# AI Launch Radar (`ghostgrid/ai-launch-radar`) Actor Track AI launches and research signals from GitHub and HuggingFace, with optional experimental directory sources. - **URL**: https://apify.com/ghostgrid/ai-launch-radar.md - **Developed by:** [GhostGrid](https://apify.com/ghostgrid) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.20 / 1,000 ai launch founds 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 ## AI Launch Radar Track AI launches and research signals from GitHub trending repos and HuggingFace models, datasets, and papers. Deduplicates results across sources to give you a unified view of what's new in AI. ### Why use it? Stay on top of AI releases without visiting 5 different sites daily. Useful for AI researchers, investors, founders, and builders who need to know about new models, datasets, and open-source projects as they emerge. ### Key features - **Multi-source aggregation** -- GitHub trending repos + HuggingFace models, datasets, and papers in one output - **Deduplication** -- same project appearing on GH and HF is merged into one record - **Trend scoring** -- each item gets a recency-weighted trend score for sorting - **Period filtering** -- daily, weekly, or monthly time windows - **Source-specific metadata** -- GitHub stars, HF downloads, paper citations per source ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `sources` | string[] | ["github","huggingface"] | Sources to query: "github", "huggingface", or both | | `max_items_per_source` | int | 20 | Max items per source (1-100) | | `time_period` | select | "daily" | daily / weekly / monthly | | `categories` | string[] | [] | Filter by category (e.g. "nlp", "computer-vision", "audio") | | `include_papers` | bool | true | Include HuggingFace papers when source includes huggingface | ### Output fields | Field | Type | Description | |-------|------|-------------| | `title` | string | Project/model/paper name | | `source` | string | "github", "huggingface", or "merged" | | `type` | string | "repository", "model", "dataset", or "paper" | | `url` | string | Source URL | | `description` | string | Description from the source | | `stars` | int | GitHub stars (github only) | | `downloads` | int | HF monthly downloads (huggingface only) | | `author` | string | Creator or organization | | `category` | string | AI category tag | | `trend_score` | float | 0.0-1.0 recency-weighted trend score | | `created_at` | string | ISO-8601 timestamp | ### Notes - GitHub trending is refreshed daily by GitHub. Expect same-day data for new repos. - HuggingFace data refreshes more frequently. New models appear within hours. - Deduplication merges same-name items across sources; merged records show both URLs. # Actor input Schema ## `sources` (type: `array`): Which sources to scrape. GitHub and HuggingFace are the reliable default sources. Product Hunt, Chrome Web Store, Futurepedia, and Toolify are experimental HTML sources. ## `max_items_per_source` (type: `integer`): Maximum number of items to collect from each source. ## `time_period` (type: `string`): Time range for trending items. Applies to GitHub and some other sources. ## `categories` (type: `array`): Optional filter for specific AI categories (e.g. 'llm', 'image-generation', 'code-assistant'). Leave empty to include all. ## `include_enrichment` (type: `boolean`): Reserved for future detail fetches. Current output is source listing data. ## Actor input object example ```json { "sources": [ "github", "huggingface" ], "max_items_per_source": 50, "time_period": "daily", "categories": [], "include_enrichment": true } ```` # 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("ghostgrid/ai-launch-radar").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("ghostgrid/ai-launch-radar").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 ghostgrid/ai-launch-radar --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ghostgrid/ai-launch-radar", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Launch Radar", "description": "Track AI launches and research signals from GitHub and HuggingFace, with optional experimental directory sources.", "version": "0.1", "x-build-id": "hMDUBdLKmS69kzhMw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ghostgrid~ai-launch-radar/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ghostgrid-ai-launch-radar", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ghostgrid~ai-launch-radar/runs": { "post": { "operationId": "runs-sync-ghostgrid-ai-launch-radar", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ghostgrid~ai-launch-radar/run-sync": { "post": { "operationId": "run-sync-ghostgrid-ai-launch-radar", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "sources": { "title": "Sources", "type": "array", "description": "Which sources to scrape. GitHub and HuggingFace are the reliable default sources. Product Hunt, Chrome Web Store, Futurepedia, and Toolify are experimental HTML sources.", "items": { "type": "string" }, "default": [ "github", "huggingface" ] }, "max_items_per_source": { "title": "Max items per source", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of items to collect from each source.", "default": 50 }, "time_period": { "title": "Time period", "enum": [ "daily", "weekly", "monthly" ], "type": "string", "description": "Time range for trending items. Applies to GitHub and some other sources.", "default": "daily" }, "categories": { "title": "Categories", "type": "array", "description": "Optional filter for specific AI categories (e.g. 'llm', 'image-generation', 'code-assistant'). Leave empty to include all.", "items": { "type": "string" }, "default": [] }, "include_enrichment": { "title": "Include enrichment", "type": "boolean", "description": "Reserved for future detail fetches. Current output is source listing data.", "default": true } } }, "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 } } } } } } } } } } ```