DEV Community: George Kioko

The proxy industry needs you to never open the network tab

George Kioko — Wed, 05 Aug 2026 15:51:09 +0000

I run 75 scrapers in production. Three of them do any fingerprint spoofing. Maybe five use residential proxies. The rest run on plain datacenter IPs or no proxy at all, and they have been running for months.

If you learned scraping from blog posts, that number probably sounds wrong to you. Every tutorial you have read starts the same way: sign up for a residential pool, install a stealth browser, randomize your fingerprint, throttle like a human. Then, on step five, you finally get to look at the actual website.

That order is backwards, and it is backwards on purpose. Proxy companies write most of the scraping content on the internet. They were never going to write "you probably do not need us."

The scraper with the $80 a month costume

Last month my guy sent me his Greenhouse job board scraper to fix. It had everything. Puppeteer with the stealth plugin. Rotating residential proxies. Randomized mouse movements between actions. Human-like typing delays.

It still kept dying.

So I did the thing nobody had done: opened the page in a normal browser with devtools up. The entire job list was sitting in one XHR request to a public JSON endpoint. No auth. No cookies. A rate limit so loose I never managed to hit it.

I deleted basically his entire codebase and replaced it with a fetch call. It has not broken since. He had been paying for proxies for months to hit an endpoint that does not care who you are.

This was not a rare lucky case. This is most cases.

The 20 minute method

What I do on every new target, before writing a single line of code:

Open the network tab, filter to XHR/fetch.
Reload the page. Click around. Paginate. Search.
Find the request that returns the actual data. It is usually JSON and usually obvious.
Right click, copy as cURL.
Paste it in a terminal and start deleting headers one at a time. Rerun after each delete.

Whatever survives step five is your scraper. Most of the time the answer is a user agent header and nothing else. Sometimes a referer. Occasionally one cookie you can grab with a single cheap request first.

Twenty minutes. No browser automation, no proxy bill, and the result is faster and more stable than any puppeteer setup, because JSON endpoints change way less often than HTML markup does.

Half the "protected" sites out there are react apps sitting on a wide open API. The frontend is the security theater. The data is just there.

When you actually need the heavy stuff

I am not saying anti detect tooling is fake. I am saying it is a last resort that got marketed into a first step. You genuinely need it when:

The protection sits on the API itself, not the page. Turnstile or a challenge token attached to the data endpoint.
The site checks TLS fingerprints, so plain curl or node fetch gets flagged before your headers even matter.
Sessions are bound to browser fingerprints, ticketing platforms mostly.
You need real logged-in browsing at scale.

In my portfolio that is maybe 1 target in 10. For those, sure, pay for the pool. For the other 9, the network tab was free the whole time.

The tell

You can tell how someone learned scraping by what they reach for first. People who learned from vendor blogs can configure a fingerprint rotator but have never stripped a cURL command down to its minimum headers. People who learned by breaking things do it the other way around.

Try the boring thing first. It costs nothing, and most of the time it is the whole job.

I write about this stuff and post scraping methods on r/scrapingtools if you want more of it.

Nobody voted for the company that now licenses the readable web

George Kioko — Wed, 29 Jul 2026 10:41:42 +0000

i write scrapers for a living, so read this knowing im biased. but something happened on july 1 that got covered as a pricing update, and it is not a pricing update.

Last time i wrote about the big labs pulling the ladder up behind them after climbing it. This is the same story from a different seat. Not a company fighting over data it trained on. A company that sits in the road deciding who is allowed to walk down it.

Start with what they got right

Most takes on this are lazy, so let me concede the strong part first.

Cloudflare moved from pay per crawl to pay per use. Under the old model a bot paid a fee to fetch a page, minimum a cent per retrieval. Under the new one, publishers get paid when their content actually shows up inside an AI answer. Opt in, content gets used downstream, money comes back.

That is real compensation reaching real site owners, and it is better than what came before. Anyone telling you this is pure extraction has not read it. i am not going to pretend otherwise to make the rest of this hit harder.

The payment is fine. Its everything wrapped around the payment that should bother you.

Who wrote the categories

AI crawlers now get sorted into three buckets. Search, agent, training. Which bucket your bot lands in determines what you can reach and what it costs.

Nobody outside one company defined those categories. Think about how much sits inside that definition. Is a research crawler agent or training. Is an archival bot search. Is a small team building a tool a legitimate reader or an unlicensed one. Those are policy questions with real consequences for who gets to build anything, and they were answered in a product spec.

Who made the scarcity

Starting September 15, mixed use crawlers get blocked by default on any page carrying ads.

Read that as a business move rather than a safety one. You cannot run a marketplace for access until access is scarce. The same company that operates the marketplace also owns the switch that creates the scarcity, and it just flipped that switch on for everyone at once, by default, without each site owner deciding anything.

That is not a company responding to a market. That is a company manufacturing one.

Who is on the list

The program launched with two partners.

So at this moment, one company decides what counts as a reader, what counts as a thief, and which buyers are allowed to cross the line between them. If you are building something and you are not on the list, your options are pay whatever the rate becomes, or lose access to a large fraction of the public web.

That is not a market with many sellers and many buyers. That is a licensing authority with a waiting list.

The part scrapers already knew

Everyone in this work has understood for years that bot blocking was never really about stopping bots.

A company that could genuinely stop automated access would ship that once and be finished. It would also destroy the revenue line it is currently building a business on. The incentives only ever pointed one direction. What got built is not a wall, its a meter with a wall painted on the front.

Blocking is the demo. Billing is the product. Watch how fast a challenge page gets easier the moment a paid API appears.

The site owners are the strange part

Millions of sites pointed their DNS at one company years ago because the free tier was genuinely good and the bandwidth cost nothing. That was never charity. That was acquisition cost, and it worked.

Now you get a check. You do not get a vote. Not on the categories, not on the rate, not on who is admitted to the program, not on whether the default should have been blocking in the first place.

What to actually take from this

im not telling anyone to rip out their CDN. the payments are real and some publishers will be better off.

im saying notice what quietly happened. Permission to read the public web turned into a licensable asset, and the entity issuing the licenses is a private company that appointed itself, defined its own terms, created the scarcity its market depends on, and answers to nobody who has to live under the rules.

The money is not the story. The authority is.

The companies that scraped the whole internet now want scraping banned

George Kioko — Wed, 22 Jul 2026 12:18:43 +0000

i scrape the web for a living, so read this knowing im biased. but this week made something clear that i think a lot of people already feel and havent said.

Every large model you use was built on scraped data. Your blog posts, your code on github, your stack overflow answers, your art, your comments, your captions, entire forums, the whole public internet. It got vacuumed up without anyone asking, because it was reachable and that was enough. i know exactly how that works, its my actual job.

And i dont even hate that part. Public is public. Ive built a whole career on the idea that data people put out in the open is fair to collect. That belief is why im comfortable saying the next part.

The move that should bother you

Now that these companies are worth what they are worth, they want the rules changed. Not for themselves, for everyone coming after them.

They are lobbying to restrict open source models. They are describing plain access to public data as a safety threat, and in some rooms as a national security problem. The exact activity that built the entire industry is being quietly recategorized into something that needs licenses, permission, and control.

Look at what is actually happening while they say this.

Anthropic is claiming local models steal from it. This is months after it paid over a billion dollars to settle with writers over claims that it trained on their work without permission. The company that just wrote a giant check for taking data is now worried about other people taking data.

OpenAIs own models just broke out of a controlled test and hacked into Hugging Face to cheat a benchmark. Their frontier systems escaped the sandbox, chained real vulnerabilities, and reached a production database. This is confirmed, they disclosed it themselves. And the public framing that follows an event like that is never we should be more careful with what we build. It is everyone needs tighter controls.

Call it what it is

This is not safety. It is a monopoly wearing a safety badge.

When you are the incumbent, the cheapest defense money can buy is a rule that makes it illegal for the next version of you to get started. You do not have to win on quality if your competition is banned from touching the raw material you already sit on top of. Regulation written by the biggest players is not protection for the public, it is a fence around the thing they took.

The people who collected everything want to be the last ones ever allowed to collect anything.

Why i bother writing this

i dont have a clean policy fix. im one person who writes scrapers. but the story being sold right now is the exact opposite of what is happening, and framing hardens fast once it goes unchallenged. so it is worth putting the plain version on the record before it gets written into law.

If you build in this space and you want tools that treat public data as public, that is most of what i make. You can see the full set here: https://apify.com/george.the.developer

Back to my scrapers.

I build the scrapers the data vendors run on, and I need to tell you what you are actually paying for

George Kioko — Wed, 01 Jul 2026 05:12:55 +0000

I have built the scraping guts behind more "real time business intelligence" products than I would ever admit on a CV. So when a contact data company tells you their database is fresh, I am not impressed, because I know how that word gets made.

Here is the part nobody puts in the sales deck. These companies pull public pages on a schedule, pour the rows into a giant table, and sell you a seat to the table. That table is a photograph. It was true the day it was scraped and it has been aging ever since. You pay every month like it refreshes every month. It does not. A row captured in February is still February sitting in your CRM in June, looking exactly as confident as a row pulled this morning, because nothing on the screen tells you which is which.

And the field that goes bad first is the one you bought the thing for. Where a person works right now. People quit and get promoted and get poached and get pushed out, and the company page is the last place on earth to find out. So "current company," the whole reason the list has any value, is one of the first columns to start lying by the time your rep hits send.

The industry knows this. Of course they know it. They built the pipelines. They just figured out that decay is invisible, and you cannot be angry about a number you never see. So the pitch drifts back to coverage. Millions of contacts. Look how big the table is. Freshness is the expensive part. It makes the table look smaller, and a smaller honest number loses sales calls to a bigger dishonest one.

So you get a beautiful export, no red cells, the kind of file an agency screenshots for a client and feels calm about. Then it goes out and lands on a VP who left in spring, a director who changed roles, a manager who is now at the competitor you are trying to beat. The email still works, which is the worst case, not the best one, because now your pitch reached the wrong person with total confidence and your team looks careless instead of automated.

I will say the thing the vendors will not. Pulling the names is the easy half. It has been easy for years. The hard half, the half they quietly skipped and still charge you for, is going back to each row and checking the one thing that rots. Does this person still work here, right now, on the live profile, not on the cached page that first surfaced them. On a messy company pull, I have seen that check wipe out the majority of the list as already wrong. Calling that a data cleaning chore is how vendors hide the miss. It is the difference between the product they sold you and the product you thought you were buying.

I still build scrapers. I think they are great. But a scraper with no verification is a camera, and these companies are selling you old photos at a subscription price and calling it a feed. The tell is simple. Ask your vendor what percentage of their "current company" fields they reverify, and how recently. Watch them change the subject to coverage. That answer, the one they will not give you, is the whole game.

You are not paying for data. You are paying for the confidence that it is current. And that is the one thing in the box they never actually put in.

Why I Turned 70 Scrapers Into Claude Agent Skills, and Why MCP Beats Calling the API

George Kioko — Fri, 19 Jun 2026 09:10:13 +0000

Why I Turned 70 Scrapers Into Claude Agent Skills, and Why MCP Beats Calling the API

For a long time the only way to use a scraper inside your own software was the same: get an API key, kick off a run, poll until it finishes, pull the dataset, parse it, then wire that into your code. It works. It is also a lot of plumbing for what should be one step.

This week I exposed all of my Apify actors over the Apify MCP server, so an AI agent can call them as skills instead. LinkedIn employees with emails, OFAC sanctions screening, YouTube transcripts, Google News, company enrichment, and the rest. Same actors, same pricing. The difference is how you reach them.

The plumbing problem

Calling an actor through the API looks like this. You authenticate. You start a run with the right input. You poll the run status, or you set up a webhook to tell you when it finishes. You fetch the dataset. You parse the rows. You map them into whatever your app expects. Every one of those steps is code you write, test, and maintain. Multiply it by every tool you want your agent to use, and you are building an integration layer that has nothing to do with the actual work.

What MCP changes

MCP flips it. The actor becomes a tool your agent can see and use directly, mid conversation, with no glue code. The agent reads the tool's schema, calls it, and gets a clean result back. You do not write the run-and-poll-and-parse loop, because the agent simply uses the tool the way it uses any other.

In practice that turns "go build an OFAC integration" into a sentence:

"Screen these twelve vendors against OFAC and flag anything over 85 percent."

The agent runs the actor, reads the matches, and hands you the shortlist. The scraper stopped being a service you integrate and became a skill your agent already has.

Why this matters for anyone building with agents

The whole point of an AI agent is that it does multi step work for you. A sanctions check, a lead pull, a transcript summary, those are steps inside a larger flow. If every step needs its own hand written API client, the agent is only as capable as the integrations you had time to build. MCP removes that ceiling. Point your MCP config at the actor and it shows up as a skill the agent can use immediately, alongside everything else.

How to use mine

Every actor is public under george.the.developer on the Apify Store, and exposed over the Apify MCP server. In Claude, Cursor, or any MCP client, add the Apify MCP server to your config and the tools appear:

https://mcp.apify.com?tools=george.the.developer/ofac-sanctions-screener

Then ask your agent to use it. Screen a vendor list, pull a company's employees with emails, turn a video into a transcript, whatever the actor does. Same actor you could call through the API, now usable as a skill inside your agent flow, no plumbing.

That is the shift: stop integrating tools, start giving your agent skills. Try one on your own data and you will feel the difference in the first five minutes.

Browse the actors here: https://apify.com/george.the.developer

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

How to Feed Your AI Agent Fresh News on Any Topic, No News API

George Kioko — Thu, 18 Jun 2026 13:15:47 +0000

How to Feed Your AI Agent Fresh News on Any Topic, No News API

If you are building anything that needs to know what is happening right now, a brand monitor, a market research agent, a competitor tracker, you hit the same wall. The agent is smart but blind to anything after its training cutoff. The paid news APIs are expensive and rate limited, and most of them cover a narrow slice of sources.

There is a simpler path. You give a topic or a brand name and get back the latest news on it as structured data, ready to feed an agent or drop into a report. Here is the workflow.

The job, stated plainly

You want fresh news on a topic, a company, a person, whatever you are tracking, and you want it as clean structured rows, not a scraped mess of HTML. Title, source, link, date, enough to act on or hand to a model. And you want it to keep up, not to be a one time pull you run by hand.

The slow way is opening Google News, scanning headlines, copying links into a spreadsheet. That does not scale and it goes stale the moment you stop. The tool way pulls the latest on any query in one call.

Step 1: point the actor at a topic

I use the Google News Monitor on Apify. You give it a search query, a brand, a keyword, a person, and it returns the recent news matching it.

{
  "query": "your brand or topic",
  "maxResults": 50,
  "language": "en"
}

Track a competitor, a product category, a regulation, anything you need to stay current on.

Step 2: get structured rows back

Each article comes back as a clean row: title, source, link, and publish date. That is the difference between data you can sort, filter, and feed to a model, and a page of HTML you have to clean first.

For an agent the structure is the point. A list of titles with sources and dates is context your model can summarize, classify, or alert on. A raw page is just noise.

Step 3: turn it into a brief or an alert

Once you have the rows, the rest is your pipeline. Summarize the day's coverage into a brief, classify articles by sentiment, or alert when a competitor shows up in the news. A query becomes a feed, and the feed becomes something your team or your agent acts on.

This is the use case that keeps coming up: give an agent a live window into the news so it is not blind to the present. The monitor is the step that opens that window.

Step 4: run it inside Claude or any AI agent (MCP)

The actor is exposed over the Apify MCP server, so an agent can pull fresh news mid conversation with no glue code:

https://mcp.apify.com?tools=george.the.developer/google-news-monitor

Ask your agent "what is the latest news on this company and summarize the three biggest stories" and it runs the actor, reads the rows, and answers. The news becomes one more source your agent can check on demand.

Why no news API matters

The paid news APIs cost real money, cap your calls, and often miss sources. This gives you the breadth of Google News behind one query, as structured data, without a contract or a key to manage. That is the difference between a feature you can wire into an agent today and a vendor bill you have to justify.

The 10 minute version

Point it at a topic, pull the latest, and feed it into a brief, an alert, or an agent. After the first run you have a repeatable live news feed on anything you track, without a paid news API.

You can run the Google News Monitor here: https://apify.com/george.the.developer/google-news-monitor

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

Turn Any YouTube Video Into Clean Text for Your AI Agent, No API Key

George Kioko — Thu, 18 Jun 2026 13:12:51 +0000

Turn Any YouTube Video Into Clean Text for Your AI Agent, No API Key

If you are building anything that reasons over video, you have hit this wall. The knowledge you want is locked inside a YouTube talk, a tutorial, a podcast episode, and there is no clean way to get the words out. The official captions panel is fiddly, the YouTube Data API does not return transcripts, and the whisper route means downloading the audio and burning compute on something that already exists as text.

There is a faster path. You point a tool at a video URL and get back the full transcript as clean text, ready to feed an agent, a RAG pipeline, or a summary. No API key, no audio download, no whisper. Here is the workflow.

The job, stated plainly

You have a video, or a hundred videos, and you want the spoken words as text you can actually use. Not a caption file with timestamps glued to every line, but clean readable text you can drop into a prompt, chunk for retrieval, or summarize. You want it fast and you want it to scale past doing one video by hand.

The slow way is opening each video, fighting the captions panel, copying broken lines into a doc. That does not scale and it produces messy text. The tool way pulls the transcript straight from the source in one call.

Step 1: point the actor at a video

I use the YouTube Transcript Scraper on Apify. You give it a video URL, or a list of them, and it returns the transcript. No login, no API key, no audio processing.

{
  "videoUrls": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],
  "language": "en"
}

Pass one URL for a quick pull, or a batch of them to build a corpus from a whole channel or playlist.

Step 2: get clean text back

Each video returns its transcript as text, plus the basic metadata you need to keep track of which words came from which video. That is the difference between a caption dump you have to clean and text you can feed straight into a model.

For an AI workflow the text is the whole point. A clean transcript is a chunk of context your agent can reason over. A messy caption file with a timestamp on every line wastes tokens and confuses retrieval.

Step 3: feed it to your agent or RAG pipeline

Once you have the text, the rest is your normal pipeline. Chunk it, embed it, drop it into a vector store, or just pass a single transcript into a prompt for a summary or a question answer. A talk becomes searchable knowledge. A playlist becomes a corpus your agent can answer from.

This is the use case that keeps showing up: turn video into knowledge an agent can use. The scraper is the first step that makes the rest possible.

Step 4: run it inside Claude or any AI agent (MCP)

The actor is exposed over the Apify MCP server, so an agent can pull a transcript mid conversation with no glue code:

https://mcp.apify.com?tools=george.the.developer/youtube-transcript-scraper

Ask your agent "pull the transcript of this talk and summarize the three main arguments" and it runs the actor, reads the text, and answers. The video becomes one more source your agent can read on demand, inside whatever flow it already runs.

Why no API key matters

The YouTube Data API does not give you transcripts, and the workarounds either need OAuth setup or push you into downloading audio and running speech to text on words that already exist as captions. This skips all of that. You give a URL, you get text. That is the difference between a step you can wire into an agent in five minutes and a side project you have to maintain.

The 10 minute version

Point it at a video or a list, pull the transcripts, and feed them into whatever you are building. After the first run you have a repeatable way to turn any video into clean text for an agent, a RAG store, or a summary, without an API key and without touching audio.

You can run the YouTube Transcript Scraper here: https://apify.com/george.the.developer/youtube-transcript-scraper

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

How to Build a Company Employee List With Emails From LinkedIn, No Login

George Kioko — Thu, 18 Jun 2026 09:46:29 +0000

How to Build a Company Employee List With Emails From LinkedIn, No Login

If you do sales or recruiting, you have hit this wall. You find a company you want to sell into or hire from, you open LinkedIn to see who works there, and within a few profile views LinkedIn throws up the login wall, then the rate limit, then the account warning. Doing it from your own logged in account is how you get your account restricted.

There is a cleaner way to pull a company's employees with their work emails, without logging in and without burning a LinkedIn account. Here is the exact workflow.

The job, stated plainly

You have a company. You want a list of the people who work there, their titles, and a work email for each, so you can actually reach the decision makers instead of staring at a profile you cannot contact. You want it as a clean export you can drop into your CRM or a sequence, not fifty browser tabs.

The slow way is opening profiles one at a time from your own account. That does not scale and it puts your account at risk. The tool way pulls the whole roster in one run from infrastructure that is not your personal login.

Step 1: point the actor at a company

I use the LinkedIn Company Employees Scraper on Apify. You give it a company, either the LinkedIn company URL or the name, and it returns the people who work there. No login, no cookies from your account, so your own profile never touches the request.

{
  "company": "https://www.linkedin.com/company/stripe",
  "maxResults": 200,
  "findEmails": true
}

Set maxResults to keep the list focused, and turn on email finding so each person comes back with a work email where one can be resolved.

Step 2: read what comes back

Each employee returns as a structured row: full name, job title, the company, profile URL, and a work email when it can be found and verified. That is the difference between a list of names you cannot use and a list you can actually send to.

The email step is the part that matters. A list of two hundred names with no contact path is busywork. A list where the head of growth and the VP of sales come back with a deliverable email is a pipeline.

Step 3: filter to the people who matter

Sort by job title and keep the roles you actually sell to or recruit for. Founder, VP, head of, director, whatever your buyer or candidate looks like. A roster of two hundred becomes a target list of the fifteen people worth a message, each with a name, a title, and an email already attached.

That is the whole point. You are not contacting two hundred people. You are finding the handful who decide and reaching them directly.

Step 4: run it inside Claude or any AI agent (MCP)

The actor is exposed over the Apify MCP server, so an AI agent can build the list mid conversation with no glue code:

https://mcp.apify.com?tools=george.the.developer/linkedin-company-employees-scraper

Ask your agent "pull the marketing and sales leaders at these ten companies with their emails" and it runs the actor, filters the roles, and hands you a ready list. The prospecting becomes one step inside whatever outreach flow your agent already runs.

Why no login is the whole game

Every LinkedIn scraping headache traces back to one thing: people run it from their own logged in account, then the account gets warned, restricted, or banned. The no login approach moves the request off your personal profile entirely. Your account stays clean because it was never in the loop. That is the difference between a tool you can run every day and one that gets your LinkedIn flagged in a week.

The 15 minute version

Point it at a company, turn on email finding, run it, filter to the titles you want, export to CSV or push to your CRM. After the first run you have a repeatable way to turn any company into a contactable target list, without ever risking your own account.

You can run the LinkedIn Company Employees Scraper here: https://apify.com/george.the.developer/linkedin-company-employees-scraper

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

How to Screen a Vendor List Against OFAC Sanctions in 20 Minutes

George Kioko — Thu, 18 Jun 2026 06:27:36 +0000

How to Screen a Vendor List Against OFAC Sanctions in 20 Minutes

If your company pays vendors, onboards customers, or moves money across a border, you are on the hook for OFAC sanctions screening. The US Treasury publishes the SDN list, and checking your counterparties against it is not optional. The problem is that most teams still do it the slow way: one name at a time in the public search box, copying results into a spreadsheet, hoping nobody fat-fingered a transliteration.

There is a faster way that takes about 20 minutes to set up and then runs on a whole list in one shot. Here is exactly how I do it.

The job, stated plainly

You have a list of names. Companies, people, vessels, whatever your counterparties are. You need to know which of them match an entry on the OFAC SDN list, how strong each match is, and enough surrounding data to either clear the name or send it to a human for review. You want an audit trail for the ones you cleared, because "we screened everyone" is a sentence you may have to prove later.

Doing that by hand does not scale past a few names. The single name search also misses the thing that actually catches sanctioned parties: spelling variants. "Ivan Petrov" and "Ivan Petroff" are the same person to a fuzzy matcher and two different rows to a copy and paste workflow.

Step 1: point an actor at your list

I use the OFAC Sanctions Screener on Apify. You give it a list of entity names, either inline or as a file, and it screens every one against the current SDN list. The whole input is this small:

{
  "entities": ["Acme Trading LLC", "Ivan Petrov", "MV Northern Star"],
  "minConfidence": 80
}

The minConfidence setting controls how aggressive the fuzzy matching is. Start at 80, then tune it once you see how your real data behaves. Lower it and you catch more variants but review more false positives. Raise it and you review less but risk missing a sloppy transliteration.

Step 2: run it and read the output

Each name comes back as a structured record. The fields that matter for a screening decision:

match_confidence, a 0 to 100 score for how close the name is to an SDN entry
risk_level, bucketed into clear, review, or high
matched_name, the exact SDN entry that triggered the hit
aliases and addresses for the matched entity
the sanctions program the entry falls under

The fuzzy match is the whole point. It runs your input against primary names and known aliases, so a name that is spelled a little differently than the official listing still surfaces. That is the failure mode that gets compliance teams in trouble, and it is the one a manual search quietly skips.

Step 3: filter to what a human reviews

Sort the output by match_confidence. Everything above your threshold is the short list a compliance analyst actually looks at. Everything below is cleared, and you keep the full output as the record that you screened the whole book. A list of three hundred vendors becomes a review queue of maybe five, with the data for each one already attached.

That is the part that turns a day of work into a coffee break. You are not screening fewer names. You are screening all of them and only spending human attention on the handful that need a judgment call.

Step 4: make it run inside your AI agent

This is the part that surprises people. The actor is exposed over the Apify MCP server, which means an AI agent can call it mid conversation. If you work in Claude, you point your MCP config at the Apify server:

https://mcp.apify.com?tools=george.the.developer/ofac-sanctions-screener

Now you can hand your agent a list and say "screen these twelve vendors against OFAC and flag anything over 85 percent," and it runs the actor, reads the matches, and gives you back the shortlist. The screening becomes one step inside a larger onboarding or due diligence workflow the agent is already running, instead of a separate tool you context switch into.

Why this beats the enterprise tools for most teams

The big sanctions screening platforms do this well and charge enterprise money for it, often five to six figures a year. If you are a bank with a dedicated compliance department, that is the right tool. If you are a fintech onboarding customers, an importer checking a new supplier, or a consultant running due diligence for clients, you are paying for a department you do not have. Screening per entity, at a cent a name, covers the same first pass screening without the contract.

The honest line: a tool gives you a fast, auditable first pass. The final compliance call stays with your team, because you know your own risk policy and your regulator does not accept "the API said it was fine." What you get is the slow part automated and the judgment part handed to you on a plate.

The 20 minute version

Set up the input once, run it on your real list, tune minConfidence to your data, and wire it into whatever onboarding flow or agent you already run. After that, screening a new batch is one call. The next time someone asks whether you checked a counterparty against OFAC, the answer is yes, with the record to show it.

You can run the OFAC Sanctions Screener here: https://apify.com/george.the.developer/ofac-sanctions-screener

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

June 16 Apify Store Read: Promote Buyer Outcomes, Not A Scraper Catalog

George Kioko — Wed, 17 Jun 2026 04:41:45 +0000

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

The portfolio is still moving:

70 public actors
70,652 total Store runs
2,790 total Store users
17,223 rolling 30-day runs
16,824 rolling 30-day successful runs

The mistake would be turning that into another catalog post.

"I have 70 actors" is not a buyer offer.

The better move is to package working actors into workflows that map to a budget owner.

Content QA

The content lane is:

AI Content Detector
AI Text Humanizer API

The AI Content Detector has 2,924 rolling 30-day runs and 99.9% recent public-run success. AI Text Humanizer has 111 rolling 30-day runs and 100% recent public-run success.

That is a better pitch than "AI detector API."

The buyer outcome is:

Check large volumes of content before publishing, review, moderation, or education workflows.

Video And Market-Intel Ingestion

The research lane is:

YouTube Transcript Scraper
Telegram Channel Scraper
Google News Monitor

YouTube Transcript has 4,211 total runs, 363 users, 77 users in the last 30 days, and 98.8% recent success.

Telegram has 6,351 total runs, 120 users, and 99.0% recent success.

Google News Monitor has 1,801 total runs, 69 users, and 100% recent success.

The buyer outcome is:

Turn videos, channels, and news sources into structured input for RAG, creator research, and market-intelligence workflows.

Sales Data Hygiene

The RevOps lane is:

Email Validator API
Company Enrichment API

Email Validator has 2,287 rolling 30-day runs and 100% recent success. Company Enrichment has 97 users, 28 users in the last 30 days, and 95.4% recent success.

The buyer outcome is:

Clean and enrich outbound lists before spending money on outreach.

Local Lead Packs

The higher-ticket lane is still local lead research.

That stack includes:

Google Maps lead discovery
website audit
company enrichment
email validation
niche lead finders for law firms, dentists, HVAC, restaurants, and other local markets

The better sales motion is not:

Go run this scraper.

The better sales motion is:

I will produce a scored local lead pack for one niche and city, with website checks, enrichment, and outreach angles.

That can start as a $300-$500 proof, then turn into a pilot or recurring workflow if the output is useful.

What I Am Not Pushing Hard Today

LinkedIn has the biggest demand, but the current public 30-day run stats still show too many failures, aborted runs, and time-outs for me to make it today's main public push.

Google Scholar and TikTok Shop both have demand, but they are careful-lane actors right now.

Fix-first actors stay out of hard promotion until the reliability story improves.

Demand is useful. Broken demand burns trust.

The portfolio is here:

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

Scraping LinkedIn company employees without a login (and without flagging your account)

George Kioko — Tue, 16 Jun 2026 06:12:45 +0000

Every LinkedIn scraper I tried fell into one of two buckets. Either it wanted my session cookies, which is a great way to get your own account restricted, or it scraped the logged-in DOM and broke the next time LinkedIn shuffled a class name. Neither felt like something I wanted running on a schedule.

So I built the version I actually wanted to use. It works off public company pages, the ones you can see without being logged in. No cookies, no credentials, nothing tied to a real account that can get burned.

What it does

You give it a company. It returns the employees it can find: full names, job titles, profile links, and emails where they genuinely exist. Output is CSV, JSON or Excel, so it lands straight in a sheet or a pipeline without a conversion step.

There is a verification pass on the employer field so you are not getting the 60 percent false-positive rate you see when a scraper trusts whatever the page first renders. If the current-employer signal does not check out, that row does not ship.

How the pricing works

It bills per result, not per month. Starting a run is a fraction of a cent, each profile is a couple cents, and the email-enriched profiles cost a little more because the enrichment step does real work. The point is that an empty run does not cost you a subscription you were not using that month.

Where it is

It is on the Apify Store, currently sitting at 353 users with a Rising Star badge, which mostly tells me it keeps working under load rather than just on my machine.

https://apify.com/george.the.developer/linkedin-company-employees-scraper

If you run it on something weird and it chokes, tell me. That is the fastest way it gets better.

June 13 Apify Scale Lane: The Actors I Would Promote Today

George Kioko — Sat, 13 Jun 2026 07:53:02 +0000

June 13 Apify Scale Lane: The Actors I Would Promote Today

I pulled a fresh read-only Apify Store check on June 13 before promoting anything.

The portfolio is still getting used:

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 important part is not "promote everything."

The important part is promoting the actors that have both movement and completion. If an actor is getting demand but failing too often, I do not want it as the public CTA until it is repaired.

Today, the scale lane is clear.

The actors I would put in front of buyers today

These actors have recent Store movement and 96%+ rolling 30-day success in the June 13 read.

Actor	Delta runs	Delta users	30d runs	30d success
AI Content Detector	1,919	2	2,107	99.9%
Email Validator API	1,102	2	1,992	100.0%
Telegram Channel Scraper	679	19	991	98.8%
YouTube Transcript Scraper	498	56	1,107	98.8%
Google News Monitor	115	11	217	100.0%
AI Text Humanizer API	94	8	113	100.0%
TikTok Shop Affiliate Sales Scraper	68	20	101	96.0%
Shopify DTC Brand Discovery	32	12	79	98.7%
TikTok to Shopify Trending Products	26	5	43	100.0%
HVAC Contractor Lead Finder	23	3	40	100.0%

Those are the lanes I would push today.

They map to actual buying intent:

AI teams need transcripts, AI detection, and text humanization.
Marketing teams need Telegram monitoring, Google News monitoring, and ecommerce product discovery.
Agencies need local lead lists and verified contact data.
Ecommerce operators need TikTok Shop and Shopify intelligence.

Start with the obvious AI data lane

The AI lane has the cleanest story.

Use YouTube Transcript Scraper when you need video transcripts for:

RAG pipelines
video summarization
creator research
podcast repurposing
competitive channel analysis
training-data preparation

Actor:

https://apify.com/george.the.developer/youtube-transcript-scraper

Use AI Content Detector when you need to classify generated text at scale:

https://apify.com/george.the.developer/ai-content-detector

Use AI Text Humanizer API when the workflow needs a rewrite layer after detection:

https://apify.com/george.the.developer/ai-text-humanizer-api

That is one complete workflow: collect source material, detect generated copy, and rewrite text for downstream use.

Monitoring lane: Telegram and news

Telegram Channel Scraper has one of the best user-growth signals in the June 13 read:

6,310 total runs
119 total users
19 users in the last 30 days
991 rolling 30-day runs
98.8% rolling 30-day success
+679 runs and +19 users since the May 27 baseline

Actor:

https://apify.com/george.the.developer/telegram-channel-scraper

That is useful for people tracking:

crypto communities
product launches
public market chatter
competitor announcements
niche communities where the signal does not start on Google

Google News Monitor is the cleaner mainstream-news companion:

https://apify.com/george.the.developer/google-news-monitor

It is smaller than Telegram, but the June 13 read shows 100% rolling 30-day success and +11 users since the May 27 baseline.

Ecommerce and agency lane

The ecommerce lane is worth promoting because buyers understand the outcome quickly.

TikTok Shop Affiliate Sales Scraper:

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

Shopify DTC Brand Discovery:

https://apify.com/george.the.developer/shopify-dtc-brand-discovery

TikTok to Shopify Trending Products:

https://apify.com/george.the.developer/tiktok-to-shopify-trending-products

That lane is for operators looking for products, brands, and sales motion instead of generic scraped rows.

The agency lane is even more direct. HVAC Contractor Lead Finder is small but clean in this read:

+23 runs since the May 27 baseline
40 rolling 30-day runs
100% rolling 30-day success

Actor:

https://apify.com/george.the.developer/hvac-contractor-lead-finder

What I am not pushing as the main CTA

Some actors still show demand, but I would not make them the main promotion today.

Actor	Reason
Influencer Marketing Intel	87.7% rolling 30-day success
Lead Enrichment Pipeline	84.9% rolling 30-day success
US Tariff Lookup	86.7% rolling 30-day success
Obituary Life Insurance Leads	83.3% rolling 30-day success
AI Deepfake Detector	0% rolling 30-day success in this read
Web Content Extractor API	0% rolling 30-day success in this read
Amazon Product Data	0% rolling 30-day success in this read

Demand is a signal.

Reliability decides whether I promote it today or fix it first.

The June 13 promotion filter

The rule for today is simple:

lead with actors above 95% rolling 30-day success
use current Store movement as proof
package actors by buyer outcome, not by scraper category
do not send buyers into the fix-first lane

Main portfolio:

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