Don’t Buy an Expensive DLP Projector. This Cheaper, Portable Model Is Just as Good


Pros

  • Compact size
  • Fairly accurate colors
  • Optical zoom

Cons

  • VIDAA OS is fine but not common
  • Maybe a little overpriced

You’d think big TV manufacturers would have plenty of engineering know-how to make a great projector. In my experience, though, that’s not always the case. The Hisense M2 Pro, however, exceeded my expectations and does a lot of things really well. It’s decently bright, has fairly accurate color, a really good contrast ratio and even an optical zoom. Combine that with a price of around $1,300 and it’s a pretty easy projector to recommend. 

The M2 Pro’s negatives are largely unrelated to performance, but to price. You can spend a bit more and get a better, brighter projector, or you can spend less and get fairly similar performance. In addition, its onboard VIDAA OS has all the major apps, but perhaps not as many of the smaller ones compared to Google TV. So, basically, the M2 Pro is very good for the money, but at a lower price it’d be amazing.

Specs and such

  • Resolution: 4K 
  • Lumens: 1,300 (claimed)
  • Zoom: 1-1.3:1
  • Lens shift: No
  • Light source type: RGB laser

The compact M2 Pro uses RGB lasers for its light source — not uncommon these days — and it pairs them with a zoom lens, which is fairly uncommon at this price. Combine these features with a built-in gimbal stand and you can put the M2 Pro just about anywhere without issue. It’s also 4K resolution, whereas many models at a similar price are 1080p.

Closeup of the lens of the Hisense H2 Pro.

Geoffrey Morrison/CNET

Hisense claims 1,300 lumens. In its most accurate mode, I measured 803. With the Brightness Enhancement feature set to Ultra, the image is noticeably green, but I measured 1,183 lumens. Given unit-to-unit variation, not to mention different measurement techniques, I’d say this is pretty close to its claimed specs. Each of the other two Brightness Enhancer modes became progressively closer to the projector’s most accurate image but with decreasing brightness boosts. High mode produced 1,006 lumens but skewed noticeably blue, while Standard mode delivered 957 lumens and was only slightly cooler than the most accurate picture mode.

Connections

  • HDMI inputs: 1
  • USB port: 1
  • Audio: eARC, 2x10W speakers
  • Internet: Wi-FI
  • Streaming interface: VIDAA OS
  • Remote: Backlit

Like most small projectors, there’s only one HDMI input on the Hisense. Since streaming is built in, this is fine. If you want to connect a streaming dongle, there is a USB output to power it. Plus, unlike most projectors in this price range, the Hisense uses its own OS called VIDAA. It’s fairly simple to operate and comes with a row of apps to choose from. All the major options are here, but if you have a more obscure favorite streamer, you may want to check it’s available first. This is the second projector I’ve reviewed with VIDAA onboard. Coincidentally, I reviewed the other one immediately before — the much more expensive Leica Cine Play 1

The back of the Hisense H2 Pro projector.

Geoffrey Morrison/CNET

The two 10-watt speakers don’t have much bass — or treble, for that matter — but they do get quite loud. Sound quality deteriorates noticeably at higher volumes, with a fair amount of distortion, but if you need the extra volume, it’s there. Like all projectors, you’re better off connecting a soundbar or to a receiver with speakers.

Picture quality comparisons

JMGO N1S 4K

The JMGO N1S 4K is similar in size, similar in original MSRP and, broadly speaking, similar in performance. It’s compact, on a pivot gimbal and uses lasers for its light source. When I reviewed the JMGO, the price was the same as the M2 Pro is now, but it has since fallen to under $800. I connected both of the projectors to a distribution amplifier and viewed them side by side on a 102-inch, 1.0-gain screen.

Once set up as similarly as possible, a number of things were instantly apparent. The first has to do with color temperature. Both are fairly close to accurate, but wrong in opposite ways: The M2 Pro looks a little blueish/greenish (lacking red), while the N1S 4K looks a little reddish. Subjectively, the N1S 4K looks slightly more natural, as the slight green push on the M2 Pro gives skin tones a mild pallor. However, on its own, the M2’s slight color tint isn’t as noticeable and there are plenty of ways to adjust it in the settings, even if you don’t have the projector professionally calibrated. Neither has the spot-on color of something like the BenQ HT2060, but they’re acceptably close enough. 

The side view of the Hisense H2 Pro tilted vertically.

Geoffrey Morrison/CNET

The M2 Pro is brighter, producing 803 lumens versus the Hisense’s 575. Light output is especially important with projectors because it also determines how large an image you can create while keeping it bright enough to watch. Viewed side by side, the M2 Pro is noticeably brighter, though subjectively the difference doesn’t seem as dramatic as the roughly 33% gap in measured brightness suggests. It’s more a case of “the Hisense looks a little brighter” than “why is the JMGO so dim?”

Contrast, too, is fairly close, with the M2 Pro measuring a highly respectable 1,482:1 compared to the N1S’s ~1,196:1 (which is more of an estimate due to how that projector handles black levels). The difference is large enough that, combined with the greater light output, the M2 Pro’s image has a little more punch. It’s not massive, but it is noticeable. Both projectors’ brightness and contrasts are better than the average of the projectors I’ve reviewed for CNET, while the M2 Pro is closer to the best performing DLP projectors, all of which are more expensive.

A quick word of caution about RGB lasers, though. Like all RGB laser projectors, the M2 Pro can present an issue for some people who wear glasses. Depending on your prescription and lens material, you may see chromatic aberration, or color fringing, around bright objects. This is especially noticeable when the objects are on a dark background, such as white credits on a black screen or streetlights at night. It will look like the image “splits” with a single-color “ghost” on either side and is separate from the rainbows some people see with DLP projectors. For what it’s worth, I notice it and find it annoying enough that I personally wouldn’t buy an RGB laser projector. If you think this might be an issue for you, check out the TK705STi instead, as it uses LEDs and doesn’t have this issue.

The Hisense H2 Pro with its remote on a black background.

Geoffrey Morrison/CNET

Speaking of the TK705STi and its standard-throw sibling, the TK705i, I didn’t have either on hand for a direct comparison. Both, however, are priced similarly to the M2 Pro. The STi is significantly brighter than the M2 Pro, though in my testing, its color was slightly less accurate and its contrast ratio was lower. It’s also a short-throw projector, which may make it a better fit for smaller rooms but a less practical choice for larger ones.

I didn’t review the TK705i, and because it uses a different lens, I can’t say how its performance compares with either projector. It’s likely closer to the TK705STi than the M2 Pro in terms of brightness, however. Both BenQ models use LED light sources, so they avoid the RGB laser issues mentioned above.

There’s also the HT2060, one of my picks for best projector. While brighter and, as mentioned earlier, more accurate, it’s not gimbaled and is “only” 1080p. It’s not exactly a direct competitor, but if you’re looking for more permanent placement for your projector, check that one out, too.

TV 2 PJ

Hisense M2 Pro

Geoffrey Morrison/CNET

I’m concerned I may have damned the M2 Pro with faint praise. There just isn’t a lot to complain about, but it’s also not the most amazing PJ I’ve seen. It’s solid. It’s an A-. It’s one of the best options for the price. My only real issue is that it’s in this weird place where spending a little more can get you a lot more performance, and spending a lot less only gets you a little less. While technically you could say it’s a little overpriced, it performs better than some other projectors around the same price, meaning “overpriced” isn’t exactly accurate either. 

I’ve been reviewing devices for a long time, and products like this are among the hardest to write about. Bad products are challenging to review because you end up double- and triple-checking your results, but they’re fairly easy to explain. Great products are easy for obvious reasons. It’s the good-but-not-great products that are tricky. The M2 Pro falls into that category. It’s a solid projector with few major flaws, but it doesn’t stand out in any one area. That makes it relatively easy to review and surprisingly difficult to write about — hence this paragraph.

I guess the spoiler in the M2 Pro conversation is the JMGO N1S 4K, which is why I mentioned it above. It’s currently going for under $800. Is the M2 Pro better? Yes, but not ~65% better as the prices suggest. The Hisense does have an optical zoom and, for better or worse, does not have Google TV. Basically, if you don’t care about that price difference, or you see it on sale, get the M2 Pro. If you want to save some money and don’t mind not having a zoom, get the N1S 4K. 





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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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