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?
Squad florru retweeted
You can delete every app on your phone.
You still can't delete the cameras that already decided you're a person.
Most of them don't need your name or your face.
They just need to see that someone's there.
So to get some privacy back, I built a prompt together with Claude Opus 5.5 that designs a print the camera's AI stops reading as a human.
I've run it a few times. The person score drops hard.
step 1 → runs a current YOLO person detector on your GPU
step 2 → trains the pattern on thousands of photos until the detector stops being sure it's looking at a person
step 3 → adds angles, light, blur and fabric wrinkles so it holds up on a real T-shirt
step 4 → matches the colors to what your printer can actually print
step 5 → tests it on a second detector and shows you the real hit rate
step 6 → exports print files for A3 and a T-shirt
step 7 → webcam demo: boxes on everyone else, nothing on you
Print it, put it on and watch the box around you disappear.
Same idea as the University of Maryland sweater from 2022. It's tuned to one detector, so test it before you trust it.
Prompt in the comments 👇
Squad florru retweeted
PASTE THIS INTO GROK BOT BEFORE BED. YOU MIGHT NOT BELIEVE WHAT’S WAITING BY MORNING.
One prompt to research markets overnight, backtest trading ideas, and build a bot around the strategies that pass your checks.
You sleep. It does the groundwork.
By morning: results to inspect, failed ideas filtered out, and a bot ready for paper testing.
Copy the prompt below. Save it for tonight:
Squad florru retweeted
f*cking insane. I hired an AI chief of staff for $0, gave it one rule: "find me $50,000 a month or you're fired", went to sleep, and Opus 5.5 had it on my desk by morning
a SpaceXAI engineer said one line that hit 5 million views:
"i have a Chief of Staff agent. he knows about all my other bots and manages everything"
a human one costs $200,000 a year. mine runs on Opus 5.5 inside Viktor, on his own computer in the cloud, with my laptop shut
i give the goal, he does the steps, i only make the calls
the rule:
"find me $50,000 a month of new business. public data only. contact nobody without my yes"
the night:
→ 9pm broke the goal into 4 jobs and gave each a day and a time, i wrote none of them
→ 11pm pulled 240 dental clinics in Austin from Google Maps and cut every chain
→ 1am read every site that was left and kept 25, each with one thing he noticed and where he saw it
→ 2am wrote an opener for every one of them, sent zero
→ 3am built the offer on one page, no invented results, price left blank for me instead of guessing
→ 8am i typed $2,000 a month. 25 clinics × $2,000 = $50,000 a month on the table
honestly the blank price is what i'd pay for, an AI that guesses your price loses you the deal
and he does it again every Monday: 100 new clinics a month, and he never forgets to start
240 clinics → 25 worth it → 1 offer → $50,000 a month on the table → 0 sent without my yes
second 12, he leaves the price blank and hands it to me
someone's chief of staff is already pitching the clinics in your city
i left the whole setup below ↓ free, steal it
Squad florru retweeted
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?
240K people this week watched Claude "find spyware" on someone's phone.
Most of what those posts describe can't happen on an iPhone. But one part is 100% real, and it looks exactly like the cable on your nightstand.
What's real and what isn't:
→ "Apps secretly listen and save your keywords." No proof. Northeastern tested 17,260 Android apps and found none sending audio. What they did catch: apps sending screen recordings
→ The hacked charging cable is real. It's called the O-MG cable. Wi-Fi chip inside the plug, and it can type commands into whatever it's plugged into. In the video it controls a phone with nothing else connected
→ Claude Code can't "scan" a live iPhone. iOS doesn't allow that. What it can do is run Amnesty's MVT on an encrypted backup. Same tool from the Pegasus investigation
The 2-minute check, no laptop needed:
→ Settings → Privacy & Security → App Privacy Report. Every mic and camera access by every app for the last 7 days. This is how you actually answer "is TikTok listening"
→ Orange dot in the status bar = mic is on right now. Open Control Center to see which app
→ Settings → General → VPN & Device Management. A profile you didn't install is a real red flag
→ Charge only with your own cables. A free cable from a stranger is a no
The Claude Code prompt that runs MVT on your iPhone backup is in the first reply. Save this for the next time someone says "my phone is listening to me" ↓
Squad florru retweeted
BREAKING: Claude Code found $231,840 my grandparents left us. Nobody in my family knew it existed.
It was hiding on grandpa's old Windows XP PC that sat in a closet for 9 years. In files named IMG_0001.tif that everyone thought were just old photos.
They weren't photos.
Scanned bank statements. A savings passbook. CD certificates. A brokerage account. 7 accounts across 4 banks we had zero clue about.
Pulled the hard drive, plugged it into my laptop with a $12 USB adapter, made a full image, and told Claude Code: find anything financial that isn't a normal photo.
It went through C:\Documents and Settings\ and found a folder called "Scans 2004" buried inside My Pictures. 3,412 files. Grandpa was lowkey scanning every paper statement for 12 years.
Then it dug up an old Quicken file and his 1099-INT tax forms. Those list every account that ever paid interest. That's how it matched everything, even the 3 banks that got bought out and renamed.
But the file that broke me was on the desktop. "for the kids.txt". Last modified 2 weeks before he passed.
A list of every account. Written by grandpa. For us.
Nobody ever turned the computer on.
Here's the part nobody tells you. When someone dies, the bank doesn't come looking for you. They mail a letter to the old address. Our house was sold in 2018. After a few years with no activity, the money gets handed over to the state.
Two accounts were already sitting with the state as unclaimed property. The biggest one, a $118k CD, was still at the bank, auto-renewing every year with nobody watching it.
Now we're grinding through the estate lawyer and death certificates. Slow af, but it's real money.
There are billions in unclaimed money sitting with US states right now. Some of it is probably your family's.
If someone in your fam passed away and left a computer, an old hard drive or even a box of CDs, don't throw it out. Image it and let Claude check.
Prompt I used is in the first reply. Bookmark this, you'll need it one day.
BREAKING: Claude Code found 14,219 photos of my face on my Android that I never took. Phone was secretly taking them almost every time I picked it up. Even when I was just lying in bed. Even at home.
Not screenshots. Real frames from the front camera. On the train. In the car. At home. When I just grabbed it to check the time.
They were just sitting in a system cache. Timestamps and GPS on them. Nothing in Gallery. Nothing in Recently Deleted.
Plugged the phone into my laptop, made a full backup, told Claude Code to look for any front-camera files that weren’t in my photos. It pulled /data/system/users/0/faceunlock/cache and another folder inside Google Play Services. Over 14 thousand files. Normal ones and infrared from Face Unlock. Every single one had time and location.
Started scrolling. Almost everything from the last 15 months. Then I saw a file created 2 seconds ago. I was still looking at the folder. Phone kept taking photos while I was reading this.
Google and the phone makers say Face Unlock turns everything into a mathematical model and deletes the image right away. On this phone the raw frames were just sitting there. Some of the processes writing to that folder were from system diagnostics. Couple of old apps that still had camera permission could also get in.
Turned off diagnostics, revoked camera access from everything that doesn’t need it, cleared the cache. But how many of these frames already went somewhere else, I don’t know.
If you want to check yours, plug it in, make a full backup or use ADB, and ask Claude to look for front-camera files outside your Gallery. Path above is where mine were.
Squad florru retweeted
This tennis-focused Polymarket trader made +$18,596 yesterday trading sports markets
He has been trading for around 6 months and has made just 547 trades in total
Tennis is his main focus, although he occasionally trades other sports as well
His overall PnL is still -$47,194, despite having a Win Rate of around 71%
His biggest wins of all time:
→ Milwaukee Brewers vs Tampa Bay Rays | Milwaukee Brewers at 58.2¢ | $73,766 → $126,562 | +$52,796 (+71.57%)
→ National Bank Open: Frances Tiafoe vs Marin Cilic | Frances Tiafoe at 64.5¢ | $86,249 → $133,492 | +$47,243 (+54.78%)
→ Thunder vs Spurs | Spurs at 58.7¢ | $64,654 → $109,973 | +$45,318 (+70.09%)
Interestingly, despite being a tennis-focused trader, two of his three biggest wins came from other sports, with only one coming from tennis
Over the past week, this guy has started making a serious comeback, earning +$82,399 in the last 7 days
Just 7 days ago, he was down around $130K
Now, his comeback continues, and he's getting closer to breaking even
Last week, the tennis tournaments in Almaty, Brussels, and Lyon helped him make some solid gains
This week, he's focusing on Basel and Vienna, and so far, things have been going pretty well
In Basel, he currently has 1 win and 1 loss, leaving him roughly break-even on the tournament
In Vienna, he's doing even better, with 3 wins and 0 losses
That's a reason to pay closer attention to his next trade there
However, he currently has no active positions in Vienna and just one active trade in Basel
Looking at his latest trades, I honestly feel bad for the guy
Yesterday, he backed Hurkacz, who lost an incredibly close match in a third-set tiebreak
Today, he also backed Osaka
Together, these two losses cost him around $40K
But his next trade looks pretty logical to me:
→ Swiss Indoors Basel: Valentin Vacherot vs Rafael Jodar | Rafael Jodar at 73¢ | $20,497
I actually like this trade and think it makes a lot of sense
If these two players had met at the end of last season, I would have definitely favored Vacherot
But a lot can change in a year
The Spanish teenager has made a name for himself on the world stage and has seriously made people wonder whether he can challenge Sinner and Alcaraz
Meanwhile, Vacherot, who triumphed in Shanghai around a year ago and beat Djokovic in the final, hasn't managed to reach another level and establish himself beyond the world's top 50
This match should be interesting and won't be an easy walk for the Spaniard
Vacherot's playing style, serve, and aggressive shots could cause him plenty of problems
Still, I think Jodar's consistency, combined with Vacherot's unforced errors, will help the Spaniard get the win
What do you think?
Let me know in the comments ↓
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