# AI Trend Radar - Hugging Face, GitHub & arXiv AI Trends (`intenal_builds/ai-trend-radar`) Actor

One live feed of what is trending in AI: Hugging Face models, datasets, Spaces and daily papers plus new GitHub AI repos, ranked by a cross-source momentum score with velocity, breakout flags and change-since-last-run for alerts.

- **URL**: https://apify.com/intenal_builds/ai-trend-radar.md
- **Developed by:** [Jaydan Eaton](https://apify.com/intenal_builds) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 trend items

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## AI Trend Radar: trending Hugging Face models, datasets, Spaces, papers & GitHub AI repos in one ranked feed

**See what's taking off in AI before it shows up in newsletters.** AI Trend Radar pulls six live public sources in one run: Hugging Face trending **models**, **datasets**, **Spaces**, Hugging Face **daily papers** (arXiv), and **new GitHub AI repositories**. It ranks everything with a single **momentum score** and flags **breakouts**. Run it on a schedule and it also tells you what is **new since the last run** and what **moved up**, so the output can feed alerts directly.

Most Hugging Face or GitHub-trending scrapers cover one source and give you raw lists. This Actor merges the sources, normalises growth into per-day velocity, and scores items against each other.

### What you get

| Field | Meaning |
|---|---|
| `overallRank`, `momentumScore` (0-100) | Cross-source ranking. 50% velocity percentile within the source, 50% the source's own trending-rank percentile |
| `signal` | `breakout` / `hot` / `rising` / `steady` |
| `likesPerDay` | Likes, upvotes or stars per day since creation (velocity) |
| `isNewSinceLastRun`, `rankChangeSinceLastRun`, `metricDeltaSinceLastRun` | Change tracking for alerts and dashboards |
| `likes`, `downloads`, `stars`, `trendingScore`, `license`, `category`, `tags`, `description` | Source metadata |

### Example output (actual top rows from a default run on 2026-10-14)

| # | source | name | category | momentumScore | signal | velocity / day | cross-source |
|---|---|---|---|---|---|---|---|
| 1 | github_repos | dsh-our-free-model | agents | 100.0 | hot | 795.31 | 1 |
| 2 | github_trending | rea | agents | 100.0 | hot | 41187.0 | 1 |
| 3 | huggingface_datasets | tiktok-5.6B-videos | vision | 99.0 | breakout | 26.01 | 1 |
| 4 | huggingface_spaces | MiniMax-H3-Turbo-Lora-UNCENSORED | audio | 99.0 | hot | 25.45 | 2 |
| 5 | huggingface_datasets | MiMo-V2.6-RL-oss | dataset | 98.0 | hot | 48.64 | 1 |
| 6 | github_repos | esp32-iphone-agent | agents | 98.0 | breakout | 659.7 | 1 |
| 7 | huggingface_spaces | jev-decision-index | other | 97.0 | hot | 24.18 | 2 |
| 8 | github_repos | magpie | agents | 96.0 | hot | 597.26 | 1 |

That run scored 256 items across 6 sources, found 7 cross-source projects and flagged 8 breakouts. Velocity is likes, upvotes or stars per day.

### New in v0.2

- **GitHub Trending feed:** AI-related repos from github.com/trending with `starsGainedInPeriod` (daily, weekly or monthly). Repos found by both GitHub feeds are merged into one row.
- **Hugging Face Space details:** runtime stage, hardware (e.g. `zero-a10g`), SDK, and the models and datasets each Space uses.
- **Category tagging:** `categories` / `primaryCategory` = llm, vision, audio, agents, code, embeddings-rag, robotics, dataset, research. Filter with `categories`.
- **Cross-source project merge:** a paper, its GitHub repo, its model and the Space that demos it share a `projectId`, with `relatedItems`, `crossSourceCount` and a boosted `projectMomentumScore`. Matching uses explicit links (paper→repo, arXiv tags, Space→models, base models) plus exact normalised-name matches across sources.
- **Digest record:** the key-value store record `DIGEST` holds the top 10, breakouts, top 3 per category, category counts, new since last run, biggest rank gainers and cross-source projects. It's ready for a newsletter, a Slack post or a dashboard.

### Use cases

- **AI newsletters and creators:** a ranked "what's hot this week" list in one API call.
- **Investors and analysts:** spot fast-rising open models, labs and repos early.
- **ML engineers:** keep track of new models, datasets and papers in your niche with `hfSearch` (e.g. `embedding`, `tts`, `qwen`).
- **Alerts:** schedule it hourly or daily with `onlyNewSinceLastRun` + `minMomentumScore: 70`, then pipe the results to Slack, email or a webhook through Apify integrations.

### Input

Pick sources, items per source, an optional Hugging Face keyword, GitHub topics and window, and filters. An optional GitHub token raises rate limits. See the Input tab.

### Pricing

Pay per event: you're charged once per output item (`trend-item`). The Actor respects your run's spending limit and stops cleanly when it's reached.

### Data sources & notes

Data is fetched live from the public Hugging Face Hub API, the GitHub Search API and the public github.com/trending page. Papers with Code has been retired (it now redirects to Hugging Face Papers, which is included). Ollama and LM Arena leaderboards are left out until their reuse terms are clear. Scores are relative rankings computed from those public numbers. They aren't forecasts. Third-party content keeps its original licenses (see `license`).

### More from Intenal_Builds

- [Pump.fun Graduated Tokens Dataset](https://apify.com/intenal_builds/pumpfun-graduated-tokens-dataset) - every pump.fun graduation on Solana with market-cap path
- [Crypto Launch Radar](https://apify.com/intenal_builds/crypto-launch-radar) - new tokens and pairs from DEX Screener and GeckoTerminal
- [Memecoin Alpha Caller Leaderboard](https://apify.com/intenal_builds/memecoin-caller-track-record) - pump.fun KOL caller hit rates and consistency

# Actor input Schema

## `sources` (type: `array`):

Which live sources to scan. All are public APIs fetched at run time.

## `maxItemsPerSource` (type: `integer`):

How many trending items to pull from each source before scoring.

## `hfSearch` (type: `string`):

Optional keyword (e.g. 'qwen', 'embedding', 'tts') applied to Hugging Face models/datasets/Spaces. Empty = overall trending.

## `spaceDetails` (type: `boolean`):

Fetch runtime stage (RUNNING, SLEEPING...), hardware, SDK and linked models/datasets for the top trending Spaces.

## `githubTopics` (type: `array`):

GitHub topics to scan for new fast-growing repositories.

## `githubCreatedWithinDays` (type: `integer`):

Only GitHub repos created in this window, ranked by stars per day.

## `githubTrendingPeriod` (type: `string`):

Which github.com/trending window to read (stars gained today / this week / this month).

## `githubToken` (type: `string`):

Optional personal access token for higher GitHub API rate limits. Not required.

## `categories` (type: `array`):

Only output items tagged with any of these categories. Empty = all.

## `minMomentumScore` (type: `integer`):

Only output items scoring at least this (0-100). Use 70+ for alerts.

## `onlyNewSinceLastRun` (type: `boolean`):

Alert mode: output only items not seen in the previous run (state kept in a named key-value store).

## `stateStoreName` (type: `string`):

Named key-value store used to remember the previous run for deltas. Use different names for different schedules.

## `maxResults` (type: `integer`):

Maximum items to output. Each output item is one billable 'trend-item' event.

## Actor input object example

```json
{
  "sources": [
    "models",
    "datasets",
    "spaces",
    "papers",
    "github",
    "githubTrending"
  ],
  "maxItemsPerSource": 50,
  "spaceDetails": true,
  "githubTopics": [
    "llm",
    "large-language-models",
    "ai-agents",
    "generative-ai",
    "machine-learning",
    "deep-learning",
    "rag",
    "mcp",
    "diffusion-models",
    "computer-vision"
  ],
  "githubCreatedWithinDays": 30,
  "githubTrendingPeriod": "daily",
  "minMomentumScore": 0,
  "onlyNewSinceLastRun": false,
  "stateStoreName": "ai-trend-radar-state",
  "maxResults": 500
}
```

# Actor output Schema

## `results` (type: `string`):

Trending AI models, datasets, Spaces, papers and repos ranked by momentum score.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("intenal_builds/ai-trend-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("intenal_builds/ai-trend-radar").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 intenal_builds/ai-trend-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,intenal_builds/ai-trend-radar"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/DXYmJdPsLL65583hB/builds/Y8CcpJpQg6zpWFgyr/openapi.json