I tested an AirTag alternative that uses LoRa mesh to track location – and it’s seriously reliable


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Seeed Studio’s SenseCAP T1000-E tracker card

pros and cons

Pros

  • Compact yet fully featured tracker card.
  • It’s more than just a tracker card – it has a full mesh client built inside.
  • The app unlocks many features, and the card can be flashed with different firmware.
Cons

  • The battery lasts two or three days.
  • It uses a proprietary magnetic charging cable.
  • It’s a deep rabbit hole that can sometimes have a steep learning curve.

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I’ve said it before, and I’ll say it again: Apple AirTags were a total game-changer for me. Being able to pinpoint and locate things like keys and wallets, wherever they are in the world, has saved me no end of heartache, headaches, and wasted time. And the proliferation of third-party tags means there are tags for all sorts of applications.

Also: I hid 4 Bluetooth trackers (including AirTags) to test their reliability – here’s how Android rivals compared

But the one drawback is that all the tags rely on cellular and Wi-Fi networks. What if you could get tags that operated on their own networks and could operate independently of the networks built by multibillion-dollar corporations? That’s exactly what the Seeed Studio’s SenseCAP T1000-E tracker card offers.

OK, how does this work?

And with most things, there are a lot of things I like about the T1000-E, and a few things I don’t. But before I go any further, let me warn you that this card could very well be a gateway drug that pulls you into the huge world of LoRa mesh.

LoRa stands for Long Range Radio and is a long-range wireless radio protocol that can be used to create a mesh.

Also: I found an AirTag alternative that’s twice as durable and works with Android phones

What’s a mesh?

It’s an open-source, off-grid, decentralized mesh network designed to run on small, low-power devices. It doesn’t need cell towers or the internet. It’s a completely stand-alone, peer-to-peer radio system. If you’re a bit like me and love getting your teeth into weird things, you’ll soon be hip-deep in mesh transceivers and setting up solar-powered nodes.

Why? Because you can.

Note: Make sure you get the T1000-E, and not the A or B variants. Only the E is designed to work with peer-to-peer mesh, and the A and B variants make use of LoRaWAN networks and require their own network gateways.

It's super thin!

Adrian Kingsley-Hughes/ZDNET

OK, back to the T1000-E. This is a card-sized tracker much like all the others. It’s credit card-sized and as thick as a bunch of cards. Inside is a 700 mAh battery that’s enough to power the card for a couple of days, along with all the tech wizardry for communication and location magic.

Also: I put away my AirTag just minutes after trying this Bluetooth tracker alternative – here’s why

There’s a super-loud buzzer, an LED status light, and a button to turn the unit on and off and control things like Bluetooth.

Built for the apocalypse

The shell is IP65-rated for dust and water intrusion, so it’s fine to be out and about in the great outdoors. I like this a lot, but to achieve this level of dust and waterproofing, the manufacturer has opted for a magnetic charging pad on the back that uses pogo pins.

IP65 means that dirt and water are no problem for the T1000-E.

IP65 means that dirt and water are no problem for the T1000-E.

Adrian Kingsley-Hughes/ZDNET

Not only does this mean that you need a proprietary cable every time you need to charge the device, but the design means that if you try to charge the card while on the move (by, say, using a power bank), there’s a good chance that it’ll come loose and stop charging. I’ve come up with solutions using elastic bands or hot glue, but I wish there were a better way to keep the connector attached.

It’s a great tracker

By default, you’ll need to download the SenseCraft app (iOS/Android) to set up and control the T1000-E.

Setting up a new T1000-E using the SenseCraft app.

Setting up a new T1000-E using the SenseCraft app.

Adrian Kingsley-Hughes/ZDNET

The app is OK, but not great. The instructions can be a bit vague; sometimes you’ll stumble across sections in Chinese, and it’s also trying to sell you more stuff (something I hate in an app for a product I’ve bought). My advice is that if you get stuck, hit the extensive support documentation wiki (complete with videos), or head over to the excellent r/meshtastic over on Reddit.

Also: I took apart the new AirTag 2 and found a serious flaw in Apple’s popular tracker

If you just want to use the tracker as a tracker, you’ve pretty much done everything you need to do. The app has a map that shows you the card’s location, or you can use it to beacon your position to others when out and about by sharing your location. 

The card will use Bluetooth to connect to the app on the smartphone, but it can also connect to LoRaWAN, Meshtastic, Amazon Sidewalk, and Helium networks.

Also: My new favorite AirTag alternative fits perfectly in my wallet – and is seriously durable

If you live in an area where there are people using mesh, that’s going to be a great option that just works. You might decide to put nodes up for you to use around your home or work, extending the mesh for everyone. 

I found that Amazon Sidewalk was also a really good, workable option, especially in built-up areas. After all, there are a lot of Amazon devices and Ring cameras out there, so there’s a pretty big mesh you can leverage.

The app has a lot of features, including tracking.

The app has a lot of features, including tracking.

Adrian Kingsley-Hughes/ZDNET

Loads of options. And yes, this could work in those apocalyptic scenarios where the power goes down, the cellular grids go quiet, and the zombies start to shuffle about.

I’ve carried it in a pocket, bag, and strapped to the outside of my rucksack, and the GPS receiver is excellent even under subpar conditions. There’s a lanyard slot on the card for attaching it to things.

But there’s more … a lot more!

But there’s a lot more to the T1000-E. It’s a lot more than a tracker card. It’s a fully functional mesh transmitter that can be used to send and receive messages between other devices. If you want to get into this, then you’ll need another mesh device (say, another tracker or a mesh transceiver). And this is where you start the slide into mesh obsession.

Also: I slipped this stealthy $15 tracker into my favorite jacket – now it’s an everyday essential

You can also delve deeper into the card. For example, you can dump the firmware that Seeed Studio loaded onto the T1000-E, and install stock Meshtastic firmware onto it using the online flasher tool.

I flashed Meshtastic firmware onto the T1000-E.

I flashed Meshtastic firmware onto the T1000-E.

Adrian Kingsley-Hughes/ZDNET

Now you can use the Meshtastic app (iOS/Android) to control the card, and I found this to be a whole lot better, especially if you want to go deeper into the card’s capabilities. This is well beyond what I can go into here, but if you want something to get you started, this video will probably kick-start your obsession.

ZDNET’s buying advice

The Seeed Studio’s SenseCAP T1000-E tracker card will set you back around $50. You’ll sometimes see it for a lot more, but avoid those. Usually, if you wait a day or so, the prices will come down.

Also: These are the closest things to AirTags for Android users (and better in some ways)

For a pure tracker card, that’s a pretty hefty price tag, but once you realize it can also serve as a messaging client and you can use it to send and receive messages for free, it starts to feel more reasonable. Mesh is great for off-grid comms and also for places like cruise ships, where the company will charge you an arm and a leg for Wi-Fi.





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Another day, another politically motivated attack in the United States.

This morning’s shooting at a Dallas ICE detention facility – where a sniper killed two detainees and wounded another before taking his own life prompted me to revisit a question that’s been troubling me: Is political violence actually increasing in America, or does it just feel that way?

To explore this, I’ve conducted what I’ll call a methodological experiment.

Rather than relying on traditional datasets, I’ve used ChatGPT and Claude to construct a synthetic index of political violence in the US since 1945. Let me be absolutely clear: this isn’t conventional data. It’s data generated through language models, with all the limitations that implies.

The Methodology (and Its Limitations)

Here’s what I did: I asked both ChatGPT and Claude to generate lists of politically motivated violent incidents since 1945, then had them score each incident’s severity on a scale where 50 represents a “normal” level.

The models assessed both casualties and symbolic significance, and I used them to cross-check each other’s work. I then quality-checked the output myself and categorised perpetrators by political affiliation where this was clearly established.

This approach is, admittedly, unorthodox. Language models are trained on existing texts and may reflect biases in their training data. They might overweight highly publicised events or recent incidents that featured prominently in their training corpus.

The “data” we’re looking at is essentially a structured synthesis of what these models have absorbed about American political violence.

Yet there’s something intriguing here. These models have processed vast amounts of information about political violence – news reports, academic studies, government documents. Their output might capture patterns that traditional datasets miss, though it might also amplify certain narratives or blind spots.

What the Synthetic Data Reveal

With those caveats firmly in mind, the patterns that emerge from this exercise are concerning. The model-generated index shows a clear upward trend in political violence over the past decade.

Looking at the breakdown by perpetrator ideology (where clearly established), the data suggest that right-wing extremist groups have been responsible for the majority of incidents in recent years, though we cannot draw conclusions about today’s attack whilst investigations are ongoing.

The synthetic data align with some empirical observations. Princeton’s Bridging Divides Initiative recorded over 600 incidents of threats and harassment against local officials in 2024 – a 74% increase from 2022. The University of Maryland found that in the first half of 2025, 35% of violent events targeted U.S. government personnel or facilities – more than twice the rate in 2024.

The Charlie Kirk Assassination and Recent Patterns

The September assassination of conservative activist Charlie Kirk marked a particularly dark moment.

The incident followed numerous recent acts of political violence, including the murder of Minnesota Democratic state Rep. Melissa Hortman and her husband, and two assassination attempts on President Trump in 2024.

What the synthetic data reveal is not just increased frequency but a shift in patterns. While overall levels of physical political violence remained low in 2024 compared to years prior, acts of vigilante violence grew as a proportion of all reported incidents.

We’re seeing less organised group violence and more lone-wolf attacks – a pattern that’s harder to predict and prevent.

The Epistemological Challenge

When we use language models to generate “data” about social phenomena, what exactly are we measuring? We’re essentially extracting structured information from the collective corpus of human writing about these events. It’s aggregating distributed information, but through an AI intermediary rather than traditional data collection methods.

This raises fascinating questions.

The models suggest that right-wing extremist violence has been responsible for a fairly large majority of U.S. domestic terrorism deaths since 2001. But how much of this reflects actual patterns versus the way these events are covered and discussed in the sources the models were trained on?

The synthetic data are, in a sense, a mirror of our collective discourse about political violence. They reflect not just what happened, but how we’ve talked about what happened. That’s both a limitation and, potentially, a feature – understanding the narrative landscape around political violence might be as important as counting incidents.

An Experimental Tool

I’ve built an interactive app (using the AI coding tool Lovable) based on this language model-generated violence index.

Users can explore the synthetic data, examine patterns across different time periods and perpetrator groups, and understand the methodology behind it. Think of it as an experiment in using AI to structure historical information rather than a definitive dataset.

The value isn’t in treating this as gospel truth, but in what it reveals about how these events are recorded, remembered, and synthesised in our collective digital memory.

When language models trained on our civilisation’s text output show rising political violence, it tells us something – even if that something is as much about narrative as about underlying reality.

This morning’s tragedy in Dallas reminds us that behind every data point – whether traditionally collected or AI-generated – there are real victims and real consequences. Understanding the patterns, however imperfectly, is the first step toward addressing them.

Try the tool here.





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