-is-hiring-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-toronto_777-hn-who-is-hiring-leads", "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/toronto_777~hn-who-is-hiring-leads/runs": { "post": { "operationId": "runs-sync-toronto_777-hn-who-is-hiring-leads", "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/toronto_777~hn-who-is-hiring-leads/run-sync": { "post": { "operationId": "run-sync-toronto_777-hn-who-is-hiring-leads", "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": { "months": { "title": "Months", "maxItems": 12, "type": "array", "description": "Months to extract in YYYY-MM format. Leave empty to use the current month.", "default": [], "items": { "type": "string" } }, "techKeywords": { "title": "Tech keywords", "maxItems": 50, "type": "array", "description": "Keywords to detect in each hiring comment.", "default": [ "AI", "LLM", "TypeScript", "Python", "React", "Node", "Go", "Rust" ], "items": { "type": "string" } }, "locationKeywords": { "title": "Location keywords", "maxItems": 50, "type": "array", "description": "Location or work-mode keywords to detect.", "default": [ "Remote", "San Francisco", "New York", "US", "Europe", "London" ], "items": { "type": "string" } }, "maxCommentsPerMonth": { "title": "Maximum comments per month", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of HN hiring comments to parse per month.", "default": 200 }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of lead records returned.", "default": 100 }, "includeRawText": { "title": "Include raw text", "type": "boolean", "description": "Whether to include the full parsed HN comment text.", "default": true } } }, "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 } } } } } } } } } } ```