# Product Hunt Launch Intelligence (`taroyamada/product-hunt-launch-intelligence`) Actor Monitor Product Hunt launches from the public Atom feed with keyword filtering, product URL matching, and launch metadata normalization. - **URL**: https://apify.com/taroyamada/product-hunt-launch-intelligence.md - **Developed by:** [åĪŠéƒŽ åąąį”°](https://apify.com/taroyamada) (community) - **Categories:** Lead generation, Business, Marketing - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN 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 ## Product Hunt Launch Intelligence Track Product Hunt launches from the public Product Hunt Atom feed. This actor normalizes launch title, tagline, product URL, discussion URL, author name, publish/update timestamps, and keyword matches without requiring Product Hunt API credentials. ### What it returns - Launch rows with `title`, `tagline`, `productUrl`, `discussionUrl`, `authorName`, `publishedAt`, `updatedAt`, and keyword matches. - Feed diagnostics describing Product Hunt public-surface limitations. ### Input - `productUrls` - `searchTerms` - `topics` - `dateFrom`, `dateTo` - `limit` - `delivery`, `webhookUrl`, `dryRun` ### Notes - Product Hunt product pages are frequently Cloudflare-protected for server-side fetchers. - This V1 actor therefore uses the public `https://www.producthunt.com/feed` Atom feed as the primary source of truth. - `productUrls` are matched against feed entries by slug; direct page fetch is intentionally skipped. # Actor input Schema ## `productUrls` (type: `array`): Product Hunt product URLs such as https://www.producthunt.com/products/zed. ## `searchTerms` (type: `array`): Filter Product Hunt feed entries by title or tagline text. ## `topics` (type: `array`): Extra keyword filters applied to title and tagline. ## `dateFrom` (type: `string`): Optional inclusive lower bound in YYYY-MM-DD. ## `dateTo` (type: `string`): Optional inclusive upper bound in YYYY-MM-DD. ## `limit` (type: `integer`): Maximum launch rows to return. ## `delivery` (type: `string`): Where to send results: dataset or webhook. ## `webhookUrl` (type: `string`): Webhook URL to POST results to when delivery=webhook. ## `dryRun` (type: `boolean`): Run without saving results to the dataset. ## Actor input object example ```json { "productUrls": [ "https://www.producthunt.com/products/zed" ], "searchTerms": [ "AI", "developer" ], "topics": [], "limit": 30, "delivery": "dataset", "dryRun": false } ```` # 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 = { "productUrls": [ "https://www.producthunt.com/products/zed" ], "searchTerms": [ "AI", "developer" ], "topics": [] }; // Run the Actor and wait for it to finish const run = await client.actor("taroyamada/product-hunt-launch-intelligence").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`ðŸ’ū 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 = { "productUrls": ["https://www.producthunt.com/products/zed"], "searchTerms": [ "AI", "developer", ], "topics": [], } # Run the Actor and wait for it to finish run = client.actor("taroyamada/product-hunt-launch-intelligence").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("ðŸ’ū 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 '{ "productUrls": [ "https://www.producthunt.com/products/zed" ], "searchTerms": [ "AI", "developer" ], "topics": [] }' | apify call taroyamada/product-hunt-launch-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=taroyamada/product-hunt-launch-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Hunt Launch Intelligence", "description": "Monitor Product Hunt launches from the public Atom feed with keyword filtering, product URL matching, and launch metadata normalization.", "version": "0.1", "x-build-id": "B0bkgs4agd9teyn4I" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/taroyamada~product-hunt-launch-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-taroyamada-product-hunt-launch-intelligence", "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/taroyamada~product-hunt-launch-intelligence/runs": { "post": { "operationId": "runs-sync-taroyamada-product-hunt-launch-intelligence", "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/taroyamada~product-hunt-launch-intelligence/run-sync": { "post": { "operationId": "run-sync-taroyamada-product-hunt-launch-intelligence", "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": { "productUrls": { "title": "Product URLs", "type": "array", "description": "Product Hunt product URLs such as https://www.producthunt.com/products/zed.", "items": { "type": "string" } }, "searchTerms": { "title": "Search terms", "type": "array", "description": "Filter Product Hunt feed entries by title or tagline text.", "items": { "type": "string" } }, "topics": { "title": "Topics", "type": "array", "description": "Extra keyword filters applied to title and tagline.", "items": { "type": "string" } }, "dateFrom": { "title": "Date from", "type": "string", "description": "Optional inclusive lower bound in YYYY-MM-DD." }, "dateTo": { "title": "Date to", "type": "string", "description": "Optional inclusive upper bound in YYYY-MM-DD." }, "limit": { "title": "Limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum launch rows to return.", "default": 30 }, "delivery": { "title": "Delivery", "enum": [ "dataset", "webhook" ], "type": "string", "description": "Where to send results: dataset or webhook.", "default": "dataset" }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Webhook URL to POST results to when delivery=webhook." }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Run without saving results 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 } } } } } } } } } } ```