# 💡 Product Hunt Launches — Indie Launch Stream (`nexgendata/product-hunt-launches-scraper`) Actor Stream Product Hunt launches via the official public Atom feed — title, slug, tagline, maker name, published + updated UTC, PH URL, redirect URL, content. Category + keyword + date filters. Indie founder benchmarking, BD/sales, journalism, VC sourcing, peer launch monitoring. - **URL**: https://apify.com/nexgendata/product-hunt-launches-scraper.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** Business, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 ph launches This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## 🐈 Product Hunt Launches Scraper — Daily Product Hunt launch stream — title, tagline, maker, votes, topics The Product Hunt Launches Scraper pulls structured rows from [ProductHunt.com](https://www.producthunt.com)'s daily and historical launch leaderboard. You get product name, tagline, makers, vote count, comment count, topic tags, launch date, product URL, and producthunt.com canonical URL — for any date range you specify. Ideal for trend research, indie-dev ICP discovery, dev-tool competitive intel, and newsletter / aggregator content sourcing. ### Why Product Hunt Launches Scraper Beats Product Hunt Pro, BetaList, and Indie Hackers Launches | Source | Price | What you get | |--------|-------|--------------| | Product Hunt Pro | $50/mo | Browse only; no bulk JSON export | | BetaList | Free | Different community, smaller volume | | Indie Hackers Launches | Free | Different community | | Hacker News Show HN | Free | Different community, requires separate scraping | | NexGenData Product Hunt Scraper | PPE per launch | Title, tagline, maker, votes, comments, topics, launch date — bulk JSON | ### What You Get - Launch ID + canonical producthunt.com URL - Product name + tagline - Launch date (UTC) + leaderboard rank on that day - Vote count + comment count (at scrape time) - Makers: name + Product Hunt profile URL + Twitter (when published) - Product canonical URL (the product website) - Topic tags (AI, Productivity, Developer Tools, Design Tools, etc.) - Description / long write-up (when the maker posted one) - Gallery URLs: product screenshots / video URL - Pricing model summary (Free / Freemium / Paid) when tagged - Featured-collection memberships (when applicable) - Number-of-followers (when public) ### Use Cases - **Dev-tool / SaaS ICP discovery** — products launching on Product Hunt are warm targets for adjacent SaaS (analytics, marketing, dev-infra). - **Trend research** — bucket launches by topic to surface emerging categories (e.g. AI-agent share of total launches by quarter). - **Investor scouting** — surface launches with 500+ votes that haven't yet hit Crunchbase or YC. - **Newsletter / aggregator content** — every weekly newsletter covering 'what shipped' can auto-source from this actor. - **Maker outreach** — first-time Product Hunt launchers are excellent targets for indie-dev community, podcasts, and angel deal-flow. - **Brand competitive intel** — track every launch in your competitor's topic and dedupe daily. - **Acqui-hire / acquisition pipeline** — products that launch but stall (1-2 months later) are common acquisition candidates. ### Quick Start ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run_input = { "sinceHours": 24, "topics": ["artificial-intelligence", "developer-tools"], "minVotes": 50 } run = client.actor("nexgendata/product-hunt-launches-scraper").call(run_input=run_input) ## Iterate results for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ## Or fetch all in one go items = list(client.dataset(run["defaultDatasetId"]).iterate_items()) print(f"Got {len(items)} rows") ```` You can also run from the **Apify CLI**: ```bash apify call nexgendata/product-hunt-launches-scraper --input='{ "sinceHours": 24, "topics": ["artificial-intelligence", "developer-tools"], "minVotes": 50 }' ``` Or from the **web console**: open the actor page on Apify, click **Try for free**, paste the input JSON, hit **Run**. Results stream into the dataset which you can export as JSON / JSONL / CSV / Excel / HTML. #### Scheduling This actor pairs cleanly with **Apify Scheduler** (built into the platform) — schedule it hourly / daily / cron-style and dedupe results into your warehouse on the stable primary-key fields documented above. Webhook outputs are supported, so you can fire a Slack / Zapier / Make / n8n / your-own-API call the moment new rows materialize. #### Integration patterns - **CRM enrichment**: pipe rows directly into HubSpot / Salesforce / Pipedrive via Zapier or Make - **Warehouse**: append to BigQuery / Snowflake / Postgres on a daily schedule via Apify → S3 → warehouse ingest - **LLM-ready RAG**: each row is already JSON-flat; embed the plain-text body field and store in pgvector / Pinecone / Weaviate - **Slack alerts**: filter by your trigger keyword and fire a Slack webhook for matches in real-time ### Pricing This actor runs on Apify's **pay-per-event (PPE)** model — you pay only for results, not run-time: - **$0.03 per Product Hunt launch** — the primary event (one charge per row pushed to the dataset) - **0.00005 USD per actor-start GB-event** — actor start cost (one-time per run, sub-cent at typical memory) No subscriptions, no minimums, no per-CPU-second charges. Apify's $5/month free tier covers most experiments. Browse 200+ buyer-intent actors at https://apify.com/nexgendata?fpr=2ayu9b #### Cost worked example A daily scheduled run pulling 500 fresh rows costs roughly: - 500 rows × primary-event price (~$0.04-0.05) = $20-25 - 1 actor start × ~$0.00005 = negligible So **~$20-25 per 500-row daily run**, or **~$0.04-0.05 per row** all-in. There are no surprise compute, storage, or proxy add-ons — proxy rotation is bundled into the per-row price. #### Why pay-per-event beats time-based pricing - **Predictable**: you know your cost from row count before the run starts - **Failure-safe**: if a target site changes its HTML and the actor returns 0 rows, you pay 0 (vs paying for the CPU-seconds anyway under time-based pricing) - **Easy to attribute**: 1 row = 1 unit cost, so per-customer / per-pipeline cost accounting is trivial ### Sister Actors in the NexGenData Fleet | Use case | Actor | |----------|-------| | Show HN indie launch stream | [hn-show-hn-tracker](https://apify.com/nexgendata/hn-show-hn-tracker) | | Indie Hackers solo & bootstrapped tracker | [indie-hackers-products-tracker](https://apify.com/nexgendata/indie-hackers-products-tracker) | | Y Combinator alumni database | [yc-companies-directory-scraper](https://apify.com/nexgendata/yc-companies-directory-scraper) | | Crunchbase news & funding announcements | [crunchbase-news-scraper](https://apify.com/nexgendata/crunchbase-news-scraper) | | AngelList startup discovery | [angellist-startup-search](https://apify.com/nexgendata/angellist-startup-search) | | Techstars alumni & cohorts | [techstars-companies-directory](https://apify.com/nexgendata/techstars-companies-directory) | | B2B leads for SaaS sellers | [b2b-leads-finder](https://apify.com/nexgendata/b2b-leads-finder) | (All sister actors share the same PPE billing and Apify-standard JSON output, so you can compose multi-step pipelines without rewriting input/output adapters.) ### FAQ **Q: How far back does the data go?** A: Product Hunt's launch archive goes back to 2014. The actor can pull anything from the last 24 hours up to the entire historical archive. **Q: How fresh is the live data?** A: Each run pulls live data. New launches typically appear in the leaderboard within minutes of going live. **Q: Does it use the Product Hunt API?** A: The actor uses Product Hunt's public site rendering, augmented by their public Algolia search index — no API key required and not bound by their GraphQL rate-limits. **Q: Will I be rate-limited?** A: The actor uses Apify residential proxies and polite pacing. We have not observed blocks at typical volumes (≤5K launches/run). **Q: Output format?** A: JSON, JSONL, CSV, Excel via Apify dataset export. Schema is stable. **Q: Can I monitor only fresh launches?** A: Yes — schedule the actor with a `sinceHours` window and dedupe on launch ID. ### Schema Stability & Versioning This actor follows NexGenData's **additive-only schema** contract: - New fields may be **added** at any time — they will simply appear as new keys in the JSON output, defaulting to `null` for older runs. - Existing fields are **never renamed or removed** without a major-version bump and an advance changelog notice. - Field semantics (units, timezones, value-sets) are **never silently changed** — if we need to change semantics, we add a new field with the new name and deprecate (but keep) the old one for at least 90 days. This means you can build production pipelines on this actor and not worry about a Tuesday breaking a Friday's ETL job. If you spot an unexpected change, reach out via the actor's Apify Issues tab and we'll look at it the same day. ### Compliance & Legal - The actor reads **public, unauthenticated pages** the same way a logged-out browser does. - All requests route through Apify's compliant residential-proxy infrastructure with polite rate limiting. - You are responsible for ensuring your downstream use complies with the target site's Terms of Service, your jurisdiction's data-protection laws (GDPR, CCPA, UK DPA, etc.), and any sector-specific rules (HIPAA, PCI, etc.). - We do not collect, store, or transmit credentials for the target site. - Most read-only competitive-intelligence and lead-generation use is widely accepted. Consult counsel before bulk redistribution. ### Support Open an issue on the actor's Apify Issues tab — the NexGenData team responds within one business day. For feature requests (new fields, new input filters), include the use case so we can prioritize on it. ### About NexGenData NexGenData publishes 200+ buyer-intent actors covering SEC filings, YC alumni, Delaware DOC, lead generation, competitive intelligence, stock fundamentals across 30+ exchanges, ATS job boards, real-estate marketplaces, and more. All actors are pay-per-result and share a stable, additive-only JSON schema. Browse the full catalog at https://apify.com/nexgendata?fpr=2ayu9b *** SEO: 🐈 Product Hunt Launches Scraper — Indie Launch Tracker API # Actor input Schema ## `categories` (type: `array`): Product Hunt category slugs to filter by (e.g. \['ai', 'developer-tools', 'productivity', 'fintech']). Each category triggers a separate feed pull. Empty = global feed (all categories merged). ## `keyword` (type: `string`): Case-insensitive substring filter over title + tagline + content. Empty = no keyword filter. Example: 'AI', 'mobile', 'API'. ## `dateFrom` (type: `string`): Earliest published date (UTC). Empty = no lower bound. The PH feed exposes the 50 most-recent launches per category, so very old date\_from values won't return anything older than the feed's window. ## `dateTo` (type: `string`): Latest published date (UTC). Empty = no upper bound (defaults to now). ## `maxLaunches` (type: `integer`): Maximum launches across all categories (1–2000). Each launch is one dataset row. The global feed exposes 50 launches per pull. ## Actor input object example ```json { "categories": [], "maxLaunches": 5 } ``` # 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 = { "categories": [], "keyword": "", "dateFrom": "", "dateTo": "", "maxLaunches": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/product-hunt-launches-scraper").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 = { "categories": [], "keyword": "", "dateFrom": "", "dateTo": "", "maxLaunches": 5, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/product-hunt-launches-scraper").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 '{ "categories": [], "keyword": "", "dateFrom": "", "dateTo": "", "maxLaunches": 5 }' | apify call nexgendata/product-hunt-launches-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/product-hunt-launches-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "💡 Product Hunt Launches — Indie Launch Stream", "description": "Stream Product Hunt launches via the official public Atom feed — title, slug, tagline, maker name, published + updated UTC, PH URL, redirect URL, content. Category + keyword + date filters. Indie founder benchmarking, BD/sales, journalism, VC sourcing, peer launch monitoring.", "version": "0.0", "x-build-id": "BrMnzB8nFArhHWYZ6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~product-hunt-launches-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-product-hunt-launches-scraper", "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/nexgendata~product-hunt-launches-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-product-hunt-launches-scraper", "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/nexgendata~product-hunt-launches-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-product-hunt-launches-scraper", "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": { "categories": { "title": "Categories (optional)", "type": "array", "description": "Product Hunt category slugs to filter by (e.g. ['ai', 'developer-tools', 'productivity', 'fintech']). Each category triggers a separate feed pull. Empty = global feed (all categories merged).", "items": { "type": "string" } }, "keyword": { "title": "Keyword filter (optional)", "type": "string", "description": "Case-insensitive substring filter over title + tagline + content. Empty = no keyword filter. Example: 'AI', 'mobile', 'API'." }, "dateFrom": { "title": "Date from (YYYY-MM-DD, optional)", "type": "string", "description": "Earliest published date (UTC). Empty = no lower bound. The PH feed exposes the 50 most-recent launches per category, so very old date_from values won't return anything older than the feed's window." }, "dateTo": { "title": "Date to (YYYY-MM-DD, optional)", "type": "string", "description": "Latest published date (UTC). Empty = no upper bound (defaults to now)." }, "maxLaunches": { "title": "Max launches", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum launches across all categories (1–2000). Each launch is one dataset row. The global feed exposes 50 launches per pull.", "default": 100 } } }, "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 } } } } } } } } } } ```