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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

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Follow me @heynavtoor for more.

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More from @heynavtoor

Nav Toor

@heynavtoor

Jun 25

Stanford researchers proved you are not being rejected by 10 companies. You are being rejected by one algorithm 10 times.

Your score is stored for 330 days. Every company that uses the same vendor sees the same number. They call it the algorithmic blackball.

Researchers at Stanford HAI, Chapman University, and Northeastern University published the largest audit of AI hiring algorithms ever conducted.

The paper is called "Algorithmic Monocultures in Hiring." Published at FAccT 2026, May 26. The data came from Pymetrics, the AI hiring platform used by major Fortune 100 companies.

Here is what they found.

When you apply for a job at a company that uses Pymetrics, you play a series of assessment games. Your scores are stored. For up to 330 days. If another company also uses Pymetrics, your application is evaluated using the same stored scores. You are not getting two separate evaluations. You are getting the same score twice.

If the algorithm rejects you once, it rejects you everywhere.

The researchers call this the "algorithmic blackball." One bad score locks you out of every company that shares the same vendor. You never find out why. You never get a second chance. You just stop hearing back.

They ran a large-scale simulation using real applicant data. The result: over 40,000 job advances were lost because applicants who would have succeeded at one company were screened out by an algorithm calibrated for a different one.

Then they measured who gets hit hardest.

25.87% of Black applicants were routed into algorithmically discriminatory hiring processes. 14.74% of Asian applicants. These are not hypothetical projections. These are rates measured in deployed, real-world hiring systems used by some of the largest employers on earth.

The same algorithm. Applied across companies. Producing the same racial disparities at every one of them.

This is already in the courts. Mobley v. Workday is a federal class-action lawsuit alleging that AI hiring tools systematically discriminate against older, Black, and disabled applicants. The case is ongoing.

In Europe, the EU AI Act classifies hiring algorithms as high-risk AI systems by default. Compliance requirements take effect August 2, 2026. Weeks away.

In the United States, there is no equivalent federal law.

The researchers make four recommendations. Measure adverse impact at the position level. Strengthen cross-employer surveillance. Monitor risks from algorithmic concentration. Create legal pathways for independent researchers to access hiring data.

The last one carries an implicit warning. This study was only possible because Pymetrics voluntarily shared its data. Most vendors would prefer their algorithms remain opaque.

The next time you apply for a job and never hear back, the rejection may not have come from a human. It may have come from a score you received 330 days ago, at a company you have already forgotten, for a role that had nothing to do with the one you just applied to.

1/ Imagine you take a test at one company. You fail. That is fine. You move on and apply to the next company.

Except you do not take a new test. The next company uses the same test vendor. They pull your old score. You fail again, with the same score, for a completely different job.

You apply to a third company. Same vendor. Same score. Same rejection.

You were not rejected three times. You were rejected once. It just followed you.

2/ Now add race.

The researchers looked at 3.37 million applicants and 4.19 million applications, all screened by the same vendor.

25.87% of applications submitted by Black applicants went to positions where the algorithm discriminated against Black applicants.

14.74% for Asian applicants.

Not because anyone at the company chose to discriminate, but because the algorithm did. The same algorithm at every company that uses it.

Read 7 tweets

Nav Toor

@heynavtoor

Jun 25

Most cold emails die in 3 seconds.

Wrong opener. Big ask. Bad subject line.

I studied the cold emails that get replies from founders, VCs, and CEOs.

The patterns repeat. I turned them into 6 Claude prompts.

The prompts are below. Copy them. Save this.

1. The Subject Line That Doesn't Look Like Marketing

Your subject line is the only thing they read before deciding to open or delete. The ones that get opened look like an internal note or a to-do item, not a pitch. A "quick question" style subject gets about 2x the reply rate of a long, salesy one. Two to five words. Hint at their world, not your product.

PROMPT

"I'm cold emailing [Person] at [Company] about [what I offer]. Their recent context: [recent funding, launch, hire, or post].

1. Write me 5 subject lines, each 2 to 5 words.
2. Each one should read like an internal message or a to-do item, not marketing.
3. Reference their context, never my product name.
4. Rank them from most to least likely to get opened, and say why the top one wins.
5. Flag any line that sounds like a sales blast so I can cut it."

2. The Research Opener (Kills "Hope This Finds You Well")

The first line decides whether they keep reading. The best openers show research, not hope. A specific observation about their recent work, funding, or post beats "hope this finds you well" every time. It proves a human wrote this for them and not for ten thousand inboxes.

PROMPT

"I'm writing the first line of a cold email to [Person] at [Company]. They recently [specific action: launched, raised, posted, hired].

1. Write me 3 opening lines that reference that specific action.
2. Keep each one under 15 words.
3. None can use "hope this finds you well" or "I came across your work."
4. Tell me which one is strongest and why.
5. Flag any version that could have been sent to anyone else."

Read 7 tweets

Nav Toor

@heynavtoor

Jun 23

A student submitted an essay she wrote by hand. Her university ran it through an AI detector. The detector said she cheated. She is autistic.

Her name is Moira Olmsted. Adelphi University. February 2026. Turnitin flagged her essay as 100% AI-generated. She was disciplined.

Two other AI detectors classified the same essay as human-written.

She sued. She won. The court called the school's decision "arbitrary and capricious."

She is not the only one.

In May 2026, a high school student in Palo Alto was expelled after an AI detector flagged his work. He faced visa revocation. He filed a federal civil rights lawsuit.

A researcher at Griffith University just proved mathematically why this keeps happening. The paper is on arXiv. The finding is one sentence.

AI text detectors have a structural flaw that no amount of better engineering can fix.

Here is what the math says.

If a university wants its detector to catch 80% of cheaters, at least 750 out of every 10,000 innocent students will be wrongly accused. That is not a software problem. It is a theorem.

If the university tries to limit false accusations to 1%, detection power collapses to 6%. It catches 6 out of every 100 AI-written papers. The other 94 get through.

There is no setting where the detector is both fair and effective.

The reason is diversity. Every student writes differently. Non-native English speakers use simpler vocabulary. Shorter sentences. Clearer structures. So does AI. A Stanford study found that 61.3% of TOEFL essays written by non-native English speakers were misclassified as AI-generated. A separate analysis tested 14 commercial detection tools. Zero out of 14 reached 80% accuracy.

The students most likely to be wrongly accused are non-native English speakers, neurodivergent students, and anyone who writes with clarity and precision. The qualities that make their writing effective are the same qualities the detector mistakes for a machine.

Vanderbilt University understood this. They disabled Turnitin's AI detection in 2023 after calculating that even a 1% error rate across 75,000 submissions would produce 750 wrongful accusations per year.

750 students accused of cheating for writing like themselves.

The paper's conclusion is not that we need better detectors. It is that the diversity of human writing itself makes accurate detection mathematically impossible.

The same thing that makes your writing yours is the thing that gets you accused.

arxiv.org/abs/2603.20254

1/ The math in one chart.

A detector that catches 80% of cheaters must wrongly accuse at least 750 out of 10,000 innocent students.

A detector that keeps false accusations below 1% catches only 6 out of every 100 AI-written papers. The other 94 get through.

There is no setting where the detector is both fair and effective. The math does not allow it.

2/ Why it cannot be fixed.

Prior work assumed one human writing style vs one AI writing style. Simple test. Two distributions.

But in a university, every student writes differently. The detector does not know YOUR writing style. It only knows what AI sounds like.

Some students naturally write in a way that overlaps with AI. Not because they cheated. Because that is how they write.

The detector cannot tell the difference. Not because it is bad. Because the difference does not exist in the text.

Read 7 tweets

Nav Toor

@heynavtoor

Jun 23

10 single developers who built free tools that Big Tech tried to kill. And lost.

Bookmark this list. These 10 people built things you use every week.

Companies worth trillions of dollars have spent over a decade trying to make them disappear.

Every one of them is still shipping today.

1. uBlock Origin

Raymond Hill lives in Quebec.

He built an ad blocker in 2014 that has been downloaded over 50 million times.

He has never accepted a single donation.

In 2024, Google killed it in Chrome. Manifest V3 capped ad blockers at 30,000 rules. uBlock Origin needs over 300,000 to work.

Mozilla announced Firefox would support it forever.

Raymond Hill still ships updates every month.

Repo → github.com/gorhill/uBlock

2. yt-dlp

In October 2020 the RIAA filed a DMCA takedown on youtube-dl. GitHub complied and pulled the repo.

Developers responded by embedding the source code in a Twitter image where every pixel encoded the code.

The EFF intervened. GitHub reversed itself in 24 days, started a $1 million developer defense fund, and changed its entire DMCA policy.

A community fork called yt-dlp was created 3 days into the takedown.

Today it has 160,000 stars and 12 million downloads a month.

Repo → github.com/yt-dlp/yt-dlp

Read 11 tweets

Nav Toor

@heynavtoor

Jun 22

My 19-year-old niece pulled out a small plastic card and said "watch this."

She opened her phone.

Free audiobooks. Free ebooks on Kindle. 30,000 movies including Criterion classics. Free New York Times. Free LinkedIn Learning courses.

All $0 a month.

She said: "It's my library card. I haven't paid for a streaming service in 3 years."

I checked the math. She saves over $100 every month.

Here's everything she showed me 👇

Step 1: Download an app called Libby.

Free audiobooks. Free ebooks. Free magazines.

You sign in once with your library card number. That's it.

The ebooks send directly to your Kindle. The audiobooks play in the app.

No subscription. No ads. No late fees.

One honest note: popular new releases may have a wait list, just like a regular library.

Step 2: Download Kanopy.

30,000+ movies, documentaries, and shows.

Criterion Collection classics. A24 films. Foreign cinema. Award winners.

Ad-free. Free with your library card.

Most US libraries cap you at 5 to 12 tickets per month. Some libraries also pause Kanopy late in the month when their budget hits.

Source: kanopy.com

Read 12 tweets

Nav Toor

@heynavtoor

Jun 19

Researchers analyzed 14 million academic papers published between 2010 and 2024. They tracked every word. They found that ChatGPT is rewriting the English language.

Not metaphorically. Literally.

After ChatGPT launched in November 2022, certain words that had been stable in academic writing for over a decade suddenly exploded in frequency. The researchers at the University of Tübingen and Northwestern University mapped every excess word and categorized them.

The words are ones you already recognize.

"Delve." "Intricate." "Meticulous." "Commendable." "Underscore." "Pivotal." "Nuanced." "Landscape." "Comprehensive." "Multifaceted." "Showcasing." "Groundbreaking." "Innovative." "Invaluable."

329 excess style words appeared in early 2024 that were not there before. The spike is unprecedented in the history of the dataset.

Here is what makes this different from every other vocabulary shift ever recorded. During COVID, excess words also appeared. Up to 188 of them in 2021. But those were content words. "Respiratory." "Remdesivir." "Ventilator." Words that described a new reality.

After ChatGPT, the excess words are not content words. They are style words. Not what people write about. How people write. The subject matter did not change. The voice did.

The researchers estimate that at least 10% of all academic papers published in 2024 were processed with ChatGPT. Not written entirely by AI. Processed. Edited. Polished. Run through the model and published with its fingerprints still on the page.

You have seen these words everywhere. In emails. In LinkedIn posts. In articles. In cover letters. In reports your colleagues sent you. You could not explain why everything started sounding the same. Now you can. The entire internet passed through the same model. And the model left the same fingerprints on everything it touched.

The researchers proved something else. The contamination is not slowing down. The number of excess words grew from 188 during COVID to 329 after ChatGPT. The curve is still climbing.

ChatGPT did not just change what we can do with language. It changed the language itself. One model. One voice. Fourteen million papers. And a vocabulary shift larger than a global pandemic.

1/ The word "delve" tells the whole story.

Before ChatGPT, "delve" appeared in roughly 1 in 1,000 PubMed abstracts. Stable for a decade. Then ChatGPT launched. The frequency shot up vertically. In 2024 it appeared in roughly 3 in 1,000 abstracts. A tripling in one year.

One word. One model. Fourteen million papers.

2/ The full list of ChatGPT's fingerprints.

These are the words that exploded after ChatGPT launched:

Delve. Intricate. Meticulous. Commendable. Underscore. Pivotal. Nuanced. Landscape. Comprehensive. Multifaceted. Showcasing. Groundbreaking. Innovative. Invaluable. Leverage. Harness. Foster. Embark. Garner. Exemplary.

329 excess style words in total. All appearing at rates that never existed before November 2022.

Read 7 tweets

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