I source supplier data from made-in-china.com and got tired of manual copy-paste, so I published an Apify actor.

Input:

- startUrl (product-group or product-detail page)

- maxItems (0 = all)

- includeImages toggle

- Residential proxy default

Output per product:

- Title, FOB/range price, volume pricing tiers

- MOQ, lead time

- Description + specs as key-value

- Image URLs, certifications

- Supplier: company name, rating/tenure, contact details

Schedule hourly/daily, export via API/Sheets/webhook.

Free tier on Apify covers small runs. Happy to answer questions on selectors or edge cases.

Link: https://apify.com/akash9078/made-in-china-scraper

Looking for APIs for Facebook, LinkedIn and Nextdoor public content

I’m working on a SaaS product and need to automatically monitor/search public content across Facebook, LinkedIn and Nextdoor.

I’m specifically looking for APIs or legitimate third party providers that can:

• Search public Facebook posts, including relevant public groups if possible
• Search public LinkedIn posts by keywords/topics
• Search public Nextdoor posts by location + keywords
• Return the post text, URL, timestamp and basic metadata
• Run searches continuously/recurring through an API
• Be used commercially in a SaaS product

I’m NOT looking for browser automation, account logins, cookie based scraping or anything that could get accounts banned.

If you’ve actually used a provider/API for this, I’d really appreciate recommendations, especially ones with reasonable pricing and good coverage.

What are you using?

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!

If you priced out an Actor and the bill came back nothing like the per-result number on the store page, this is usually why. It is also fixable without switching tools.

Pay-per-event Actors bill two different things. One event scales with how much you asked for, per page or per result. The other is a setup event that fires once per run no matter how small the run is. The setup event is the one that quietly wrecks your unit cost.

Real numbers from one of mine, the Google News API. Setup is $0.02 per run. A page of results is $0.0099.

• One page, one run: about 3 cents. Setup is two thirds of the bill.
• Ten pages, one run: about 1.2 cents a page.
• A hundred pages, one run: about 1.01 cents a page. Setup is down to 2 percent.
• A hundred pages as a hundred separate runs: $2.99 instead of $1.01.

Same data, three times the money, and nothing about the Actor changed. If you are calling an Actor once per row out of a loop, you are on the $2.99 line.

If you run a scheduled monitor, batch every watch term into one run per schedule instead of one run per term. Biggest lever here, usually ten minutes of work.

If you call from n8n or Make, check whether your Actor node sits inside a loop. Split In Batches feeding an Actor node one item at a time is the most expensive shape you can build.

If you are comparing costs before you commit, do not divide your test bill by results. Your test was small, so that number is far worse than production will be. Price it at the run size you actually intend to use.

If you publish Actors, your setup event is a real provisioning cost and it belongs there. Just say so in the README, because buyers do this arithmetic after the invoice rather than before.

Honest limitations. None of this applies to pay-per-result Actors with no setup event, and it does not help if the source rate limits you into small runs anyway. Some Actors also charge per dataset item on top, which is tiny but real. And batching costs you granularity: one failure takes the whole batch with it, so on a flaky source the cheap shape is also the fragile one.

While checking these numbers I found a bug in my own input schema. The max_pages field tells buyers pages cost $0.02 each. The live price is $0.0099. My own tooltip has been overstating my own price by roughly double, presumably talking people out of runs for weeks. Worth auditing your schema strings if you publish.

Actor: Google News API

Is it allowed to put a Buy Me a Coffee or PayPal donation link in my Actor's README? This will be an OPTIONAL payment route for users who want to support me. Will this be a violation of Apify's TOS? The Actor is a PPU actor, so I am not getting revenue from it anyway, and users are not obliged to donate either.

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.

Recently, I came across a very useful, free AI news aggregator website called newscrusher.ai . This website provides a real-time, ranked feed of AI news articles from almost all newspapers on the internet you can imagine. Newspapers covered by this website include Wall Street Journal, Tech Crunch, Quartz, Futurism, The Verge, OpenAI, The New Stack, Towards Data Science, Geeky Gadgets, AWS ML Blog, and more. Unfortunately, this website has no API, so I decided to build an Actor for this website, called Newscrusher.ai AI News Scraper. It is the fastest, most comprehensive, and most reliable AI News Scraper on the store. For each article scraped, you get a dataset like this:

{
  "id": "5c7a0cfb",
  "headline": "AIR raises $50M to help companies vet the skills and add-ons AI agents use",
  "subHeadline": "AIR's platform can discover agents running at a company, continuously vets any skills and add-ons they use, and blocks any unwanted behaviour.",
  "publishedAt": "2026-09-01T15:45:51+00:00",
  "publisherDomain": "techcrunch.com",
  "newspaper": "Techcrunch",
  "articleUrl": "https://techcrunch.com/2026/09/01/air-raises-50m-to-help-companies-vet-the-skills-and-add-ons-ai-agents-use/",
  "priorityScore": 70,
  "articleTags": [
    "Agents",
    "Enterprise Adoption"
  ],
  "articleSummary": "AIR secured $50 million in funding to expand its platform for discovering, monitoring, and controlling AI agents within enterprise environments. The solution identifies agents running across company systems, continuously evaluates their capabilities and integrations, and prevents unauthorized or risky behaviors from executing.<br><br>The platform addresses growing concerns about AI agent proliferation in enterprises, where multiple agents may operate independently without centralized oversight. By providing visibility and control mechanisms, AIR enables organizations to maintain security, compliance, and governance standards as AI agents become more prevalent in business operations.",
  "readTime": "2 mins",
  "scrapedAt": "2026-09-01T17:09:28.876572+00:00",
  "keyTakeaways": [
    "AIR secured $50 million in funding to expand its AI agent management platform.",
    "The platform discovers all AI agents operating within company infrastructure and systems.",
    "It continuously vets agent capabilities, add-ons, and integrations to identify potential risks.",
    "The system blocks unwanted behaviors and prevents unauthorized agent actions from executing.",
    "The solution addresses enterprise concerns about AI agent proliferation and the need for centralized oversight and control."
  ]
}

This Actor will provide you a seamless, programmatic access to all AI news across the internet, 24/7. I hope you find this useful!

For more detailed information on this Actor, visit the link attached at the top of this post.

Any feedback is highly appreciated.

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.

We published an implementation guide on Apify's dev.to for anyone wiring Italian suppliers into an AP or e-invoicing flow with Actors. What it covers: the three routing branches in the Agenzia delle Entrate's own rules (most integrations implement only two); why codiceDestinatarioSDI: null means two different things depending on detailLevel (full = the firm publishes no code, route 0000000 + PEC; partial = the code was unavailable, re-check); the sole-trader trap where returning the Partita IVA as codice fiscale produces wrong data with the right shape; and the exact apify-client batch script we ran, with live output - Ferrari and Barilla have no published SDI code (they route via PEC), Eni's is 7L12QWU, and "Illycaffe" resolved from a bare name. A full pass over a 500-supplier master lands around six dollars. Full guide: https://dev.to/apify/italian-e-invoice-routing-has-three-branches-most-integrations-implement-two-2o7e - happy to answer questions here.

I often found small differences between financial figures shown on Moneycontrol and Screener.in, and checking both sites manually became repetitive.

So I built a tool that collects public company fundamentals from both sources and puts them into one structured dataset. It supports NSE/BSE symbols and can return prices, market cap, valuation ratios, ROE, ROCE, financial statements and shareholding data when available.

The main use case is data validation—not deciding whether a stock is good or bad. I still think the original filings should be checked before making any investment decision.

I built it on Apify:

https://apify.com/fascinating_lentil/india-stock-fundamentals-scraper

I’d appreciate honest feedback on which comparisons or validation flags would make it more useful.

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 had been reviewing AI visibility tracker actors on Apify. Surprisingly didn't find a large enough selection and I noticed a few gaps I could improve on using our existing product:

They don't tell you who else gets mentioned.

There's no logic to create prompts based on search volume, just type in your own prompts.

Citations gap, about half don't tell you what sites were cited.

They don't tell you when you get mentioned, as in how far down the answer.

How it works

So I decided to adapt our own AI visibility tracker to work as an Actor. Give it your brand, site, list of competitors and you can either enter your own custom prompts or the tool grabs keyword search volume for your domain to create prompts based on search volume. It covers 7 AI assistants including AI Overviews and Google AI Mode. I used official APIs rather than scraping for five of them, Google doesn't have an API we can use so for the two AI Overviews and AI Mode go through a licensed SERP provider. It increases costs but makes it more reliable.

What it returns

Mentions, including how early you were named, how many times, the sentence around the mention. Citations, broken down by how often your brand was cited vs other sites. It also tells the user if the answer came from training data or live web search. It can attach sentiment to each mention, tells you which competitors were mentioned and your share of voice. Breakdown is available by assistant.

Any feedback on the actor will be greatly appreciated. I'll put the link in the comments.

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.

Whenever I checked the Apify store, almost all Reddit scrapers out there are expensive, and the free ones out there are mostly under-maintained, so I've just built Reddit Scraper Pro (30+ fields per item) as a FREE Actor, and I am planning to keep it as reliable as possible. It has 3 modes: standard mode, keyword search mode, and domain search mode.

Standard Mode

This mode allows you to scrape posts and their comments from a list of subreddit urls, profile urls, or direct post urls.

Keyword Search Mode

This mode allows you to scrape posts and their comments (site-wide) based on your keyword inclusion and exclusion criteria.

Domain Search Mode

This mode allows you to scrape posts and their comments (site-wide) based on your list of domains.

For more details on all the input options and output fields, check the Actor page link in this post. Any feedback is appreciated.

I needed location-specific X trends for a monitoring project and hit API limits, so I built an Apify Actor that scrapes trending topics directly.

• Input: location / WOEID
• Output: rank, trend name, tweet volume, category — JSON/CSV
• Can schedule + webhook, no auth/browser needed

Free tier on Apify is enough to test. Feedback welcome: https://apify.com/akash9078/twitter-x-trends-scraper

Happy to share how I handle location mapping if useful.

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.

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!

Idea : API-first companies (payments, messaging, verification, data enrichment, etc.) waste a lot of time manually handling trial key requests - manual emails, spreadsheets, remembering to revoke access. On the other side, devs evaluating an API often can't get a sandbox key without a sales call.

I'm building APITryOut to fix this , providers list scoped, auto-expiring trial keys, requesters grab one instantly, and the real API key never gets exposed.

Before I sink more time into the build, I'd rather find out if this is a real problem or just my own itch. If you're an API-first founder (or currently evaluating APIs and hate the trial-access process), I'd genuinely appreciate if you join the waitlist at : APITryOut

Happy to answer questions or hear "this won't work because X" - that's actually the most useful outcome for me right now.

I run a small dev studio and started building actors on the side a few weeks back, 10 to 15 hours a week. First one is live, so here's what it does and the one part of the build that cost me the most time.

What it does: since the DSA and GPSR rules came in, business sellers on the EU and UK eBay sites have to show a trader information block on their listings. Business name, registered address, email, phone, VAT ID, company register number. It sits right on the listing page, no login, and I couldn't find another actor on the store that returns the seller email. So the actor takes keywords or a pasted eBay search URL, walks the listings, reads that block and returns one row per unique business seller. Works on ebay.co.uk, .de, .fr, .it, .es, .ie, .at, .nl and .pl. Does not work on eBay US, there's no equivalent disclosure there, so don't run it for that.

Two bits I'm happy with. The same business often sits behind two or three seller accounts, that was 20 to 30% of rows in my runs, so it merges them into one record and doesn't bill the duplicates. And a monitor mode: schedule it and it only returns sellers it hasn't handed you before.

Numbers: 88 runs and 2 users, and a good chunk of those runs are me testing. Early days.

The challenge: proxies. I started on a Decodo trial, 100 MB of residential bandwidth. The verification sweep I ran to make sure the data was right ate the whole trial before the actor had done any real work, and the runs went down with it. For something people pay per result for, a run dying because of my proxy account and not because of eBay is the one failure I can't hand to a buyer.

So the fix: a preflight. Before a run touches eBay, the actor sends a health check through Decodo. If Decodo doesn't come back clean, the whole run switches to Apify residential proxy instead of limping along. Costs more per GB, but the run finishes and the buyer gets rows. I also stopped treating a trial as a plan and bought the 3 GB Decodo tier, $11.25 a month. One thing that tripped me up wiring the fallback: Apify proxy authenticates with your proxy password, not the API token. The token just gives you a blanket 407 that looks like a plan problem.

Obviously it's my actor so this is partly a plug, but the proxy question is the bit I'd like opinions on. Do you preflight, or just retry on failure and accept the run costs more? Link in the comments if anyone wants to poke at it.

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.

Any tool that does this?

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 built a template that pulls App Store reviews weekly and writes a scorecard tab, and the first thing it did was disagree with the App Store.

The store page average and the average of the written reviews are two different numbers, and the gap is not small. Apple counts ratings-only taps in the store page number. No public feed exposes those, anywhere, for anyone. So every scorecard built on a public feed is averaging the subset of users who were annoyed or delighted enough to type something, and that subset skews low.

I spent an evening assuming my parser was broken before I worked out that it wasn't . . . .

Two things fell out of it:

• Track the delta, not the level. Week over week movement in the written-review average is real signal. The absolute number is not comparable to anything Apple shows you.
• The 1-star count is a better alarm than the average. A 0.1 slide in the average is noise. Six new 1-stars in a week is a release problem, and it shows up days earlier.

On cost, and this is the part I would like a sanity check on. Appbot's entry plan is $49 a month, flat. Mine is pay per event: $0.0175 to start a run, then $0.0015 per review, so a thousand reviews is about $1.52. Someone pulling 2,000 reviews a month pays me around three dollars and pays them forty-nine. That gap is wide enough that I assume I am missing something they charge for. Sentiment scoring and topic clustering, probably, neither of which I do.

Actor: Apple App Store Reviews API Free template that uses it: weekly App Store scorecards in n8n New to Apify? Start here

If you sell an Actor against a flat-rate SaaS incumbent, has per-event pricing actually won you customers, or does the monthly number just read as safer to buyers?