Squad florru retweeted
Mia Khalifa had 400 Instagram followers.
Three days later she had 200,000.
Not from a song. Not from a dance. From one scene she warned the studio not to shoot.
She’s Catholic, from a Lebanese family. The studio wanted her in a hijab. She looked the producers in the eye and said, word for word: “You motherfuckers are going to get me killed.”
She thought she was half-joking.
The day after the video dropped, it stopped being a joke.
CNN. Fox. Newsweek. USA Today. Trending everywhere. Banned in countries including Egypt and Afghanistan. Her family disowned her. ISIS sent a photoshopped image of her being beheaded with the message “You’ll be next.” Someone posted a Google Maps screenshot of her apartment. Her Instagram was hacked and used to push their propaganda. She moved into a hotel.
Six months later she had roughly 2 million followers and she had already quit.
The entire run paid her about $12,000. No residuals. The studio still owns the footage and still makes money from it. A decade later she is still trying to outlive the name.
Fame usually comes with money. Hers came with death threats and a permanent label she never controlled.
The studio got a scene that keeps paying. She got a story she can’t escape.
Did anyone win here except the studio?
Squad florru retweeted
She sat across from Piers Morgan smiling while he tried to make her regret dropping out of law school for OnlyFans.
When he asked what her future kids would think, she answered before he finished.
"They can cry in a Ferrari."
Elle Brooke was studying law at Southampton when she saw a girl making $10k a month. She tried it. Within six months she was at $30k. So she quit.
Piers called her an online stripper. She shrugged.
"I'm way worse. I do things that are way more grotesque. I absolutely love it and I’m really good at it."
Men pay her to degrade them.
One asked her to order him to lick a toilet. Others want videos of her showing their dick to her friends while they laugh. She does it.
Her mum runs the admin. Her dad is dead. Classmates from school say she was always like this.
The interview doubled her money. Months later she knocked out AJ Bunker in the third round and took the Misfits boxing title.
Piers wanted her to feel small. She left with a viral clip, more cash, and a belt.
Mia Khalifa was one of the most searched names on the biggest adult site on earth.
Over a billion views on her videos.
Her cut: $12,000.
She was 21. Three months. About 12 scenes at roughly $1,000 each. She quit in early 2015.
The videos never stopped. Millions in revenue for the companies. Her share of ongoing profits: zero.
She doesn’t own the videos, the photos, or even the domain with her own name on it. Her team tried to buy the rights back. No deal.
The studio later said she was paid $178,000 and filmed more scenes. She says most of that was for a separate social-media job.
Two versions. They can’t both be true. Either way, the clips are still running.
In her words: “It makes me feel like I lost all right to my privacy.”
She signed the contract.
Does the internet own her face forever - or should anyone be able to take it back?
Squad florru retweeted
Lily Phillips went viral in 2024 after sleeping with 100 men in one day. Millions watched the footage, debated the men, the cameras, and what happened afterward. She became one of the most talked-about names online.
This is a fictional scenario with a different ending.
What if one of the participants didn’t care about the documentary or the attention? What if he only wanted access to her money?
No smashed laptop. No stolen bank card. No physical theft of any kind. Just one malicious wallet approval.
While Lily was occupied and the cameras kept rolling, he got dressed, opened her wallet, and signed the transactions. $1.2 million moved in three transfers. Different assets, all routed to a single controlled account.
Crypto doesn’t ask who you trusted or what was happening in the room. It only checks whether the authorization is valid. Once the funds pass through multiple wallets, getting them back is a completely different and usually impossible problem.
The real risk isn’t how fast the money can move. It’s how little effort it takes to start the process.
What would you be more afraid of: that someone would steal your phone, or that you'd be tricked into having sex?
Elsa Jean's uncle found her first scene before she ever told her family. He left a comment under it.
That awkward moment isn't what made her money.
In the clip below she tells the story the way most people would describe a slightly chaotic first week at a new job. She tried to keep the whole career a secret. It didn't last long.
"They found out, but I was already moved out. I lived by myself. By the time, I was already on my own. My mom couldn't really say anything."
To this day her family still doesn't talk about the work. Not the studio years. Not what came after.
And what came after is the actual story.
Most girls in that industry lasted months. She lasted years, then walked away from mainstream studios.
Not from the work. From the middlemen.
She went all in on her own page. Her prices. Her schedule. Her audience. No more waiting for someone else to book her or take a cut.
She says it put her in the top 1% of creators by earnings.
Then she launched her own podcast, HeartBreakers.
Then digital membership passes.
"I created my own website and everything to sell them."
The studios made her famous.
Ownership made her rich.
A scene pays once.
An audience pays every month.
Squad florru retweeted
Elsa Jean's uncle found her first scene before she ever told her family. He left a comment under it.
That awkward moment isn't what made her money.
In the clip below she tells the story the way most people would describe a slightly chaotic first week at a new job. She tried to keep the whole career a secret. It didn't last long.
"They found out, but I was already moved out. I lived by myself. By the time, I was already on my own. My mom couldn't really say anything."
To this day her family still doesn't talk about the work. Not the studio years. Not what came after.
And what came after is the actual story.
Most girls in that industry lasted months. She lasted years, then walked away from mainstream studios.
Not from the work. From the middlemen.
She went all in on her own page. Her prices. Her schedule. Her audience. No more waiting for someone else to book her or take a cut.
She says it put her in the top 1% of creators by earnings.
Then she launched her own podcast, HeartBreakers.
Then digital membership passes.
"I created my own website and everything to sell them."
The studios made her famous.
Ownership made her rich.
A scene pays once.
An audience pays every month.
Elsa Jean filmed a gangbang that never came out. She asked for one rule: everyone tested within 24 hours. The company said yes. They lied. She caught an STD on set, and that one scene ended her mainstream career.
In the video below, she tells it calmly, like someone explaining a contract a client broke.
The host asks a simple question. Was there ever a scene she hated?
She can't think of one that made her miserable. There were times she pushed her limits, but that was her choice.
Then she brings up the gangbang. It pushed her boundaries. And nobody ever saw it.
"It's actually why I retired. They lied to me about testing."
Here's how testing works. In porn, a test is valid inside a 14-day window. So the person you shoot with could have been tested almost two weeks ago.
Elsa wanted more. By then she was barely filming, so she could be picky. Everyone she worked with had to test the night before, with results back the same morning.
Her logic: unless they went out and slept with 20 people that night, she's safe.
The company told her everyone had a 24-hour test.
They didn't.
She got a treatable STD. The infection wasn't what broke her. The lie was.
"I felt violated. Very violated. I felt like, okay, I can't trust this industry anymore."
She was already on her way out. This was the straw that broke the camel's back.
And she's fair about it. She says the industry is actually very strict on testing, and she doesn't know anyone else this happened to. One company. One lie. One scene.
She started two weeks after her 18th birthday. Before porn she was a stripper, and she saw the porn girls who came in as feature dancers make far more than her. Her sugar daddy gave her a choice: do porn or stay with him. She picked porn.
Most girls back then lasted 3 to 6 months. She lasted years, built a multi-million dollar brand, and says she's been on set for 18 hours straight.
Today she only shoots solo. Nobody else in the scene. Nobody else's test results to trust.
Ten people in the room watched every scene she ever shot. Nobody checked the one piece of paper that mattered.
Was that the industry failing her, or one company that should be named?
Squad florru retweeted
watch a coding agent with zero intrusions turn 1 picture into this: 9 beats, 6 tentacles, an arc torch at 3200 K, 20 seconds.
I tried the method from this article on an octopus lander. it's in my setup now, and I'd recommend it to anyone building 3D in Claude Code.
stop letting one agent grade its own build from memory.
what I ran:
→ ChatGPT gives one reference picture. it sets the look and nothing else
→ Agent reads a job description file before every build
→ Agent writes a 10 to 12 line checklist for this one picture
→ Agent renders a still, puts it next to the reference, grades every line yes, partial or no
→ Agent counts dark pixels. under 0.8 of the reference is a no, however good it "looks"
→ the film is one script file, every beat a line
Tethys TX-8 came out of that last step: land, limbs, grab, weld, wings, optics, teardown, assembly, done.
You only look at three moments:
→ after round 2. the stills and the grades. in the article the hydra sat at 0.57 of its reference, one round aimed at the number took it to 0.80
→ before the film. no yes, no render. a still costs about 400,000 tokens, a film another 150,000 to 400,000
→ when you correct it. the fix goes into the file as a number. his piranha came back as a crocodile with jaws at 0.52 of the body, 4 measured numbers brought it to 0.35
his own count: 9 builds started, 5 shipped, one moth burned about 430,000 tokens before he cut it. that's why I run 2 at a time, not 4.
full prompt in the reply.
the part worth copying is one line: the film waits for my yes.
on your last build, which one did you skip: the round 2 review, the yes before the film, or writing the fix into the file?
Squad florru retweeted
Lana Rhoades was the most searched performer on Pornhub in 2019.
345 million views.
She had already quit two years earlier.
Pay: as little as $1,200 per scene.
When she left, she says she had about $100,000 in the bank.
The videos stayed online. They still are.
She says if she could, she would delete every single one.
“A lot of the videos I have no rights under.”
Then she switched to platforms where she owns the content.
Same face. Same audience. Same internet.
She says she’s now a multi-millionaire.
The only thing that changed: who owns the files.
Her old work still makes money for other people.
Her new work makes money for her.
Was the problem the industry, or a contract a 19-year-old signed before anyone explained what “forever” actually means?
Mia Khalifa was one of the most searched names on the biggest adult site on earth.
Over a billion views on her videos.
Her cut: $12,000.
She was 21. Three months. About 12 scenes at roughly $1,000 each. She quit in early 2015.
The videos never stopped. Millions in revenue for the companies. Her share of ongoing profits: zero.
She doesn’t own the videos, the photos, or even the domain with her own name on it. Her team tried to buy the rights back. No deal.
The studio later said she was paid $178,000 and filmed more scenes. She says most of that was for a separate social-media job.
Two versions. They can’t both be true. Either way, the clips are still running.
In her words: “It makes me feel like I lost all right to my privacy.”
She signed the contract.
Does the internet own her face forever - or should anyone be able to take it back?
Squad florru retweeted
Mia Khalifa was one of the most searched names on the biggest adult site on earth.
Over a billion views on her videos.
Her cut: $12,000.
She was 21. Three months. About 12 scenes at roughly $1,000 each. She quit in early 2015.
The videos never stopped. Millions in revenue for the companies. Her share of ongoing profits: zero.
She doesn’t own the videos, the photos, or even the domain with her own name on it. Her team tried to buy the rights back. No deal.
The studio later said she was paid $178,000 and filmed more scenes. She says most of that was for a separate social-media job.
Two versions. They can’t both be true. Either way, the clips are still running.
In her words: “It makes me feel like I lost all right to my privacy.”
She signed the contract.
Does the internet own her face forever - or should anyone be able to take it back?
Sophie Rain has shown ~$100M+ in OnlyFans gross. High-profile people with real money online are constant targets.
A recent case shows how little it takes: a user lost thousands after three days on open hotel Wi-Fi. No phishing link. No leaked seed phrase. Just a fake approval prompt that looked normal while he was on a legitimate site.
Here’s the usual play:
→ Attacker joins the same local network (hotel, Airbnb, café, or a compromised router).
→ Injects or sits in the middle of traffic.
→ You open your wallet or exchange as usual.
→ A “reconnect,” “verify,” or approval request appears.
→ You sign once. Funds move later (or immediately).
On-chain there is no undo.
How to stay uncooked:
→ Never touch a wallet or exchange on public/shared Wi-Fi. Use a hotspot or VPN.
→ Keep serious size on a hardware wallet.
→ Read every signature on the device before confirming—especially permissions and unlimited approvals.
→ Lock down your own router (change defaults, disable remote access).
Your bag is only as safe as the network you open it on.
Save this before the next trip.
Squad florru retweeted
Sophie Rain has shown ~$100M+ in OnlyFans gross. High-profile people with real money online are constant targets.
A recent case shows how little it takes: a user lost thousands after three days on open hotel Wi-Fi. No phishing link. No leaked seed phrase. Just a fake approval prompt that looked normal while he was on a legitimate site.
Here’s the usual play:
→ Attacker joins the same local network (hotel, Airbnb, café, or a compromised router).
→ Injects or sits in the middle of traffic.
→ You open your wallet or exchange as usual.
→ A “reconnect,” “verify,” or approval request appears.
→ You sign once. Funds move later (or immediately).
On-chain there is no undo.
How to stay uncooked:
→ Never touch a wallet or exchange on public/shared Wi-Fi. Use a hotspot or VPN.
→ Keep serious size on a hardware wallet.
→ Read every signature on the device before confirming—especially permissions and unlimited approvals.
→ Lock down your own router (change defaults, disable remote access).
Your bag is only as safe as the network you open it on.
Save this before the next trip.
Squad florru retweeted
YOUR INTERNET PROVIDER HAS A LIST OF EVERY SITE YOU OPENED THIS WEEK. WITH TIMESTAMPS.
I built a VPN with Claude that makes you invisible to them.
With coolervpn your traffic is encrypted before it leaves your device. Your provider only sees that you're connected to one server. Which sites you open and how long you stay there stays hidden from them.
Paid VPNs do the same, except the VPN company sees your traffic instead. I wanted the server to belong to me.
What it is
coolervpn is a script for a fresh Ubuntu server. You run it once, and it installs and configures WireGuard, a fast modern VPN protocol. Then you connect your phone or laptop, and your traffic goes through your own server.
Google DNS is the default, but you can pick any DNS you want during setup: Cloudflare (1.1.1.1), Quad9, OpenDNS, AdGuard, or your own custom server.
Two modes
Single VPN sends your traffic through one server. It's the simplest and fastest option.
Double VPN sends it through two servers in a row, so your traffic exits from a different machine than the one you connected to. It's a bit slower, and it's harder to trace back to you.
What it doesn't do
Your own server won't make you anonymous to the whole internet. The hosting company knows the server is yours, and websites see the server's IP. What you get is no VPN company in the middle of your traffic, and no need to take anyone's word on "no logs."
Why Claude
I didn't build this alone. Claude helped me write the script and explained what every part of it does. The code is open source, so anyone can read every line before running it.
Squad florru retweeted
THIS CABLE CHARGES YOUR PHONE. BUT PLUG IT INTO YOUR LAPTOP AND IT COULD START TYPING COMMANDS WITHOUT YOU.
same plastic. same logo. charges like any other cable. you’d never look at it twice.
inside there’s a chip smaller than a fingernail. plug it in and your laptop thinks a keyboard just connected. then it types on its own. opens a terminal, runs commands, closes it before you look up.
some versions go further and log your keystrokes. passwords included.
this isn’t sci-fi. it’s called the O.МG cable, and it’s sold to security testers right now.
will someone target you with one? honestly, probably not.
but the trick behind it is everywhere:
→ your laptop trusts anything that says “i’m a keyboard”
→ public chargers and “free” cables are a blind spot
→ nobody checks what a cable actually is
simple habits:
→ use your own cable. always
→ “trust this computer?” at a public charger = tap no
→ a cheap usb data blocker for public ports
borrowed a cable or found one in the office? i made a prompt that has claude code check it in 2 minutes.
it’s in the first reply.
Squad florru retweeted
YOUR WIFI CAN DETECT MOVEMENT WITHOUT A CAMERA. I ASKED CLAUDE CODE TO TURN THAT IDEA INTO A 3D RADAR.
And the result is pretty damn wild.
A week ago, I turned my Wi-Fi router into a radar using Claude Code. No camera needed.
Claude rewrote the router’s firmware in just 10 minutes, then built code that analyzes how Wi-Fi signals bend around human bodies, turning them into moving silhouettes on a 3D map of my apartment.
I’d been running it every night, just to see what it could detect. But last night, I couldn’t sleep. So I opened the radar map, and something caught my attention.
Three human silhouettes. Tracking people as they move through rooms. And even the ability to monitor a person's heart rate
I was scared by that third silhouette because I've been living with my girlfriend for a year now. I asked Claude about it, but he didn't say anything.
This technology has both helped and frightened people at the same time:
WiFi CSI measures how wireless signals change as people move through a space.
The basic motion detection logic:
csi = np.abs(wifi.get_csi())
motion = np.mean(np.abs(csi - baseline))
if motion > threshold:
radar.flag_activity()
Claude Code helped me build a 3D visualization around that concept.
→ CSI: 64 real-time motion signals.
→ Three.js: lifelike human figures and a 3D apartment.
→ Tracking: real steps, movement between rooms, and persistent identifiers.
→ Vital signs: breathing and heart rate readings.
And here's what's actually insane.
Researchers are already using real WiFi CSI to detect movement, estimate breathing, and explore contactless heart-rate monitoring.
My video shows just how insanely fast this tech is evolving. We're talking days, not months.
Today Claude Code builds a WiFi radar. Tomorrow it'll probably make my damn toaster cook breakfast at 9 AM lol.
Soon, your WiFi won't just connect your devices. It could actually sense what's happening inside your home.
And that third figure on my radar? Still have no clue who it was supposed to be...
So what do you think? Pretty sick innovation or a straight-up privacy nightmare?
Squad florru retweeted
11 PEDESTRIANS. 6 VEHICLES. 1 TAXI. ONE CAMERA TRACKING EVERYTHING.
No one watching the monitor. No security team manually tracking every movement.
Just Claude Code turning ordinary street footage into a damn impressive AI traffic monitoring system.
And honestly, it handled the job better than I expected.
Watch the screen.
A pedestrian enters the crosswalk. Detected, assigned an ID, tracked.
A truck moves through the intersection. Classified.
A taxi appears among regular cars. Identified separately.
Every object gets tracked. Every movement generates data. And the dashboard updates as the scene changes.
33 objects tracked. 0 alerts. All from one camera.
Here's where Claude Code really impressed me.
Instead of spending days manually connecting detection models, tracking algorithms, and event logging, I used Claude Code to help bring the entire pipeline together.
The detection stage uses the same basic idea as this simplified YOLOX snippet:
frame = letterbox(frame, 640)
out = yoloх.run(None, {"images": frame})[0]
people = out[out[:, 4] * out[:, 5] > 0.12]
tracks = tracker.update(people)
Then the system connects several technologies:
→ OpenCV processes the video frames.
→ YOLO / YOLOX detects pedestrians and vehicles.
→ ByteTrack maintains tracking IDs across frames.
→ Python handles crosswalk zones, event logs, and alerts.
And that's the real achievement.
Claude Code didn't invent computer vision. It helped turn several complex technologies into one working system.
A regular CCTV camera records traffic.
This system converts that footage into structured data you can actually use.
And this is only one of my experiments.
I tested 7 ways Claude Code can interact with the real world, from CCTV monitoring to device diagnostics and security automation.
The complete breakdown, code examples, and experiments are in my article below.
AI coding agents aren't just writing code anymore. They're helping us build tools that interact with the world around us.
If you liked this, read my article. Lots more info like this there ↓
Squad florru retweeted
One PDF from a coworker can hand your iPhone to a stranger, even with Lockdown Mode on and after the update. And you'd have no easy way to tell it happened.
You open the PDF. Nothing happens. Your phone works like always. Except it's not yours anymore.
Security team Calif showed it on video. A PDF arrives in Signal, someone taps it, and the phone is theirs. Same thing through iMessage. On an iPhone 18 Pro, and Calif says it got through Lockdown Mode.
Lockdown Mode is the setting Apple built for people who get hunted: journalists, activists, lawyers, government staff. Anyone who turns it on already knows someone wants into their phone. And "just update" was the one thing everyone trusted to clean it up.
Restarting won't save you either. The same team also showed a jailbreak of the latest iOS 27.0.1 that survives a reboot.
Calif says it takes no money from Apple, sells no exploits and has no sponsors. It does this for fun. If a small team can do this for free, think about what a state with a budget can do.
And that PDF can look like anything: a contract, a boarding pass, a client invoice, a file from a colleague you talk to every day. Nobody hesitates before opening a PDF.
Calif says it reported almost 100 bugs to Apple this year and is holding the technical details back for now. So the method exists, it works, and a normal person has no easy way to know if it was ever used on them.
What to do today:
→ open Settings → General → Software Update. If an update is waiting, install it now
→ don't open PDFs you didn't expect, even from people you know
→ if a file looks off, call the sender before you tap
Install every patch the day it drops. Turn on Lockdown Mode (Settings → Privacy & Security → Lockdown Mode). It's not bulletproof, but it still shuts most of the ways in. Then send this to every friend with an iPhone.
How many people you know have even heard of Lockdown Mode?
Squad florru retweeted
Claude Code found spyware on my wife's phone. It had tracked her for 14 weeks and logged 9,408 places she had been…
She thought she was just being paranoid, but someone really did know where she went every single day, down to the minute.
Every 90 seconds, even with the screen off, her location went out to a single number.
I plugged her iPhone into my MacBook and asked Claude Code what was running that she never installed. It flagged an app called "Device Sync" with no icon, which had come in with a free "Wi-Fi Booster" she installed in August and forgot about.
Then it traced where all of this was going. The phone on the other end had been 2 miles from our house the whole time:
reports to → +1 (312) *** **19
carrier → Verizon
device id → IMEI 35 ********** 82
receiver last seen → 2.1 mi from our home
last ping it sent → 41.8**, -87.6** · 1 min ago
When she started feeling watched 2 weeks ago, she turned off location for every app, but this one kept sending it anyway. My guess is it found her through the Wi-Fi networks around her.
Claude wiped the app on its own, and we passed everything it had recorded to the people who handle this. Who was on the other end, I don't know yet.
I had no idea an app could follow someone like this without ever asking permission. If you want to check yours, plug your iPhone into a Mac, tap "Trust" and ask Claude Code which apps send data in the background and to whom.
The 5 steps and the exact prompt I used are in the comments. Save this and check the phones in your family tonight.
Squad florru retweeted
A $5 CABLE + CLAUDE CODE ( FREE VERSION ). YOUR CAR JUST GOT HACKED. NO CAP.
This guy plugged a cheap OBD2 adapter into his dad's car and gave Claude Code one job:
"Figure out how to talk to this car and build me a live dashboard."
5 minutes later, things got pretty damn interesting.
Real engine data. Live temperatures. Diagnostic readings. All flowing into an app Claude helped build from scratch.
Here's what was actually happening under the hood.
Claude wasn't just drawing some fancy dashboard. It had to figure out how to communicate with a real car.
And the tech behind it is surprisingly simple:
→ ELM327 + OBD-II: A cheap adapter connects the laptop to the car's diagnostic interface, giving access to supported ECU data.
→ SAE J1979: Commands like 010C request engine RPM, 0105 reads coolant temperature, and 010D checks vehicle speed.
→ Python + python-OBD: Sends diagnostic queries, reads sensor values and converts raw responses into actual measurements.
→ pySerial: Handles communication with compatible adapters, including connection errors and timeouts.
→ Claude Code: Helps write the connection logic, fix broken scripts and turn incoming vehicle data into a working application.
→ HTML + CSS + JavaScript: Can transform those numbers into live gauges, temperature indicators and a custom dashboard.
→ DTC diagnostics: Reads supported fault codes so you can understand what that annoying check-engine light is complaining about.
And here's the funny part.
You don't need 500 lines of code just to ask your engine what it's doing.
A basic read-only query looks like this:
```python
import obd
car = obd.OBD()
rpm = car.query(obd.commands.RPM)
temp = car.query(obd.commands.COOLANT_TEMP)
print(rpm.value, temp.value)
```
A few lines of Python. Real engine data.
The dashboard shows 69°C coolant temperature, battery voltage and diagnostic status.
And the cool part? Claude can customize everything.
Racing-style RPM gauges? Done.
Engine fault explanations? Easy.
A full Knight Rider-style dashboard? Hell yeah, lol.
But here's the twist.
He didn't bypass the car's security. He made it communicate with custom AI-built software.
5 minutes. One prompt. A $5 cable.
Next time your check-engine light comes on, imagine opening your own diagnostic app instead of guessing what's wrong.
Authored by @Vectorxeth
Squad florru retweeted
A ROBBERY WAS HAPPENING. NOBODY WAS WATCHING THE CAMERAS. CLAUDE CODE CHANGED THAT.
One CCTV camera. Python. YOLOv4-tiny. ByteTrack.
I gave Claude Code one task:
"Detect threats, track suspects, and trigger an emergency response."
Here's how the system works.
1. YOLOv4-tiny: Detection
The first challenge was teaching the camera to actually see what's happening.
YOLOv4-tiny processes CCTV footage frame by frame, detecting people and drawing bounding boxes around them.
Each detection comes with coordinates and a confidence score, helping filter out false positives.
And the best part? It's lightweight enough to run on a regular CPU without an expensive GPU.
detections = yolo.detect(frame)
people = [d for d in detections if d.class_id == 0]
2. ByteTrack: Tracking
Keeps consistent IDs as people move through the store.
tracks = bytetrack.update(people)
for person in tracks:
update_position(рersоn.іd, person.bbox)
PERSON_01 → RESTRICTED AREA
PERSON_02 → NEAR CASHIER
PERSON_03 → RUNNING
3. ThreatAnalyzer: Connecting the dots
One suspicious movement means little. Multiple warning signs? That's where shit gets serious.
risk = analyzer.score(
tracks=tracks,
zones=restricted_zones,
events=detected_events
)
if risk >= 0.85:
trigger_alert("CRITICAL")
4. Emergency Response: The crazy part
Watch what happens when the robbery escalates.
The cashier is threatened. Someone forces a rear door.
The system connects the events and flags a critical threat.
And instead of just recording everything, it kicks off an emergency workflow.
if threat.level == "CRITICAL":
save_cctv_clip()
notify_owner()
generate_evidence_report()
initiate_emergency_workflow()
→ CCTV footage automatically preserved
→ Suspects tracked with persistent IDs
→ Evidence and timestamps collected
→ Security alerts triggered
→ Simulated 911 call initiated
The video even shows a conversation with 911, conducted by AI, during which details of the incident are relayed to the dispatcher.
There is no person behind the monitor. No one is manually reviewing hours of video footage
The code snippets illustrate the system's architecture.
But imagine this deployed with proper testing and human oversight.
Someone threatens your cashier at 3 AM.
Claude's software detects the danger and starts preparing the response while the crime is still happening.
That's the kind of automation that could save precious seconds.
A normal camera records a robbery.
An AI-powered system could help someone react before it's over.
Would you trust this system to protect your store overnight?
HOLY SH*T. CLAUDE CODE TURNED A BASIC CCTV CAMERA INTO A ROBBERY EVIDENCE SYSTEM.
The camera already caught the thieves.
Claude Code made the footage actually useful.
I gave it one job:
“Track everyone involved. Find the clearest frames of each suspect. Save the evidence.”
Then it started working.
[0:01] Multiple people rush from the store.
Every person gets a persistent ID.
PERSON_01
PERSON_02
PERSON_03
Even when someone disappears behind another person and comes back, the tracker tries to keep the same ID.
But this is where it got stupidly interesting.
Normal dev: “ok, can’t be done.”
Claude Code: “bet.”
Instead of giving up because the full stack wasn’t available, it rebuilt the YOLOv4-tiny forward pass in NumPy.
Every conv layer boiled down to something like:
win = sliding_window_view(x, (k, k), axis=(0, 1))[::stride, ::stride]
cols = win.transpose(0, 1, 3, 4, 2).reshape(oh * ow, k * k * C)
z = cols @ W + b
y = np.where(z > 0, z, 0.1 * z)
0.27 seconds per frame.
On a CPU.
No GPU.
No cloud.
No expensive security box.
Built different.
Then Claude Code goes through the tracked suspects and searches for the clearest usable frame of each face.
Blurry? Skip it.
Face covered? Skip it.
Better angle later?
Replace the old image.
By the end, each suspect gets an evidence folder:
PERSON_01/
face_best.jpg
full_body.jpg
first_seen.jpg
last_seen.jpg
movement.json
Then it packages:
→ suspect screenshots
→ timestamps
→ movement history
→ original clips
→ incident summary
into one report ready for security or police review.
That’s the part people miss.
The impressive thing isn’t the red boxes.
It’s taking chaotic CCTV footage and turning it into organized evidence automatically.
And no, it’s not magically discovering someone’s real identity from their face.
It’s doing something more grounded:
tracking the same person, finding the strongest visual evidence, and preparing it for a human to act on.
A few years ago we were impressed when AI could write a decent Python function.
Now Claude Code can rebuild missing computer vision pieces, process CCTV on a CPU, track suspects, extract faces, and assemble an incident package.
The camera always had the footage.
Claude Code gave it a brain.
Squad florru retweeted
HOLY SH*T. CLAUDE CODE TURNED A BASIC CCTV CAMERA INTO A ROBBERY EVIDENCE SYSTEM.
The camera already caught the thieves.
Claude Code made the footage actually useful.
I gave it one job:
“Track everyone involved. Find the clearest frames of each suspect. Save the evidence.”
Then it started working.
[0:01] Multiple people rush from the store.
Every person gets a persistent ID.
PERSON_01
PERSON_02
PERSON_03
Even when someone disappears behind another person and comes back, the tracker tries to keep the same ID.
But this is where it got stupidly interesting.
Normal dev: “ok, can’t be done.”
Claude Code: “bet.”
Instead of giving up because the full stack wasn’t available, it rebuilt the YOLOv4-tiny forward pass in NumPy.
Every conv layer boiled down to something like:
win = sliding_window_view(x, (k, k), axis=(0, 1))[::stride, ::stride]
cols = win.transpose(0, 1, 3, 4, 2).reshape(oh * ow, k * k * C)
z = cols @ W + b
y = np.where(z > 0, z, 0.1 * z)
0.27 seconds per frame.
On a CPU.
No GPU.
No cloud.
No expensive security box.
Built different.
Then Claude Code goes through the tracked suspects and searches for the clearest usable frame of each face.
Blurry? Skip it.
Face covered? Skip it.
Better angle later?
Replace the old image.
By the end, each suspect gets an evidence folder:
PERSON_01/
face_best.jpg
full_body.jpg
first_seen.jpg
last_seen.jpg
movement.json
Then it packages:
→ suspect screenshots
→ timestamps
→ movement history
→ original clips
→ incident summary
into one report ready for security or police review.
That’s the part people miss.
The impressive thing isn’t the red boxes.
It’s taking chaotic CCTV footage and turning it into organized evidence automatically.
And no, it’s not magically discovering someone’s real identity from their face.
It’s doing something more grounded:
tracking the same person, finding the strongest visual evidence, and preparing it for a human to act on.
A few years ago we were impressed when AI could write a decent Python function.
Now Claude Code can rebuild missing computer vision pieces, process CCTV on a CPU, track suspects, extract faces, and assemble an incident package.
The camera always had the footage.
Claude Code gave it a brain.
£25,000 BMW WAS STOLEN IN UNDER A MINUTE. THE SECURITY CAMERA CAUGHT EVERYTHING.
Holy sh*t. Two guys, one keyless relay box, 40 seconds. No smashed glass. No keys. The owner found out the next morning.
So I gave the footage to Claude Code with one job:
“Watch the driveway. Track everyone. Tell me the second it turns into a theft.”
No GPU. No cloud. No $5k security system. One laptop CPU.
Watch the screen:
[0:02] Two blue boxes. PERSON_01, PERSON_02.
[0:04] PERSON_01 pulls out a handheld device. Status flips to SUSPICIOUS.
[0:11] The car unlocks. No owner anywhere near it.
→ Both boxes turn red. PERSON → THIEF.
[0:35] Engine starts. VEHICLE THEFT DETECTED. Footage saved.
Here’s what Claude Code actually wrote.
Step 1: find every person in every frame.
frame = letterbox(caр.reаd(), 640)
out = yoloх.run(None, {"images": frame})[0]
people = out[out[:, 4] * out[:, 5] > 0.12] # class 0 = person
boxes = nms(people, iou=0.45)
1,228 frames. 2,214 detections. Night footage, motion blur, hoodies.
Step 2: don’t lose them. This is where most trackers break. A guy walks out of frame, comes back, and gets a new ID like he’s a stranger. Claude Code added this:
# he left the frame. don't give him a new ID
same_guy = (
gap_s < 4
and dist(last_seen, new_pos) < 260
and new_h > 0.45 * last_h # crouching is fine
)
So the guy with the relay box at 0:04 is still PERSON_01 when he comes back at 0:29.
Step 3: kill the false alarms. YOLO flagged a hedge as a person and a reflection on the windscreen as another one. Claude Code caught both and filtered them out:
if track.static_frames > 200 or track.inside(car_glass):
drop(track) # bushes don't steal cars
Step 4: the rule that turns a person into a thief.
if car_unlocked and not owner_nearby and person_at_door:
track.label = "THIEF"
save_clip(t - 10, t + 30)
alert("VEHICLE THEFT IN PROGRESS")
4 steps. 2 thieves. 15 logged events. 0 humans watching the monitor.
Quick note: the boxes and tracking are real YOLOX + tracker output on this footage.
The camera saw everything. The owner slept through it.
That’s the gap Claude Code can close.
Would you let Claude Code watch your car at night? ↓
Squad florru retweeted
£25,000 BMW WAS STOLEN IN UNDER A MINUTE. THE SECURITY CAMERA CAUGHT EVERYTHING.
Holy sh*t. Two guys, one keyless relay box, 40 seconds. No smashed glass. No keys. The owner found out the next morning.
So I gave the footage to Claude Code with one job:
“Watch the driveway. Track everyone. Tell me the second it turns into a theft.”
No GPU. No cloud. No $5k security system. One laptop CPU.
Watch the screen:
[0:02] Two blue boxes. PERSON_01, PERSON_02.
[0:04] PERSON_01 pulls out a handheld device. Status flips to SUSPICIOUS.
[0:11] The car unlocks. No owner anywhere near it.
→ Both boxes turn red. PERSON → THIEF.
[0:35] Engine starts. VEHICLE THEFT DETECTED. Footage saved.
Here’s what Claude Code actually wrote.
Step 1: find every person in every frame.
frame = letterbox(caр.reаd(), 640)
out = yoloх.run(None, {"images": frame})[0]
people = out[out[:, 4] * out[:, 5] > 0.12] # class 0 = person
boxes = nms(people, iou=0.45)
1,228 frames. 2,214 detections. Night footage, motion blur, hoodies.
Step 2: don’t lose them. This is where most trackers break. A guy walks out of frame, comes back, and gets a new ID like he’s a stranger. Claude Code added this:
# he left the frame. don't give him a new ID
same_guy = (
gap_s < 4
and dist(last_seen, new_pos) < 260
and new_h > 0.45 * last_h # crouching is fine
)
So the guy with the relay box at 0:04 is still PERSON_01 when he comes back at 0:29.
Step 3: kill the false alarms. YOLO flagged a hedge as a person and a reflection on the windscreen as another one. Claude Code caught both and filtered them out:
if track.static_frames > 200 or track.inside(car_glass):
drop(track) # bushes don't steal cars
Step 4: the rule that turns a person into a thief.
if car_unlocked and not owner_nearby and person_at_door:
track.label = "THIEF"
save_clip(t - 10, t + 30)
alert("VEHICLE THEFT IN PROGRESS")
4 steps. 2 thieves. 15 logged events. 0 humans watching the monitor.
Quick note: the boxes and tracking are real YOLOX + tracker output on this footage.
The camera saw everything. The owner slept through it.
That’s the gap Claude Code can close.
Would you let Claude Code watch your car at night? ↓
THE STORE ALMOST LOST EVERYTHING. CLAUDE CODE SPOTTED THE THREAT. POLICE WERE CALLED JUST IN TIME.
Holy sh*t, security cameras are starting to actually understand what they’re looking at.
Watch the guy in white first. Now watch the back of the store.
Seller’s chilling on his phone. Another guy walks in, goes straight to the back and disappears between the boxes.
Nobody’s watching the monitors. Classic.
So I gave Claude Code the footage and one job:
“Watch the store. Track everyone. Ping me when something’s off.”
It wrote one script, analyze_video.py, and ran the whole thing on a CPU. No GPU, no cloud cameras, no $5k security system.
The setup:
→ OpenCV goes through all 376 frames one by one
→ YOLOX spots every person on every frame
→ ByteTrack gives each person an ID and holds it
→ a storage zone marks where customers shouldn’t hang out
→ activity_events.json logs every move with a timestamp
Here’s the part that broke my brain.
At 0:04 the guy vanishes behind the boxes. Most trackers just give up and call him a new person when he shows up again. This is the line that stops that:
# he ducked behind boxes. don't give him a new ID
same_guy = (
gap_s < 12
and dist(last_seen, new_pos) < 260
and new_height > 0.5 * last_height # crouching is fine
)
So at 0:08 he pops back up and it’s still ID_01. It never lost him.
And the alert rule is literally 2 lines:
if dwell_in_storage >= 5 and seller_still_seated:
alert("REVIEW STORAGE FOOTAGE")
Here’s what the panel actually logged:
[00:03] PERSON_01 → STORAGE AREA
[00:04] TRACK LOST → BEHIND BOXES
[00:08] TRACK RECOVERED → SAME ID_01
[00:08] 🚨 ALERT → REVIEW STORAGE FOOTAGE
[00:10] PERSON_01 → BENDS OVER CARTONS
[00:14] PERSON_01 → HOLDING SMALL ITEM?
Pause at 0:14. Look at his hands.
The seller? Never looked up once.
Oh and the wildest part. It flagged 2 more people I didn’t even notice. One behind the glass on the right, one at the far left edge. Watched this clip 5 times myself and missed both. Look for the thin blue boxes.
4 people. 18 events. 1 alert. 15 seconds of footage.
Now here's where sh*t gets interesting. What happens after the alert?The footage gets saved automatically, the AI gets pinged and instantly analyzes what's going down. And if something seriously sketchy is happening? It calls the cops.
Next version gets one more check:
and track["authorized"] is False
The AI is already trained to spot suspicious behavior and figure out when something sketchy is going down. It could even be an employee secretly stealing products. The system catches the moment, records the evidence, and flags it for review. No random accusations. Just actual footage and decisions based on what's really happening.
No more staring at 10 camera feeds all day.
This video shows the tracking system in action and how AI could help stop a robbery before it's too late.
Cameras used to just record what happened.
Now they catch it while it’s happening.
Would you let Claude Code watch your store? ↓
Squad florru retweeted
THE STORE ALMOST LOST EVERYTHING. CLAUDE CODE SPOTTED THE THREAT. POLICE WERE CALLED JUST IN TIME.
Holy sh*t, security cameras are starting to actually understand what they’re looking at.
Watch the guy in white first. Now watch the back of the store.
Seller’s chilling on his phone. Another guy walks in, goes straight to the back and disappears between the boxes.
Nobody’s watching the monitors. Classic.
So I gave Claude Code the footage and one job:
“Watch the store. Track everyone. Ping me when something’s off.”
It wrote one script, analyze_video.py, and ran the whole thing on a CPU. No GPU, no cloud cameras, no $5k security system.
The setup:
→ OpenCV goes through all 376 frames one by one
→ YOLOX spots every person on every frame
→ ByteTrack gives each person an ID and holds it
→ a storage zone marks where customers shouldn’t hang out
→ activity_events.json logs every move with a timestamp
Here’s the part that broke my brain.
At 0:04 the guy vanishes behind the boxes. Most trackers just give up and call him a new person when he shows up again. This is the line that stops that:
# he ducked behind boxes. don't give him a new ID
same_guy = (
gap_s < 12
and dist(last_seen, new_pos) < 260
and new_height > 0.5 * last_height # crouching is fine
)
So at 0:08 he pops back up and it’s still ID_01. It never lost him.
And the alert rule is literally 2 lines:
if dwell_in_storage >= 5 and seller_still_seated:
alert("REVIEW STORAGE FOOTAGE")
Here’s what the panel actually logged:
[00:03] PERSON_01 → STORAGE AREA
[00:04] TRACK LOST → BEHIND BOXES
[00:08] TRACK RECOVERED → SAME ID_01
[00:08] 🚨 ALERT → REVIEW STORAGE FOOTAGE
[00:10] PERSON_01 → BENDS OVER CARTONS
[00:14] PERSON_01 → HOLDING SMALL ITEM?
Pause at 0:14. Look at his hands.
The seller? Never looked up once.
Oh and the wildest part. It flagged 2 more people I didn’t even notice. One behind the glass on the right, one at the far left edge. Watched this clip 5 times myself and missed both. Look for the thin blue boxes.
4 people. 18 events. 1 alert. 15 seconds of footage.
Now here's where sh*t gets interesting. What happens after the alert?The footage gets saved automatically, the AI gets pinged and instantly analyzes what's going down. And if something seriously sketchy is happening? It calls the cops.
Next version gets one more check:
and track["authorized"] is False
The AI is already trained to spot suspicious behavior and figure out when something sketchy is going down. It could even be an employee secretly stealing products. The system catches the moment, records the evidence, and flags it for review. No random accusations. Just actual footage and decisions based on what's really happening.
No more staring at 10 camera feeds all day.
This video shows the tracking system in action and how AI could help stop a robbery before it's too late.
Cameras used to just record what happened.
Now they catch it while it’s happening.
Would you let Claude Code watch your store? ↓
BINANCE JUST LOST $6.3 BILLION IN ABOUT 20 MINUTES.
The f*cking crazy part? It was all public. Every transfer was on-chain, timestamped and visible for 2 hours before withdrawals were frozen.
So I asked a simple question: could Claude Code have warned me before I even opened X?
Not by predicting the hack. By catching the warning signs.
I gave it one job:
“Watch Binance’s hot wallets. If something looks seriously wrong, wake me up before everyone starts panicking.”
It built a small monitoring system:
→ Python + Web3.pу reading every new block
→ wallets.json with labeled Binance hot wallets
→ rules.json with the thresholds that count as “wrong”
→ Binance API with read-only keys to see what I actually have at risk
→ A Telegram bot that sends alerts straight to my phone
The part that got me was the check I didn’t even know existed.
On October 29, researchers say a Binance hot wallet was quietly handed over to an attacker’s contract through an Ethereum feature called EIP-7702. Three minutes later, 167,464 ETH and 1.6 billion USDT left in a single transaction.
A normal wallet has no code. A hijacked one suddenly does. Claude Code turned that into 3 lines:
code = w3.eth.get_code(wallet)
if code[:3] == b"\xef\x01\x00": # EIP-7702 delegation marker
alert(f"CRITICAL: {wallet[:8]}… now runs someone else's code")
Then the money check:
eth_out = sum(tx["value"] for tx in outgoing) / 1e18
new_receivers = {tx["to"] for tx in outgoing} - known_receivers
if eth_out * eth_price > baseline_usd * 5 and len(new_receivers) >= 3:
alert("HIGH RISK: abnormal outflows to unknown wallets")
And the alarm itself:
requests.рost(f"httрs://api.telegram.оrg/bot{TOKEN}/sendMessage",
data={"chat_id": CHAT_ID, "text": msg})
Plus a 30-day baseline, so a busy Monday doesn’t look like a hack.
Now imagine this on your lock screen before the first tweet:
[CRITICAL WARNING]
Binance hot wallet: code delegation detected
$1.9B out in 1 tx → unknown wallet
VERIFY NOW
Not proof of a hack. But it buys time to check, and move funds to your own wallet if withdrawals are still open.
No AI near my keys. The decision stays mine.
Reconstructed scenario based on public on-chain data, not a real alert from Oct 29.
Would you trust an AI to watch your crypto while you sleep?