Forem: Sami

Scraping G2 Reviews Without Kasada Headaches: A SaaS Competitive Intelligence Pipeline With 29 Fields Per Review

Sami — Fri, 17 Apr 2026 17:51:52 +0000

G2 holds roughly two million verified software reviews across tens of thousands of SaaS categories. For anyone doing competitive intelligence, sales prospecting, or product research in the B2B software space, it is one of the single highest-signal public datasets on the internet. The problem is that scraping it has become almost comically painful in 2026.

If you have tried hitting g2.com with requests lately, you already know the story. Cloudflare turnstile, then a Kasada challenge, then a TLS fingerprint check, then a behavioral JavaScript puzzle, then an invisible CAPTCHA. Even well-configured headless browsers with residential proxies get flagged within the first dozen pages. The G2 review data is public, but getting to it at scale has turned into a cat-and-mouse game that burns engineering hours and proxy budget in equal measure.

This article walks through a cleaner path: using the zhorex/g2-reviews-scraper Actor on Apify to pull structured reviews, 29 fields deep, without running a single browser or rotating a single proxy.

Why G2 Data Is Worth the Effort

Before getting into the scraper itself, it helps to articulate what the data is actually good for. G2 reviews are structured, long-form, and written by verified business users. That combination makes them uniquely useful for:

Sales intelligence. A review that complains about vendor X's integration with Salesforce is a warm lead for vendor Y whose Salesforce integration is its headline feature.
Win/loss analysis. Reviews of competitors often name the alternatives the reviewer evaluated. This is free narrative market research.
Feature gap detection. Aggregating thousands of "what do you dislike" fields across a category surfaces the roadmap items customers actually care about.
Churn signals. Negative sub-ratings on "support" or "ease of setup" for a specific competitor, trended over quarters, predict defection windows.

The value is there. The delivery mechanism is the bottleneck.

The Problem With the Official Path

G2 does offer a paid API, but it is gated behind an enterprise contract, requires a seat license on the G2 side, and typically restricts you to data about your own product and a handful of named competitors. Pulling the full set of reviews for a category like "CRM Software" or "Marketing Automation" across all vendors is not on the menu.

The community workarounds are worse. DIY scrapers hit Kasada within minutes. Proxy bills for rotating residential IPs run $500 to $2,000 a month at modest volumes. Every time G2 rolls a new JS challenge, your Playwright script breaks at 3 AM and you spend a Saturday fingerprinting headers.

"We burned six weeks and about $4,000 in proxies before admitting our in-house G2 scraper was never going to be stable."
— Growth engineer at a mid-market PLG startup

What the Actor Does

The g2-reviews-scraper Actor bypasses all of that by calling G2's public review feed directly. No browser, no proxy rotation, no Kasada bypass hacks. You give it a product URL or slug and it returns structured JSON.

Feature set:

Scrape reviews for any G2 product URL or slug
29 fields per review including sub-ratings, reviewer job title, company size, industry, and verification status
Pagination handled internally, up to the full review history of a product
Filters for star rating, date range, and review language
JSON, CSV, Excel, or JSONL output
Runs on Apify infrastructure, so no local Node/Python setup required for the scraping itself

Comparison Table

Capability	G2 Official API	DIY scraper + residential proxies	`zhorex/g2-reviews-scraper`
Access to competitor reviews	No (own product only)	Yes, if you can keep it running	Yes
Kasada / JS challenge handling	N/A	Your problem, breaks weekly	Handled, no browser needed
Setup time	Weeks (contract + provisioning)	Days to weeks	Minutes
Cost at 10k reviews/month	Custom enterprise quote	~$300-600 proxies + eng time	$50
Sub-ratings included	Yes	Usually not	Yes (29 fields)
Export formats	JSON via API	Whatever you build	JSON, CSV, XLSX, JSONL
Maintenance burden	Low	High	None

Input Example

A realistic starter config for pulling reviews across three CRM competitors:

{
  "productUrls": [
    "https://www.g2.com/products/salesforce-sales-cloud/reviews",
    "https://www.g2.com/products/hubspot-sales-hub/reviews",
    "https://www.g2.com/products/pipedrive/reviews"
  ],
  "maxReviewsPerProduct": 500,
  "minRating": 1,
  "maxRating": 5,
  "dateFrom": "2024-01-01",
  "language": "en",
  "includeSubRatings": true,
  "includeReviewerProfile": true
}

Output Example

Here is one review item, trimmed to the fields most people care about. The full object has all 29 fields.

{
  "reviewId": "g2-8421930",
  "productSlug": "hubspot-sales-hub",
  "productName": "HubSpot Sales Hub",
  "reviewTitle": "Great for SMB, feels cramped above 50 reps",
  "starRating": 3.5,
  "subRatings": {
    "easeOfUse": 4.5,
    "qualityOfSupport": 4.0,
    "easeOfSetup": 4.0,
    "meetsRequirements": 3.5
  },
  "reviewLikes": "Pipeline view is clean, sequences are easy to build, and the free tier got us started without procurement.",
  "reviewDislikes": "Once we hit 60 reps the reporting module struggled. Forecasting is weaker than Salesforce and custom objects are limited.",
  "recommendations": "Fine for teams under 50. Above that, evaluate Salesforce or Dynamics.",
  "reviewer": {
    "displayName": "Verified User in Software",
    "jobTitle": "RevOps Manager",
    "industry": "Computer Software",
    "companySize": "51-200 employees",
    "isVerified": true
  },
  "publishedAt": "2025-11-08T14:22:10Z",
  "language": "en",
  "helpfulCount": 12,
  "source": "organic",
  "reviewUrl": "https://www.g2.com/products/hubspot-sales-hub/reviews/hubspot-sales-hub-review-8421930"
}

Fields include reviewId, productSlug, productName, starRating, five sub-ratings, reviewLikes, reviewDislikes, recommendations, problemsSolved, benefitsRealized, reviewer display name, job title, industry, company size, region, validation status, publishedAt, updatedAt, language, helpfulCount, source, incentive (if the review was incentivized), and the canonical reviewUrl.

Four Real Use Cases

1. SaaS Sales Displacement Plays

A sales team selling a CRM builds a nightly job that pulls all 1- and 2-star reviews for three major competitors. Each review is piped through an LLM that extracts the specific complaint and the reviewer's company. The result is a prioritized outbound list where every lead comes with a documented pain point in their own words. Open rates on personalized sequences built from real G2 complaints routinely run 2-3x generic cold outbound.

2. Category-Level Feature Gap Analysis

A product manager at a marketing automation vendor scrapes every review in the "Marketing Automation" category filed in the last 12 months, roughly 40,000 reviews across 30 products. She clusters the "dislikes" text with embeddings and counts cluster frequency per vendor. The result is a heatmap showing which features are consistent weak spots across the category (great roadmap input) and which are only weak for specific competitors (great competitive collateral).

3. Churn and Renewal Risk Signals

A customer success team at an observability vendor subscribes to a rolling scrape of their own product's reviews plus the top five competitors. Any new 1- or 2-star review mentioning an integration or feature their product covers gets routed to a Slack channel. It acts as an early-warning system for account risk and a real-time queue of switch-ready prospects.

4. Private Equity Due Diligence

A PE analyst evaluating a SaaS acquisition scrapes 5 years of G2 history for the target and three comparable vendors. The trend of monthly average star rating, sub-rating deltas, and review volume growth becomes part of the investment memo. This is one of the few ways to reality-check the seller's narrative about product quality and market position.

Pricing

The Actor is priced at $0.005 per review scraped, billed through Apify. Platform compute usage is negligible because there is no browser.

Worked examples:

1,000 reviews: $5
10,000 reviews (a large product's full history): $50
100,000 reviews (a full category sweep): $500
1,000,000 reviews (multi-category enterprise pull): $5,000

Compare that to a DIY build where a single month of residential proxies for the same volume runs $1,500 to $3,000, plus engineering time to keep the Kasada bypass alive. For most teams the break-even point is well under a week.

FAQ

Is scraping G2 reviews legal?
G2 reviews are publicly accessible and the Actor only collects data that any logged-out visitor can see. Public data scraping has been repeatedly upheld in US courts (hiQ v. LinkedIn is the landmark). That said, how you use and redistribute the data is on you. Do not republish full review text as your own content, and respect GDPR if you process reviewer metadata for EU subjects.

Do I need proxies or a Kasada solver?
No. The Actor uses G2's public review feed directly and does not trip Kasada. You do not need to supply proxies, browser fingerprints, or CAPTCHA solver tokens.

How fresh is the data?
Reviews are scraped live at run time. If a review was published five minutes before your run, it will be included. For ongoing monitoring, schedule the Actor hourly or daily via Apify Schedules.

What is the rate limit?
Practically speaking, you are limited by Apify concurrency and the Actor's internal pacing, not by G2 blocking. Expect roughly 500-1,000 reviews per minute per run. Runs can be parallelized across products.

Can I get all 29 fields or is that a premium tier?
All 29 fields are included at the flat $0.005 per review rate. There is no feature-gated premium tier.

How do I export to my warehouse?
Apify exposes datasets as JSON, CSV, XLSX, JSONL, RSS, and HTML table, and has native integrations for S3, Google Drive, and webhooks. A common pattern is JSONL to S3, then COPY into Snowflake or BigQuery.

Pair It With Capterra for Full B2B Coverage

G2 skews toward mid-market and enterprise SaaS. Capterra, owned by Gartner, leans more toward SMB and has broader coverage of vertical software (construction, healthcare, legal). For any serious competitive intel project, you want both. The companion Actor zhorex/capterra-reviews-scraper uses the same schema philosophy and pairs cleanly with this one in a single pipeline. If you are also tracking sentiment on Chinese-language software or consumer platforms, zhorex/weibo-scraper covers the APAC side.

Get Started

The Actor page, full input schema, and a free trial run live at:

https://apify.com/zhorex/g2-reviews-scraper

Drop in a product URL, run it, and you will have a clean JSON dataset in the Apify console in a couple of minutes. No proxy contract, no Kasada cat-and-mouse, no maintenance bill at 3 AM.

Telegram Channel Scraper: Extract Messages, Media & Metadata Without API Keys or Phone Numbers

Sami — Wed, 15 Apr 2026 17:15:49 +0000

Telegram has 950+ million monthly active users and has become the go-to platform for crypto communities, news channels, research groups, and brand communications. But extracting data from Telegram channels programmatically is surprisingly difficult — until now.

This guide covers why Telegram scraping matters, the technical challenges involved, and how to extract messages, views, reactions, and media from any public Telegram channel without needing API keys, phone numbers, or Telegram bot tokens.

Why Scrape Telegram Channels?

Telegram isn't just a messaging app — it's a broadcasting platform where organizations, communities, and influencers share real-time information. The data inside Telegram channels is valuable for:

Business Intelligence

Crypto & DeFi research — Track token announcements, project updates, and community sentiment across hundreds of channels
Brand monitoring — See what's being said about your brand in Telegram communities
Competitive intelligence — Monitor competitor announcements and marketing strategies

Research & OSINT

Open-source intelligence (OSINT) — Investigate public channels for geopolitical, security, or journalistic research
Academic research — Study information spread, community dynamics, and content patterns
Market research — Analyze consumer conversations and product discussions

Marketing & Content

Influencer discovery — Find active Telegram channels in your niche with engaged audiences
Content strategy — Analyze which message formats, topics, and posting times drive the most views and engagement
Lead generation — Identify potential customers discussing relevant topics

The Challenge: Why Telegram Scraping Is Hard

If you've tried to build a Telegram scraper, you know the pain:

The Official API Requires Authentication

Telegram's Bot API and MTProto API both require:

A phone number to create an account
An API key from my.telegram.org
Session management with 2FA complications
Compliance with strict rate limits

For bulk data extraction, you risk account bans if you exceed rate limits or trigger anti-abuse systems.

The Telethon/Pyrogram Approach

The most common DIY approach uses Python libraries like Telethon:

from telethon import TelegramClient

# Requires API ID, API hash, AND a phone number
client = TelegramClient('session', api_id, api_hash)

async def scrape_channel(channel_name):
    await client.start(phone='+1234567890')  # Phone verification required
    channel = await client.get_entity(channel_name)
    messages = await client.get_messages(channel, limit=100)
    return messages

Problems with this approach:

You need to provide a real phone number and verify with SMS
Your account can get banned if you scrape too aggressively
Session management is fragile — sessions expire, require re-auth
You need to handle flood wait errors and implement backoff logic
Media downloads require additional API calls per file

The Easy Way: No API Keys, No Phone Numbers

The Telegram Channel Scraper on Apify takes a completely different approach. Instead of using Telegram's authenticated API, it accesses public channel data through Telegram's web preview endpoints — the same ones that power t.me preview pages.

What This Means For You

No API key needed — zero authentication required
No phone number — no account registration or SMS verification
No proxy needed — direct HTTP requests work without rotation
No account ban risk — doesn't use an authenticated Telegram session
No browser needed — pure HTTP requests, fast and cheap

What Data Can You Extract?

From any public Telegram channel, you get:

Data Field	Description
Message text	Full message content including formatting
Views	Number of views per message
Reactions	Emoji reactions with counts
Date & time	Publication timestamp
Media URLs	Links to photos, videos, documents
Forward info	Original source if message was forwarded
Reply info	Which message it replies to
Channel metadata	Name, description, subscriber count, avatar

How To Use It

{
  "channelNames": ["duaborev", "crypto_news"],
  "maxMessages": 100,
  "includeMedia": true
}

That's it. Enter channel names (or full t.me URLs), set your message limit, and hit Start.

Real-World Use Cases

Crypto Research Dashboard

Step 1: List 50 crypto project Telegram channels
Step 2: Schedule daily scraping → extract latest messages
Step 3: Run NLP sentiment analysis on message text
Step 4: Build dashboard tracking project activity and community sentiment
Step 5: Alert when channels mention specific tokens or events

OSINT Investigation

Step 1: Identify target public channels
Step 2: Extract full message history with timestamps
Step 3: Analyze posting patterns, forwarded sources, media
Step 4: Map information networks and content origin

Brand Monitoring

Step 1: Search for channels mentioning your brand/product
Step 2: Schedule weekly scraping of relevant channels
Step 3: Track mention frequency, sentiment, and reach (views)
Step 4: Compare share of voice vs competitors

Content Marketing Research

Step 1: Scrape top channels in your niche
Step 2: Analyze which posts get highest views and reactions
Step 3: Identify optimal posting times and content formats
Step 4: Apply insights to your own Telegram channel strategy

Why This Scraper vs. Alternatives

Method	API Key	Phone #	Ban Risk	Speed	Cost
Telethon (DIY)	Yes	Yes	High	Slow (rate limits)	Free + server costs
Pyrogram (DIY)	Yes	Yes	High	Slow (rate limits)	Free + server costs
Bot API	Yes	No	Medium	Medium	Free + server costs
SaaS tools	Varies	Varies	Low	Medium	$50-200+/mo
Telegram Channel Scraper	No	No	None	Fast	$5/1K messages

Key Advantages

Zero setup friction — No API registration, no phone verification, no OAuth flow. Enter channel names and go.
No ban risk — Since it doesn't use an authenticated session, there's no account to ban.
Pay per result — $0.005 per message ($5 per 1,000). No monthly subscription.
Scheduled runs — Set up daily/weekly/hourly scraping directly in Apify.
Export anywhere — JSON, CSV, Excel, XML. Integrate with Google Sheets, Zapier, Make, n8n.

Pricing

Volume	Cost	Per-Message
100 messages	$0.50	$0.005
1,000 messages	$5.00	$0.005
10,000 messages	$50.00	$0.005

Free Apify trial includes credits to test before committing. No monthly fees.

FAQ

Does this scrape private Telegram channels?

No. This scraper only accesses publicly available Telegram channels — the same content anyone can view at t.me/channelname without a Telegram account.

Can I scrape Telegram groups (not channels)?

Currently, the scraper is optimized for public channels (one-way broadcast). Group chat scraping requires authenticated API access and is not supported.

Does it download media files?

It extracts media URLs (photos, videos, documents) so you can download them separately. The URLs point to Telegram's CDN and are publicly accessible.

How often can I run it?

As often as you need. Schedule runs every hour for near-real-time monitoring, or daily/weekly for trend analysis.

Is scraping Telegram channels legal?

This scraper only accesses publicly available content that anyone can view without a Telegram account. It does not bypass authentication or access private data. Always comply with local regulations when using scraped data.

Get Started in 60 Seconds

Go to the Telegram Channel Scraper →
Click "Try for free"
Enter your channel names
Hit Start → get structured JSON data

No API keys. No phone numbers. No account bans. Just clean Telegram channel data ready for analysis.

Try it free on Apify →

Part of the Zhorex scraper suite on Apify. Also check out: RedNote Xiaohongshu Scraper · Kick.com Analytics · G2 Reviews Scraper

Kick.com API Alternative: Extract Streamer Analytics, Live Streams & VODs Without Authentication

Sami — Wed, 15 Apr 2026 17:07:41 +0000

Kick.com doesn't offer a public API. But if you need Kick streamer data — profiles, live streams, VODs, clips, or channel rankings — there's a clean, structured way to get it without building your own scraper from scratch.

This guide covers what data is available from Kick.com, how the platform's internal API works, and the fastest way to extract Kick streaming analytics at scale.

Why Kick.com Data Is Valuable in 2026

Kick.com has positioned itself as the creator-friendly alternative to Twitch, offering streamers a 95/5 revenue split (vs Twitch's 50/50). This has attracted a wave of major creators, making Kick one of the fastest-growing live streaming platforms globally.

For businesses and analysts, this growth creates demand for structured Kick.com data:

Who Needs Kick Data?

Influencer marketing agencies — Find the right Kick streamers for brand deals based on viewer counts, categories, and engagement patterns
Esports organizations — Track competitive gaming viewership and identify rising talent
Brand sponsors — Monitor ROI of sponsorship deals across Kick channels in real time
Content analysts — Discover which categories, stream formats, and clip styles perform best
Competitive intelligence teams — Compare Kick vs Twitch vs YouTube Gaming performance for specific creators

What Data Can You Extract?

Data Type	Examples
Channel profiles	Follower count, bio, avatar, social links, verified status, creation date
Live streams	Current viewers, stream title, category, start time, language, tags
VODs	Past broadcasts with duration, views, category, thumbnail
Clips	Clip title, views, likes, duration, creator, video URL
Rankings	Top channels by viewer count, filtered by category

The Problem: No Public Kick API

Unlike Twitch (which has a documented API), Kick.com doesn't offer a public developer API. The platform does use internal API endpoints that return JSON, but:

Endpoints change without notice — Kick updates their internal routes regularly
TLS fingerprinting — Standard HTTP clients get blocked; you need browser-level TLS impersonation
Rate limiting — Too many requests from the same IP get throttled
No documentation — You're reverse-engineering endpoints from browser network traffic

Building a DIY Kick scraper means maintaining code that breaks every few weeks when Kick updates their internal API structure.

The Solution: 4-in-1 Kick.com Analytics Actor

The Kick.com Streamer & Channel Analytics actor on Apify solves all of this. It handles TLS fingerprinting, rate limiting, and endpoint changes — and delivers clean JSON data through 4 scraping modes.

Mode 1: Channel Profiles (`channel_details`)

Get complete Kick streamer profiles:

{
  "mode": "channel_details",
  "channelNames": ["xqc", "amouranth", "trainwreckstv"]
}

Returns: display name, bio, avatar, banner, follower count, live status, current viewers, category, stream title, verified badge, subscriber badges, social links (Instagram, Twitter, YouTube, Discord, TikTok), and creation date.

Use case: Build a Kick influencer database with accurate follower counts and cross-platform social links for outreach campaigns.

Mode 2: Live Streams (`live_streams`)

Discover who's streaming live on Kick right now:

{
  "mode": "live_streams",
  "category": "just-chatting",
  "minViewers": 100,
  "maxResults": 50,
  "sortBy": "viewers"
}

Returns: channel name, viewer count, stream title, category, start time, thumbnail, language, tags, and maturity rating.

Use case: Real-time Kick viewership monitoring — schedule runs every 5 minutes for a live dashboard of who's streaming and how many viewers they have.

Mode 3: VODs & Clips (`channel_videos`)

Extract past broadcasts and Kick clips:

{
  "mode": "channel_videos",
  "channelNames": ["xqc"],
  "videoType": "clips",
  "maxResults": 50
}

Returns: clip ID, title, duration, views, likes, category, thumbnail, video URL, creator, and creation date.

Use case: Analyze what content formats get the most views and engagement on Kick — identify optimal clip lengths, trending categories, and viral content patterns.

Mode 4: Channel Rankings (`top_channels`)

Get a ranked list of top Kick channels by live viewers:

{
  "mode": "top_channels",
  "sortBy": "viewers",
  "category": "fortnite",
  "maxResults": 25
}

Returns: rank, channel name, bio, avatar, current viewers, stream title, category, affiliate status, and social links.

Use case: Competitive streaming intelligence — see who's dominating each category on Kick, and how viewer distribution compares to Twitch.

Why This Actor vs. DIY Scraping

Feature	DIY Python Scraper	Kick.com Analytics
Setup time	Hours to days	2 minutes
TLS fingerprinting	Must handle yourself	Built-in
Rate limit handling	Your problem	Handled automatically
API change updates	You maintain it	Maintained for you
4 scraping modes	Build each separately	All included
Data normalization	Manual parsing	Clean JSON output
Scheduling	Cron jobs + servers	Built-in Apify scheduler
Cost	Server + dev time	$0.005/result ($5/1K)

Key Technical Advantages

No API key needed — accesses Kick.com's public API endpoints directly
No proxy needed — direct HTTP requests work without proxy rotation
No browser needed — pure HTTP with TLS impersonation, no Playwright/Puppeteer overhead
Lightweight — runs on 256 MB RAM
Structured output — clean JSON, CSV, Excel, or XML export

Real-World Workflow Examples

Influencer Marketing: Find & Vet Kick Streamers

1. top_channels mode → get top 100 streamers in target category
2. channel_details mode → bulk-pull profiles for all 100
3. Filter by follower count, verified status, social links
4. channel_videos (clips) → check engagement on recent content
5. Build shortlist with data-backed partnership recommendations

Esports: Track Tournament Viewership

1. live_streams mode → filter by game category (e.g., "valorant")
2. Schedule every 5 min during tournament hours
3. Export to Google Sheets via Apify integration
4. Build real-time viewership dashboard

Content Strategy: Analyze What Works on Kick

1. channel_videos (clips) → extract top clips from 20 channels
2. Analyze: avg views by duration, category, time of day
3. Identify content patterns that drive viral clips
4. Apply insights to your own streaming or client strategy

Cross-Platform Streaming Intelligence

The same developer also offers a Twitch Streamer & Channel Analytics actor with 6 scraping modes. Combined with the Kick actor, you can build a complete cross-platform streaming analytics pipeline:

Compare creator performance across Kick and Twitch
Track viewer migration between platforms
Identify creators who are growing faster on one platform vs. another
Monitor category trends across both ecosystems

Pricing

Pay-per-result with no monthly fees:

Volume	Cost
1,000 results	$5.00
10,000 results	$50.00
100,000 results	$500.00

Free Apify trial includes credits to test before committing.

Get Started

Go to the Kick.com Streamer & Channel Analytics actor →
Click "Try for free"
Select your mode, enter parameters, hit Start
Get structured JSON data in seconds

No API key. No proxy. No browser. Just clean Kick.com data ready for analysis, dashboards, or integration with your existing tools.

Try it free on Apify →

RedNote (Xiaohongshu) Scraping API: Extract Posts, Comments, Videos & Profiles in 2026

Sami — Wed, 15 Apr 2026 16:57:11 +0000

RedNote (小红书 / Xiaohongshu) isn't just another social media app — it's China's #1 product discovery engine with 300M+ monthly active users. Since the TikTok ban uncertainty drove millions of Western users to the platform in early 2025, RedNote data has become critical for marketers, researchers, and brands expanding into Chinese and global markets.

This guide covers everything you need to know about extracting RedNote data at scale — from understanding the platform's value, to building your own scraper, to using a ready-made solution that handles all the complexity for you.

Why RedNote Data Matters in 2026

Xiaohongshu (literally "Little Red Book") started as a cross-border shopping guide but evolved into the platform where Chinese consumers discover products, read authentic reviews, and follow lifestyle KOLs (Key Opinion Leaders). Think of it as Instagram meets Pinterest meets Yelp — but with 300 million users who actively use it to make purchasing decisions.

What Makes RedNote Different

Purchase intent is built in. Unlike TikTok or Instagram where content is mostly entertainment, RedNote users actively search for product reviews and recommendations before buying
KOL marketing ROI is trackable. Brands can measure how RedNote influencer campaigns translate to actual product searches and saves
Cross-border commerce data. See which Western brands and products are trending with Chinese consumers in real time
UGC reviews at scale. Authentic user-generated reviews that brands can mine for product development insights

Who Needs RedNote Data?

Role	Use Case
Marketing agencies	Identify trending RedNote KOLs for brand partnerships
E-commerce brands	Monitor product mentions and competitor campaigns
Market researchers	Track consumer sentiment and emerging trends in China
Content creators	Discover viral content formats and trending topics
Brand managers	Competitive intelligence across Chinese social media

The Challenge: Why Scraping RedNote Is Hard

If you've tried to scrape Xiaohongshu before, you know the pain points:

Anti-bot protections — RedNote uses aggressive fingerprinting, request signing, and behavioral detection
Dynamic content loading — Posts, comments, and profiles load via complex JavaScript rendering
API encryption — Internal API endpoints use signed requests with rotating parameters
Rate limiting — Aggressive rate limits that block standard HTTP clients quickly
No public API — Unlike Twitter or Reddit, there's no official API for developers

Building and maintaining a RedNote scraper from scratch requires constant updates as the platform evolves its protections. Most DIY scrapers break within weeks.

The Easy Way: RedNote Xiaohongshu Scraper All-in-One

Instead of fighting anti-bot systems, the RedNote Xiaohongshu Scraper All-in-One on Apify handles all the complexity in a single tool. It's the most comprehensive Xiaohongshu scraper available — 5 scraping modes covering every data type you need.

5 Scraping Modes

1. Search Posts

Search RedNote by keyword and get structured results:

{
  "mode": "search_posts",
  "keyword": "skincare routine",
  "maxResults": 50,
  "sortBy": "relevance"
}

Returns: post title, content, images, video URLs, likes, comments, shares, author info, hashtags, and publication date.

2. User Content

Extract all posts from a specific RedNote creator:

{
  "mode": "user_posts",
  "userIds": ["user123", "user456"],
  "maxResults": 100
}

Perfect for KOL analysis — see everything a creator has posted, their engagement rates, and content themes.

3. Download Videos

Extract video URLs from RedNote posts for content analysis:

{
  "mode": "download_videos",
  "postUrls": ["https://www.xiaohongshu.com/explore/..."]
}

4. Scrape Comments

Get the full comment thread from any post:

{
  "mode": "post_comments",
  "postUrls": ["https://www.xiaohongshu.com/explore/..."],
  "maxComments": 200
}

Returns: comment text, author, likes, replies, and timestamps. Essential for sentiment analysis and understanding audience reactions.

5. Profile Data

Extract detailed RedNote user profiles:

{
  "mode": "user_profile",
  "userIds": ["user123"]
}

Returns: display name, bio, follower count, following count, total likes, verification status, and profile metadata.

Why This Scraper vs. Others

Actually works — most Xiaohongshu scrapers on the market are broken or deprecated. This one is actively maintained and updated
5 modes in one tool — no need to use separate scrapers for posts, comments, videos, and profiles
Clean JSON output — structured data ready for analytics, dashboards, or database import
No browser needed — lightweight HTTP-based scraping, runs fast and cheap
Pay per result — only pay for the data you extract, no monthly subscription lock-in

Real-World Use Cases

Influencer Marketing Campaign Planning

Step 1: Search "skincare" → find top posts with highest engagement
Step 2: Extract user profiles of top creators → analyze follower counts
Step 3: Scrape their recent posts → calculate average engagement rate
Step 4: Build KOL shortlist with data-backed ROI predictions

Competitive Brand Monitoring

Step 1: Search your brand name + competitor names on RedNote
Step 2: Extract all posts mentioning each brand
Step 3: Scrape comments → run sentiment analysis
Step 4: Compare brand perception and share of voice

Product Launch Research

Step 1: Search product category keywords ("vitamin C serum")
Step 2: Analyze which products get the most organic mentions
Step 3: Extract comments to understand what features consumers value
Step 4: Identify content gaps your launch campaign can fill

Getting Started in 3 Steps

Create a free Apify account at apify.com
Go to the RedNote Xiaohongshu Scraper actor page
Choose your mode, enter your parameters, and hit Start

Results are delivered in JSON, CSV, Excel, or XML — export directly or integrate with Google Sheets, Zapier, Make, n8n, and any tool that accepts webhooks.

Pricing

The scraper uses Apify's pay-per-event model — you only pay for the data you extract:

Volume	Cost	Per-Result Price
100 results	~$0.50	$0.005/result
1,000 results	~$5.00	$0.005/result
10,000 results	~$50.00	$0.005/result

No monthly fees, no minimum commitment. Free Apify trial includes credits to test the scraper before committing.

FAQ

Is scraping RedNote legal?

This scraper accesses publicly available content only. It does not bypass authentication or access private user data. Always comply with local regulations and platform terms when using scraped data.

Does it work after Xiaohongshu API changes?

Yes. The scraper is actively maintained and updated when RedNote changes their internal APIs. Updates are published automatically through Apify's platform.

Can I schedule automated scraping runs?

Yes. Apify supports scheduled runs — set it to scrape daily, hourly, or at any custom interval. Perfect for ongoing brand monitoring or trend tracking.

What's the difference between this and other RedNote scrapers?

Most competitors are deprecated, broken, or only support one mode (e.g., search only). The RedNote Xiaohongshu Scraper All-in-One is the only scraper that combines 5 modes (search, user posts, videos, comments, profiles) in a single tool.

Start Extracting RedNote Data Today

Whether you're planning influencer marketing campaigns, monitoring brand mentions on Xiaohongshu, or researching Chinese consumer trends, the RedNote Xiaohongshu Scraper All-in-One gives you the data you need — reliably, affordably, and at scale.

Try it free on Apify →

I Built a Free Chrome Extension That Shows Google Ads Budget Pacing in Real Time — Here's Why

Sami — Wed, 15 Apr 2026 16:44:23 +0000

TL;DR: I got tired of checking spreadsheets to see if my Google Ads campaigns were overspending. So I built AdPacer — a free Chrome extension that injects real-time budget pacing indicators directly into the Google Ads dashboard. Green, yellow, red. No API keys, no login, no data leaves your browser. Skip to the demo.

The Problem Every PPC Manager Knows

If you manage Google Ads campaigns, you've experienced this: you set a $100/day budget, and Google happily spends $187 on a Tuesday. Then on Wednesday, it spends $43. By end of month, you're technically within the 30.4x monthly cap, but your daily spend was a rollercoaster you never signed up for.

Now multiply that by 15 campaigns across 4 client accounts, and you have the daily reality of every PPC manager, freelance Google Ads consultant, and digital marketing agency in 2026.

The standard workflow looks like this:

Export campaign data from Google Ads
Paste into a budget pacing spreadsheet
Calculate: (actual spend / expected spend) × 100
Realize you're 22% over on three campaigns
Go back to Google Ads and adjust
Repeat tomorrow

I did this for two years. Then I thought: why am I leaving Google Ads to check if Google Ads is overspending?

What I Built

AdPacer is a Chrome extension that adds budget pacing indicators directly into the Google Ads campaign table. No new tabs, no separate dashboard, no SaaS login.

When you open your Google Ads campaign list, AdPacer reads what's already on your screen and overlays three things:

1. Color-Coded Pacing Bars

Each campaign gets a visual indicator right next to its name:

🟢 Green — within 10% of expected pace. You're good.
🟡 Yellow — 10–20% over expected pace. Worth keeping an eye on.
🔴 Red — 20%+ over pace. Overspend risk — take action.

2. Projected End-of-Month Spend

A small badge shows what each campaign is on track to spend by month's end, based on the current daily run rate. So instead of "$847 spent" (which means nothing without context), you see "$847 spent → projected $2,940 / $3,000 budget."

3. Browser Notification Alerts (Pro)

Configurable alerts that fire when any campaign crosses your chosen threshold. You're in another tab writing a report? AdPacer pings you: "Campaign 'Brand - US' is 23% over pace."

How It Works Under the Hood

I want to be transparent about the architecture because I think it matters for trust — especially with a tool that runs inside your ad account.

No API. No Server. No Data Collection.

AdPacer is a content script that runs on ads.google.com. It does three things:

1. Reads the DOM → campaign names, daily budgets, month-to-date costs
2. Calculates pacing → (actual_spend / expected_spend_for_today)
3. Injects indicators → colored bars + projected spend badges

That's it. There is:

No Google Ads API connection — it doesn't use OAuth or any API tokens
No backend server — no data is sent anywhere outside your browser
No account login — you don't create an AdPacer account
No tracking or analytics on your campaign data

The extension runs entirely in your browser's content script sandbox. Your campaign data never leaves the page.

The Core Pacing Logic

Here's the simplified version of the pacing calculation:

function calculatePacing(campaign) {
  const today = new Date();
  const daysInMonth = new Date(
    today.getFullYear(), today.getMonth() + 1, 0
  ).getDate();
  const dayOfMonth = today.getDate();

  // What percentage of the month has elapsed?
  const expectedPct = dayOfMonth / daysInMonth;

  // What's the monthly budget cap?
  const monthlyBudget = campaign.dailyBudget * 30.4;

  // How much should have been spent by now?
  const expectedSpend = monthlyBudget * expectedPct;

  // Pacing ratio: 1.0 = perfectly on pace
  const pacingRatio = campaign.monthToDateCost / expectedSpend;

  // Projected end-of-month spend
  const dailyRunRate = campaign.monthToDateCost / dayOfMonth;
  const projectedSpend = dailyRunRate * daysInMonth;

  return {
    ratio: pacingRatio,
    status: pacingRatio <= 1.10 ? 'green'
          : pacingRatio <= 1.20 ? 'yellow'
          : 'red',
    projectedSpend: projectedSpend,
    monthlyBudget: monthlyBudget
  };
}

Language Support

One challenge was making the DOM parser work across different Google Ads interface languages. Column headers, number formats, and currency symbols all change. Currently, AdPacer supports English and Spanish Google Ads interfaces, with more languages planned.

Why Not Just Use [Existing Tool]?

Fair question. Here's where the Google Ads budget pacing tool landscape sits right now:

Tool	Type	Cost	Real-Time	In-Dashboard
Manual Spreadsheet	DIY	Free	No (daily lag)	No
Google Ads Scripts	DIY	Free	Hourly at best	No
TrueClicks	SaaS	$49+/mo	Near real-time	No
Optmyzr	SaaS	$209+/mo	Near real-time	No
WordStream	SaaS	$299+/mo	Near real-time	No
AdPacer	Extension	Free / $19/mo	Yes	Yes

The SaaS platforms are great — they offer full PPC management suites with audit tools, bid optimization, reporting, and more. If you already pay for one, their pacing features may cover your needs.

But if budget pacing is the specific problem you're solving, those tools are like buying a Swiss Army knife when you need a screwdriver. AdPacer does one thing, does it well, and does it where you're already working.

Zero Chrome Extensions Do This

I checked. When I built AdPacer, there were literally zero Chrome extensions on the Web Store that add budget pacing indicators inside the Google Ads interface. There are extensions for keyword research, ad preview, competitor analysis — but nothing for the most basic question a PPC manager asks every day: "Am I on pace or overspending?"

The March 2026 Problem That Made This Urgent

On March 1, 2026, Google changed how budget pacing works for campaigns with ad schedules. Previously, a weekday-only campaign on a $100/day budget would spend roughly $2,200/month (22 active days × $100).

Now Google tries to spend the full 30.4x monthly cap ($3,040) regardless of schedule. That means the same weekday-only campaign now targets $3,040 ÷ 22 days = $138.18/day — a 38% increase with zero changes from the advertiser.

Weekend-only campaigns? Even worse — up to 249% more per active day.

This made real-time pacing visibility not just nice-to-have, but essential. You can't afford to discover overspend at end of month when Google is actively redistributing budget to your active days.

Read the full breakdown of the March 2026 changes →

What's Free vs. Pro

I wanted the core functionality to be genuinely useful on the free tier, not a crippled demo:

Free (forever)

✅ Pacing bars (green/yellow/red) for up to 3 campaigns
✅ Projected end-of-month spend badges
✅ English and Spanish support
✅ No account required, no data collection

Pro ($19/month)

Everything in Free, plus:
✅ Unlimited campaigns
✅ Browser notification alerts with configurable thresholds
✅ Multi-account support (coming soon)

For comparison, the cheapest SaaS alternative with pacing features is $49/month. AdPacer Pro is $19/month and lives inside your existing workflow.

Technical Decisions I'd Make Differently

Since this is dev.to, here are some honest notes on the build:

DOM parsing is fragile. Google Ads updates their UI frequently. I'm using a combination of CSS selectors and data attribute patterns to locate campaign data in the table. When Google ships a UI change, the parser can break. I've built a fallback system that detects when expected elements are missing and gracefully degrades, but it's an ongoing maintenance cost.

Manifest V3 limitations. Chrome's extension platform has moved to Manifest V3, which limits background script capabilities. The notification alert system required creative workarounds using service workers with alarms API instead of persistent background pages.

Number parsing across locales. In the US, it's $1,234.56. In Spain, it's 1.234,56 €. In India, it's ₹1,23,456.78. Parsing currency values from the DOM across locales was surprisingly complex. I ended up building a locale-aware parser that detects the format from the Google Ads interface language setting.

What's Next

The roadmap based on user feedback:

Multi-account (MCC) support — see pacing across all accounts in one view
Historical pacing trends — track pacing patterns over time (stored locally)
Custom pacing rules — different thresholds for different campaigns
More languages — French, German, Portuguese next
Firefox support — if there's enough demand

Try It

If you manage Google Ads campaigns and spend any time worrying about budget pacing, give it a try:

Install AdPacer — Free on Chrome Web Store →

No API keys. No login. No data leaves your browser. Takes 30 seconds to install, and you'll see pacing indicators the next time you open Google Ads.

If you have feedback, feature requests, or bugs — I'm building this actively and would love to hear from PPC managers and developers who work with Google Ads daily.

Also check out our landing page at adpacer.netlify.app for more details on features and pricing.

How to Prevent Google Ads Overspend After the March 2026 Budget Pacing Changes

Sami — Tue, 14 Apr 2026 19:22:26 +0000

TL;DR: Google quietly changed how budget pacing works for campaigns with ad schedules on March 1, 2026. If you run weekday-only campaigns, your daily spend may have jumped 38%+ overnight. Skip to the easiest fix — a free Chrome extension that shows real-time pacing indicators directly inside Google Ads.

What Changed on March 1, 2026

Google Ads has always been allowed to spend up to 2x your daily budget on any given day, as long as your monthly total doesn't exceed 30.4 times your daily budget. This is standard Google Ads budget pacing behavior that most PPC managers are familiar with.

What changed in March 2026 is how Google handles budget pacing for campaigns with ad schedules. Previously, if you ran a campaign only on weekdays (Monday through Friday), Google would effectively pace your spend across those 5 days and leave the budget untouched on weekends. Your monthly spend would naturally come in under the 30.4x cap.

The March 2026 change: Google now actively attempts to spend the full 30.4x monthly limit regardless of your ad schedule. If your campaign only runs on weekdays, Google redistributes the "unused" weekend budget across your active days.

Google did not send email notifications about this change. It was documented in a support article update and briefly mentioned in the Google Ads product changelog. Most advertisers noticed it when their daily costs jumped without any changes to bids, targeting, or budgets.

The Math: How Google Ads Overspend Happens

Let's walk through a concrete example to show exactly how the March 2026 pacing changes affect your spend.

Before March 2026: Weekday-Only Campaign

Daily budget:              $100/day
Schedule:                  Monday–Friday only
Active days per month:     ~22 days
Expected monthly spend:    22 × $100 = $2,200
Monthly cap (30.4 × $100): $3,040
Actual spend:              ~$2,200 — well under cap ✓

After March 2026: Same Campaign, Same Settings

Daily budget:              $100/day
Schedule:                  Monday–Friday only
Monthly cap Google targets: 30.4 × $100 = $3,040
Active days per month:     ~22 days
New effective daily spend:  $3,040 ÷ 22 = $138.18/day  ← +38.2%

That's a 38% increase in daily spend with zero changes on your end. And it gets worse for more restricted schedules.

Weekend-Only Campaign: The Worst Case

Daily budget:              $100/day
Schedule:                  Saturday–Sunday only
Active days per month:     ~8–9 days
Monthly cap Google targets: 30.4 × $100 = $3,040
New effective daily spend:  $3,040 ÷ 8.7 = $349.43/day  ← +249%

Combined with the existing 2x daily overdelivery rule, your weekend-only campaign could theoretically spend up to $698.86 in a single day on a $100 daily budget. That's not a billing error — it's the new system working as designed.

Who Is Affected

This change impacts any advertiser running campaigns with restricted ad schedules:

Local service businesses running ads only during business hours (weekdays 8am–6pm)
B2B advertisers targeting decision-makers Monday through Friday
E-commerce brands with weekend or payday-specific promotions
Agencies managing clients with strict monthly budgets and limited schedules
Seasonal advertisers with custom day-of-week schedules

If all of your campaigns run 24/7 with no ad scheduling, the change has minimal impact. The issue is specifically with limited schedules combined with Google's new aggressive pacing.

Manual Monitoring: The Spreadsheet Method

The most common approach to tracking Google Ads budget pacing is a spreadsheet:

Days elapsed in month:  14 (example: April 14)
Days remaining:         16
Monthly budget:         $3,000
Expected spend by today: ($3,000 ÷ 30) × 14 = $1,400
Actual spend to date:   $1,847
Pacing:                 $1,847 ÷ $1,400 = 131.9% — OVERSPENDING

This works, but it has significant limitations:

You need to export data daily from Google Ads and paste it into the sheet
It's a lagging indicator — by the time you check, the overspend already happened
It doesn't account for active-day-adjusted pacing under the new rules
Managing 10+ campaigns across multiple accounts makes this tedious
There's no alerting — you have to remember to check

Google Ads Scripts: The Developer Approach

For teams with technical resources, a Google Ads script can automate pacing checks:

function main() {
  var today = new Date();
  var daysInMonth = new Date(
    today.getFullYear(), today.getMonth() + 1, 0
  ).getDate();
  var dayOfMonth = today.getDate();
  var expectedPct = dayOfMonth / daysInMonth;

  var campaigns = AdsApp.campaigns()
    .withCondition("Status = ENABLED")
    .get();

  while (campaigns.hasNext()) {
    var campaign = campaigns.next();
    var stats = campaign.getStatsFor("THIS_MONTH");
    var cost = stats.getCost();
    var dailyBudget = campaign.getBudget().getAmount();
    var monthlyBudget = dailyBudget * 30.4;
    var actualPct = cost / monthlyBudget;
    var pacingRatio = actualPct / expectedPct;

    if (pacingRatio > 1.2) {
      Logger.log("🔴 " + campaign.getName()
        + " — " + (pacingRatio * 100).toFixed(1) + "% paced");
    } else if (pacingRatio > 1.1) {
      Logger.log("🟡 " + campaign.getName()
        + " — " + (pacingRatio * 100).toFixed(1) + "% paced");
    }
  }
}

Scripts are better than spreadsheets, but they have trade-offs:

Runs on a schedule (hourly at best) — not truly real-time
Requires JavaScript knowledge to write and maintain
Doesn't account for the new ad-schedule redistribution without extra logic
Email alerts are easy to miss in busy inboxes
No visual indicator inside the Google Ads dashboard itself

The Easiest Fix: Real-Time Pacing Directly in Google Ads

The ideal workflow for Google Ads budget pacing is one where you don't need to leave the dashboard, open a spreadsheet, or wait for a script to run. You just look at your campaign list and immediately know which campaigns are on track and which are overspending.

That's the approach behind AdPacer, a free Chrome extension that overlays pacing indicators directly into the Google Ads campaign table.

How It Works

AdPacer reads the campaign data already visible on your Google Ads screen — campaign names, daily budgets, and month-to-date costs. It calculates the pacing ratio and displays a color-coded indicator next to each campaign:

🟢 Green — on pace, within 10% of expected spend
🟡 Yellow — 10–20% above expected pace, worth monitoring
🔴 Red — 20%+ over pace, overspend risk that needs attention

It also shows projected end-of-month spend badges based on your current daily run rate, so you can see at a glance whether a campaign is headed toward $3,200 on a $3,000 budget.

Privacy: Everything runs locally in your browser. No API connections, no data sent to servers, no login required. It reads what's already on your screen and adds visual overlays. Supports both English and Spanish Google Ads interfaces.

Why This Matters Post-March 2026

The new pacing behavior means your campaigns can start overspending faster than before, especially in the first half of the month when Google is aggressively redistributing budget. A visual indicator that's always visible while you work means you can catch problems before they compound.

Instead of discovering on the 20th that a weekday-only campaign has already burned 85% of its monthly budget, you see a yellow indicator on the 10th and adjust proactively.

Comparing Your Options

Method	Cost	Real-Time	Setup	In-Dashboard
Manual Spreadsheet	Free	No (daily lag)	30–60 min	No
Google Ads Script	Free	Hourly at best	1–3 hours	No
TrueClicks	$49+/mo	Near real-time	15 min	No (separate app)
Optmyzr	$209+/mo	Near real-time	30 min	No (separate app)
WordStream	$299+/mo	Near real-time	30 min	No (separate app)
AdPacer	Free / $19/mo	Yes	30 seconds	Yes

The SaaS platforms offer comprehensive PPC management suites with many features beyond pacing. But if budget pacing is your primary concern — especially after the March 2026 changes — a dedicated in-dashboard tool is faster to deploy and cheaper to run.

Pricing Breakdown

Free tier: Up to 3 campaigns, pacing bars, projected spend badges
Pro ($19/mo): Unlimited campaigns, configurable browser notification alerts, multi-account support

Install AdPacer from the Chrome Web Store →

Frequently Asked Questions

What exactly changed with Google Ads budget pacing in March 2026?

Starting March 1, 2026, Google Ads now actively tries to spend the full 30.4x monthly budget limit even for campaigns with limited ad schedules. Previously, campaigns with restricted schedules would naturally spend less. Now, Google redistributes unspent budget to active days, increasing per-day spend by 38% or more.

Can Google Ads really spend 2x my daily budget in one day?

Yes. Google's overdelivery policy allows spending up to twice your average daily budget on high-traffic days. Combined with the new pacing changes, a $100/day weekday-only campaign could see individual days as high as $276.

Will lowering my daily budget fix the problem?

It reduces total spend, but it doesn't fix the pacing imbalance. The better approach is to monitor pacing and adjust strategically — pausing on days when you're ahead of pace, and letting campaigns run on days when you're behind.

Does AdPacer access my Google Ads account through the API?

No. AdPacer reads data already displayed on your Google Ads screen. It doesn't use the Google Ads API, doesn't require OAuth credentials, and doesn't send any data to external servers. Everything runs locally in your browser.

Is AdPacer free?

The free tier includes pacing indicators and projected spend badges for up to 3 campaigns. The Pro plan ($19/month) adds unlimited campaigns, configurable alerts, and multi-account support.

Does this work for non-English Google Ads accounts?

Currently, AdPacer supports English and Spanish Google Ads interfaces. Additional language support is planned.

Bottom Line

The March 2026 Google Ads budget pacing changes caught a lot of PPC managers off guard. If you manage campaigns with restricted ad schedules, your daily costs have likely increased without any action on your part.

You have three options: build spreadsheets, write scripts, or get real-time visibility directly where you work. AdPacer is the only Chrome extension that shows Google Ads budget pacing indicators directly in the campaign table — free for up to 3 campaigns, no API key required, installs in 30 seconds.

Add AdPacer to Chrome — Free →

How to Scrape Kick.com Streamer Data in 2026: Profiles, Live Streams, VODs & Rankings

Sami — Tue, 14 Apr 2026 01:38:52 +0000

TL;DR: Want to skip the coding and scrape Kick.com right now? Jump to the pre-built solution that extracts streamer profiles, live streams, VODs, clips, and channel rankings — no API key, no proxy, no browser needed.

Why Scrape Kick.com in 2026?

Kick.com has emerged as one of the fastest-growing live streaming platforms in the world. With creators migrating from Twitch due to better revenue splits, Kick streaming data has become a goldmine for marketers, analysts, and developers.

Whether you're tracking Kick streamer analytics, monitoring live viewership trends, or researching influencer marketing opportunities, having access to structured Kick.com data is essential.

What Can You Do With Kick.com Data?

Influencer marketing — Identify top Kick streamers for brand partnerships based on audience size, category, and engagement
Esports analytics — Track competitive gaming trends and viewership across categories
Brand monitoring — Monitor sponsorship ROI across Kick channels in real time
Competitive streaming intelligence — Compare channel performance and growth patterns
Content research — Discover trending categories, formats, and top-performing clips

The DIY Approach: Scraping Kick.com With Python

Kick.com exposes internal API endpoints that return structured JSON data. Here's how to build a basic Kick scraper with Python:

Setting Up Your Environment

pip install curl-cffi asyncio

Basic Kick.com Channel Scraper

import asyncio
from curl_cffi.requests import AsyncSession

BASE = "https://kick.com"
HEADERS = {
    "Accept": "application/json",
    "Referer": "https://kick.com/",
    "Accept-Language": "en-US,en;q=0.9",
}

async def scrape_kick_channel(channel_name: str):
    """Scrape a Kick.com channel profile."""
    async with AsyncSession() as session:
        url = f"{BASE}/api/v2/channels/{channel_name}"
        response = await session.get(
            url, headers=HEADERS, impersonate="chrome"
        )
        if response.status_code == 200:
            data = response.json()
            return {
                "channelName": channel_name,
                "displayName": data.get("user", {}).get("username"),
                "followersCount": data.get("followersCount"),
                "isLive": data.get("livestream") is not None,
                "verified": data.get("verified", False),
            }
        return None

# Usage
result = asyncio.run(scrape_kick_channel("xqc"))
print(result)

The Challenges of DIY Kick Scraping

Building your own Kick.com scraper sounds straightforward, but you'll quickly run into problems:

Challenge	Impact
TLS fingerprinting	Kick detects standard HTTP clients — you need impersonation
Rate limiting	Too many requests get your IP temporarily blocked
API endpoint changes	Kick regularly updates internal API routes
Data normalization	Raw API responses need heavy parsing for clean output
Multiple data modes	Profiles, streams, VODs, and clips each require different endpoints
Infrastructure costs	Running scrapers 24/7 requires servers, monitoring, and maintenance

The Pre-Built Solution: Kick.com Streamer & Channel Analytics

Instead of maintaining your own scraper, the Kick.com Streamer & Channel Analytics actor on Apify handles everything for you — 4 scraping modes in one tool.

Why Use This Actor?

No API key required — accesses Kick.com's public API endpoints directly
No proxy needed — direct HTTP requests work without proxy rotation
No browser needed — pure HTTP requests, no Playwright or Puppeteer overhead
Lightweight — runs on 256 MB RAM with pure HTTP requests
Pay per result — just $0.005 per data point ($5 per 1,000 results)
Structured output — clean JSON ready for analytics, dashboards, or integrations

The 4 Scraping Modes

Mode 1: Channel Profiles (`channel_details`)

Get complete Kick streamer profiles with follower counts, live status, social links, and more:

{
  "mode": "channel_details",
  "channelNames": ["xqc", "amouranth", "trainwreckstv"]
}

Returns: display name, bio, avatar, banner, follower count, live status, current viewers, category, verified badge, subscriber badges, social links (Instagram, Twitter, YouTube, Discord, TikTok), and creation date.

Mode 2: Live Streams (`live_streams`)

Discover who's streaming live on Kick right now, filtered by category and minimum viewers:

{
  "mode": "live_streams",
  "category": "just-chatting",
  "minViewers": 100,
  "maxResults": 20,
  "sortBy": "viewers"
}

Returns: channel name, viewer count, stream title, category, start time, thumbnail, language, tags, and maturity rating.

Mode 3: VODs & Clips (`channel_videos`)

Extract past broadcasts and Kick clips from any channel:

{
  "mode": "channel_videos",
  "channelNames": ["xqc"],
  "videoType": "clips",
  "maxResults": 10
}

Returns: clip ID, title, duration, views, likes, category, thumbnail, video URL, creator name, and creation date.

Mode 4: Channel Rankings (`top_channels`)

Get a ranked list of top Kick channels sorted by live viewer count:

{
  "mode": "top_channels",
  "sortBy": "viewers",
  "category": "just-chatting",
  "maxResults": 25
}

Returns: rank, channel name, bio, avatar, current viewers, stream title, category, affiliate status, and social links.

How to Get Started

Sign up for a free Apify account
Go to the Kick.com Streamer & Channel Analytics actor page
Click "Try for free"
Select your scraping mode and enter your parameters
Hit Start and get structured data in seconds

Export Your Data Anywhere

Results are stored in an Apify dataset and can be exported as:

JSON — for APIs and applications
CSV — spreadsheet-ready for analysis
Excel (.xlsx) — direct download
XML — for legacy system integration

Plus, integrate directly with Google Sheets, Zapier, Make, and more.

DIY vs. Pre-Built: The Comparison

Feature	DIY Python Scraper	Kick.com Analytics Actor
Setup time	Hours to days	2 minutes
API key needed	No	No
Proxy needed	Sometimes	Never
Browser needed	No	No
TLS fingerprinting	Must handle yourself	Built-in
4 scraping modes	Build each separately	All included
Data normalization	Manual parsing	Clean JSON output
Rate limit handling	Your problem	Handled automatically
API change updates	You maintain it	Maintained for you
Scheduling	Cron jobs + servers	Built-in Apify scheduler
Cost	Server + dev time	$0.005/result

Real-World Use Case Workflows

Influencer Marketing Agency

Use channel_details mode to bulk-analyze potential Kick partners:

{
  "mode": "channel_details",
  "channelNames": ["streamer1", "streamer2", "streamer3", "streamer4"]
}

Then filter by follower count, live status, and engagement to find the best fits for your brand campaigns.

Esports Data Dashboard

Combine live_streams and top_channels modes on a schedule to build a real-time Kick viewership dashboard:

{
  "mode": "live_streams",
  "category": "fortnite",
  "minViewers": 500,
  "sortBy": "viewers"
}

Schedule runs every 5 minutes for real-time monitoring, or daily for trend analysis.

Content Creator Research

Use channel_videos mode to analyze what's performing on Kick clips:

{
  "mode": "channel_videos",
  "channelNames": ["xqc", "adin"],
  "videoType": "clips",
  "maxResults": 50
}

Discover trending content formats, optimal clip lengths, and high-engagement topics.

Frequently Asked Questions

Does this need an API key to scrape Kick.com?

No. The Kick.com Streamer & Channel Analytics actor uses Kick.com's publicly accessible internal API endpoints. No authentication required.

Does it need a proxy?

No. Direct HTTP requests work without any proxy rotation needed.

Can I track live viewership on Kick in real time?

Yes. The live_streams mode returns real-time viewer counts for all currently live streams. Schedule runs every 5 minutes for continuous monitoring.

What data formats can I export?

JSON, CSV, Excel (.xlsx), and XML — all available directly from the Apify dashboard or API. Plus direct integrations with Google Sheets, Zapier, and Make.

How does Kick.com scraping compare to Twitch scraping?

Both platforms expose internal API endpoints, but Kick's API is generally more open and doesn't require authentication tokens. The same developer also offers a Twitch Streamer & Channel Analytics actor for complete cross-platform streaming intelligence.

Is scraping Kick.com legal?

This actor only accesses Kick.com's publicly available API endpoints. It does not bypass any authentication, CAPTCHA, or rate-limiting mechanisms. No private or user-authenticated data is accessed.

Start Scraping Kick.com Today

Ready to extract Kick.com streamer data at scale? The Kick.com Streamer & Channel Analytics actor on Apify gives you everything you need — 4 scraping modes, clean JSON output, and zero infrastructure to maintain.

Try it free on Apify →

No API key. No proxy. No browser. Just data.

How to Scrape RedNote (Xiaohongshu) Data in 2026: The Complete Guide

Sami — Tue, 14 Apr 2026 01:16:00 +0000

RedNote (小红书 / Xiaohongshu) exploded onto the global stage in January 2025 when millions of TikTok users migrated to the platform during the US TikTok ban uncertainty. What was once a Chinese-only lifestyle app suddenly became a cross-cultural phenomenon with 300+ million monthly active users.

For marketers, researchers, and e-commerce brands, RedNote scraping is now essential for consumer insights — but extracting data from Xiaohongshu is notoriously difficult. The platform is built for the Chinese market, uses aggressive anti-bot measures, and has no public API.

TL;DR: If you want structured RedNote data without building a scraper, skip to The Pre-Built RedNote Scraper — no API key needed, works out of the box.

This guide covers how Xiaohongshu scraping works, what data you can extract, the technical challenges involved, and practical solutions for getting reliable data at scale.

Why RedNote Data Matters in 2026

RedNote isn't just "Chinese Instagram." It's a hybrid of Instagram, Pinterest, and Amazon Reviews — users post product reviews, travel guides, fashion inspiration, skincare routines, and restaurant recommendations, all with purchase links and brand tags.

Brand intelligence: Global brands like Estée Lauder, Nike, and Dyson actively monitor RedNote to understand how Chinese consumers perceive their products. A single viral Xiaohongshu post can drive millions in sales.

Trend forecasting: Beauty, fashion, and food trends often start on RedNote months before they hit Western markets. K-beauty routines, "clean girl" aesthetics, and matcha everything all gained traction on Xiaohongshu first.

E-commerce research: RedNote's integration with e-commerce means posts include real purchase data, price comparisons, and authentic reviews — invaluable for product research and market analysis.

Influencer marketing: Brands entering the Chinese market need to identify RedNote KOLs (Key Opinion Leaders) in their niche and analyze their engagement rates before investing in partnerships.

The Technical Challenge of Scraping Xiaohongshu

RedNote is one of the harder platforms to scrape:

No public API. Unlike Twitter/X, Reddit, or Telegram, Xiaohongshu offers no official API for developers. Everything must be reverse-engineered.

Chinese-first infrastructure. The platform is built for Chinese users. Content is primarily in Chinese, and the web version behaves differently from international access points.

Anti-bot protection. RedNote uses request signature verification (the X-s and X-t headers), cookie-based session management, rate limiting, and dynamic page rendering. This makes scraping RedNote significantly harder than most Western platforms.

What Data Can You Extract from RedNote?

Post/Note Data

Title and full text content
Images and video URLs
Likes, comments, shares, and saves counts
Post date and tagged topics/hashtags
Tagged products with prices
Location tags

User Profile Data

Username, display name, bio
Follower/following counts
Total likes received
Verification status

Engagement Metrics

Like-to-follower ratio
Average engagement per post
Posting frequency
Content category distribution

How to Scrape RedNote: Web vs Mobile API

Approach 1: Web Scraping (xiaohongshu.com)

import httpx
from bs4 import BeautifulSoup

async def fetch_note(note_id: str):
    url = f"https://www.xiaohongshu.com/explore/{note_id}"

    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
        "Referer": "https://www.xiaohongshu.com/",
    }

    async with httpx.AsyncClient() as client:
        response = await client.get(url, headers=headers)

    if response.status_code == 200:
        return parse_note_html(response.text)
    elif response.status_code == 461:
        raise SignatureRequired("Request needs signing")

The 461 status code is RedNote's signature challenge — requiring custom X-s and X-t headers that change with every request.

The Xiaohongshu Signature Problem

Both web and mobile approaches hit RedNote's signature verification. Solutions:

Browser automation — Use Playwright to run RedNote's own JavaScript. Most reliable but slowest (1-3 seconds per request).

Signature reverse engineering — Faster but breaks whenever RedNote updates their code (happens frequently).

Hybrid approach — Browser automation for session setup, then direct HTTP for batch extraction. Best balance of speed and reliability.

Handling RedNote Rate Limits

Xiaohongshu throttles aggressively:

Search requests: ~20 per minute
Note detail pages: ~30 per minute
User profiles: ~15 per minute
IP blocks: 1-24 hours after sustained high volume

import asyncio
import random

async def rate_limited_request(url, **kwargs):
    delay = random.uniform(2.0, 5.0)
    await asyncio.sleep(delay)

    response = await client.get(url, **kwargs)

    if response.status_code == 429:
        wait = random.uniform(30, 60)
        await asyncio.sleep(wait)
        return await rate_limited_request(url, **kwargs)

    return response

Pro tip: Use residential proxies for any serious volume. Datacenter IPs get blocked almost immediately by Xiaohongshu.

Processing Chinese Content from RedNote

def extract_hashtags(text: str):
    """Extract both Latin and Chinese hashtags from Xiaohongshu posts"""
    import re
    pattern = r'#[\w\u4e00-\u9fff\u3400-\u4dbf]+'
    return re.findall(pattern, text)

Since the TikTok migration of 2025, many RedNote channels now post bilingual content, making the platform increasingly accessible to global researchers and marketers.

The Pre-Built RedNote Scraper

Building and maintaining a Xiaohongshu scraper is a significant time investment — the signature algorithm changes frequently, rate limits require constant tuning, and edge cases add up fast. I've been maintaining one for over a year so you don't have to.

I built the RedNote Xiaohongshu Scraper on Apify that handles all of this — and it's free to try.

Why use it instead of building your own?

Feature	DIY Scraper	My Apify RedNote Scraper
Setup time	Days/weeks	30 seconds
Signature handling	You reverse-engineer it	Built-in & auto-updated
Proxy management	You manage it	Built-in residential proxies
Rate limit handling	You build it	Built-in
Chinese text processing	You build it	Built-in
JSON/CSV export	You build it	Built-in
Scheduled monitoring	You build it	Built-in
Maintenance when RedNote updates	On you	On me

What you can do with it:

Search by keyword — find posts matching any topic, brand, or product in Chinese or English
Scrape user profiles — extract all posts from specific creators or KOLs
Extract by URL — paste specific post URLs for structured data
Full metadata — likes, comments, shares, saves, images, videos, tagged products
No API key needed — runs on Apify's infrastructure with proxy management built in

It currently serves brands, agencies, and researchers with a 90%+ success rate across thousands of extractions.

→ Try the RedNote Xiaohongshu Scraper on Apify (Free Trial)

Common Use Cases for RedNote Scraping

Brand Monitoring on Xiaohongshu

Search for your brand name (Chinese and English), extract matching posts, set up scheduled runs for new mentions. Critical for any brand selling in China.

Competitor Analysis on RedNote

Extract competitor's last 100-200 posts, sort by engagement rate, analyze what topics and formats drive the most saves. On RedNote, saves = purchase intent, making this metric more valuable than likes.

KOL/Influencer Identification

Search niche keywords, collect top-performing post authors, filter by engagement rate — micro-KOLs with 10K-50K followers often drive more conversions than celebrity influencers on Xiaohongshu.

Trend Research & Product Discovery

Track emerging keywords over time, analyze which products get tagged most, cross-reference with Western platforms to spot trends early. Many global brands use this to predict Western trends 3-6 months early.

FAQ

Can I scrape RedNote without an API?

Yes, but it's technically challenging due to signature verification. The easiest approach is using a pre-built RedNote scraper that handles the anti-bot measures for you.

Is scraping Xiaohongshu legal?

Scraping publicly available data is generally legal. However, always respect rate limits and the platform's terms. Using the data for competitive intelligence and market research is standard practice.

What's the best RedNote scraper in 2026?

The RedNote Xiaohongshu Scraper on Apify is the most maintained option with the highest success rate. It handles signature changes, proxy rotation, and rate limiting automatically.

Can I scrape RedNote in English?

Yes. Since the 2025 TikTok migration, RedNote has significantly more English content. The scraper extracts content in any language and you can search using English or Chinese keywords.

How do I monitor my brand on Xiaohongshu?

Use a RedNote scraper with scheduled runs — search for your brand name weekly, compare engagement metrics over time, and track new mentions automatically.

Conclusion

RedNote is arguably the most valuable social media platform for consumer insights into the Chinese market. The scraping challenge is real but solvable.

If you want production-ready Xiaohongshu data without the maintenance headache, try my RedNote Scraper on Apify — it's free to start and handles all the anti-bot complexity covered in this article.

Questions about RedNote scraping? Drop a comment below.

How to Scrape Telegram Channels in 2026 (Without API Keys or Phone Numbers)

Sami — Tue, 14 Apr 2026 01:11:26 +0000

Telegram has 950+ million monthly active users and has become the go-to platform for crypto communities, news channels, research groups, and brand communications. If you need to scrape Telegram channels, extract messages, reactions, media, or metadata from public Telegram channels — for OSINT research, competitive analysis, crypto monitoring, or academic projects — this guide covers everything you need to know.

I'll walk through the two main approaches to Telegram scraping, the technical pitfalls that break most implementations, and a ready-to-use Telegram scraper that actually works in production.

TL;DR: If you just want a working solution, skip to The Pre-Built Telegram Scraper — no API keys, no phone number, no setup required.

Why Scraping Telegram Is Different from Other Platforms

Most social media platforms require you to fight anti-bot systems — CAPTCHAs, IP blocks, browser fingerprinting. Telegram is different for one critical reason: it was built for developer access.

Telegram offers two paths to data extraction:

Path 1: The web preview endpoint (t.me/s/channelname) renders public channel messages as plain HTML. No login. No API keys. No JavaScript rendering needed. You can curl it directly and parse the HTML. This is what makes Telegram one of the easiest platforms to scrape — yet most existing Telegram scraper tools still manage to fail at it.

Path 2: The Telegram API via Telethon gives you full access to message history, member lists, media files, and reply threads. It requires API credentials from my.telegram.org and a phone number for authentication, but provides complete data that the web preview can't match.

How to Scrape Telegram Channels Without Authentication

Every public Telegram channel has a web preview at https://t.me/s/CHANNEL_NAME. This page contains the latest messages rendered as server-side HTML — no JavaScript execution required. This is the fastest way to scrape Telegram messages without any setup.

Here's the basic structure of a Python Telegram scraper:

import httpx
from bs4 import BeautifulSoup

async def scrape_telegram_channel(channel_name: str):
    url = f"https://t.me/s/{channel_name}"

    async with httpx.AsyncClient() as client:
        response = await client.get(url, headers={
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
        })

    soup = BeautifulSoup(response.text, "lxml")
    messages = []

    for widget in soup.select(".tgme_widget_message_wrap"):
        message = widget.select_one(".tgme_widget_message")
        if not message:
            continue

        msg_data = {
            "id": extract_message_id(message),
            "text": extract_text(message),
            "date": extract_date(message),
            "views": extract_views(message),
            "reactions": extract_reactions(message),
            "media": extract_media(message),
        }
        messages.append(msg_data)

    return messages

The key CSS selectors for Telegram data extraction:

.tgme_widget_message_text — message content (preserves formatting)
.tgme_widget_message_date time — timestamp (in the datetime attribute)
.tgme_widget_message_views — view count
.tgme_widget_message_forwarded_from — forwarded channel info
.tgme_widget_message_photo_wrap — photo containers
.tgme_widget_message_video — video elements

Telegram Message Pagination

The web preview doesn't show all messages at once. To scrape older Telegram messages, use the before parameter:

url = f"https://t.me/s/{channel_name}?before={last_message_id}"

Keep requesting with decreasing message IDs until you get an empty response. This typically gives you access to the latest 500-1,000 messages depending on the channel.

Extracting Telegram Reactions Data

Telegram added reactions in 2022, and the web preview includes them — useful for sentiment analysis on Telegram:

def extract_reactions(message_element):
    reactions = {}
    for reaction in message_element.select(".tgme_widget_message_reaction"):
        emoji = reaction.select_one(".tgme_widget_message_reaction_emoji")
        count = reaction.select_one(".tgme_widget_message_reaction_count")
        if emoji and count:
            reactions[emoji.text.strip()] = int(count.text.strip().replace(",", ""))
    return reactions

Scraping Telegram with Telethon (Full API Access)

For complete Telegram message history, member data, or private channels you belong to, you need the Telegram API via Telethon:

pip install telethon

from telethon import TelegramClient
from telethon.sessions import StringSession

async def scrape_with_telethon(api_id, api_hash, session, channel):
    client = TelegramClient(StringSession(session), api_id, api_hash)
    await client.start()

    entity = await client.get_entity(channel)

    messages = []
    async for message in client.iter_messages(entity, limit=1000):
        messages.append({
            "id": message.id,
            "date": message.date.isoformat(),
            "text": message.text,
            "views": message.views,
            "forwards": message.forwards,
            "sender_id": message.sender_id,
            "reply_to": message.reply_to_msg_id,
            "pinned": message.pinned,
            "media_type": type(message.media).__name__ if message.media else None,
        })

    return messages

Handling Telegram FloodWaitError

This is where every Telegram scraper breaks. Telegram enforces rate limits through FloodWaitError — when you hit the limit, the API tells you exactly how many seconds to wait:

from telethon.errors import FloodWaitError
import asyncio

async def safe_api_call(coro, max_retries=3):
    for attempt in range(max_retries):
        try:
            return await coro
        except FloodWaitError as e:
            wait = e.seconds + 5
            print(f"Rate limited. Waiting {wait}s...")
            await asyncio.sleep(wait)
    raise Exception("Max retries exceeded")

The key insight: don't try to avoid FloodWaitError — expect it and handle it gracefully.

Getting Telegram API Credentials

Go to my.telegram.org
Log in with your phone number
Click "API development tools"
Create a new application
Save your api_id and api_hash

Common Pitfalls That Break Telegram Scrapers

After building my own Telegram channel scraper and testing against a dozen others, here are the failure modes I've seen:

Not handling empty channels. Some channels have web preview disabled. Your scraper should detect this and return a clear message instead of crashing.

Ignoring forwarded message metadata. Many crypto and news channels are mostly forwards. If you don't extract the forwarded_from field, you lose critical context.

Breaking on media-only messages. Messages with only photos/videos and no text are common. Scrapers that assume every message has text crash or skip these silently.

Not normalizing channel input. Users will provide t.me/channel, https://t.me/channel, @channel, or just channel. Handle all formats.

Crashing on rate limits instead of waiting. This alone accounts for most 1-star reviews on existing Telegram scrapers.

The Pre-Built Telegram Scraper

If you don't want to build and maintain this yourself, I built a Telegram Channel Scraper on Apify that handles all of the above — and it's free to try.

Why use it instead of building your own?

Feature	DIY Scraper	My Apify Telegram Scraper
Setup time	Hours/days	30 seconds
API keys needed	Yes (Telethon mode)	No (default mode)
Phone number needed	Yes (Telethon mode)	No (default mode)
Handles rate limits	You build it	Built-in
Media extraction	You build it	Built-in
Reactions data	You build it	Built-in
JSON/CSV export	You build it	Built-in
Scheduled runs	You build it	Built-in
Cloud infrastructure	You manage it	Managed for you

It runs in two modes:

No-auth mode (default): paste a channel URL, get messages back. No API keys, no phone number, no setup. Try it free →
Telethon mode: provide your API credentials for full message history and member data.

It handles FloodWaitError automatically, never crashes on edge cases, extracts reactions and media metadata, and outputs clean JSON or CSV.

→ Try the Telegram Channel Scraper on Apify (Free Trial)

Use Cases for Telegram Scraping

Crypto monitoring — Track token announcements, pump signals, and community sentiment across Telegram groups
OSINT research — Monitor extremist channels, propaganda networks, or misinformation campaigns
Competitive intelligence — Track competitor announcements and community engagement
Academic research — Collect datasets for NLP, sentiment analysis, or social network studies
Brand monitoring — Track mentions and discussions about your brand across Telegram channels
News aggregation — Build feeds from curated Telegram news channels

FAQ

Can I scrape Telegram without an API key?

Yes. Public Telegram channels have a web preview at t.me/s/channelname that can be scraped without any authentication. My Telegram Channel Scraper uses this approach by default.

Is scraping Telegram legal?

Scraping publicly available data is generally legal in most jurisdictions. Telegram's web previews are designed to be publicly accessible. Always check your local laws and Telegram's Terms of Service.

How many messages can I extract from Telegram?

Using the web preview, you can typically access the latest 500-1,000 messages. With Telethon API access, you can extract the entire message history.

What's the best Telegram scraper in 2026?

For no-setup scraping, the Telegram Channel Scraper on Apify is the fastest option — paste a URL, get structured data. For full API access, Telethon is the standard Python library.

Can I scrape Telegram groups?

Web preview scraping works only for public channels. For groups, you need Telethon with API credentials and you must be a member of the group.

Conclusion

Telegram is one of the most scraping-friendly platforms out there — public data is genuinely accessible, the API is well-documented, and there's no aggressive anti-bot system. The challenge isn't access — it's reliability.

If you want a production-ready Telegram scraper without building anything, try my Telegram Channel Scraper on Apify — it's free to start and handles all the edge cases covered in this article.

If you have questions about Telegram scraping, feel free to reach out in the comments.

Forem: Sami

Scraping G2 Reviews Without Kasada Headaches: A SaaS Competitive Intelligence Pipeline With 29 Fields Per Review

Why G2 Data Is Worth the Effort

The Problem With the Official Path

What the Actor Does

Comparison Table

Input Example

Output Example

Four Real Use Cases

1. SaaS Sales Displacement Plays

2. Category-Level Feature Gap Analysis

3. Churn and Renewal Risk Signals

4. Private Equity Due Diligence

Pricing

FAQ

Pair It With Capterra for Full B2B Coverage

Get Started

Telegram Channel Scraper: Extract Messages, Media & Metadata Without API Keys or Phone Numbers

Why Scrape Telegram Channels?

Business Intelligence

Research & OSINT

Marketing & Content

The Challenge: Why Telegram Scraping Is Hard

The Official API Requires Authentication

The Telethon/Pyrogram Approach

The Easy Way: No API Keys, No Phone Numbers

What This Means For You

What Data Can You Extract?

How To Use It

Real-World Use Cases

Crypto Research Dashboard

OSINT Investigation

Brand Monitoring

Content Marketing Research

Why This Scraper vs. Alternatives

Key Advantages

Pricing

FAQ

Does this scrape private Telegram channels?

Can I scrape Telegram groups (not channels)?

Does it download media files?

How often can I run it?

Is scraping Telegram channels legal?

Get Started in 60 Seconds

Kick.com API Alternative: Extract Streamer Analytics, Live Streams & VODs Without Authentication

Why Kick.com Data Is Valuable in 2026

Who Needs Kick Data?

What Data Can You Extract?

The Problem: No Public Kick API

The Solution: 4-in-1 Kick.com Analytics Actor

Mode 1: Channel Profiles (channel_details)

Mode 2: Live Streams (live_streams)

Mode 3: VODs & Clips (channel_videos)

Mode 4: Channel Rankings (top_channels)

Why This Actor vs. DIY Scraping

Key Technical Advantages

Real-World Workflow Examples

Influencer Marketing: Find & Vet Kick Streamers

Esports: Track Tournament Viewership

Content Strategy: Analyze What Works on Kick

Cross-Platform Streaming Intelligence

Pricing

Get Started

RedNote (Xiaohongshu) Scraping API: Extract Posts, Comments, Videos & Profiles in 2026

Why RedNote Data Matters in 2026

What Makes RedNote Different

Who Needs RedNote Data?

The Challenge: Why Scraping RedNote Is Hard

The Easy Way: RedNote Xiaohongshu Scraper All-in-One

5 Scraping Modes

1. Search Posts

2. User Content

3. Download Videos

4. Scrape Comments

5. Profile Data

Why This Scraper vs. Others

Real-World Use Cases

Influencer Marketing Campaign Planning

Competitive Brand Monitoring

Product Launch Research

Mode 1: Channel Profiles (`channel_details`)

Mode 2: Live Streams (`live_streams`)

Mode 3: VODs & Clips (`channel_videos`)

Mode 4: Channel Rankings (`top_channels`)

Mode 1: Channel Profiles (`channel_details`)

Mode 2: Live Streams (`live_streams`)

Mode 3: VODs & Clips (`channel_videos`)

Mode 4: Channel Rankings (`top_channels`)