I wasn’t trying to break the rules—I was just trying to survive.
A few months ago, I had $15 in my bank account, a toddler to take care of, and the gnawing feeling that I was running out of options. I live in a place where opportunities don’t come easy. Payments are hard to collect, tools and platforms are geo-restricted, and even when you have talent, the global market feels just out of reach.
But one thing I knew for sure? Social media is where the money is. Specifically, 𝕏 (formerly Twitter). Brands, creators, marketers—they all need to get their message out, and I saw an opportunity there.
The problem was, nobody had cracked the code for scaling outreach effectively.
That’s where my story begins.
I’m a developer by trade, so when the idea hit me, I spent days obsessing over it. What if there was a way to target leads on 𝕏 with absolute precision? What if you could find the exact people who’d want to buy what you’re selling and message them all in one go? No spam, no guessing—just a clean, focused strategy.
So I rolled up my sleeves, opened my laptop, and started building.
The process wasn’t glamorous—lots of trial and error, late nights, and caffeine-fueled coding sessions. I had no budget, no team, and no resources—just determination and a problem I was hell-bent on solving. After a few months of pushing myself to the limit, I finally had something: a tool that could scrape millions of profiles from 𝕏 and automate personalized outreach.
At first, I wasn’t sure if it would work. But when I tested it on a small campaign, the results blew my mind. In just days, I saw conversions that I’d never thought were possible. People started responding. Deals started closing. The tool worked.
I knew I had something big on my hands, so I decided to package it and sell licenses to marketers, agencies, and creators. I set the price high because I believed in the value it provided—$4,997 for a license. And to my surprise, people were willing to pay. Within weeks, I sold dozens of licenses.
And then the floodgates opened.
By the time summer hit, I’d sold hundreds of licenses and made over $500K. It wasn’t just a tool anymore—it was a movement. People started posting about how the tool helped them close deals, grow their businesses, and dominate their niches. For the first time in my life, I felt like I was winning.
But then came the storm.
In November, I woke up to an email from 𝕏’s legal team—a cease-and-desist letter. And it wasn’t just a slap on the wrist; it was a 13-page document outlining every way I’d violated their Terms of Service and federal laws. They accused me of unauthorized data scraping, breaking the Computer Fraud and Abuse Act, and other things I couldn’t even pronounce.
They even quoted my tweets, where I (foolishly) bragged about the tool’s capabilities:
- “Scraping 2M profiles a day with sniper precision.”
- “Sold $100K in licenses this month. Illegal? Maybe. Effective? Absolutely.”
I won’t lie—it shook me. I’d built something that was genuinely helping people, but it had also crossed some serious lines.
They banned my account, shut down my API access, and threatened me with further legal action if I didn’t stop immediately. It felt like the end of the road.
But here’s the thing: I don’t regret a second of it.
That cease-and-desist wasn’t just a threat—it was proof that I’d built something powerful enough to get the attention of one of the biggest companies in the world. It validated everything I’d been working toward.
Sure, I can’t share testimonials or public social proof because of legal restrictions, but the numbers speak for themselves: Hundreds of licenses sold, $500K in revenue, and countless businesses transformed.
Now, I’m pivoting. The tool as it was might be dead, but the vision isn’t. I’m working on new ways to help businesses grow, focusing on strategies that work within the rules but still deliver game-changing results.
The journey hasn’t been easy, but if there’s one thing I’ve learned, it’s this: Sometimes, the only way to make a real impact is by stepping outside the lines.
So, to anyone out there grinding, building, and hustling—don’t let fear hold you back. The world rewards boldness.
This isn’t the end of my story. It’s just the beginning.
UPDATE: 1/09/2025
Rolling out for a compliant version V2
Reach out - xleadscraper@tutamail.com for early access
17/11/2025
EVERYONE ASKS ME: “How did you scrape 2M profiles per day without getting banned immediately?”
Instead.
The right question is: “How did you architect a distributed system that made Twitter’s rate limiting completely irrelevant?”
Let me show you the technical infrastructure that made $500K possible (and what I’m doing differently now):
Most developers think Twitter rate limiting works like this:
“You get X requests per 15 minutes. Stay under that = safe.”
Completely wrong.
Twitter’s rate limiting is multi-dimensional:
DIMENSION 1: Per-endpoint limits
(obvious, everyone knows this)
DIMENSION 2: Per-IP-address scoring
(less obvious, some know)
DIMENSION 3: Behavioral anomaly detection
(almost nobody understands this)
DIMENSION 4: Network-wide pattern recognition
(this is what killed me)
Let me break down each layer and how I bypassed them (and why I eventually got caught).
LAYER 1: PER-ENDPOINT LIMITS
Twitter’s API gives you:
300 requests per 15 min for user lookups
900 requests per 15 min for search
100 requests per 15 min for timeline access
Most tools hit these limits in 3 minutes and then wait.
Amateurs.
Here’s what I did:
ENDPOINT ROTATION STRATEGY
Instead of hammering one endpoint, I built a request router that:
Distributed requests across 47 different API endpoints
Each endpoint has independent rate limits
System automatically switches between endpoints
Never maxes out any single rate limit bucket
Example:
Instead of using “user_lookup” 300 times
I used:
user_lookup (200 requests)
user_timeline (150 requests)
user_tweets (180 requests)
list_members (140 requests)
Then reconstructed the full profile from combined data.
Twitter sees 4 different usage patterns.
I get 670 profile lookups instead of 300.
Same data. Different architecture.
But that only gets you 2x improvement.
To get to 2M profiles per day, you need layer 2.
LAYER 2: IP DISTRIBUTION & TOKEN MANAGEMENT
Here’s what most devs don’t understand:
Twitter doesn’t just track per-token limits.
They track per-IP behavior patterns.
If one IP address is making 10,000 API calls per hour (even with valid tokens):
FLAGGED.
So I built what I called “The Hydra Architecture”:
DISTRIBUTED AUTH TOKEN POOL:
83 different Twitter developer accounts
Each account had 3-5 app tokens
Total: 247 independent authentication tokens
Each token could operate at max API limits
Tokens rotated through different IP addresses
IP ROTATION INFRASTRUCTURE:
Residential proxy network (not datacenter IPs)
1,200+ IP addresses across 40 countries
Each IP used for max 50 requests before rotation
Request timing randomized to look human
Geographic distribution matched organic Twitter usage patterns
LOAD BALANCING ALGORITHM:
Requests distributed across token + IP combinations
No single token ever approached rate limits
No single IP showed suspicious patterns
System looked like 247 different people using Twitter normally
This is how I went from 2,000 profiles per day to 2,000,000.
Not by “hacking the API”
But by making the system look like 247 legitimate users.
Twitter’s automated systems saw nothing suspicious.
Until layer 3 caught me.
LAYER 3: BEHAVIORAL ANOMALY DETECTION
Here’s where I fucked up.
I got the technical infrastructure perfect.
But I ignored human behavioral patterns.
Twitter’s ML models don’t just look at rate limits.
They analyze:
TEMPORAL PATTERNS:
When requests happen
How long between requests
Consistency of timing
INTERACTION PATTERNS:
What endpoints get used together
Natural sequences of API calls
Realistic user behavior flow
REQUEST DIVERSITY:
Are you looking at diverse profiles?
Or hammering specific niches?
Does your usage match organic patterns?
My system had perfect rate limiting.
But terrible behavioral mimicry.
Example of what normal Twitter usage looks like:
8:47 AM - check timeline
8:49 AM - read 3 tweets
8:52 AM - like one tweet
8:53 AM - check notifications
9:14 AM - post a tweet
9:47 AM - check timeline again
Human behavior is RANDOM and INEFFICIENT.
My system looked like this:
8:00:00 - user_lookup request
8:00:01 - user_lookup request
8:00:02 - user_lookup request
8:00:03 - user_lookup request
Perfect timing. Zero variation. Completely robotic.
Even though I was within rate limits.
Even though I was using residential IPs.
Even though I had 247 different tokens.
The PATTERN was obviously automated.
This is what I should have built:
BEHAVIORAL RANDOMIZATION LAYER:
Random delays between requests (300ms to 8 seconds)
Occasional “idle time” (2-15 minutes of no activity)
Mixed request types (don’t just do user_lookup repeatedly)
Realistic interaction sequences (timeline → profile → tweet → profile)
Time-of-day variation (heavier usage during working hours)
Geographic consistency (if IP is in Germany, use during German hours)
I built none of this in v1.
Because I was optimizing for SPEED.
Not STEALTH.
Big mistake.
Twitter’s behavioral models flagged me within 8 weeks.
But they didn’t remove me immediately.
Because of layer 4.
LAYER 4: NETWORK-WIDE PATTERN RECOGNITION
This is the layer that killed me.
And it’s the layer almost nobody understands.
Twitter doesn’t just analyze YOUR behavior.
They analyze the behavior of everyone YOUR ACCOUNTS interact with.
Here’s what happened:
I had 83 developer accounts scraping profiles.
Those accounts were connected to:
Hundreds of user accounts using my tool
All those users were DM’ing similar profiles
All those profiles were in similar niches
All those DMs had similar timing patterns
Twitter’s network analysis started seeing:
“Wait, these 400 accounts are all messaging the same 50,000 people within a 72 hour window?”
“And all 400 accounts seem connected to these 83 developer tokens?”
“And these 83 tokens are making similar API call patterns?”
They didn’t see one bad actor.
They saw an entire NETWORK of coordinated automation.
Game over.
This is what the cease-and-desist letter referenced:
“Large-scale coordinated inauthentic behavior”
“Network of accounts engaged in automated activity”
“Platform manipulation across multiple vectors”
They didn’t just remove ME.
They identified the entire network of users and shut down the infrastructure.
All 83 developer accounts: TERMINATED
All associated app tokens: REVOKED
My personal Twitter account: BANNED
30+ customer accounts: SUSPENDED
I didn’t just lose my business.
I burned 30+ paying customers who got caught in the blast radius.
That’s what kept me up at night.
Not the $500K.
But the people who trusted me and got banned because of MY architectural decisions.
HERE’S WHAT I’M DOING DIFFERENTLY IN V2:
The compliant version isn’t just “following rate limits better”
It’s architecturally different at every layer:
LAYER 1: OFFICIAL API USAGE ONLY
No endpoint tricks or workarounds
Stay well below published rate limits (50% max utilization)
Use OAuth properly (no fake developer accounts)
LAYER 2: SINGLE-USER ARCHITECTURE
Each license = one user’s authentication
No shared IP pools or proxy networks
No token sharing or rotation schemes
LAYER 3: HUMAN-PATTERN MIMICRY
Randomized timing with realistic delays
Mixed activity patterns (not just scraping)
Idle periods and natural usage rhythms
Time-zone aware behavior modeling
LAYER 4: NETWORK ISOLATION
Each user’s activity is completely independent
No coordinated targeting or timing
No shared infrastructure that could link users
Plausible deniability at network level
Is it slower? YES.
Can you scrape 2M profiles per day? NO.
But will it get you banned? ALSO NO.
The new architecture maxes out at:
~10,000 profiles per day per user
Across those 10K profiles, you can apply the same behavioral intent detection algorithms.
Same intelligence.
Same targeting precision.
Just sustainable.
HERE’S THE TECHNICAL TRADEOFF:
V1 Architecture:
Speed: 2M profiles/day
Intelligence: High (behavioral intent detection)
Risk: Guaranteed remove within 3-6 months
Cost: $500K revenue + cease-and-desist
V2 Architecture:
Speed: 10K profiles/day
Intelligence: High (same algorithms)
Risk: Near-zero (compliant by design)
Cost: Sustainable long-term
Most people think 10K vs 2M is a massive downgrade.
But here’s what they miss:
YOU DON’T NEED 2M PROFILES
You need 50-200 QUALIFIED LEADS per day.
If you’re doing proper behavioral intent detection:
10,000 profiles analyzed → 200 with high buying intent
That’s more than enough for any sales operation.
The problem with v1 wasn’t the speed.
It was that speed was a vanity metric.
Nobody can effectively follow up with 10,000 leads per day anyway.
I was optimizing for the wrong thing.
V2 optimizes for:
QUALIFIED LEADS PER DAY (not total profiles)
SUSTAINABLE OPERATIONS (not short-term revenue)
ZERO remove RISK (not maximum speed)
Completely different design philosophy.
FOR THE TECHNICAL PEOPLE:
Here’s the actual infrastructure stack for v2:
AUTHENTICATION LAYER:
OAuth 2.0 with PKCE (proper user auth)
Token refresh handled client-side
No server-side token storage or sharing
Each user authenticates their own Twitter account
RATE LIMIT MANAGEMENT:
Distributed rate limit tracking per-user
Automatic backoff when approaching limits
Request queuing with priority scoring
Smart caching to minimize redundant API calls
BEHAVIORAL ANALYSIS:
Client-side NLP processing (no data leaves user’s system)
Incremental profile building over 7-14 days
Intent scoring algorithms run locally
Privacy-preserving architecture
COMPLIANCE MONITORING:
Real-time TOS checking (I update when Twitter changes rules)
Automatic feature disabling if compliance risk detected
User education and warnings built into UI
Legal-reviewed documentation and usage guidelines
This isn’t just “following rate limits”
This is building compliance INTO THE ARCHITECTURE
Not bolting it on afterwards.
THE LESSON FOR DEVELOPERS:
Most devs building tools think:
“Build it fast → Get users → Fix compliance later”
This is backwards.
The right approach:
“Design for compliance → Build within constraints → Scale sustainably”
Compliance isn’t a BLOCKER.
It’s a DESIGN CONSTRAINT.
And constraints breed creativity.
V1 was fast because I ignored all constraints.
V2 is better because I designed around them.
The technical challenge isn’t “how do I bypass rate limits?”
It’s “how do I deliver maximum value WITHIN rate limits?”
Completely different problem.
Much harder to solve.
But the solution is defensible.
DEFENSIBLE = VALUABLE
V1 made $500K but died in 6 months.
V2 will make less per month but run for years.
What’s more valuable?
$500K one-time or $100K/year for 5 years?
I’ll take the $500K over 5 years.
Because I can actually keep it.
HERE’S WHAT MOST PEOPLE MISS:
The cease-and-desist wasn’t just legal risk.
It was REPUTATION RISK.
30+ customers got banned because of my tool.
How many of them will trust me again?
How many will recommend my new product?
Short-term revenue optimization destroyed long-term trust.
That’s the real cost.
Not the legal fees.
But the burned relationships.
V2 prioritizes:
CUSTOMER PROTECTION over maximum revenue
SUSTAINABLE OPERATIONS over fast growth
REPUTATION BUILDING over short-term wins
This is the boring, responsible approach.
And it’s the right one.
FOR PEOPLE BUILDING ON PLATFORMS:
You don’t own Twitter.
You don’t own Instagram.
You don’t own TikTok.
You’re renting space on someone else’s infrastructure.
Act accordingly.
The rules aren’t suggestions.
They’re THE PRICE OF ADMISSION.
If you can’t build profitably within the rules, you don’t have a business.
You have a ticking time bomb.
I learned this the expensive way.
You can learn it from my mistakes.
FINAL ARCHITECTURE INSIGHT:
The best systems aren’t the fastest.
They’re the most SUSTAINABLE.
V1 was a dragster: insanely fast, burns out quickly.
V2 is a Tesla: fast enough, runs forever.
Which would you rather drive across the country?
Same principle applies to software architecture.
Optimize for longevity.
Not just speed.
This is what I’m building now.
100 licenses.
Launching soon.
For people who understand:
Technical excellence isn’t about bypassing constraints.
It’s about maximizing value WITHIN constraints.
That’s the real engineering challenge.
And it’s a lot harder than just ignoring rate limits.
Questions: xleadscraper@tutamail.com
Vinay
P.S. For the people who think “compliant = weak”:
The original tool made $500K in 6 months.
Then died.
Net present value: $500K
A compliant tool making $10K/month for 5 years:
Net present value: $600K
Plus you get to sleep at night.
And keep your Twitter account.
And not have lawyers sending you scary letters.
Math is pretty simple.
P.P.S. The hardest part of rebuilding wasn’t the code.
It was admitting that “moving fast and breaking things” applied to OTHER PEOPLE’S THINGS.
Not PLATFORM RULES.
Big difference.
Learn it now or learn it expensively later.
Your choice.
