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Bitrig, An AI Development Assistant from SwiftUl Team Members

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Four members of the SwiftUI team at Apple left the company, and now they’ve started a company that writes SwiftUI for you.

The product is Bitrig. You describe an app in plain English, and it generates real Swift and SwiftUI code. What comes out the other end is a project you can open in Xcode, read, and edit by hand. The pipeline runs all the way to TestFlight and the App Store, provisioning profiles and all.

It started as an iPhone-only app last October and now it’s on the Mac. I’ve only poked around the edges so far, but it seems like a good idea if you want some help with native app development.

I’m putting the Mac app on my machine this week to find out.

The post Bitrig, An AI Development Assistant from SwiftUl Team Members appeared first on MacSparky.

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Belfong
4 hours ago
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A few years ago, I worked hard to learn SwiftUI. Now, I feel like it's kinda pointless when AI can do it for everyone. Did I waste my effort? I hope not. Vibe coding is a great thing to get started but there's still lots of customization and tweaking to make an app do what you want, natively.
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Bluesky Is Full of Anti-AI Zealots

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Mike Masnick, in a thread on Bluesky:

Multiple people I know have told me that they love the idea of Bluesky, and want it to succeed, but have abandoned it for X because they use agentic tools in their work and find them incredibly useful, and feel that any mention of their usage here leads to hate and ridicule.

I wouldn’t bring this up if it was just one person, but it was multiple people since the beginning of August who mentioned in passing something to the effect of either “I’d spend more time on Bluesky if people weren’t so anti-AI” or “As much as I hate to be there all the useful AI talk is on X.” [...]

I’m not telling anyone what to do or what tech to use or what to like or not like. I’m just relaying that the reputation this place now has is one that a large segment of the population who are NOT fascists, saying they don’t feel comfortable here because a main area of their interest is considered worthy of being mocked and attacked. And even as I’ve explained that I’ve been able to have positive conversations about productive AI usage here with many of you, the reputation of the community here as insisting their own lived experience can’t possibly be real has driven them away.

It’s not just “the reputation”. That really is the culture of Bluesky. I’ve obviously written more about AI this month than usual — in particular, my two long pieces regarding watermarking LLM-generated text through word-choice diddling. I don’t really write much about AI generally.

Broadly, I’m making two arguments against text watermarking. First, that the embedding of a detectable signal (even if subtle) must necessarily degrade the quality of the generated prose (even if only slightly), and that this is an unacceptable trade-off for a feature that has no benefit to the user and in fact can only be used against the user. Second, that even if I’m wrong about the first part, and a clever enough watermarking scheme can embed a detectable signature without any degradation of prose quality, this is a bad idea and should be rejected.

Reaction to my takes on text watermarking have largely been about these two arguments. Some people agree with me that what watermarking proponents are claiming is not possible — the text quality must degrade. Others agree that while it must have an effect on text quality, it in fact is so subtle that it truly does not matter and even a careful reader will not notice an overall degradation. Others disagree, tell me I don’t understand the cleverness of the systems, and are convinced that somehow this watermarking truly embeds a detectable signal in prose without any degradation of text quality whatsoever. There’s a range of disagreement, but it’s all reasonable. Likewise on the broader question of whether it’s a good idea or not to even try to do this.

The reaction on Bluesky to my recent work is like nothing else. It’s largely in the vein that watermarking can’t possibly make AI-generated text worse because it’s already random gibberish, and that the only explanation for my being opposed to this is that I must be using AI to produce my own writing and I’m upset that my scheme will soon be detectable. Read the replies to this one; they speak for themselves.

I continue to think my friend Daniel Jalkut has espoused the one true take: “My take on AI is, essentially, everybody who’s against it is too against it and everybody who’s for it is too for it.” It is way less inflammatory to espouse an AI-skeptical stance on Twitter/X than it is to simply state that AI can be truly useful on Bluesky.

Anyway, Masnick, at the end of the day yesterday:

The people bitching about me turning off replies on my thread earlier today are doing an excellent job reinforcing why I was right to turn off replies.

Link: bsky.app/profile/masnick.com/post/3mtk7cuvbok2x

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Belfong
6 hours ago
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It’s interesting how a social platform will attracts certain people and then a culture is built around it.
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LLM-Based Social Engineering Scams

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OpenAI disrupted a social engineering group from Cambodia that used ChatGPT. Its scope is impressive:

The network simultaneously conducted multiple types of scams, often blending elements from different schemes. For instance, operators used dating personas to build trust before introducing fraudulent investment opportunities involving cryptocurrencies and spot gold trading. Other users engaged in lengthy romantic conversations with targets using fictitious identities, posed as representatives of online gambling platforms offering fake bonuses and winnings, or impersonated law enforcement agencies to tell targets they needed to pay fines for committing serious criminal offenses.

Although the narratives varied, users across the network consistently displayed the same underlying pattern of deceptive behavior. For example, they created and operated fake dating profiles, fictitious investment experts, and fraudulent law enforcement personas. They also generated images of forged documents, including passports, legal notices, stock-purchase confirmations, and gambling platform interfaces.

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Belfong
2 days ago
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Beware of various schemes used by scammers. If it is too good to be true, it probably is.
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Intel 14A defect density is dropping faster than the company expected — 'we have not seen this performance since 22nm,' says CFO

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Intel's confidence in its 14A (1.4nm-class) fabrication process is rising as defect density drops. The company's own design teams are at work developing products that will use the technology, while external customers are now asking about 14A volumes Intel can get them, according to David Zinsner, chief financial officer of Intel, who spoke at Deustche Bank's 2026 Technology Conference. The CFO went as far as saying that 14A is Intel's best process since 22nm technology from the 2010's. But while the comment is optimistic, there is a caveat.

"When you look at the defect density, 14A is tracking better than the target curve we had for 14A," Zinsner said at Deutsche Bank's 2026 Technology Conference. "It is also doing better than any of the previous nodes in terms of how quickly we are bringing down the defects. In fact, we have not seen this performance since 22nm, which is arguably one of the best nodes Intel has ever put out."

Intel intends to begin risk production of its own products on 14A fabrication process in the second half of 2027 and then initiate its high-volume manufacturing in 2028, so 14A is two years away from mass production. So, what Intel's CFO said at the conference is that at this point in 14A's development, its defect reduction trajectory looks at least as healthy as the trajectory of the company's exceptionally successful 22nm process at a comparable point in its development (i.e., in 2010). There are a couple of catches with such phrasing, though. Firstly, the defect density on 14A now is not necessarily equivalent to a defect density on 22nm two years away from mass production. Secondly, due to advances of wafer processing and inspection equipment, what Intel counts as a defect now may not be the same thing as what it counted as a defect 16 years ago. Furthermore, defect density itself does not directly equal product yield.

Intel's 22nm was the company's first manufacturing process to rely on FinFET transistors and at the time was the most advanced process technology in the world; the rest of the industry moved to FinFET devices only with their 14nm and 16nm-class nodes in 2014 and 2015. By contrast, 14A will use second-generation gate-all-around (GAA) RibbonFET transistors, second-generation backside power delivery called PowerDirect, and will be able to use High-NA EUV lithography due to extremely complex patterning. Keeping all of that in mind, Intel's claim that 14A defect density reduction is progressing unusually well this far ahead of HVM is certainly good news.

Meanwhile, comparing 14A to 22nm's successors should be quite comforting for Intel as 14nm mass production was delayed by a year due to insufficient yield, the first-generation 10nm node was a failure, 20A was cancelled, 18A defect density was high even as it hit HVM milestone, while the company did not share almost any information about the progress of its Intel 4 and Intel 3 nodes.

There are good signs for 14A though: external customers are already developing products for this node, whereas the interest from external customers is now practical rather than theoretical.

"We are now seeing demand from our internal customers on 14A [and] they are actually probably the most cynical bunch out of anybody," Zinsner said. "The fact that they are now designing products on 14A was a good confidence boost for us as well. Then, engagements with customers externally, from a foundry perspective has significantly increased. Lip-Bu and the team are now meeting on weekly basis with customers. They are moving away from just looking at data to thinking about 'how much capacity can I get?' 'what does that supply look like?' So, we are now at a point where we have conviction around customers on 14A externally as well."

Intel initiated mass production of its 22nm-based Ivy Bridge processors in late 2011 and early 2012 and released them commercially in late April 2012. The product was highly successful (though overclockers did not like it because of inefficient thermal interface material between the die and integrated heatspreader), and 22nm fabrication technology served the company for many years, first for CPUs in 2012 through 2016, then for other products. Intel's 14A also promises to be a long-lasting node for Intel.



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Belfong
3 days ago
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Intel 14A = 14 ångström = 1.4 nm (rough comparison). So it is pretty advance. Apple newest Mac mini is manufactured on a 2 nm TSMC process.
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OpenAI, Anthropic, AWS, Microsoft, and 100+ companies warn there is "a limited window" to prepare for AI-enabled cyberattacks and call for "collective action" (Sam Sabin/Axios)

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Sam Sabin / Axios:
OpenAI, Anthropic, AWS, Microsoft, and 100+ companies warn there is “a limited window” to prepare for AI-enabled cyberattacks and call for “collective action”  —  OpenAI, Anthropic, Amazon Web Services, Microsoft and more than 100 other companies warned Thursday …

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Belfong
3 days ago
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Does it mean Cybersecurity companies play an even important role in Enterprise security?
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Pop Goes the Weasels

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I was at PT on Tuesday and a pop music loop was playing.

These were extremely popular songs from the last few years. I recognized most of them, even though I don't listen to pop music. 

What I noticed for the first time, though, because they were back to back to back, was how incredibly algorithm-driven they were. They all used the same elements with only slight variations. They all sounded incredibly artificial. 

They didn't even sound like songs as much as an amalgamation of desirable characteristics as determined by analyzing other successful pop songs. 

Lots of pop songs are like that, really. Not driven by the artist, but by the producer or the record label, with teams of writers providing lyrics. It's always been that way, but with the rise of sophisticated computer-driven production features, it's even more so now.

Everything gets fixed. Nothing is real.

That's when it hit me: this is where AI is going big. 

Top pop songs already sound so artificial that using AI to create the entire song isn't going to sound much different from what's already topping the charts. 

I'll put a stake in the ground here: within the next 24 months, fully AI-generated pop songs are going to be heavily represented in the charts. No artist to manage, barely discernible difference in quality, exponentially lower production cost--it's a record label's dream situation.

I, for one, doubt I'll be able to tell the difference unless the songs are identified as AI-created. I doubt many other people will, either.

Here's the other possibility, and I bet this is already happening: record labels feeding all their most successful pop material (and that of the other labels) into a custom LLM that then creates songs. If they don't want to say it's AI, they then hire someone to sing the song. Technically, that's not AI. Or is it? Who knows?

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Belfong
4 days ago
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I think it’s already happening. What I didn’t expect was that even human made pop songs of say, 10 years ago, were already systematically written based on patterns.
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