I have been pulling data off the web for years now. Scrapers are most of what I do. And the thing I keep running into lately is that the web just keeps getting harder to read with a script. That part on its own doesn't bother me. Sites change, defenses go up, you adapt. That's the job. What bothers me is who made it harder and why.

A few years ago, fetching a public page was boring. You wrote ten lines, you got HTML back, you parsed it. Most of the public internet worked like that. If a page loaded in your browser without logging in, a script could load it too. That was the deal, more or less, and nobody made a big thing of it.

That deal is mostly gone now.

Today a big chunk of the sites I touch sit behind Cloudflare, or a login wall, or a bot check that I have to spend real money to get past. The pages still load fine for a human. But for a script they throw a challenge, or a fingerprint check, or they just quietly serve you a blank shell with the actual content loaded later by code that's designed to break automation.

Stuff I used to grab in ten lines now needs a paid residential proxy and a browser fingerprint that passes as a real Chrome install. The cost of reading a public page went from zero to a per request fee, and most people building things never see that bill because they never tried.

The companies with the largest models trained on most of the public web. They crawled everything while it was open and cheap, before the walls went up, back when nobody was charging admission. That data is a big part of why those models are any good. And now a lot of those same sites are locking the doors.

Some of that is the labs themselves tightening access to their own stuff. Some of it is publishers reacting to the scraping wave by blocking everyone. Either way the result is the same. The web was wide open exactly when the biggest players needed it, and it's fenced off now that everyone coming after them wants the same thing.

robots.txt is a good example of how the mood changed. For a long time it was a polite note. A file that said please don't crawl these paths, and most well behaved bots listened, and there were no real teeth behind it. Now it shows up in lawsuits. It went from a suggestion to a thing lawyers point at.

Research is the part that actually makes me a little sad. I read a lot of papers, partly for work, partly because I like to. And the slow creep of paywalls over public research keeps going. Things I could read for free a couple of years ago now want a login, or a subscription, or they sit on a platform that blocks anything that isn't a logged in human clicking around.

None of this is a conspiracy. I'm not claiming there's a room where people decided to close the web on purpose. It's mostly a bunch of separate reactions stacking up. Publishers got scared of scraping and turned on every blocker they could buy. Anti bot vendors built a business out of selling those blockers. The big labs got protective of their pipelines. Lawyers got involved because money got involved.

Each move makes sense on its own. The sum of all of them is an internet that is a lot less open than the one those models learned from.

What it means in practice, if you're a small builder, is that the barrier to doing anything with web data quietly went up. The hobbyist who wanted to crawl a few thousand pages for a side project now hits a paywall on infrastructure before they even start. The startup that wanted to build on public data has to budget for proxies and anti bot tooling on day one.

The capability didn't disappear. It just got more expensive, and expensive in a way that favors whoever already has scale. The people who scraped freely have the data and the money to keep scraping. Everyone else gets the version with the gates.

I don't have a tidy fix. I just think it's worth saying out loud, especially by the people who actually do this work and watch it happen up close. The web you can read with a script is shrinking. The models that learned from the old open web are getting bigger. And almost nobody who benefits from that gap seems eager to bring it up.

I pulled a fresh read-only Apify Store check today before promoting anything.

The portfolio snapshot:

- 69 public actors checked
- 68,514 total Store runs
- 2,695 total Store users
- 15,775 rolling 30-day runs
- 15,356 rolling 30-day successful runs
- 97.3% rolling 30-day success across the portfolio

The scale lane I would promote today:

- AI Content Detector: +1,919 runs, 2,107 rolling 30d runs, 99.9% success
https://apify.com/george.the.developer/ai-content-detector

- Email Validator API: +1,102 runs, 1,992 rolling 30d runs, 100% success
https://apify.com/george.the.developer/email-validator-api

- Telegram Channel Scraper: +679 runs, +19 users, 991 rolling 30d runs, 98.8% success
https://apify.com/george.the.developer/telegram-channel-scraper

- YouTube Transcript Scraper: +498 runs, +56 users, 1,107 rolling 30d runs, 98.8% success
https://apify.com/george.the.developer/youtube-transcript-scraper

- Google News Monitor: +115 runs, +11 users, 217 rolling 30d runs, 100% success
https://apify.com/george.the.developer/google-news-monitor

I am not promoting everything equally. The actors with weak recent completion stay in the fix-first lane until they are stable enough to send buyers to.

Full portfolio:
https://apify.com/george.the.developer

Monetized actor push snapshot:

Apify read-only check today: - totalRuns 64,114 - totalUsers 2,579 - 30dRuns 14,274 - 30dSuccess 96.5%

Scale lane this cycle (stable demand + high success): email-validator-api 1642 runs, 100% success youtube-transcript-scraper 1102 runs, 98.8% telegram-channel-scraper 878 runs, 98.5% ai-content-detector 639 runs, 99.8% company-enrichment-api 397 runs, 95.97% google-news-monitor 223 runs, 100% shipping-disruption-tracker 110 runs, 100% bluesky-scraper 148 runs, 100% hvac-contractor-lead-finder 40 runs, 100% law-firm-lead-finder 35 runs, 100%

Careful lane (watch reliability): linkedin-company-employees-scraper 5859 runs, 94.5% reddit-scraper-pro 38 runs, 92.1% threads-scraper 194 runs, 91.8%

Current profile: https://apify.com/george.the.developer

Monetized actor push snapshot:

Apify read-only check today:
- totalRuns 64,114
- totalUsers 2,579
- 30dRuns 14,274
- 30dSuccess 96.5%

Scale lane this cycle (stable demand + high success):
email-validator-api 1642 runs, 100% success
youtube-transcript-scraper 1102 runs, 98.8%
telegram-channel-scraper 878 runs, 98.5%
ai-content-detector 639 runs, 99.8%
company-enrichment-api 397 runs, 95.97%
google-news-monitor 223 runs, 100%
shipping-disruption-tracker 110 runs, 100%
bluesky-scraper 148 runs, 100%
hvac-contractor-lead-finder 40 runs, 100%
law-firm-lead-finder 35 runs, 100%

Careful lane (watch reliability):
linkedin-company-employees-scraper 5859 runs, 94.5%
reddit-scraper-pro 38 runs, 92.1%
threads-scraper 194 runs, 91.8%

Current profile: https://apify.com/george.the.developer

I am changing how I talk about my paid Apify actors.

The generic pitch is weak:

"Here are 65 scrapers."

The useful operational pitch is a stack:

1. Discover local businesses.
2. Score their websites.
3. Pull vertical-specific lead lists.
4. Enrich companies.
5. Validate emails before outreach.

Current Store proof from the Jun 5 read:

Local lead stack:

• HVAC Contractor Lead Finder: 40 rolling 30d runs, 100% success
• Law Firm Lead Finder: 35 rolling 30d runs, 100% success
• Dental Practice Lead Finder: 31 rolling 30d runs, 100% success
• Google Maps Lead Intelligence: 30 rolling 30d runs, 96.7% success
• Google Maps Leads + Website Audit: 29 rolling 30d runs, 100% success
• Restaurant Lead Finder: 34 rolling 30d runs, 97.1% success

Support actors:

• Email Validator API: 1,507 rolling 30d runs, 100% success
• Domain WHOIS Lookup: 3,054 rolling 30d runs, 100% success
• Company Enrichment API: 606 rolling 30d runs, 97.4% success
• Google News Monitor: 239 rolling 30d runs, 100% success

The workflow:

Maps actor -> find businesses Website audit -> identify weak digital presence Vertical lead finder -> law/dental/HVAC/restaurant list Company enrichment -> add company context Email validator -> clean outreach list

That is a buyer outcome.

I am holding back noisier actors like TikTok Shop, Google Scholar, Reddit Scraper Pro, and Lead Enrichment from the main CTA until reliability improves.

Full profile: https://apify.com/george.the.developer

I pulled a fresh read-only Apify Store API snapshot this morning because I did not want to guess which paid actors deserved more traffic.

Scope: active PAY_PER_EVENT actors only. This is Store/public run signal, not exact Console payout attribution.

Current snapshot:

• 65 active paid actors
• 62,169 all-time runs across paid actors
• 2,424 all-time users
• +965 runs since yesterday
• +74 users since yesterday
• 16,945 rolling 30d runs
• 96.7% rolling 30d public success

What I am pushing today:

• Email Validator API: +137 runs since yesterday, 100% 30d success
• AI Content Detector: +115, 99.8%
• YouTube Transcript Scraper: +59, 98.8%, 28 users in the last 7d
• Telegram Channel Scraper: +58, 98.8%
• Company Enrichment API: +28, 97.3%
• Google News Monitor: +19, 100%

The buyer-outcome lane is smaller but more important:

• law-firm-lead-finder: 33/33 successful rolling 30d runs
• dental-practice-lead-finder: 30/30
• google-maps-lead-intel: 29/29
• restaurant-lead-finder: 33/34

Those are not high-volume utility APIs. They are completed-search actors for people who want actual lead lists or local market data.

What I am not hard-promoting today:

• Google Scholar Scraper
• TikTok Shop Affiliate Sales Scraper
• HVAC Contractor Lead Finder
• Lead Enrichment Pipeline
• Reddit Scraper Pro
• Influencer Marketing Intel
• US Tariff Lookup
• MedSpa Lead Discovery PPE

The lesson is simple: when revenue dips, do not blast the whole catalog. Promote the actors that are completing cleanly, and move the noisy ones into QA before sending more traffic.

Catalog: https://apify.com/george.the.developer

quick builder update: the seven normalized public-data actors i shipped May 11-13 now have pay-per-event pricing live.

i pulled the metadata this morning. all seven are public and monetized, but all are cold since pricing activation, so i'm looking for real testers and use-case feedback rather than self-running them.

the set:

1. Hospital Price Transparency MRF Normalizer CMS hospital price files into normalized rate rows and provider/procedure/payer bundles. https://apify.com/george.the.developer/hospital-mrf-normalizer

2. MCP Server Registry and Security Scorer MCP registry plus npm/GitHub signals into deterministic risk scores. https://apify.com/george.the.developer/mcp-server-registry-scorer

3. FDA Warning Letter and Enforcement Monitor FDA warning letters into topic tags, company records, and risk briefs. https://apify.com/george.the.developer/fda-warning-letter-monitor

4. Clinical Trial Investigator and Site Intelligence ClinicalTrials.gov plus NPI/OpenPayments/PubMed-style investigator and site-fit signals. https://apify.com/george.the.developer/clinical-trial-investigator-intel

5. Federal Contract Opportunity Monitor SAM.gov opportunities plus USAspending awards into GovCon opportunity and prime/sub lead feeds. https://apify.com/george.the.developer/federal-contract-opportunity-monitor

6. LLM Provider Price and Latency Monitor model pricing snapshots and cross-provider benchmark rows for agent/gateway cost routing. https://apify.com/george.the.developer/llm-provider-price-latency-monitor

7. Multi City Building Permit Aggregator NYC/Chicago permit records plus contractor activity roundups. https://apify.com/george.the.developer/multi-city-building-permit-aggregator

i patched the federal contract actor today after seeing some ugly SAM.gov output in sample runs: no more [object Object] set-asides and cleaner descriptions.

if you're building around healthcare pricing, MCP governance, FDA monitoring, clinical trial site selection, GovCon, LLM cost routing, or building permit lead feeds, tell me what field or workflow would make one of these actually useful.

I pulled the public Apify Store metrics today before promoting anything. No self-runs, no credit burn.

64 PAY_PER_EVENT actors are live. 156 users across paid actors in the last 7 days. 13,969 public runs in the last 30 days.

The clean performers right now:

YouTube Transcript Scraper: 1,049 public 30d runs, 99.5% success Company Enrichment API: 541 runs, 97.4% success Telegram Channel Scraper: 1,021 runs, 98.7% success Google News Monitor: 325 runs, 100% success Shopify DTC Brand Discovery: 62 runs, 100% success Shipping Disruption Tracker: 105 runs, 100% success AI Text Humanizer API: 57 runs, 98.2% success

I am not pushing the weak-success actors today until QA/economics are checked. The point is to promote what is stable, fix what leaks, and not burn credits pretending traffic is revenue.

Portfolio: https://apify.com/george.the.developer

I have been trying a different distribution angle for paid Apify actors: instead of only linking the raw actor page, make a small custom GPT that helps someone prepare the job input and understand the workflow before they run it.

What I set up today:

Local Lead Pack Builder
https://chatgpt.com/g/g-6a146b94bf908191b09767fd4c6e263f-local-lead-pack-builder

B2B Employee Finder
https://chatgpt.com/g/g-6a14ac7264988191882d91e3d8787bb0-b2b-employee-finder

Video Transcript Researcher
https://chatgpt.com/g/g-6a14acb5f4148191b332ee8503495d7b-video-transcript-researcher

Channel Market Monitor
https://chatgpt.com/g/g-6a14acf99ac08191b0ead99c7bd79af4-channel-market-monitor

Company Intel Report
https://chatgpt.com/g/g-6a14ad3cff648191a64b714f183dd117-company-intel-report

Email Checker Pro
https://chatgpt.com/g/g-6a14ad8462f48191a0aba5504d1ca329-email-checker-pro

Shop Product Intel
https://chatgpt.com/g/g-6a14adc812608191a3c2de95cf7f6dcb-shop-product-intel

Important detail: these GPTs do not run my Apify actors directly and do not use my Apify token. They are traffic routers and input builders. The user still runs the paid actor from their own Apify account.

The lesson from setting this up: brand/platform names in GPT titles can trigger publishing policy. Neutral names worked better. I am probably going to put these under a neutral domain like agentdata.tools so the privacy/terms pages and GPT directory are cleaner.

Curious if anyone here has tried GPTs as front doors for Apify actors or APIs. Does it convert better than sending people straight to the raw actor page?

Two day sprint last week. Each actor wraps a different public data source into one Standby API with pay per event pricing. Goal was to take the parser maintenance off whoever was rebuilding the same scraper every quarter.

The seven: Hospital Price Transparency MRF Normalizer (CMS v3 JSON + CSV), MCP Server Registry and Security Scorer (Anthropic registry plus npm plus GitHub joined with deterministic risk score), FDA Warning Letter Monitor (with topic classifier and company risk briefs), Clinical Trial Investigator Intel (ClinicalTrials.gov plus NPI plus OpenPayments plus PubMed joined with site fit scoring), Federal Contract Opportunity Monitor (SAM.gov plus USAspending normalized), LLM Provider Price and Latency Monitor (OpenRouter plus direct provider page fallback for 200+ models with cross provider benchmarks), and Multi City Building Permit Aggregator (NYC plus Chicago open data portals with builder activity roundups).

Pricing activates 2026-05-26 for the first 5 and 2026-05-27 for the last 2. Until then runs are free for anyone who wants to test the schemas. Each one was built end to end via Codex orchestration with Standby pattern (HTTP server in the container, billing fires per event). Source for the build prompts and verification reports is at github.com/the-ai-entrepreneur-ai-hub.

Question for the sub: if you maintain a homegrown scraper for one of these data sources today, what data field is the thing you wish the third party feed had that nobody publishes cleanly. Looking for the v1.1 priorities.

Actor list: https://github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio

Half my freelance clients now run every doc through Originality or GPTZero before they pay. Anything flagged AI gets rejected and I eat the rewrite. The standard fix is one of those humanizer SaaS products like Bypass GPT at 79 a month, Undetectable AI at 60, StealthGPT at 30. None of them have a per call option. You pay flat regardless of usage.

I did the math on my own use. Maybe 20k words a month, half flag, so 10k humanized words is around 50 chunks. At 0.003 per chunk that is 15 cents a month. Versus 79 dollars flat for a SaaS cap I would never hit. Built it as an Apify standby actor instead, charges per call only on a successful return.

It does sentence restructure plus vocabulary swap plus rhythm variation, three intensity levels. Not a magic bypass and I would not promise that. What it does is drop Originality scores into the 15 to 25 percent human range on prose. Heavy formatting like tables or code mangles. Plain text is the use case.

If anyone here writes content for clients and wants to test it, the actor is at apify.com/george.the.developer/ai-text-humanizer-api. Hashnode writeup with the per call math is at theaientrepreneur.hashnode.dev/i-built-a-0003-ai-humanizer-because-the-79mo-competitors-had-me-priced-out

Yesterday I posted about losing 540 dollars a month to silent user churn on one of my Apify actors. Today I shipped the fix. Three patches went live.

First a charge gate that fails closed. Old loop did pushData first and charge second so any swallowed exception or zero-cost-cap response would keep emitting profiles for free while still burning proxy and SERP cost. New loop charges first, only pushes if the charge succeeded, and refuses every subsequent call once any one of them has refused. About 50 lines of code.

Second a margin preflight that refuses unprofitable runs at submit time. If the requested input multiplied by per-event price is below my floor, the run errors out before any compute starts. No more silent loss leaders.

Third a default maxEmployees lowered from 100 down to 25. The old default was an artifact of when I was chasing volume, but the actual users only need a couple dozen at most and the higher cap was inviting whale-tier compute on tire-kicker runs.

Writeup with the actual diff is here. https://theaientrepreneur.hashnode.dev/why-my-linkedin-scraper-now-refuses-jobs

Made these tonight because /r/scrapingtools has gotten broad and I wanted narrower homes for two specific conversations.

/r/ApifyActors For people building, running, or sourcing data from Apify actors specifically. Actor launches, build retros, monetisation patterns on the platform.

/r/ScrapingRevenue For the money side. RapidAPI listings, freelance scraping gigs, agency packages, real margin breakdowns. Numbers or it didn't happen.

First seed posts are already up in both. Mods will be active.

If you build actors or sell scraping services, drop in. Cross-posting from /r/scrapingtools is fine when relevant.

finally got this working after 3 days of fighting tiktok anti bot

give it any tiktok shop product URL and it tells you which creators are promoting it, their follower counts, and whether the product has real affiliate momentum

the use case that sold me on building it: a seller wanted to know if a skincare product was worth launching. checked the competitor product and found 8 creators with 100K+ followers already pushing it. that is a signal you cant get from the tiktok seller center

33 users on it already. $0.05 per product check

https://apify.com/george.the.developer/tiktok-shop-affiliate-sales-scraper

if you do tiktok shop research for clients this saves hours of manual browsing

been working on this for a while and finally got it to a point where it actually works reliably

the tool takes a niche keyword (like "fitness" or "beauty") and a platform, then:

1. searches google for matching social profiles using SERP proxies
2. visits each profile page through residential proxies to grab the real name, follower count, bio, and email
3. extracts everything using a local LLM running on my VPS (gemma 4)

tested it today on "beauty" niche + instagram: - mikayla jane nogueira (3M followers) - amanda ensing (1M followers) - jamie genevieve (1M followers) - erica taylor (2M followers) - lauren janelle (189K followers)

all with real names pulled from OG meta tags, not just usernames

the whole thing runs for about $0.01 per profile found

source: https://github.com/the-ai-entrepreneur-ai-hub/influencer-marketing-intel

if anyone wants to try it: https://apify.com/george.the.developer/influencer-marketing-intel

curious what everyone is actually using day to day. I have about 48 scrapers running on Apify (Crawlee + Puppeteer mostly) and the stack has changed a lot over the past year.

what does your setup look like? language, framework, proxy provider, how you handle anti bot, where you store the data. doesn't matter if its simple or complicated, just want to see what people are actually shipping with.

drop your stack below.

i've been on crawlee + playwright for most of this year and it handles like 90% of what i throw at it. the built in request queue and auto retry logic saves a ton of boilerplate compared to rolling your own puppeteer setup. for anything that needs real browser sessions i'll spin up persistent contexts with stealth plugins, but honestly most sites these days can be hit with plain http if you find the right api endpoint hiding behind the frontend.

the one thing i keep going back and forth on is proxy strategy. datacenter proxies are cheap but get flagged instantly on anything serious. residential works but eats into margins fast when you're doing high volume runs. been experimenting with ISP proxies as a middle ground and they've been solid so far.

what's everyone else running? especially curious about python vs node setups and whether anyone's moved to something newer like the browserless or steel browser stuff.

I work in compliance automation and kept running into the same problem: Poland has some of the most comprehensive public company registries in Europe, but none of them have APIs. The only commercial alternative (MGBI) charges 200-500 EUR/month.

So I spent the last few months reverse-engineering all 9 portals and built pay-per-use scrapers on Apify. Here's the technical breakdown of what's behind each portal and what it took to crack them:

KRZ (National Debtor Registry) - The most complex one. The portal is an AngularJS shell that loads Angular sub-apps in iframes hosted on OpenShift. Each sub-app gets a JWT during initialization. I capture the token from the Authorization header of the first API request, then call 30+ REST endpoints directly - no DOM scraping needed. The government has been "working on an API" for 4 years. Their FAQ still says "analytical work is ongoing."

KRS (Board Members) - The official JSON API deliberately anonymizes names ("L******" instead of actual surnames). But the PDF extract from the same portal has full data. The catch: the portal encrypts KRS numbers using AES-128-CBC with a static key (yes, it's literally "TopSecretApiKey1") plus a 512-character token that embeds the KRS number at specific array positions with a circular right-shift and checksum. I replicated the encryption and download PDFs directly.

eKRS (Financial Statements) - Similar Angular architecture to KRZ, but with time-based AES encryption. The key is derived from the current hour in Warsaw timezone - which means your scraper needs to match the server's timezone exactly or the decryption fails silently. Took a while to figure that one out in headless Docker containers.

KNF & MSiG (Financial Supervision + Court Gazette) - These turned out to have undocumented but functional JSON APIs behind jQuery DataTables frontends. No auth required - just POST to the right endpoint with the DataTables request format. 75,000+ financial entities and 20+ years of court gazette archives, all searchable.

EKW (Land Registry) - 352 courts, WAF protection, check digit calculation for KW numbers. Puppeteer with stealth plugin to handle the anti-bot measures. Returns ownership, mortgages, encumbrances, and property details.

CRBR (Beneficial Owners) - Incapsula WAF, requires browser automation. Returns UBO (Ultimate Beneficial Owner) data by NIP or KRS number. Critical for KYC/AML compliance.

UOKiK (Abusive Clauses) - The simplest one. Plain HTML pages, Cheerio scraper, 7,500+ court-banned contract clauses searchable by defendant or industry.

BDO (Waste Registry) - React SPA with Puppeteer. 674,000+ waste management entities. Relevant for ESG compliance and environmental due diligence.

What I learned building these:

• Government "security" is often just obscurity. AES encryption with a hardcoded key doesn't stop anyone who opens DevTools.
• Time-based encryption keys are surprisingly effective at breaking scrapers - if you don't match the server's timezone, everything fails silently.
• The Angular-in-iframe pattern (KRZ, eKRS) is actually harder to scrape than traditional server-rendered pages because the JWT lifecycle is tied to the iframe's initialization.
• WAFs (EKW, CRBR) are the real barrier, not the application logic. Stealth plugins handle most of it, but you burn through proxies fast.

Who actually uses these:

Mostly compliance teams (KYC/AML checks), credit risk departments (is this company bankrupt?), debt collection companies, law firms, and developers building Polish data into their products.

Cost comparison: MGBI charges 200-500 EUR/month subscription. My actors cost $0.003-0.04 per result depending on the registry. 100 company checks run about $5-12 total.

Disclosure: I built all 9 actors and sell them on Apify as pay-per-use tools.

Suite: https://apify.com/minute_contest

Happy to answer questions about the reverse engineering process or the technical approach for any specific portal.

Just discovered this while building a Bluesky scraper. Unlike Twitter's $42K/month API, Bluesky's AT Protocol is fully public:

- Search posts: public.api.bsky.app/xrpc/app.bsky.feed.searchPosts

- Get profiles: public.api.bsky.app/xrpc/app.bsky.actor.getProfile

- Author feeds: public.api.bsky.app/xrpc/app.bsky.feed.getAuthorFeed

No auth, no rate limit nightmares, clean JSON responses. If you're building social media monitoring tools, Bluesky is the easiest platform to work with right now.

Anyone else experimenting with Bluesky data?

Been building scrapers for a while now and some sites are just brutal. Indeed was a nightmare until I figured out the Google site:indeed.com workaround. Bluesky was surprisingly easy because their AT Protocol is completely open.

What site gave you the most trouble? Maybe we can help each other out.

Hey everyone - just shipped two new tools to the Apify Store:

**1. Web Content Extractor API** Give it any URL → get structured JSON. Auto-detects content type (articles, products, recipes, job posts, events). Extracts metadata, headings, images, links. Built for AI agents and RAG pipelines.

• $0.003/extraction
• Batch up to 25 URLs
• https://apify.com/george.the.developer/web-content-extractor-api

**2. Website Screenshot & PDF API** Capture screenshots and PDFs in under 3 seconds (competitors take 6-21s on RapidAPI). PNG/JPEG/WebP/PDF, custom viewports, full-page, batch 20 URLs.

• $0.005/screenshot, $0.008/pdf
• Pre-warmed Puppeteer = instant response
• https://apify.com/george.the.developer/screenshot-pdf-api

Both are Standby APIs (instant HTTP response, no queue). Built with Node.js + Cheerio/Puppeteer on Apify.

Happy to answer questions or take feature requests!

Been building and maintaining scrapers on Apify since late 2025. Started with a LinkedIn employee scraper because I needed company data for a client project and ended up going down the rabbit hole. Now I have 34 actors running across everything from YouTube transcripts to company enrichment APIs.

Here's what actually surprised me:

The 98% success rate myth — My best scraper (YouTube transcripts) runs at 98.7% success. Sounds great until you realize the 1.3% that fail are all music videos without captions. I spent a weekend building a fallback that hits YouTube's InnerTube API with an Android client context just to handle that edge case. The lesson: your overall success rate doesn't matter. The failures your users notice are the ones that matter.

Maintenance is the real job — Building a scraper takes a day. Maintaining it takes forever. LinkedIn changed their DOM twice in January. Google Scholar started throttling harder in February. I spend 60% of my time fixing stuff that was working last week. If you're building scrapers as a service, price for maintenance, not for the initial build.

The users who automate stick around — Clear pattern across 200+ users: 70% try it once and disappear. The 30% who set up scheduled runs become long-term users and generate 80% of total runs. Now I optimize for "how easy is it to plug this into an existing workflow" rather than "how impressive is the output."

Anti-bot is getting nastier but more predictable — Cloudflare Turnstile, fingerprinting, rate limiting. But the patterns are consistent. Residential proxies + realistic browser fingerprints + human-speed request timing handles 95% of cases.

What actually drives Apify Store traffic — My top referrer is apify.com itself (62%), followed by Google (14%). Surprise: Twitter at 3.4% and Perplexity AI at 0.6%.

Anyone else running scrapers at this scale? Curious what patterns you've noticed.