Makita Table Saws Were Discontinued In The US, But You Can Still Buy Them Here







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Makita is one of the go-to brands for both pros and DIYers alike, and much of that is due to the manufacturer’s reliable selection. From simple hand tools and accessories to heavy-duty equipment and more, there’s a lot you’ll want to know about Makita tools before you consider buying one. But what happens when the company discontinues a specific tool, like the Makita table saw, in the United States? If you’re searching for one, you do have options, but they’re very limited.

Beyond Tools, an online Australian retailer, has two models of the Makita table saw: a 254/260-millimeter model for $637 and the 255-millimeter model for $878. However, its inventory is low, and shipping is not free. Both new and used Makita saws are also listed on eBay, where you’ll pay anywhere from $329 to $1,500 or more, depending on what you buy. Italian-based Mister Worker has the Makita 1650W 260-millimeter table saw for $1,862.20, and shipping varies by location. In fact, depending on where you are, you could pay more than $1,000 for shipping alone. 

The problem is that neither Lowe’s nor Home Depot typically carries Makita table saws either in-store or online. The same is true for Ace Hardware as well. While you could get lucky and randomly find one from a U.S. retailer online, nearly all of the top tool retailers are either out of stock or don’t regularly carry Makita table saws. Even Amazon and Walmart are hit-or-miss, and in both cases, you could be buying from a third-party seller and not directly from Makita.

How table saws compare with alternative options

Makita, as a company, has not specifically addressed why it discontinued table saws in the U.S. Though there is some speculation about the company’s motives, none of it has been confirmed by Makita. The manufacturer does continue to offer a selection of corded and cordless models, including cordless circular saws, miter saws, and reciprocating saws. This may suggest that Makita’s focus shifted to other options, rather than continuing to focus on the table saw category.

The issue is that a table saw, by design, is much different from these other saws, and as a result, it’s better for performing specific tasks. A table saw is built as a stationary cutting system, which means the blade stays in one place while the user guides the wood across it. This allows for highly consistent, repeatable rip cuts and precise straight edges. Additionally, unlike circular saws and reciprocating saws that rely on hand control, table saws use a stabilized fence system. This makes a difference in woodworking specifically, where accuracy and repetition matter.

The Makita Corded Plunge Saw track system could potentially be seen as a viable alternative to the table saw. This is especially true when working with large sheet materials. Using this Makita saw system involves running the circular saw along a guided rail, which allows users to make long, straight cuts. But you’re still moving the tool in this system instead of the wood, which makes it a different process overall.





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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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