Users Say This Popular Hand Tool Brand’s Quality Is On The Decline







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Nothing stays the same forever, and it’s always sad to see something you loved change for the worse. This is the predominant sentiment you’ll find expressed by online communities toward a once-beloved Made in the USA brand: Klein Tools. This sentiment is often seen in Reddit’s tool user subreddits, like r/electricians, which is hardly surprising, since Reddit is the world’s biggest online forum (despite numerous controversies plaguing the website) and electricians are some of Klein Tools’ main buyers.

Tradespeople are often found posting pictures of damaged, recently bought Klein hand tools, asking if the rest of the community has had similar issues or if the thread’s author just got unlucky. The bulk of the answers blame Klein Tools, not the users. According to the community, quality issues affect many of Klein’s hand tools, from wire strippers that don’t close as tightly as they should and let materials slip to pliers with rubber handle grips that become loose. And while the community rarely speaks about Klein’s electronic tools, they’re hardly fail-proof. In 2021, the company recalled a non-contact voltage tester after selling about 1.7 million units.

Skepticism and outright hatred of Klein Tools is so popular that the author of an old Reddit post on r/electricians was mocked by the community for acting as if disliking Klein Tools was an unpopular opinion. Things have only gotten worse for Klein in recent years, as posts about the presumed poor quality of its tools multiply, and a new word was coined to explain what happened to the company: de-Klein, presumably pronounced “decline.”

Can you still trust Klein Tools?

Plenty of Klein Tools’ most popular products have excellent user reviews, meaning they’re probably good enough for most users. Professional electricians, especially those who spend their time on tools and trade online forums, are prone to noticing quality issues that will escape others. But does this make their opinion more valuable to you? Not necessarily. DIYers won’t notice many of the issues reported by professionals — poor quality or not, you won’t strip the rubber grip off a hammer by using it twice a year. It’s also possible that some of the shattered tools paraded on Reddit were broken by improper use, not faulty design.

On the other hand, there are too many of these posts for it to be just a trend. Specialized publications are starting to change their tune about Klein as well, even if they aren’t quite as radical as some users. Professional reviewers at Pro Tools Reviewed liked the brand’s new laser level, but that’s the only Klein tool they reviewed in the last few years. We at SlashGear named Klein Tools one of the best hand tool brands, but with the huge caveat that, unless you’re an electrician, it’s more expensive and doesn’t offer as much choice as many other brands.

Of course, this doesn’t mean you should replace some perfectly functional tools just because they’re made by Klein, but new purchases are a different story. For DIYers, Klein Tools is still a great brand. For professionals, it may be worth giving a shot to another premium toolmaker, like Wiha or Knipex. The worst that can happen is that you spend $5 extra on a pair of pliers.





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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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