# 🎬 HN Show HN Tracker — Indie Product Launch Stream (`nexgendata/hn-show-hn-tracker`) Actor Track Hacker News Show HN, Ask HN, front-page, and story streams via Algolia API. Title, URL, author, points, comments, story\_text, HN permalink, top 5 comments per story optional, date + points filters. Indie launches, PM trend hunting, journalism, recruiting. - **URL**: https://apify.com/nexgendata/hn-show-hn-tracker.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** Social media, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 hn stories 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 ## 🎬 HN Show HN Tracker — Indie Product Launch Stream Stream every **Show HN** launch and **Ask HN** thread directly from Hacker News's Algolia search index. Filter by date, points, and stream type. Optionally pull the top 5 comments per story for built-in sentiment + discussion intel. If you build for developers, write about technology, run a startup accelerator, or invest in early-stage SaaS — Show HN is the single best free signal for "this just shipped and devs care about it." This Actor turns that signal into a clean structured feed. > **How this differs from our `hacker-news-scraper`:** the existing actor scrapes HN's *front page* (top 30 stories at any moment). This Actor is **stream-focused** — it pulls the entire historical Show HN, Ask HN, or general story stream paginated by date. Different use cases, complementary tools. --- ### What you get per story Each dataset row is one HN story (Show HN / Ask HN / front_page / story): - **Identity** — `object_id` (HN item ID), `stream` (which feed it came from), `tags` (Algolia `_tags`) - **Title + content** — `title`, `url` (the external link), `story_text` (Show HN's "this is what I built" body or Ask HN question text) - **Permalink** — `hn_url` (https://news.ycombinator.com/item?id=...) - **Author** — `author` (HN username) - **Engagement** — `points` (upvotes), `num_comments` - **Timing** — `created_at` (ISO 8601 UTC), `created_at_i` (Unix epoch), `updated_at` - **Comments** (optional) — top 5 comments per story with `comment_text`, `author`, `parent_id`, `created_at` --- ### 6 use cases 1. **Product Manager trend hunting** — Pull every Show HN with `points >= 100` from the last 90 days. Cluster by keywords (AI, MCP, Postgres, Stripe) to see what devs actually upvote vs what gets ignored. 2. **Indie founder peer benchmarking** — See every Show HN this month sorted by points. Reverse-engineer the launch posts that hit the front page (title structure, time of day, body length) and copy what works. 3. **Journalism / writeup pipeline** — Filter Show HN to `points >= 200` (top ~5%) — these are the genuine breakout launches. With `includeComments: true` you also get the dev-community reaction in one shot. 4. **Recruiting / talent intel** — Show HN authors are usually the founder/builder. Pulling Show HN authors who post repeatedly across multiple launches surfaces serial indie hackers. 5. **Competitive intel** — Set up a daily `dateFrom: yesterday` Show HN run; alert on any title containing your keywords. Catch competitor launches the same day they ship. 6. **Investor / VC sourcing** — Every Show HN with the founder's email or website in the body is a free pre-seed pipeline. Filter `points >= 50` to skip the lottery posts. --- ### Quick start (JSON input) ```json { "streams": ["show_hn"], "dateFrom": "2025-01-01", "dateTo": "2025-01-31", "minPoints": 50, "includeComments": false, "maxStoriesPerStream": 500 } ```` ### Python SDK ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("nexgendata/hn-show-hn-tracker").call(run_input={ "streams": ["show_hn", "ask_hn"], "minPoints": 100, "maxStoriesPerStream": 200, }) for story in client.dataset(run["defaultDatasetId"]).iterate_items(): print(f"[{story['stream']}] {story['points']} pts — {story['title']}") print(f" link: {story['url']} HN: {story['hn_url']}") ``` ### cURL ```bash curl "https://api.apify.com/v2/acts/nexgendata~hn-show-hn-tracker/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"streams":["show_hn"],"minPoints":100,"maxStoriesPerStream":50}' ``` ### Integrations Pipe results to **Zapier**, **Make.com**, or **n8n** for daily Slack digests, Google Sheets append, or CRM enrichment. Common pairings: Slack alert when a competitor name shows up in a Show HN body; Notion database for tracking weekly indie launches; Airtable view for editorial pipeline. *** ### Pricing (Pay Per Event) | Event | Price | |---|---| | Actor start | **$0.00005** | | Per story returned | **$0.02** | **Cost calculator:** - 100 stories = **$2.00** - 500 stories (a month of Show HN) = **$10.00** - 5,000 stories (~1 year of Show HN) = **$100.00** - Full historical Show HN backfill (~520K stories) = **$10,400** (one-time, only worth it for serious research) For ongoing daily monitoring (~30–80 new Show HN posts/day), expect $0.60–$1.60 per day = under $50/month. Compare to manual scraping (free + your time + getting blocked), or paying a research firm $500+ for a one-shot HN trend report. *** ### FAQ **Why not use the official HN API?** The official `hacker-news-firebaseio.com` API is terrific for individual items but gives you no native search, no date filtering, no points filtering, and no per-stream tags. Algolia's HN search index — the same one powering hn.algolia.com — solves all that. We use it. **Are Show HN comments included by default?** No, to keep cost predictable. Set `includeComments: true` to fetch the first 5 comments per story (1 extra API call per story; latency adds ~150ms/story). **Can I get older than 2007?** No. HN itself launched February 2007 and Algolia indexes from then forward. Our `dateFrom` accepts any value back to that horizon. **What does `front_page` really mean?** Algolia tags any story that ever appeared on the HN front page. It's the highest-quality filter for "broadly-noticed stories" (more selective than `points >= 50` because front-page placement also requires moderator-friendly framing). **What happens if minPoints is too aggressive?** You just get fewer results. The Actor caps at `maxStoriesPerStream` regardless and stops paginating when no hits remain. **Does this conflict with the existing nexgendata `hacker-news-scraper`?** No — that Actor scrapes the live HN front page (top 30, point-in-time snapshot). This Actor pulls historical streams across years and supports per-stream tag filters. They cover different jobs. *** ### Related actors - 📰 [Hacker News Scraper](https://apify.com/nexgendata/hacker-news-scraper) — live front-page top stories (point-in-time, not historical) - 💼 [HN Who's Hiring Scraper](https://apify.com/nexgendata/hn-whos-hiring-scraper) — monthly Who's Hiring + Who Wants to Be Hired threads parsed - ⭐ [GitHub Trending](https://apify.com/nexgendata/github-trending) — cross-reference Show HN launches with GitHub stargazer momentum *** [Try this Actor on Apify →](https://apify.com/nexgendata/hn-show-hn-tracker) | [Get a free Apify account →](https://apify.com/nexgendata?fpr=2ayu9b) # Actor input Schema ## `streams` (type: `array`): Which HN feed(s) to track. Allowed values: 'show\_hn' (Show HN launches), 'ask\_hn' (Ask HN questions), 'front\_page' (front-page stories), 'story' (all stories). ## `dateFrom` (type: `string`): Earliest story creation date (UTC, inclusive). Leave blank for no lower bound. Algolia indexes go back to Feb 2007. ## `dateTo` (type: `string`): Latest story creation date (UTC, inclusive). Leave blank for no upper bound (defaults to now). ## `minPoints` (type: `integer`): Filter to stories with at least this many upvotes. 0 = no filter; 50 = ~front-page tier; 100+ = trending. ## `includeComments` (type: `boolean`): If true, fetch the first 5 comments per story (text + author + parent\_id). Adds 1 extra API call per story; useful for sentiment / discussion intel. ## `maxStoriesPerStream` (type: `integer`): Cap stories per stream (1–5000). Each story is one dataset row. ## Actor input object example ```json { "streams": [ "show_hn" ], "minPoints": 0, "includeComments": false, "maxStoriesPerStream": 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 = { "streams": [ "show_hn" ], "dateFrom": "", "dateTo": "", "minPoints": 0, "includeComments": false, "maxStoriesPerStream": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/hn-show-hn-tracker").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 = { "streams": ["show_hn"], "dateFrom": "", "dateTo": "", "minPoints": 0, "includeComments": False, "maxStoriesPerStream": 5, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/hn-show-hn-tracker").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 '{ "streams": [ "show_hn" ], "dateFrom": "", "dateTo": "", "minPoints": 0, "includeComments": false, "maxStoriesPerStream": 5 }' | apify call nexgendata/hn-show-hn-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/hn-show-hn-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🎬 HN Show HN Tracker — Indie Product Launch Stream", "description": "Track Hacker News Show HN, Ask HN, front-page, and story streams via Algolia API. Title, URL, author, points, comments, story_text, HN permalink, top 5 comments per story optional, date + points filters. Indie launches, PM trend hunting, journalism, recruiting.", "version": "0.0", "x-build-id": "3Wv95vf5uh1hrhsQz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~hn-show-hn-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-hn-show-hn-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~hn-show-hn-tracker/runs": { "post": { "operationId": "runs-sync-nexgendata-hn-show-hn-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~hn-show-hn-tracker/run-sync": { "post": { "operationId": "run-sync-nexgendata-hn-show-hn-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "streams" ], "properties": { "streams": { "title": "HN streams", "type": "array", "description": "Which HN feed(s) to track. Allowed values: 'show_hn' (Show HN launches), 'ask_hn' (Ask HN questions), 'front_page' (front-page stories), 'story' (all stories).", "items": { "type": "string" } }, "dateFrom": { "title": "Date from (YYYY-MM-DD, optional)", "type": "string", "description": "Earliest story creation date (UTC, inclusive). Leave blank for no lower bound. Algolia indexes go back to Feb 2007." }, "dateTo": { "title": "Date to (YYYY-MM-DD, optional)", "type": "string", "description": "Latest story creation date (UTC, inclusive). Leave blank for no upper bound (defaults to now)." }, "minPoints": { "title": "Minimum points", "minimum": 0, "type": "integer", "description": "Filter to stories with at least this many upvotes. 0 = no filter; 50 = ~front-page tier; 100+ = trending.", "default": 0 }, "includeComments": { "title": "Include top 5 comments per story", "type": "boolean", "description": "If true, fetch the first 5 comments per story (text + author + parent_id). Adds 1 extra API call per story; useful for sentiment / discussion intel.", "default": false }, "maxStoriesPerStream": { "title": "Max stories per stream", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Cap stories per stream (1–5000). Each story is one dataset row.", "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 } } } } } } } } } } ```