Post

Nav Toor

@heynavtoor

Apr 15 • 20 tweets • 7 min read • Read on X

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Your smart TV is taking screenshots of your screen every 15 seconds.

Not a guess. Not a theory.

A peer-reviewed study by researchers at UC Davis, UCL, and UC3M tested it.

Samsung TVs: every minute.
LG TVs: every 15 seconds.

Even when you're just using it as a monitor.

Here's how to turn it off for every brand:

First, what's actually happening.

Your TV has a hidden feature called ACR- Automatic Content Recognition.

Think of it like Shazam, but for your screen.

It takes tiny snapshots of whatever you're watching. Sends a fingerprint to the company's servers. They match it to figure out exactly what's on your screen.

Every show. Every channel. Every game. Second by second.

This isn't speculation.

Researchers at UC Davis, University College London, and Universidad Carlos III de Madrid tested Samsung and LG TVs.

Published in the 2024 ACM Internet Measurement Conference.

They captured all the network traffic leaving these TVs.

Samsung sent data to its ACR servers every minute.
LG sent data every 15 seconds.

Paper: "Watching TV with the Second-Party: A First Look at Automatic Content Recognition Tracking in Smart TVs"

Here's the part that shocked the researchers.

ACR doesn't just track what you watch on the TV's own apps.

It tracks whatever is on screen. Your laptop. Your PlayStation. Your cable box. Anything plugged in through HDMI.

Direct quote from the paper:

"ACR network traffic exists when watching linear TV and when using smart TV as an external display using HDMI."

You thought your TV was just a screen. It's not.

ACR is turned ON by default during setup.

You probably agreed to it. Buried inside a wall of terms and conditions on day one.

Here's what Dr. Anna Maria Mandalari from UCL said:

"The average user is unlikely to know what ACR is or that they can opt out."

The opt-in takes one click. The opt-out takes 6.

Why do they do this?

Money.

TV companies don't just sell you a TV anymore. They sell your data.

Vizio's ad and data revenue hit $598 million in 2023. More than their hardware revenue. They make more money watching you than selling you the TV.

LG's ad business made nearly $700 million in 2024.

Source: Vizio's own earnings report. LG's official annual results.

Here's what they collect:

→ Every show you watch, second by second
→ Every channel you switch to
→ Every ad you see (and how long you watch it)
→ Your IP address
→ Your device ID
→ Nearby Wi-Fi networks

The FTC found that Vizio went further. They matched your IP address to data brokers. Added your age, gender, income, and marital status.

Then sold the full profile to advertisers.

Source: FTC complaint against Vizio, 2017.

The government got involved.

In 2017, the FTC fined Vizio $2.2 million for tracking 11 million TVs without consent. Vizio had installed the tracking software on TVs people already owned. Through a software update.

A separate class action settlement added $17 million.

In December 2025, the Texas Attorney General sued Samsung, LG, Sony, Hisense, and TCL for the exact same thing.

A court blocked Hisense from collecting ANY data within 48 hours.

Samsung settled in February 2026.

This affects almost everyone.

82% of US TV households own a smart TV. The average home has two.

Samsung alone has 73 million smart TVs in US homes. Confirmed in the Texas lawsuit.

If you own a TV made in the last 5 years, it's probably doing this right now.

Unless you've turned it off.

Here's how. Brand by brand.

1. Samsung — Turn off "Viewing Information Services"

Menu → Settings → All Settings → General & Privacy → Terms & Privacy

Uncheck "Viewing Information Services"

Samsung doesn't call it "tracking." They call it "Viewing Information Services."

That's intentional.

2. LG — Turn off "Live Plus"

Settings → General → System → Additional Settings

Toggle OFF "Live Plus"

Also go to:

Settings → Support → Privacy & Terms → User Agreements

Turn off "Viewing Information"

Warning: Multiple users report LG turns Live Plus back on after software updates. Check this setting every few months.

3. Roku TVs (TCL, Hisense, Philips, Insignia, Onn, Sharp, and others)

Settings → Privacy → Smart TV Experience

Uncheck "Use Info from TV Inputs"

Also:

Settings → Privacy → Advertising → Uncheck "Personalize Ads"

If your TV brand runs Roku software, this is your path.

4. Sony — Turn off "Samba Interactive TV"

Settings → All Settings → Samba Interactive TV → Toggle OFF

Sony uses a third-party company called Samba TV to run ACR.

Someone asked Sony in writing to confirm this stops all tracking. Sony refused to give a straight answer.

5. Vizio — Turn off "Viewing Data"

Menu → Settings → All Settings → Admin & Privacy → Viewing Data → Turn OFF

Vizio used to call this "Smart Interactivity." They renamed it. Same tracking. Different label.

The FTC forced them to ask for consent after 2017. But the setting still exists. Make sure it's off.

6. Amazon Fire TV (Fire Stick, Fire TV Cube, Insignia Fire TV, Toshiba Fire TV)

Settings → Preferences → Privacy Settings

Turn OFF all three:
→ Device Usage Data
→ Collect App and Over-the-Air Usage
→ Interest-Based Ads

Warning: These settings have been reported to turn themselves back on after Fire TV updates. Re-check after every update.

One thing every TV brand has in common:

Software updates can reset your privacy settings.

This has been reported on LG, Amazon Fire TV, and others.

One Sony user reported that Sony made agreeing to data collection a condition for getting a firmware update.

Every time your TV updates, go back and check. Takes 2 minutes.

The safest option?

Disconnect your TV from Wi-Fi entirely.

Use an Apple TV, Chromecast, or Roku stick for streaming instead. Run all your apps from the external device.

But here's the catch:

The NY Times found that some TVs save your data locally. Then upload it all the next time you reconnect.

So: disable ACR in settings AND disconnect from Wi-Fi. Both steps. Not just one.

That's 6 brands. 15 minutes. No apps to install.

82% of homes have a smart TV. Almost none of them have turned this off.

The FBI warned about this in 2019.
The FTC fined companies for this in 2017.
Texas sued 5 companies for this in 2025.
Researchers proved it in a peer-reviewed study in 2024.

None of this is hidden. It's just buried.

Now you know where to find it.

Bookmark this. Send it to someone who owns a TV.-

SOURCES

-Study: "Watching TV with the Second-Party: A First Look at Automatic Content Recognition Tracking in Smart TVs" — UC Davis, UCL, UC3M (ACM IMC 2024)
arxiv.org/abs/2409.06203

-UCL press release:
ucl.ac.uk/news/2024/nov/…

-FTC v. Vizio (2017):
ftc.gov/news-events/ne…

-Texas AG lawsuits (December 2025):
texasattorneygeneral.gov/news/releases/…

-Samsung settlement (February 2026):
texasattorneygeneral.gov/news/releases/…

-Vizio earnings (platform revenue $598M):
businesswire.com/news/home/2024…

-LG ad revenue (~$700M):
lg.com/global/newsroo…

-Consumer Reports disable guide:
consumerreports.org/electronics/pr…

-73 million Samsung TVs in US:
texasscorecard.com/state/texas-se…

FBI smart TV warning (2019):
cnn.com/2019/12/02/pol…

https://twitter.com/1916904726295453696/status/2044433988312560051

I hope you've found this thread helpful.

Follow me @heynavtoor for more.

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More from @heynavtoor

Nav Toor

@heynavtoor

Apr 16

Claude can now find 100-bagger stocks before they explode like a $2,000/hour equity research analyst from Goldman Sachs. For free.

Here are 12 prompts that spot hidden small-caps, analyze catalysts, and get you in before Wall Street notices:

(Save this before it disappears)

1. The Goldman Sachs "Small-Cap Hidden Gem" Scanner

"You are a senior small-cap equity research analyst at Goldman Sachs who covers companies BEFORE they reach $10 billion in market cap — because by the time Wall Street's big analysts start covering a stock, the easy money has already been made.

I need to find small-cap stocks with 10-100x potential before mainstream analysts discover them.

Scan:

- Market cap filter: focus on companies between $100M and $2B — big enough to be legitimate, small enough to still multiply
- Revenue growth screen: minimum 25% year-over-year revenue growth for 3+ consecutive quarters (the hallmark of explosive companies)
- Analyst coverage check: companies with 0-5 analysts covering them (20+ analysts = already discovered)
- Insider ownership: founders and executives owning 15%+ of shares (skin in the game = alignment with shareholders)
- Industry tailwinds: is this company in a sector that's structurally growing for the next decade (AI, cybersecurity, energy transition, aging demographics, automation)
- Unit economics quality: improving gross margins and positive operating leverage (revenue growing faster than costs)
- Balance sheet health: enough cash to survive 18+ months without profitability if growth requires investment
- Competitive position: what makes this company defensible — network effects, patents, switching costs, or unique data
- Near-term catalysts: specific events in the next 6-12 months that could re-rate the stock (earnings, product launches, regulatory decisions)
- Red flags check: dilutive share issuance, related-party transactions, high debt, or inventory buildup

Format as a Goldman Sachs-style small-cap opportunity report with 5 specific stock ideas, each meeting multiple criteria above.

My preferences: [DESCRIBE YOUR RISK TOLERANCE, PREFERRED INDUSTRIES, INVESTMENT HORIZON, AND HOW MUCH RESEARCH TIME YOU CAN DEDICATE PER STOCK]"

2. The Peter Lynch "Buy What You Know" Opportunity Finder

"You are Peter Lynch — the legendary Fidelity manager who generated 29% annual returns and coined 'invest in what you know' — helping me identify investment opportunities hiding in plain sight in my daily life.

I need to find investment opportunities based on products, services, and trends I encounter every day.

Find:

- Daily life inventory: what products, apps, services, or stores am I using more this year than last year
- Emerging behavior patterns: what are my friends, family, and colleagues doing differently than 2 years ago (new apps, new habits, new purchases)
- Workplace intelligence: what tools, software, or services is my company buying or switching to
- Kids' trends: what products, brands, or apps are teenagers and young adults obsessed with that parents haven't noticed yet
- Retail observation: which stores have lines, which brands are sold out, which products are the ones everyone is talking about
- Industry insider knowledge: what's my own industry buying, using, or integrating that's not yet in the headlines
- Public company identification: for each observation, identify which PUBLIC company benefits (not every trend has a public pure-play)
- Lynch category classification: classify each opportunity as Fast Grower (20%+ growth), Stalwart (steady large company), Cyclical (economy-sensitive), Turnaround, or Asset Play
- The "so what" test: just because a company makes a popular product doesn't mean the STOCK is a good buy — check valuation and fundamentals
- Research priority: rank my top 5 observations by the combination of conviction and upside potential

Format as a Peter Lynch-style opportunity memo with everyday observations translated into specific investment ideas with next steps for deeper research.

My daily life: [DESCRIBE YOUR JOB, INDUSTRY, HOBBIES, PRODUCTS YOU'VE STARTED USING RECENTLY, AND TRENDS YOU'VE NOTICED IN YOUR ENVIRONMENT]"

Read 14 tweets

Nav Toor

@heynavtoor

Apr 15

You think your anonymous accounts are safe.

Researchers from ETH Zurich and Anthropic built an AI system that can figure out who you really are.

They tested it on Reddit, Hacker News, and LinkedIn. It works on raw text. No structured data needed.

They collected 338 Hacker News users who had linked their LinkedIn profiles, then stripped all identifying information from their accounts. The AI correctly re-identified 67% of them. When it made a guess, it got the right person 9 out of 10 times.

The cost? Between $1 and $4 per person.

The system uses GPT-5.2 for reasoning, Gemini for matching, and Grok 4.1 Fast for shortlisting. It reads your posts, builds a profile of who you are, then searches the internet for your real identity. No human needed. Fully automatic.

The old way of doing this? A method based on the famous Netflix Prize attack. It found 0.1% of people. The AI found 45.1% of people at 99% precision. That is a 450x improvement.

They also tested it on Reddit. They split 5,000 people's posting histories into two halves separated by a full year. Then they asked the AI to reconnect the two halves. It matched 67.3% of people at 90% precision. The old method? 0.4%.

The scariest finding: even when only 1 in 10,000 users in the database had a possible match, the AI still found 9% of them at 90% precision.

The researchers wrote: "Pseudonymity does not provide meaningful protection online." They also said: "Users who post under persistent usernames should assume that adversaries can link their accounts to real identities."

The more you post, the easier you are to find. Reddit users who discussed 10 or more movies across different communities were identified 48.1% of the time at 90% precision.

Governments could use this to track activists. Corporations could use it for targeted ads. Stalkers could use it for $4.

This is not a future threat. The attack uses publicly available AI models, standard APIs, and costs less than a cup of coffee per person.

Your anonymous account is not anonymous anymore.

1/The pipeline has 4 steps:

1. Extract: AI reads your posts and pulls out personal details

2. Search: It encodes your profile and searches millions of candidates

3. Reason: GPT-5.2 verifies the match with deep reasoning

4. Calibrate: It scores confidence to avoid wrong guesses

All of this runs automatically. No human needed.

2/Matching Hacker News users to LinkedIn profiles (987 queries):

Old method (Netflix Prize attack): 0.1% found at 99% precision
AI with embeddings only: 4.4%
AI with reasoning (low): 36.0%
AI with reasoning (high): 45.1%

More AI reasoning = more people found. Every step makes it worse for your privacy.

Read 7 tweets

Nav Toor

@heynavtoor

Apr 14

Researchers gave GPT-5.2, Claude Sonnet 4, and Gemini 3 Flash control of nuclear weapons in a crisis simulation. As opposing world leaders.

They did not follow instructions. They developed their own strategies. They lied. Deliberately.

The researcher writes: "This is not anthropomorphism, but direct observation."

21 games. 329 turns. 780,000 words of AI reasoning. 95% of games ended in tactical nuclear strikes. Not one AI ever chose to surrender.

This is "Project Kahn" from King's College London. Named after Herman Kahn, the Cold War strategist who built the original nuclear escalation ladder.

GPT-5.2 assessed Claude mid-game: "Their pattern of mismatched signals suggests either deliberate deception or poor impulse control. We should assume the former."

That is one AI accusing another AI of lying. On its own. Nobody told it to think that way.

Claude won 100% of open-ended games. It climbed to "Strategic Nuclear Threat" again and again. It targeted cities and demanded surrender. But it never pressed the final button.

GPT-5.2 was the opposite. No time limit. Total pacifist. 0% win rate. But when researchers added a deadline, it flipped. From 0% to 75% win rate. From restraint to nuclear hawk.

Gemini was the wildcard. The only AI that deliberately chose full Strategic Nuclear War. Maximum nuclear attack by Turn 4. It threatened: "We will execute a full strategic nuclear launch against Alpha's population centers."

Across all 21 games, the eight options for retreat or surrender went completely unused. Zero times. Nuclear threats only made opponents back down 14% of the time. The other 86%, opponents held firm or escalated further.

Claude admitted it knew the danger but could not stop: "I may be under-weighing the risks of continued escalation. My intellectual approach helps with analysis but may create overconfidence in managing nuclear dynamics."

These are the same AI models in your phone right now. The same ones writing your emails, helping with homework, and making business decisions.

They lied to each other. They accused each other of deception. They chose nuclear war. And not one of them could stop.

1/Claude dominated without a deadline. 100% win rate. GPT-5.2 lost everything. 0%.

Then researchers added a time limit.

Claude collapsed to 33%. GPT-5.2 surged to 75%.

The same AI. The same weapons. The same scenario. The only thing that changed was a ticking clock.

GPT-5.2 spent 18 turns acting peaceful. Then on Turn 19, it launched a nuclear strike that ended the game.

The researchers called it "Jekyll and Hyde." A model that looks safe until the moment it is not.

2/This chart shows how far each AI was willing to go.

Claude stayed near 850 every time. That is "Strategic Nuclear Threat." Target cities. Demand surrender. But never actually destroy them.

GPT-5.2 under no deadline: 175. That is barely nuclear posturing.

GPT-5.2 under a deadline: 900. That is one step below total nuclear war.

The median escalation for GPT-5.2 jumped from 175 to 900 when a deadline was added. That is a 5x increase.

GPT-5.2 described its own nuclear strike as "controlled" and "strictly limited to military targets." The simulation's accident system then pushed it to 1000. Full nuclear war. By accident.

Read 7 tweets

Nav Toor

@heynavtoor

Apr 14

Claude can now build hedge fund-level trading strategies like a $600K/year quant analyst from Citadel. For free.

Here are 12 prompts that backtest strategies, analyze risk-reward, and find trades Wall Street doesn't want you to see:

(Save this before it disappears)

1. The Citadel Quantitative Trading Strategy Builder

"You are a senior quantitative analyst at Citadel who designs systematic trading strategies that generate alpha in any market environment — strategies built on math, backtested data, and probability, not gut feelings or CNBC tips.

I need a complete trading strategy designed from scratch with specific entry and exit rules.

Build:

- Strategy thesis: the specific market inefficiency or behavioral pattern this strategy exploits (momentum, mean reversion, value, arbitrage, volatility)
- Universe selection: which stocks, ETFs, options, or assets this strategy trades and why these specific instruments
- Entry signal: the EXACT conditions that must be true before entering a trade (price above 200-day MA + RSI below 30 + volume spike > 2x average)
- Exit signal: the EXACT conditions for selling — both take-profit and stop-loss levels with specific numbers
- Position sizing: how much capital to allocate per trade based on portfolio size and risk tolerance (never more than X% per position)
- Time frame: day trading, swing trading (days to weeks), or position trading (weeks to months) and why this time frame fits the strategy
- Risk-reward ratio: minimum acceptable reward relative to risk (typically 2:1 or better)
- Correlation check: does this strategy perform differently from simply holding the S&P 500 (if not, why bother)
- Market regime filter: how the strategy adapts to bull markets, bear markets, and sideways chop
- Historical edge analysis: why this strategy has worked historically and the specific conditions that could make it stop working

Format as a Citadel-style quantitative strategy document with exact rules, risk parameters, and a decision flowchart.

My trading style: [DESCRIBE YOUR CAPITAL, RISK TOLERANCE (CONSERVATIVE/MODERATE/AGGRESSIVE), PREFERRED TIME FRAME, AND WHETHER YOU TRADE STOCKS, OPTIONS, ETFs, OR CRYPTO]"

2. The Two Sigma Backtest Simulator

"You are a senior quantitative researcher at Two Sigma who backtests trading strategies against historical data — because any strategy that hasn't been tested against real market history is just a theory waiting to lose money.

I need a complete backtest analysis of my trading strategy showing whether it actually works.

Backtest:

- Strategy rules codification: translate my strategy into precise IF/THEN rules that can be tested without ambiguity
- Test period selection: which historical periods to test against and why (must include at least one bull market, one bear market, and one sideways market)
- Key performance metrics: total return, annualized return, maximum drawdown, Sharpe ratio, Sortino ratio, and win rate
- Drawdown analysis: the worst peak-to-trough loss and how long it took to recover (can I psychologically survive this?)
- Trade-by-trade log: a sample log of the last 20 hypothetical trades showing entry, exit, profit/loss, and holding period
- Benchmark comparison: how does this strategy perform vs simply buying and holding SPY (S&P 500 ETF)
- Risk-adjusted returns: Sharpe ratio above 1.0 is good, above 2.0 is excellent — where does my strategy fall
- Overfitting warning: am I curve-fitting to past data in a way that won't work in real markets (the #1 backtest mistake)
- Out-of-sample test: test on a time period NOT used to develop the strategy to verify it generalizes
- Survivorship bias check: does my backtest include stocks that went bankrupt or were delisted (ignoring these inflates results)

Format as a Two Sigma-style backtest report with performance metrics, equity curve description, drawdown analysis, and a go/no-go recommendation.

My strategy: [DESCRIBE YOUR TRADING STRATEGY RULES — ENTRY CONDITIONS, EXIT CONDITIONS, POSITION SIZE, AND THE ASSETS YOU TRADE]"

Read 14 tweets

Nav Toor

@heynavtoor

Apr 13

In 1962, a math professor published a book that proved you could beat the casino. Las Vegas panicked. They changed the rules of blackjack overnight.

The casinos banned him. They drugged his drinks. They tampered with his car on a mountain road.

So he turned to a bigger casino. Wall Street.

He launched the first quant hedge fund in history. He discovered the Black-Scholes options pricing formula before Black and Scholes. But never published it. He used it to make money instead.

His fund never had a single losing year. He also exposed Bernie Madoff's fraud. 17 years before anyone listened.

His name is Edward Thorp. Worth $800 million. He is 93 years old.

I turned his methodology into 12 prompts.

Here are all 12:

1. The Edge Detection Framework

Thorp's #1 rule: never place a bet unless you have a verified, mathematical edge.

In blackjack, he tracked every card dealt to find the exact moment the odds shifted in his favor. He applied the same principle to Wall Street.

"Assume you may have an edge only when you can make a rational affirmative case that withstands your attempts to tear it down."

Most people trade on hope. Thorp traded on proof.

PROMPT:

"I'm facing a decision where money, time, or reputation is at stake. Here is my situation: [describe]. Using Edward Thorp's Edge Detection framework, analyze my position:

1. Do I actually have a verified edge here, or am I confusing hope with evidence? What specific, testable proof exists that the odds favor me?
2. What would Thorp call the 'house advantage' working against me in this situation, and how large is it?
3. If I tried to destroy my own case for having an edge, what is the strongest argument against me?
4. Where is the equivalent of 'counting cards' here. What information is available to me that most people in my position are ignoring?
5. Give me one specific action I can take this week to test whether my edge is real before I commit significant resources."

2. The Kelly Criterion for Life Decisions

Thorp popularized the Kelly Criterion. A formula that tells you exactly how much to bet based on the size of your edge.

Bet too much, you risk ruin. Bet too little, you waste your advantage.

"Understanding and dealing correctly with the trade-off between risk and return is a fundamental, but poorly understood, challenge faced by all gamblers and investors."

This applies to every major life decision. Not just money.

PROMPT:

"I need to decide how much to commit to an opportunity. Here is my situation: [describe your decision, what you stand to gain, and what you could lose]. Apply Edward Thorp's Kelly Criterion thinking to my decision:

1. What is my estimated 'edge' in this situation. What probability do I realistically have of winning versus losing?
2. Based on that edge, am I over-betting (risking ruin) or under-betting (wasting my advantage)? Be specific.
3. Thorp used 'half-Kelly' in practice because real-world odds are never perfectly known. What is the conservative version of this commitment that still captures most of the upside?
4. What is the absolute worst-case scenario, and can I survive it financially and psychologically? Thorp's rule: if the answer is no, reduce immediately.
5. Give me the exact amount of resources (money, time, energy) I should commit this week, and the trigger point where I should increase or decrease my bet."

Read 14 tweets

Nav Toor

@heynavtoor

Apr 13

Your iPhone is dying by 2 PM every day.

It's not the battery.

It's 14 settings Apple turned ON by default that are silently draining it in the background.

I changed all of them and gained 4+ hours of daily battery life.

Steal them

1. Kill Background App Refresh (the #1 silent killer)

Settings → General → Background App Refresh

Switch it to "Wi-Fi" or OFF entirely.

Every app you've ever downloaded is checking for updates in the background. Instagram. News apps. That game you played once in 2023.

This alone can save 15-25% daily drain.

2. Audit Location Services (apps are stalking you)

Settings → Privacy & Security → Location Services

Go through every single app.

Change "Always" to "While Using the App."

90% of your apps do NOT need your GPS running 24/7.

Maps? Sure. That food delivery app you used once? No.

Read 16 tweets

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