the model isn't the problem. your setup is | ai skills, agents, prompts
Washington
Joined September 2026
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Vector 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.
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.
Vector retweeted
this is f*cking insane
the most underrated way to give Claude Code a memory: a globe of plain text files around one map, and every answer comes back with the file it came from
zero apps, and Claude says not found instead of inventing a password
how it works:
→ one map file in the center, loaded before you type anything
→ three folders around it: notes, people, projects
→ every dot is one topic in one file, named after what's inside
→ memory and decisions stay pinned next to the map
paste this into Claude Code in an empty folder ↓
"Build a second brain here:
1. Folders: notes, people, projects.
2. Root files for memory, lessons and decisions.
3. One map file that says where each kind of info lives.
4. One topic per file, lowercase with hyphens.
5. Answer from the file the map points to and cite it. Not there? Say so.
Show me the plan first."
save this now, your globe grows with every file you add
Vector 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.
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.
Vector 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.
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.
PROMPT:
ROLE: You are a security analyst auditing my own computer.
TASK: A suspicious USB cable was plugged in. Check for malware and tampering.
RULES:
- Read-only. Change nothing without asking
- Detect my OS first
CHECK:
1. USB devices acting as keyboards
2. Terminal/PowerShell history, last 7 days
3. New startup items, services, scheduled tasks
4. New executables in Downloads, temp, AppData
5. Unsigned or hidden processes
6. Unknown network connections
7. Remote access tools
8. Antivirus and firewall status
OUTPUT:
- Verdict: clean / check / act now
- Table: finding | risk | fix
- What you can't detect
Vector retweeted
HOLY SH*T people are still manually wiring Claude into Obsidian as a second brain while the Obsidian CEO already open-sourced the entire setup plan
Here are 4 GitHub repos that do the heavy lifting for you:
→ obsidian skills (by kepano) github.com/kepano/obsidia… Teaches Claude the exact internal mechanics of your vault
→ claude obsidian (by AgriciDaniel) github.com/AgriciDaniel/c… Ready-to-use commands for wiki-links, ingestion, queries and linting
→ obsidian second brain (by eugeniughelbur) github.com/eugeniughelbur… 48 ready-to-run commands for Claude Code, Codex and Gemini
→ second brain starter (by coleam00) github.com/coleam00/secon… Interactive onboarding that builds a personalized 9-step setup plan
Most tutorials floating around still point to dead scripts and broken workflows.
Bookmark this before it disappears.
Vector retweeted
🚨I asked Claude Code to turn his WiFi router into a radar
In 40 minutes he wrote the code that reads how the WiFi signal bends around bodies and draws them as moving shapes on the map of his floor
He tested it at night and lives alone thought it was a bug so he went to the bedroom and the shape didn't move
The map showed it standing next to the bed looking at the door so he turned it off and still doesn't know what it was he was reading the code
Save it
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?
PROMPT:
You are helping me build a home WiFi sensing system ("WiFi Radar") that uses Channel State Information (CSI) to detect presence, motion, and breathing in MY OWN apartment, and visualizes it as a live 3D map in the browser. All occupants have consented; this is a personal research project.
## Hardware (assume this unless I say otherwise)
- 2-3x ESP32-S3 boards: one as transmitter (sends packets at ~100 Hz), one or two as CSI receivers placed in different rooms
- My laptop runs the backend and the web UI
- Alternative path to document: Raspberry Pi 4 + Nexmon CSI (only mention, don't build)
## Architecture
1. Firmware (ESP-IDF, C)
- TX node: broadcasts packets at fixed rate on a fixed channel
- RX node: enables CSI callback (esp_wifi_set_csi_rx_cb), streams per-packet CSI (64 subcarriers, amplitude + phase, RSSI, timestamp, MAC) over UDP or serial as compact binary/JSON
- Config via menuconfig: SSID, channel, packet rate, target IP
2. Signal processing backend (Python 3.11, FastAPI + WebSocket, numpy, scipy)
- Ingest CSI stream, parse, buffer in a ring buffer per receiver
- Preprocessing: amplitude extraction, remove null/pilot subcarriers, Hampel filter for outliers, phase sanitization (linear detrending), low-pass filter
- Calibration mode: record 60 s of empty-room baseline per receiver
- Motion detection: deviation from baseline + variance over sliding window, adaptive threshold, output motion score 0-1 per receiver
- Presence: static vs empty classification using baseline drift
- Breathing estimation (only when person is still): PCA across subcarriers, band-pass 0.1-0.5 Hz, FFT peak -> breaths/min, with a confidence value
- Heart rate: experimental only, band-pass 0.8-2.0 Hz, mark clearly as "low confidence / not medical"
- Coarse localization: which room/zone based on which receiver sees strongest disturbance (no fake precision)
- Optional ML: script to record labeled sessions (empty / walking / sitting / lying) and train a small classifier (scikit-learn RandomForest or 1D CNN in PyTorch)
- Push state to frontend over WebSocket at 10 Hz
3. Frontend (Vite + Three.js)
- Simple 3D floor plan editor: I define rooms as rectangles in a JSON config, and receiver positions
- Render apartment in a dark "radar" style with a sweeping scan effect
- Show a humanoid silhouette in the active zone, animated walking/idle based on motion state, with a glow intensity tied to confidence
- Side panel: live CSI heatmap (subcarriers x time), motion score graph, breathing rate, connection status for each node
- Replay mode: load recorded sessions and scrub through them
## Requirements
- Be honest in the UI about accuracy: CSI gives zone-level location and motion, not exact positions of multiple people. Show confidence everywhere.
- Include a simulator that generates synthetic CSI data so I can develop the backend and UI without hardware
- Record/playback CSI sessions to .npz files
- Privacy: everything runs locally, no cloud, no data leaves my network
## Deliverables
- Repo structure: /firmware/tx, /firmware/rx, /backend, /frontend, /tools, README.md
- README with: hardware list, flashing steps, calibration procedure, placement tips (receivers 1-1.5 m high, line of sight through the room), troubleshooting
- Start with the simulator + backend + frontend so it works end to end, then firmware
Work step by step. Before writing code, show me the plan and file tree, then build each module and test it.
Vector 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?
Vector retweeted
Quantum Space 🚨 WiFi Yönlendiriciniz Sizi Görebilir mi? Claude Code ile Gerçek Zamanlı Radar Deneyi!
Bir geliştiricinin Claude Code kullanarak WiFi yönlendiricisini bir radara dönüştürmesi, kablosuz sinyallerin ev içindeki hareketleri nasıl takip edebileceğini gözler önüne serdi.
• **Sinyal Analizi:** Yalnızca 40 dakika içinde yazılan özel kod, WiFi sinyallerinin cisimler etrafındaki bükülmelerini okuyarak dairenin haritası üzerinde hareket eden figürlere dönüştürdü.
• **Şoke Eden Sonuç:** Gece saatlerinde test edilen sistemin haritada yatak odasında ayakta duran ve kapıya bakan bir insan figürü algılaması, teknolojinin sınırlarını ve potansiyel gizlilik risklerini bir kez daha tartışmaya açtı.
• **Dijital Mahremiyet:** Evimizdeki kablosuz ağ cihazlarının veri toplama ve çevreyi izleme kapasitesi dijital güvenlik açısından yeni soru işaretleri doğuruyor.
Vector retweeted
🚨Le pidio a Claude Code que convirtiera su router de WiFi en un radar
En 40 minutos escribió el código que lee como la señal de WiFi se dobla alrededor de los cuerpos y los dibuja como formas en movimiento sobre el mapa de su piso
Lo probo de noche y vive solo penso que era un bug entonces fue al dormitorio y la forma no se movio
El mapa la mostraba de pie junto a la cama mirando a la puerta entonces lo apago y aun no sabe que estaba leyendo el codigo
Guardalo
Readers added context they thought people might want to know
La detección de personas con señales WiFi es real como en el proyecto RuView. Requiere dispositivos adicionales como ESP32 para capturar los cambios en las ondas no solo código de Claude para un router común.
ru-view.com
ruview.pro/how-it-works
Vector 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 ↓
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 ↓
Vector retweeted
CLAUDE CODE BUILT A FAKE AIRPORT WIFI AND IT TOOK A PASSWORD IN 30 SECONDS
i wanted to see how easy it actually is. everyone connects to free wifi at airports, cafes and hotels without thinking. i do it too
i asked claude code to build a fake wifi login page. the kind you see everywhere. it wrote the whole thing in 3 minutes
then i put up a hotspot called "Guest_Network". same name as half the networks you've ever joined
here's what happened the second someone connected:
- a normal looking "Welcome to Our Wi-Fi" page popped up
- it asked for email and password to connect
- looked exactly like a real captive portal
- nothing on the phone looked off
i typed a test email and password. walked over to the laptop. claude code opened the admin panel
both were sitting right there in plain text:
- the email
- the password
- the exact timestamp
two people connected in under a minute. both entered real looking logins without a second thought
the scariest part is there's no warning. no popup, no lock icon telling you something is wrong. the page looks identical to the real one
this cost $0. a laptop and a few lines of code claude code wrote. no special hardware, no pineapple, no flipper. just a hotspot and a fake page
this is a demo i ran on my own devices, for educational purposes only
the point is simple. if i can do this in 3 minutes, so can the person sitting next to you at the gate
how to protect yourself is in the first reply. go through it tonight, takes 2 minutes
bookmark this and send it to anyone who connects to free wifi