AI tools help owners strip unwanted software from smart TVs

Developers are using AI coding agents to remove unwanted software from smart TVs, replace cluttered home screens, and disable packages they associate with advertising or tracking. They want to get to Netflix or an HDMI input without waiting for a sponsored interface to load.

Two projects, dumbtv and cleanyourtv, package that work into reusable workflows. As TechCrunch reported on October 19, 2026, developers are sharing repositories and prompts that let an AI agent help modify a television over the local network.

How much authority should the agent have? Replacing a launcher and disabling system packages carry different risks. And proving that a TV has stopped collecting viewing data takes more evidence than a cleaner screen.

The projects make customization easier to attempt, but their early results include failed modifications and reports of broken devices.

What the tools change

Gathin T., the creator of cleanyourtv, says the project began with ads on his parents’ television. In an October 17 post, he described giving Claude Code the TV’s model number, having it find the device over Wi-Fi, remove 25 Xiaomi packages, and write a replacement launcher.

The project promises to strip unwanted software from Android TV and Google TV devices and provide an Apple TV-like home screen. According to the report, it doesn’t require root access and allows a return to the factory-default state.

Avoiding root removes some complexity. A device’s existing administrative and debugging capabilities can provide enough access for customization, without granting broad control over the operating system. That still leaves plenty of room to disable something important.

Dumbtv’s stated goals are apps, HDMI inputs, no TV-provided ads, no viewing tracking, and no unrequested updates. Those outcomes haven’t been independently verified across every supported television.

TechCrunch reports that dumbtv has been tested end-to-end on a Fire TV Edition television and Android/Google TV devices from Sony, TCL, Philips, Hisense, and Xiaomi, alongside Shield. Its creator, Muzzammil Yasin Sabuwala, recommends pointing an AI agent at the repository and plans to expand support.

Distributing this work through a repository gives developers somewhere to collect device-specific knowledge, fixes, and recovery instructions. That knowledge is much harder to retain across isolated chat sessions.

The brand list still needs care. Compatibility depends on the model, operating system, firmware version, and package configuration. Testing one television from a manufacturer says little about the rest of its catalog.

AI reduces the work of investigating unfamiliar devices

People have customized consumer electronics for years. AI coding agents reduce how much specialized knowledge they need before starting.

An agent can help interpret a model number, inspect available software, propose changes, and generate a launcher. That can save hours of searching forums, comparing package lists, and learning unfamiliar tooling.

Gathin says a few thousand people repeated his process over two days, naming Xiaomi, TCL, Sony, Fire TV, LG, and even projectors. The figure comes from the creator and hasn’t been audited for compatibility. His broader list also extends beyond cleanyourtv’s stated Android TV and Google TV scope.

Other users describe similar experiments. Ryan Doyle says Claude Code helped remove ads and unwanted software, improve responsiveness, and customize a screensaver after he supplied a TV listing. Mihir Gupta reports disabling 25 apps and replacing a 192 MB stock launcher with an 83 MB custom one.

The demonstrations are useful; the performance evidence is thin. The launcher-size figures don’t specify whether they measure storage, memory, or something else, so they can’t support a precise claim about RAM savings or latency. Doyle’s video shows his reported experience on one device, without a controlled comparison.

A simpler launcher plausibly spends less time rendering recommendations and promotional content. Measuring the benefit requires launch latency, input responsiveness, memory use, and behavior after a reboot. Software size alone doesn’t tell you whether the system is faster.

Checking the privacy claims

Smart TV privacy needs its own testing.

Automatic content recognition, or ACR, identifies what’s playing through screenshots, audio samples, or other digital fingerprints. As the source explains, this can support viewing-data sales and personalized advertising.

A sponsored tile disappearing provides no evidence about whether viewing-data collection has stopped. On an Android-based TV, the launcher, vendor services, streaming apps, and other background components can run independently. Checking a privacy claim means identifying the collection mechanisms and observing their behavior.

For engineers, that calls for three separate acceptance criteria:

• Interface: unwanted recommendations and TV-provided ads disappear.
• Performance: measured interactions become faster without breaking expected functions.
• Privacy: identified tracking components stop collecting or transmitting the data in question.

Bundling all three under “debloating” makes results harder to evaluate and can give users more confidence than the evidence warrants. The reporting contains user demonstrations and project claims, but no independent network-level verification that these tools eliminate viewing surveillance.

Installed streaming services also need separate scrutiny. Removing television-level advertising establishes nothing about ads or telemetry inside those apps.

Risks and limitations

Both projects are works in progress. The source reports users proposing fixes across platforms, agents declining vague requests to “hack my TV,” and experiments failing partway through.

One user says Claude bricked an Android TV; another says a Fire TV modification stalled halfway. These are individual accounts, without established failure rates or verified diagnoses. They still justify caution, particularly when an agent recommends disabling a package whose purpose the user doesn’t understand.

Package names won’t tell you all the dependencies. Software that looks unnecessary may support a function elsewhere in the system. The reporting specifically notes warnings about agents disabling important packages or breaking things.

Cleanyourtv’s promise of a return to factory defaults is useful, but recovery needs operational detail. Can the user restore a functioning launcher after a failed change? Recovery instructions should be available before execution, along with a record of exactly what the agent changes.

Treat the work like infrastructure maintenance: inspect first, approve a bounded change, test it, then continue. Keep package changes separate from launcher development and preserve a known-good route back. An agent’s confident explanation isn’t evidence that a component is disposable.

Security updates need particular care. Dumbtv’s goal of preventing unrequested updates may appeal to owners tired of interface changes, but indiscriminate blocking can also prevent security fixes. The source doesn’t establish how the project distinguishes those cases.

Users who mainly want a calmer home screen can try existing Android launcher replacements such as ATV Launcher, Projectivy Launcher, and FLauncher. They won’t necessarily address tracking or background software, but they reduce how much of the system needs changing.

Maintaining the modifications

Some owners dislike their TV’s supplied software enough to commission a replacement. AI makes that cheaper to attempt; maintaining the result across device variants remains substantial work.

Shared projects will need model-specific test results, firmware information, explicit package-change records, and recovery procedures. Without those, the next user has to guess whether a successful demo applies to their television.

Any impact on advertising remains speculative. A few viral posts and creator-reported installations don’t establish a mass shift in consumer behavior.

For now, owners can get a less cluttered interface without writing every component themselves. A dependable cleanup tool also needs to explain what it changes, measure what improves, and provide a tested way back when a modification fails.

Source: TechCrunch, published October 19, 2026, at 16:13:22 UTC; updated October 19, 2026, at 20:04:21 UTC.