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From paper launches to melting connectors: The worst GPUs of all time, ranked

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worst GPUs of all time

Nvidia GeForce RTX 4090: The $1,600 power connector that melted

When Nvidia launched the RTX 4090 in late 2022, it was the fastest consumer GPU ever made. But within weeks, reports started flooding forums: the new 12VHPWR power connector was literally melting. The problem? A poorly designed adapter that could overheat if the cable wasn’t seated perfectly — or if it was bent too sharply inside a case.

Nvidia eventually blamed user error, but the damage was done. For a flagship card that cost $1,600 at launch, this wasn’t just a defect. It was a worst GPU moment in an otherwise stellar generation. The RTX 4090 remains a beast for gaming and AI workloads, but its launch will forever be tied to fire hazards and RMA horror stories.

The connector fiasco in numbers

Over 100 confirmed cases of melted connectors were documented by Gamers Nexus alone. Nvidia revised the adapter months later, but the trust was gone. If you’re building a high-end PC today, you’re still gambling on that 12VHPWR plug — and that’s a problem Nvidia has yet to fully solve.

Intel Arc A770: The comeback that never was

Intel entered the discrete GPU market in 2022 with the Arc A770, promising to challenge Nvidia and AMD. The hardware was ambitious: 16GB of VRAM, ray tracing support, and XeSS upscaling. But the drivers were a disaster. Games that ran fine on any Nvidia or AMD card would crash, stutter, or refuse to launch on Arc.

Intel fixed many of the driver issues over the following year, but for early adopters, the Arc A770 was a frustrating, unfinished product. By the time it worked well, buyers had already moved on. It’s a textbook case of why worst GPUs aren’t always slow — sometimes they’re just broken.

AMD Radeon RX 7900 XTX: The vapor chamber that failed

AMD‘s Radeon RX 7900 XTX launched in December 2022 as a flagship competitor to Nvidia’s RTX 4080. But within weeks, reviewers and users noticed something alarming: the card would overheat and throttle under load, with GPU hotspot temperatures exceeding 110°C. The culprit was a defective vapor chamber in some reference models.

AMD acknowledged the issue and offered replacements, but the damage to the RX 7900 XTX’s reputation was real. For a $1,000 card, this was unacceptable. The worst graphics cards in history share a common thread: they fail at the basics. The 7900 XTX failed at staying cool.

Nvidia GeForce RTX 4060 Ti: The 8GB embarrassment

In May 2023, Nvidia released the RTX 4060 Ti with 8GB of VRAM — at a time when games like Hogwarts Legacy and The Last of Us Part I were already exceeding 8GB at 1080p. Gamers were furious. Why would Nvidia ship a $400 card with such limited memory?

The answer was obvious: market segmentation. Nvidia wanted to push buyers toward the more expensive 16GB version (which cost $500 and barely sold). But the 8GB RTX 4060 Ti became a punchline. It’s one of the worst GPUs of all time not because it’s slow, but because it’s insulting.

Nvidia GeForce RTX 4080 12GB: The card that got cancelled

Originally announced as the RTX 4080 12GB alongside a 16GB version, this card caused immediate backlash. The two “4080” models had completely different specs: different memory buses, different core counts, and different performance levels. Gamers called it deceptive, and Nvidia quickly backtracked.

In October 2022, Nvidia “unlaunched” the 12GB model, rebranding it as the RTX 4070 Ti months later. The whole episode was a PR disaster. It’s a rare case of a worst graphics card that never even hit shelves — but the confusion and anger it caused still haunt Nvidia’s reputation.

AMD Radeon RX 6500 XT: The 4GB card that couldn’t even play modern games

Released in January 2022, the RX 6500 XT was supposed to be an entry-level card for budget gamers. But AMD cut so many corners that it was nearly unusable. It had only 4GB of VRAM — at a time when even $200 cards had 6GB or 8GB. Worse, it lacked hardware encoding for video streaming and had no 4K output support.

In benchmarks, the RX 6500 XT was slower than the RX 570 from 2017. It couldn’t run Call of Duty: Warzone at 1080p without stuttering. It’s widely considered one of the worst GPUs of the modern era, and a low point for AMD’s Radeon division.

Nvidia GeForce GTX 970: The 3.5GB lie

The GTX 970 was a popular mid-range card when it launched in 2014. But users soon discovered a dirty secret: Nvidia advertised it as having 4GB of VRAM, but the last 0.5GB was accessed at a much slower speed. In practice, the card performed like it had 3.5GB of fast memory and 0.5GB of slow memory.

The class-action lawsuit that followed forced Nvidia to pay out $30 million in refunds. The GTX 970 is a classic example of a worst GPU that sold millions — but left a bitter taste for everyone who bought one.

Intel Arc A750: The driver nightmare continues

Intel’s second attempt at a discrete GPU, the Arc A750, launched alongside the A770 in 2022. It was cheaper, but it inherited the same driver problems. Games that relied on older DirectX 9 or 11 APIs ran poorly or not at all. Intel promised fixes, but they came slowly.

By the time the drivers were stable — around mid-2023 — the A750 was already obsolete in terms of performance. For budget-conscious buyers, it was a gamble that rarely paid off. The A750 is another entry on the list of worst graphics cards that could have been great if Intel had just waited until the software was ready.

Nvidia GeForce RTX 3080 12GB: The paper launch that angered everyone

In January 2022, Nvidia released an updated RTX 3080 with 12GB of VRAM and a slightly faster core. But stock was virtually nonexistent. Scalpers bought up every unit, and the card was sold at double its $699 MSRP for months. Gamers who couldn’t find a standard RTX 3080 were left frustrated.

The 12GB version was a paper launch — a product that existed mostly on paper and in press releases. It’s a reminder that the worst GPUs of all time aren’t always the slowest; sometimes they’re the ones you can’t even buy.

Nvidia GeForce RTX 4060: The 60-class card that barely improved

When Nvidia released the RTX 4060 in June 2023, expectations were low. The RTX 3060 had been a solid 1080p card, and the 4060 needed to beat it. It did — by about 15%. But that was only with DLSS 3 Frame Generation enabled. Without it, the 4060 was barely faster than the 3060, and sometimes slower in ray tracing.

At $299, it wasn’t a bad deal. But for a generation-to-generation upgrade, it was disappointing. The RTX 4060 is a worst GPU in the sense that it represents everything wrong with modern GPU launches: tiny performance gains, reliance on upscaling, and a lack of meaningful innovation.

The common thread: Why these GPUs failed

Whether it’s melting connectors, broken drivers, or deceptive marketing, the worst graphics cards in history share one thing: they broke the trust between manufacturers and users. A GPU is the most expensive component in a gaming PC. When it fails — literally or metaphorically — it leaves a lasting impression.

If you’re shopping for a new GPU today, learn from these mistakes. Read reviews from multiple sources. Check for known issues like connector problems or driver bugs. And never, ever buy a card based on promises of future fixes. The best GPU is one that works out of the box.

For more on avoiding bad hardware, check out our guide on how to choose a graphics card and our ranking of the best GPUs for gaming in 2025.

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From Gems to Employees: How to Turn AI Tools Into Autonomous Workers

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The Weekend Is Almost Here: Don’t Miss This AI Roundup

Friday’s looming. You’ve got one foot out the door, but before you log off, there’s a batch of AI intel worth your attention. Save it for later if you must — just don’t skip it.

This week’s edition zeroes in on three areas that matter if you’re building a serious AI workflow: converting Google Gems and custom GPTs into reusable Skills, training autonomous AI employees that actually meet your standards, and the latest industry moves from Gemini and others.

What Are Gems and GPTs — and Why Convert Them?

You’ve probably tinkered with custom GPTs in ChatGPT or played with Gems in Gemini. They’re handy, sure. But they’re also siloed. A GPT lives inside ChatGPT; a Gem stays in Gemini. That’s fine for personal use, but it falls apart when you want a consistent process across your whole operation.

The fix? Turn them into Skills. A Skill is a packaged, reusable capability that any AI agent can call on — regardless of the underlying model. Think of it as the difference between hiring a freelancer who only works in one app and hiring someone who can plug into your entire tech stack.

This isn’t just a nice-to-have. If you’re serious about building autonomous AI employees, Skills are the building blocks. They let you standardize how your AI handles specific tasks, so you’re not reinventing the prompt wheel every Monday morning.

How the Conversion Works

  • Audit what you have: List every custom GPT and Gem you’ve created. Which ones actually save time?
  • Extract the core logic: Strip away the interface and isolate the instructions, knowledge files, and workflow steps.
  • Repackage as a Skill: Define clear inputs and outputs. A Skill should be callable by any agent, not tied to a chat window.

It sounds technical, but the payoff is real. Once your best prompts become Skills, they’re versionable, shareable, and — crucially — usable inside automated pipelines.

Training AI Employees That Match Your Standards

Here’s the uncomfortable truth: most people’s AI assistants are mediocre because they’re trained by accident, not by design. You let the model guess what “good” looks like. Then you’re surprised when the output is generic.

Training an autonomous AI employee is different. You’re not just writing a prompt — you’re onboarding someone. That means setting expectations, providing examples of excellent work, and establishing guardrails for when things go sideways.

Start with a single role. Pick one repetitive task — say, drafting client emails or summarizing industry reports. Build a Skill for it, then train the agent on your feedback loop. Show it what a 9-out-of-10 response looks like. Correct it when it drifts. Over time, it gets faster and sharper.

And here’s the key: it works while you sleep. That’s the whole point of autonomous. You set the standard, the AI meets it, and you’re not in the loop for every single output.

A Simple Training Framework

  1. Define the role: Write a one-paragraph job description for your AI employee.
  2. Give it a Skills stack: Assign the Skills it needs to do the job.
  3. Run a pilot: Test on low-stakes tasks first. Measure accuracy, not speed.
  4. Iterate: Feed corrections back into the Skill definitions.

Done right, you’ll have a workforce that scales without headcount. That’s not sci-fi — it’s just good process design.

Industry News: Gemini and the Wider AI Landscape

Meanwhile, the big players aren’t standing still. Gemini has been rolling out updates that blur the line between chatbot and coworker. New capabilities are pushing toward longer context windows, better tool use, and more reliable multi-step reasoning.

What does that mean for you? The gap between consumer AI and enterprise AI is shrinking. Tools that felt experimental six months ago are now production-ready. But that also means the bar for differentiation is higher. Anyone can chat with a bot. Few can deploy a fleet of autonomous AI employees that actually deliver.

The winners this year won’t be the ones with the fanciest models. They’ll be the ones who figured out how to turn AI into a repeatable, trainable workforce. That starts with Skills — and ends with results.

So before you head out for the weekend, ask yourself: are you still playing with AI, or are you actually putting it to work?

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Judge Hands X a Split Verdict in Trademark Fight With Twitter Rival Tweet.app

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Tweet.app trademark ruling

A Startup Built on Trademark Scraps

Most social media startups begin with a big idea. Operation Bluebird began with a legal loophole. The Virginia-based company, founded by two lawyers, launched with the explicit goal of picking up the trademarks Elon Musk discarded when he renamed Twitter to X. Its homepage doesn’t hide this. It says the company wants to go back and grab what Musk dropped when he “threw the bird away on his way out.”

That’s not your typical mission statement. But it might be a clever one.

On Wednesday, a federal court in Delaware delivered a split ruling in the trademark fight between X Corp. and Operation Bluebird. Judge Colm F. Connolly granted X’s request for a preliminary injunction on eight Twitter-related marks. That means the startup can’t call itself Twitter.now. The core “Twitter” name stays with X, at least for now.

But here’s the twist. The judge denied X’s motion regarding two other marks: the word “tweet” and the Twitter bird logo. He wrote that Operation Bluebird was “likely to succeed in proving both that X Corp. discontinued the bona fide use of the Tweet mark and Bird logo and that it intends not to resume the use of the marks.”

In plain English: X gave up on those words and images. The public kept using them, and now a rival can too.

The Lawyers Behind the ‘Tweet’ Revival

Operation Bluebird isn’t run by Silicon Valley dreamers. It’s led by Michael Peroff, an Illinois-based attorney, and Stephen Coates, who once worked as a trademark lawyer at Twitter. Their legal pedigree makes their claim of wanting to build a fresh social network feel a bit thin. The real prize here is likely the trademarks themselves, which carry value independent of any actual product.

Still, the startup is going through the motions. It has rebranded its website as Tweet.app and opened its doors to early testers. The company told TechCrunch that more than 172,000 people requested a handle before launch. That number probably reflects lingering public affection for the Twitter brand — a name the startup can no longer use.

There’s a catch, though. To reserve a handle and join, users must pay $20. That fee likely helps cover the mounting legal bills.

What the Judge Actually Decided

This ruling isn’t final. It’s a preliminary injunction, which means the court is weighing the likelihood of success on the merits. Judge Connolly sided with X on the eight marks tied directly to the Twitter name. He sided with Operation Bluebird on the Tweet mark and the bird logo.

The case will now proceed to a full trial to determine whether X retains any rights to the Twitter marks, given that the company now operates under the X banner in most places.

For now, the practical outcome is this:

  • X keeps exclusive rights to the “Twitter” name.
  • Operation Bluebird can use the word “tweet” and the bird logo.
  • The startup must rebrand away from Twitter.now.

A Question of Abandonment

The core legal question is whether X abandoned these marks through non-use. In trademark law, abandoning a mark means discontinuing its use with no intent to resume. Musk’s aggressive rebrand to X in 2023 left the word “tweet” and the bird logo in limbo. The company kept the Twitter handle on its own platform but stopped using the bird in most official capacities.

Coates, now president of Operation Bluebird, framed the ruling as a victory for the public. “They kept the word. They let go of the bird, and they let go of the tweet,” he wrote in an announcement shared via email with TechCrunch. “A tweet was never a corporation. It’s one person saying something. That word survived three years of a company trying to replace it, because the public declined to stop using it. We think that tells you who it belongs to.”

That’s a poetic argument. Whether it holds up in court remains to be seen.

What This Means for X and Its Rivals

For X, this ruling is a mixed bag. It protects the core Twitter trademark, which the company still uses in some contexts. But it opens the door for competitors to use the word “tweet” and the bird imagery — elements that remain culturally significant even if X has moved on.

For anyone tracking Elon Musk’s X rebrand and its legal fallout, this case is worth watching. It could set a precedent for how courts treat abandoned trademarks in the fast-moving world of social media. If X loses the full case, it might have to accept that the bird and the tweet belong to the public now.

The startup’s approach is unusual, but it’s not without precedent. Companies have long scooped up abandoned trademarks and repurposed them. The difference here is scale: Twitter’s marks are among the most recognized in internet history.

For now, Tweet.app lives. Twitter.now is dead. And the bird, it seems, has found a new perch.

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I finally found an easy way to make Windows remember exactly where every app window belongs

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Windows remember app positions

Every morning, the ritual repeats. Open the same half-dozen apps, then spend five minutes dragging, resizing, and nudging each window into its designated spot across two monitors. By lunch, something shifts—a notification steals focus, a window snaps to the wrong screen—and the carefully built layout crumbles. It’s maddening.

I’d tried every trick I knew. Windows built-in snap layouts help, but they only work one window at a time. Third-party tools exist, but most feel like overkill or cost money. Then I stumbled on a free utility hiding inside PowerToys: Workspaces. It’s the closest thing to a magic button I’ve found for Windows remember app positions—and it’s genuinely easy.

What is PowerToys Workspaces?

Workspaces is a relatively new addition to Microsoft’s PowerToys suite, which has been around for years as a grab-bag of productivity utilities. It lets you capture a snapshot of your current desktop layout—which apps are open, where they sit, their size, and even which monitor they’re on—and save it as a named workspace.

Later, with one click, you can relaunch all those apps and have them snap back into their exact positions. No dragging. No resizing. No remembering which browser profile goes on the left screen. It’s like a bookmark for your entire desktop.

Why I stopped using manual snap layouts

Windows Snap Layouts (the hover-over-the-maximize-button feature) is fine for a quick two-window split. But it fails when you need a precise arrangement across multiple monitors or want to restore a full set of apps after a reboot. Workspaces solves that because it handles the whole layout as one unit, not window by window.

How to set up Workspaces in PowerToys

Here’s the part that sold me: setup takes less than a minute. You don’t need to configure anything complicated—just capture what’s already on your screen.

  1. Install PowerToys from the Microsoft Store or GitHub if you haven’t already.
  2. Open PowerToys and select Workspaces from the left sidebar.
  3. Arrange your apps the way you want them on your monitor(s).
  4. Click Capture Workspace—PowerToys takes a snapshot of every open window’s position and size.
  5. Give your workspace a name, like “Work” or “Coding Setup.”
  6. That’s it. You can now launch that workspace anytime from the Workspaces editor or a shortcut.

One note: Workspaces works best with apps that support command-line launching or have standard window handles. Most everyday programs—browsers, editors, terminals, chat apps—work flawlessly. Occasionally, a stubborn app might not restore perfectly, but in my testing, that’s rare.

Restoring a layout with one click

Once you’ve saved a few workspaces, using them is even simpler. Open the Workspaces editor, hover over a saved layout, and hit the launch button. PowerToys opens all the apps and arranges them exactly as captured. You can also assign a keyboard shortcut to each workspace, which turns the whole process into a single keystroke.

I now have three saved workspaces: one for writing (browser, editor, notes app), one for development (terminal, IDE, preview window), and one for communication (email, Slack, calendar). Morning startup went from a five-minute chore to a ten-second flick of a key.

What makes this different from other layout tools

I’ve tested utilities like FancyZones (also part of PowerToys) and paid tools like DisplayFusion. FancyZones is excellent for creating custom snap regions, but it doesn’t launch apps or restore a full set. DisplayFusion does window management across monitors, but it costs money and has a steeper learning curve.

Workspaces hits a sweet spot. It’s free, it’s built into a tool you might already use, and it requires zero configuration for basic use. If you’re juggling multiple monitors or just tired of rearranging after every reboot, this is the easy way to make Windows remember app positions without buying anything or wrestling with scripts.

Tips for getting the most out of Workspaces

  • Capture after a clean setup: Close apps you don’t want in the layout before capturing, so you don’t freeze in a stray window.
  • Use separate workspaces for different tasks: Don’t try to cram everything into one layout. Save distinct setups for distinct modes of work.
  • Combine with FancyZones: If you want more granular control over snap regions, set up FancyZones first, then capture a workspace that uses those zones.
  • Test with your key apps: Some apps (like elevated admin tools) may not restore automatically. Test your core set to see if anything needs a manual nudge.

If you’re like me and you’ve spent months fighting your own desktop, give Workspaces a shot. It’s the rare productivity fix that actually sticks. And for more Windows productivity tips, check out our guide on customizing your taskbar for faster workflows or setting up a multi-monitor display like a pro.

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