# Hacker News Brand Monitor (`arched_friend/hn-brand-monitor`) Actor

Track every mention of your brand, product or competitors on Hacker News. Stories and comments with points, comment counts, author and a direct link, filtered by recency and traction.

- **URL**: https://apify.com/arched\_friend/hn-brand-monitor.md
- **Developed by:** [Peach O](https://apify.com/arched_friend) (community)
- **Categories:** News, Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$4.00 / 1,000 mention founds

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?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Hacker News Brand Monitor: Every Mention Of You And Your Competitors

**Know the moment your product comes up on Hacker News, in the stories and in the comments where the real opinions live.**

A single HN thread can send more qualified traffic than a month of anything else, and the conversation happens whether you are watching or not. Most teams find out days late, when the thread is cold and the correction they wanted to post no longer matters.

Give this a list of terms and get every story and comment that mentions them, newest first, with points, comment count, author, and a link straight to the exact post.

Built for founders watching their own launch, marketers tracking share of voice against competitors, and support teams who would rather answer a complaint on the thread than read about it later.

### How it works

```mermaid
flowchart LR
    A[Terms to watch] --> B[Search stories<br/>and comments]
    B --> C[Filter to the<br/>time window]
    C --> D{Does the text<br/>really contain<br/>the term?}
    D -->|no| E[Dropped as<br/>a loose match]
    D -->|yes| F[Merge duplicates<br/>across terms]
    F --> G{New since<br/>the last run?}
    G --> H[(Your mention feed)]
```

Hacker News search ranks on relevance and splits a query into separate words, so it happily returns threads that never say the thing you asked about. Every result is checked against the actual text before it reaches your dataset. In testing, a search for `planet scale` returned six results and only one genuinely mentioned it.

### What you get

One row per mention. A real row from a live run:

```json
{
  "id": "49290923",
  "type": "story",
  "matchedKeyword": "apify",
  "title": "I built a lint for AI-generated evidence",
  "author": "fmajchrzak",
  "points": 2,
  "commentCount": 0,
  "createdAt": "2026-08-13T19:40:38Z",
  "url": "https://apify.com/...",
  "linkedDomain": "apify.com",
  "hnUrl": "https://news.ycombinator.com/item?id=49290923"
}
```

Comments come through with the body decoded to plain text, so no `&#x27;` or `<p>` tags land in your spreadsheet:

```json
{
  "type": "comment",
  "author": "someuser",
  "title": "Ask HN: What is your testing setup?",
  "excerpt": "if you give them access to Playwright and have them write playwright tests then it works well",
  "hnUrl": "https://news.ycombinator.com/item?id=49610112",
  "storyUrl": "https://news.ycombinator.com/item?id=49609887"
}
```

Stories carry `points` and `commentCount`. Comments carry neither, because Hacker News does not publish a public score for them, so those fields are `null` rather than a misleading zero.

### Running it as an alert feed

Turn on `onlyNewMentions` and put it on a schedule. The Actor remembers what it has already reported, so each run returns only what appeared since the last one and the dataset becomes an inbox rather than a growing pile.

```json
{
  "keywords": ["yourbrand", "your competitor", "your founder name"],
  "days": 2,
  "onlyNewMentions": true,
  "minPoints": 0
}
```

Pair that with a daily schedule and a webhook, and a mention reaches Slack while the thread is still on the front page.

### Example input

```json
{
  "keywords": ["supabase", "planet scale"],
  "days": 7,
  "includeStories": true,
  "includeComments": true,
  "minPoints": 10,
  "requireExactMatch": true,
  "maxResultsPerKeyword": 100
}
```

`minPoints` applies to stories only. A comment has no score of its own, so a points threshold never quietly deletes the comments you came for.

### Run it from the command line

```bash
curl -X POST "https://api.apify.com/v2/acts/arched_friend~hn-brand-monitor/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "keywords": ["yourbrand"],
    "days": 7,
    "onlyNewMentions": false
  }'
```

### Pricing

$0.003 per mention returned. A term with no mentions in the window costs nothing.

| | This Actor | Social listening SaaS | Checking by hand |
| --- | --- | --- | --- |
| 200 mentions a month | $0.60 | $99 to $500 a month | Forgotten by week two |
| Comments included | Yes | Often stories only | |
| Loose match filtering | Yes | Varies | |
| New only mode | Yes | Yes | |
| Data you own | Dataset, API, webhook | Their dashboard | |

### Notes and limits

- Search covers the full Hacker News archive back to 2007, so a long lookback works for research as well as monitoring.
- A single item that matches several of your terms is reported once, with every matched term listed, so it is never billed twice.
- Points and comment counts are read at the moment of the run. A story still climbing will show a lower score than it ends on, so re-running near the end of the day gives the settled number.

### Related products

- **Brand Mention Monitor** for the same watch across news and the wider web.
- **Google Reviews Monitor** for what customers say where they buy.
- **App Store Review Tracker** for the same signal inside your app reviews.

# Actor input Schema

## `keywords` (type: `array`):

Brand names, products or competitors to watch, one per line. A term with a space is matched as a phrase, so Planet Scale will not match the word planet on its own.

## `days` (type: `integer`):

How far back to search. Set this to match your schedule, so a daily run looks back one or two days.

## `includeStories` (type: `boolean`):

Include submitted stories, which is where a launch or an article about you shows up.

## `includeComments` (type: `boolean`):

Include comments, which is where most opinions about a product are actually written.

## `minPoints` (type: `integer`):

Only keep stories with at least this many points. Comments have no score of their own, so this never removes them.

## `minComments` (type: `integer`):

Only keep stories with at least this many comments, which is a good proxy for a thread worth reading.

## `requireExactMatch` (type: `boolean`):

Drop results that do not actually contain the term. Hacker News search ranks on relevance and splits words apart, so leaving this off returns threads that never mention you.

## `onlyNewMentions` (type: `boolean`):

Remember what has already been reported and return only mentions that are new. Turn this on for a scheduled run so the dataset is an alert feed.

## `maxResultsPerKeyword` (type: `integer`):

Caps how many mentions to pull for each term, which also caps what a run can cost.

## `timeoutSeconds` (type: `integer`):

How long to wait for each search request before retrying it.

## Actor input object example

```json
{
  "keywords": [
    "supabase"
  ],
  "days": 7,
  "includeStories": true,
  "includeComments": true,
  "minPoints": 0,
  "minComments": 0,
  "requireExactMatch": true,
  "onlyNewMentions": false,
  "maxResultsPerKeyword": 100,
  "timeoutSeconds": 20
}
```

# Actor output Schema

## `mentions` (type: `string`):

One row per story or comment, newest first, with the text, author, points, comment count and a direct link to the thread.

## `runSummary` (type: `string`):

Mention counts per term, the top scoring stories, the most active authors and the total points across the run.

# 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 = {
    "keywords": [
        "supabase"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("arched_friend/hn-brand-monitor").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 = { "keywords": ["supabase"] }

# Run the Actor and wait for it to finish
run = client.actor("arched_friend/hn-brand-monitor").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 '{
  "keywords": [
    "supabase"
  ]
}' |
apify call arched_friend/hn-brand-monitor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,arched_friend/hn-brand-monitor"
        }
    }
}
```

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/jO6HybOI1XQ0Rg2ed/builds/PIeevVF2g3frFyA1n/openapi.json