# Fresh HN Feed (`prince_gabriel/fresh-hn-feed`) Actor Freshness-ranked Hacker News feed powered by FreshContext DAR scoring. - **URL**: https://apify.com/prince\_gabriel/fresh-hn-feed.md - **Developed by:** [Immanuel Gabriel](https://apify.com/prince_gabriel) (community) - **Categories:** Lead generation, Agents, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.05 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Fresh HN Feed Apify Actor Fresh HN Feed is a minimal Apify Actor wrapper around the deployed Fresh HN Feed Cloudflare Worker API: ```text https://fresh-hn-feed.gimmanuel73.workers.dev ```` It does not scrape Hacker News and does not duplicate the Worker. It calls the live API, writes each returned signal to the default Apify Dataset, and stores the full API response in the default Key-Value Store as `OUTPUT`. Fresh HN Feed uses FreshContext DAR scoring: ```text rt_score = log1p(score) * exp(-0.05 * age_hours) ``` This ranks Hacker News stories by a temporal decay model instead of raw points alone. ### Input | Field | Type | Default | Description | | --- | --- | --- | --- | | `mode` | `"feed"`, `"fresh"`, or `"trending"` | `"feed"` | Selects the live API endpoint. | | `limit` | number | `10` | Number of signals to return. Clamped to `1..100`. | | `min_score` | number | `0` | Minimum decay-adjusted `rt_score`. | | `includeExplain` | boolean | `false` | If true, attaches `/v1/hn/explain/:id` scoring details to each dataset item. | ### Example Input ```json { "mode": "feed", "limit": 10, "min_score": 0.5, "includeExplain": true } ``` ### Example Dataset Item ```json { "id": 48090029, "title": "I'm going back to writing code by hand", "url": "https://blog.k10s.dev/im-going-back-to-writing-code-by-hand/", "score": 299, "comment_count": 120, "rt_score": 4.1, "age_hours": 6.5, "published_at": "2026-05-11T01:23:51.000Z", "semantic_fingerprint": "24f6ffe2e29b1769", "inserted_at": "2026-05-11T07:54:18.000Z", "mode": "feed", "fetched_at": "2026-05-11T08:00:00.000Z", "source_api": "https://fresh-hn-feed.gimmanuel73.workers.dev/v1/hn/feed?limit=10&min_score=0.5", "explanation": { "raw_score": 299, "age_hours": 6.5, "lambda": 0.05, "rt_score": 4.1, "formula": "log1p(score) * exp(-lambda * age_hours)" } } ``` The Key-Value Store `OUTPUT` record contains the normalized input, the full live API response, dataset item count, and generation timestamp. ### Use Cases - AI agents that need current developer and startup signals. - Trend monitoring without raw Hacker News noise. - Developer research and competitive intelligence. - Content discovery for newsletters, briefs, and social posts. - Lightweight freshness-aware market scanning. ### Local Run Install dependencies: ```bash npm install ``` Run locally: ```bash apify run ``` ### Push to Apify ```bash apify push ``` # Actor input Schema ## `mode` (type: `string`): Which Fresh HN Feed endpoint to call. ## `limit` (type: `integer`): Maximum number of signals to return. Values are clamped to 1-100. ## `min_score` (type: `number`): Minimum FreshContext decay-adjusted score to include. ## `includeExplain` (type: `boolean`): Fetch /v1/hn/explain/:id for each signal and attach it to the dataset item. ## Actor input object example ```json { "mode": "feed", "limit": 10, "min_score": 0, "includeExplain": false } ``` # Actor output Schema ## `results` (type: `string`): Dataset items, one per Hacker News signal returned by the Fresh HN Feed API. ## `fullApiResponse` (type: `string`): The full Fresh HN Feed API response and normalized Actor input stored as OUTPUT in the default key-value store. # 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("prince_gabriel/fresh-hn-feed").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("prince_gabriel/fresh-hn-feed").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 prince_gabriel/fresh-hn-feed --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=prince_gabriel/fresh-hn-feed", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fresh HN Feed", "description": "Freshness-ranked Hacker News feed powered by FreshContext DAR scoring.", "version": "0.1", "x-build-id": "oysBmFVTU8h1t8tTn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/prince_gabriel~fresh-hn-feed/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-prince_gabriel-fresh-hn-feed", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/prince_gabriel~fresh-hn-feed/runs": { "post": { "operationId": "runs-sync-prince_gabriel-fresh-hn-feed", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/prince_gabriel~fresh-hn-feed/run-sync": { "post": { "operationId": "run-sync-prince_gabriel-fresh-hn-feed", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "mode": { "title": "Feed mode", "enum": [ "feed", "fresh", "trending" ], "type": "string", "description": "Which Fresh HN Feed endpoint to call.", "default": "feed" }, "limit": { "title": "Limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of signals to return. Values are clamped to 1-100.", "default": 10 }, "min_score": { "title": "Minimum Rt score", "minimum": 0, "type": "number", "description": "Minimum FreshContext decay-adjusted score to include.", "default": 0 }, "includeExplain": { "title": "Include explanations", "type": "boolean", "description": "Fetch /v1/hn/explain/:id for each signal and attach it to the dataset item.", "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 } } } } } } } } } } ```