# HN "Who is Hiring" Structured Job Extractor (`dataset-smith/hn-who-is-hiring-extractor`) Actor Turns Hacker News monthly 'Who is hiring?' threads into clean structured job data: company, roles, location, remote policy, salary, tech stack, apply links & emails. - **URL**: https://apify.com/dataset-smith/hn-who-is-hiring-extractor.md - **Developed by:** [Ackoo Lu](https://apify.com/dataset-smith) (community) - **Categories:** Jobs, Business - **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" Structured Job Extractor Turn the famous Hacker News monthly **"Ask HN: Who is hiring?"** threads into clean, structured job data — ready for job boards, recruiting tools, market research, and AI training sets. Every month that thread has **300+ tech job postings** written as messy free text. This Actor parses each one into structured fields: | Field | Example | |---|---| | `company` | `SmarterDx` | | `roles` | `["Senior Backend Engineer", "ML Engineer"]` | | `location` | `New York` | | `remote_policy` | `Remote (US only)` | | `is_remote` | `true` | | `salary` | `150-250k+ + equity` | | `tech_stack` | `["python", "react", "postgres", "aws"]` | | `apply_urls` | `["https://smarterdx.com/careers"]` | | `apply_emails` | `["jobs@example.com"]` | | `posted_text` | full original posting | | `hn_url` | direct link to the HN comment | ### Why use it - **No login, no captcha, no proxies** — uses the public Hacker News API. - **Fresh monthly** — point it at `"latest"` and always get the current thread. - **Structured & filterable** — query by tech stack, remote, salary, location. - **Clean apply links & emails** — extracted from messy comment HTML for you. ### Input ```json { "month": "latest", "maxItems": 0 } ```` - `month` — `"latest"` or a specific thread like `"June 2026"`. - `maxItems` — cap the number of postings (`0` = all). ### Output One dataset item per job posting (see table above). Export as JSON, CSV, or Excel. ### Use cases - Build or augment a **tech job board** with curated remote jobs. - **Recruiters / sourcers**: fresh inbound-friendly companies + contact links each month. - **Market & talent research**: which stacks/companies are hiring, salary signals, remote trends. - **AI / dataset builders**: structured job-posting corpus. Run locally with `python3 src/main.py` (writes `output.json`), or on Apify with pay-per-result pricing. # Actor input Schema ## `month` (type: `string`): Which monthly thread to parse, e.g. "June 2026". Use "latest" for the most recent one. ## `maxItems` (type: `integer`): Limit number of job postings returned. 0 = all of them. ## Actor input object example ```json { "month": "latest", "maxItems": 0 } ``` # 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 = { "month": "latest" }; // Run the Actor and wait for it to finish const run = await client.actor("dataset-smith/hn-who-is-hiring-extractor").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 = { "month": "latest" } # Run the Actor and wait for it to finish run = client.actor("dataset-smith/hn-who-is-hiring-extractor").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 '{ "month": "latest" }' | apify call dataset-smith/hn-who-is-hiring-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dataset-smith/hn-who-is-hiring-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HN \"Who is Hiring\" Structured Job Extractor", "description": "Turns Hacker News monthly 'Who is hiring?' threads into clean structured job data: company, roles, location, remote policy, salary, tech stack, apply links & emails.", "version": "0.1", "x-build-id": "IFEibcNpCK46Akv2A" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dataset-smith~hn-who-is-hiring-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dataset-smith-hn-who-is-hiring-extractor", "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/dataset-smith~hn-who-is-hiring-extractor/runs": { "post": { "operationId": "runs-sync-dataset-smith-hn-who-is-hiring-extractor", "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/dataset-smith~hn-who-is-hiring-extractor/run-sync": { "post": { "operationId": "run-sync-dataset-smith-hn-who-is-hiring-extractor", "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": { "month": { "title": "Thread month", "type": "string", "description": "Which monthly thread to parse, e.g. \"June 2026\". Use \"latest\" for the most recent one.", "default": "latest" }, "maxItems": { "title": "Max postings", "minimum": 0, "type": "integer", "description": "Limit number of job postings returned. 0 = all of them.", "default": 0 } } }, "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 } } } } } } } } } } ```