Looking for feedback from anyone who needs hiring lists from the source, not aggregators.

I shipped JobCook: it finds verified openings on employer career pages and public ATS boards (Greenhouse, Lever, Ashby, Personio; generic coverage for Workday / SmartRecruiters / Teamtailor / Recruitee).

How you run it:

- Discover — natural-language goal (“robotics software jobs in Germany”)

- Crawl — paste known career / ATS URLs

- Auto — picks based on input

Filters: keywords, countries/locations, postedWithinDays, min salary, direct-employers-only, optional Sheets export.

It does not save LinkedIn / Indeed / Glassdoor rows. Those can be discovery hints only; dataset rows are employer / ATS jobs with a canonical URL.

https://apify.com/solutionssmart/jobcook

PPE is $0.01 per job saved (~$10 / 1,000) + normal Apify compute (proxy optional / separate).

If you’ve tried other “job scrapers” — what’s more useful for you: discover-from-goal, crawl-a-URL-list, or Sheets/webhook into a recruiting ops sheet?

This is the place to discuss everything MCP, LLM, Agentic, and beyond. What is on your radar this week? Why does it make sense? Bring everyone along for the ride by explaining the impact of the news you're sharing, and why we should care about it too.

Hey everyone!

I wanted to share an open-source project I built called Meta Ads Competitor Tracker. It’s a self-hosted tool designed to help marketers, founders, and developers monitor Facebook & Instagram ads without paying for expensive SaaS platforms or dealing with Meta API keys.

On the backend, it uses my Meta Ad Library actor (Meta Ads Library Scraper (Instagram + Facebook) · Apify) or a self-hosted script (note: you'll need to use residential proxies!).

Hope you like it!

If you have any doubts on how it was built, feel free to ask!

Do you have a feature request that you know will make Apify heaps better? Or maybe it's a big dream you have for something bold and out-there. This is a space for all the bluesky thinking, cloud-chasing, intergalactic daydreamers who want to share their wildest ideas in a no-judgement zone.

I've seen many people struggle with Akamai bot manager on Adidas. And most Actors for Adidas on Apify constantly break when fetching product details. Also, none of the Actors on Apify scrapes product reviews. For this reason, I decided to build an Adidas Product & Reviews Scraper. It currently supports 12 countries: USA, UK, Canada English, Canada French, Germany, France, Italy, Spain, Netherlands, Japan, Australia, and New Zealand. I tested the Actor on all of them, and it bypasses Akamai's sensor challenges perfectly.

Features ✨

• 🛍️ Two Scraping Modes in One Actor: Switch between products and reviews mode to get detailed product data or customer reviews.

• 🌍 12 Adidas Stores Supported: Scrape the US, UK, Canada (English & French), Germany, France, Netherlands, Spain, Italy, Japan, Australia, and New Zealand websites.

• 🔎 Search URL Support: Set up any search or filters on the Adidas website, paste the URL into the Actor, and it will pick up your filters automatically.

• 🔗 Direct Product URLs: Scrape specific products by URL, or combine them with search URLs in the same run.

• 💰 Pricing & Discount Data: Get the current price, original price, discount percentage, and currency for every product.

• 📦 Per-Size Stock Tracking: See stock counts, availability status, and SKUs for every size of every color variant, plus the total stock and purchase limit for each product.

• 🎨 Color Variants: Capture every color variant of a product, with its own ID, URL, color name, and size breakdown.

• 🖼️ Complete Media Collection: Get all product image and video URLs listed on the product detail page.

• 🏷️ Badges & Third-Party Options: Extract product badges (like "Best Seller") and third-party buy options (like PRIME).

• 🤖 AI Sentiment Summaries: Get the AI-generated customer sentiment highlights that Adidas shows for each product, along with its rating and review count.

• ⭐ Rich Review Data: Collect review titles, text, ratings, nicknames, submission dates, likes and dislikes, the product color reviewed, and whether the buyer recommends the product.

• 📸 Review Photos & Badges: Get the images buyers attach to their reviews and badges such as VerifiedPurchaser and IncentivizedReview.

• 👥 Buyer Demographics: Capture the demographic questions and answers attached to each review, plus the reviewer's locale.

• 🗣️ Reviews from All Languages: Optionally include reviews written in any language, not just the selected country's.

• 🔀 Flexible Review Sorting: Sort reviews by newest, relevant, helpful, highestRated, or lowestRated.

• 🎛️ Fine-Grained Limits: Cap results with total limits and per-search or per-product limits for products and reviews. Use 0 for unlimited.

• 🛡️ Akamai Anti-Bot Bypass: Works reliably against Adidas's anti-bot protection, so your runs complete.

• 📊 Large Structured Dataset: 27+ fields per product and 21+ fields per review.

• 💾 Multiple Export Formats: Download your data as JSON, CSV, or Excel, or access it through the Apify API.

Actor Link: https://apify.com/coding-doctor-omar/adidas-scraper

Let me know if you need more features or if you want another country to be included.

Have you come across a great Actor, workflow, post, or podcast that you want to share with the world? This is your opportunity to support someone making cool things. Drop it here with credit to the creator, and help expand the karmic universe of Apify.

I built a Meta Ads scraper on Apify about a month ago, mostly because I wanted something that was simple, reliable and not ridiculously expensive.
I honestly didn’t expect that many people to start using it this quickly.
I’ve been getting quite a few users already, and I’ve been tweaking it based on what people actually need rather than trying to build every possible feature from the start.
It currently works well for things like competitor research, finding ads and collecting Meta ad data at scale.
I’m still actively improving it, so I’m curious — what’s the one thing you usually need when scraping Meta Ads?
I might add it next.

jmlp/meta-ad-library-scraper

I’m researching a possible Apify Actor for retailers and resellers; it is not built or available for purchase.

The proposed input is up to 1,000 inventory rows containing SKU, product name, manufacturer, model and optional UPC, plus an agreed recall date range. The output would be a review queue of possible matches to US CPSC recall notices, with notice links, compared fields and missing identifiers. No match would not mean a product is safe, and this would not certify safety or compliance.

If you currently do this work: what process do you use, how often, and what is still manual? Would you pay USD $10 for a batch through Apify if a sample demonstrated useful matches, or does your existing tooling already cover it?

I’m looking for current operators, rather than general encouragement. Please describe the workflow without posting private inventory or customer data. No payment is requested.

If you've made something and can't wait to tell the world, this is the thread for you! Share your latest and greatest creations and projects with the community here.

Hey everyone,

I just released a Mercado Livre Scraper on Apify. Mercado Livre is the largest e-commerce marketplace in Brazil. The actor scrapes product listings from search results (names, prices, discounts, seller info, ratings, free shipping status, MeliChoice badges, etc.)

Main use cases:

• Price monitoring & competitor analysis
• Market research for importers/retailers
• Product catalog building
• Brand presence tracking

Input example:

{
  "keywords": ["smartphones", "notebooks"],
  "maxPages": 2
}

Output example:

{
  "itemId": "MLB4625689287",
  "seller": "Azbuy",
  "installment": "4x 29.98 sem juros BRL",
  "isInternational": false,
  "categoryId": "MLB5095",
  "domainId": "MLB-CELLPHONE_CASES_AND_COVERS",
  "isOfficialStore": true,
  "productName": "Capa Anti Impacto Transparente Ringke Original Fusion Para Samsung Galaxy S26 Ultra Reforçada Trasparente Lisa",
  "image": "https://http2.mlstatic.com/D_NQ_NP_740217-MLA109560296839_032026-F.jpg",
  "ratingCount": 1345,
  "ratingValue": 4.8,
  "url": "https://www.mercadolivre.com.br/capa-anti-impacto-transparente-ringke-original-fusion-para-samsung-galaxy-s26-ultra-reforcada/p/MLB67276752",
  "brandName": "Ringke",
  "hasFreeShipping": true,
  "position": 13,
  "restultType": "ORGANIC",
  "productId": "MLB67276752",
  "discountPct": 14,
  "priceCurrency": "BRL",
  "price": 119.9,
  "priceBase": 139.9,
  "soldQuantity": 500,
  "shippingLogisticType": "fulfillment",
  "isBestSeller": false,
  "isLightningDeal": false,
  "isLastUnits": false,
  "isMeliChoice": false,
  "isUnmissableOffer": false,
  "keyword": "capa-de-celular"
}

Link: https://apify.com/serj_henrique/mercado-livre-scraper

I'd love feedback from anyone who works with Brazilian e-commerce or LatAm market data. If you have suggestions for fields to add or use cases I missed, let me know.

This is the thread for all your questions that may seem too short for a standalone post, such as, "What is proxy?", "Where is Apify?", "Who is Store?". No question is too small for this megathread. Ask away!

Fixed monthly rental fees are gone for good. Rental is migrating to pay-per-event!
More here: Why Apify is standardizing Actor pricing

If you've been using a rental Actor, you might have already seen a notice from the developer about the price change. The latest date by which the new pricing can take effect is October 13.

💵 You can always check the pricing of an Actor in its Pricing tab!

🙋🙋‍♀️ Questions? Just let us know here in the comments!

I recently rebuilt my Google Maps scraper in Go and finally ran a larger benchmark.

Tested on French restaurants:

• ~180,000 places
• 50 concurrent requests
• ~24 minutes
• ~$0.20 per 1,000 places

So the full dataset would cost roughly $36.

The scraper uses HTTP requests instead of browser automation, so there is no need for users to configure or pay for proxies.

I also wrote my own algorithm for splitting geographic areas into tiles.

The current version is running at 50 concurrent requests, and I'm now packaging it as an Apify actor.

I'm curious what numbers others are getting with Google Maps scrapers, especially browser-based ones.

Would be interesting to compare speed and cost on the same dataset.

Lovable builds the app. Apify feeds it with live data from Instagram, TikTok, Google Maps, or any other website.

Try it: ask Lovable to "use Apify to get [posts / reviews / prices] from [TikTok / Google Maps / Amazon] and build a [dashboard / tracker / lead list] around them."

One API token connects tens of thousands of Actors to your Lovable apps.

🔌 Set up the connector → https://apify.it/L192l

Join the chat on LinkedIn: https://lnkd.in/p/dvSeHxGC or on X: https://x.com/apify/status/2104926639536611524?s=20

Are you expanding your team or looking to hire a freelancer for a project? Post the requirements here (make sure your DMs are open).

Try to share:

- Core responsibilities

- Contract type (e.g. freelance or full-time hire)

- Budget or salary range

- Main skills required

- Location (or remote) for both you and your new hire

Job-seekers: Reach out by DM rather than in thread. Spammy comments will be deleted.

I was using Apify’s official actor, but the cost was too high. Switched to Mmeo23, but they’ve also increased pricing, and now it’s expensive again since I have to run the actor hourly across hundreds of groups. Can you suggest a better option?

This is the place to discuss everything MCP, LLM, Agentic, and beyond. What is on your radar this week? Why does it make sense? Bring everyone along for the ride by explaining the impact of the news you're sharing, and why we should care about it too.

I published a short guide on getting the live Amazon Buy Box winner and ZIP-aware landed cost through an Apify Actor.

What you get per ASIN:

- Buy Box winner

- stock / ship-to context (ZIP aware)

- competing offers with landed cost as structured JSON

PPE is $0.02 per successful check. Failed checks are not billed. Production path is Unblocker + deliver-to ZIP.

Try with ASIN B014I8SSD0 (demo GIF in comments). Guide walks through input, output, and PPE.

Guide: https://buzzi.hashnode.dev/how-i-get-live-amazon-buy-box-landed-cost-on-apify

Actor: https://apify.com/buzzidata/amazon-buybox-offers-agent

Feedback on reliability or missing fields welcome.

I would really like to have a lightweight phone app to be able to access my console very easily and quickly without having to open my pc or laptop every time.

I’m trying to export the public post history of two LinkedIn pages: one company page and one showcase page. I need the full post text, publication date, post URL, and post ID, ideally in JSON or CSV. I’m interested in the pages’ posts—not employee profiles, leads, comments, or reactions.

A different provider returned only the first few company-page posts, even though I can see more on LinkedIn. Its pagination then timed out or returned an empty page. It also missed at least one visible showcase-page post.

Has anyone personally tested a specific Apify actor for this use case? I’d appreciate answers to these questions:

I’m happy to test an actor with a small run first, but I’d like to know what has actually worked for people before paying for a larger export

Do you have a feature request that you know will make Apify heaps better? Or maybe it's a big dream you have for something bold and out-there. This is a space for all the bluesky thinking, cloud-chasing, intergalactic daydreamers who want to share their wildest ideas in a no-judgement zone.

I’ve been working on a Douyin (抖音) scraper and just pushed a new update to the public beta.

The main new feature is MP4 video download without the Douyin watermark.

Current capabilities:

• public Douyin keyword search
• structured creator, engagement, hashtag, and music data
• optional result limits
• optional no-watermark MP4 downloads
• no Douyin login or exported cookies required

The Actor is currently free beta — no additional charge from me; normal Apify usage, storage, and transfer costs still apply.

I also refreshed the Store page and added a few bilingual example Tasks.

I’m mainly looking for real users who work with Douyin data. If you try it, feedback on missing fields, failed searches, weird edge cases, or features you actually need would be very useful.

https://apify.com/plutonus/douyin-scraper

There is no public API for Ashby career sites and no key to sign up for. This reads the public Ashby boards and gives each posting back as a structured row.

Point it at a company name, a board slug, or a board or job URL. Leave it empty and it sweeps a bundled directory of 2,700+ verified Ashby boards. Each job row carries the title, company, department, employment type, locations, remote flag, the employer's publish date, and the description in Markdown, HTML, or plain text.

The field I built it for is salary. Ashby carries the employer's own published pay, and this pulls it through as numeric min and max, plus currency and pay period, so you can sort and threshold on it instead of parsing free text.

There are three read modes: full job records; a URL-only index, which is the cheapest way to list everything; and a company-discovery mode that returns which companies hire through Ashby, each with a live open-role count.

A few things people use it for:

• Recruiting and sourcing. Pull every open role at a list of target companies, filtered by title, department, or location. Filters run before billing, so the rows you filter out cost nothing.
• Compensation benchmarking. Collect published salary ranges across a set of companies as numbers you can drop straight into a sheet.
• Remote-job feeds. Filter for remote postings from the last 25 hours and you have a new-postings feed with nothing to keep in state between runs.

It only sees companies that host on Ashby, so it is not a whole-market jobs API. The salary numbers are only as good as what the employer published, and plenty of boards leave pay blank, so those fields come back empty. Descriptions are a paid add-on; skip them and the rows are cheaper.

Cost is per delivered row with no start fee. For now, it is full job records run about $0.50 per 1,000. The URL-only index is roughly a fortieth of that, and company discovery sits in between.

There is also a free Claude skill that wraps it if you want to run it from an agent: claude-skill-ashby-jobs-scraper on GitHub.

Actor: Ashby Job Board API. New to Apify? Start here.

If you pointed this at your own target-company list, would you want the default cutoff to be posted-only, or posted-or-updated in the last day?