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Meta (Facebook) CEO Mark Zuckerberg testified in a trial alleging that social media platforms like Facebook are designed to be addictive, particularly for young users. The trial examines whether tech companies should be held accountable for the negative mental health impacts of their products.

Every AI app builder on the market right now generates cross-platform code. Bolt, Lovable, Rork, they all output React Native or Expo under the hood, or worse, web apps wrapped in a native shell. And I get why. React has more documentation, more training data for LLMs, and it's easier to build a platform around, one codebase, two platforms, faster time to market.

But the apps are bad.

I tried building a polished app with one of these tools and the moment I needed a premium, native-feeling design, I hit a wall. Everything looked like AI slop. Animations were janky. Platform conventions were ignored. Try getting one of these approved on the App Store without weeks of cleanup. Apple doesn't love Expo wrappers, and users can feel the difference whether they can articulate it or not.

So when I started building Nativeline, I made what most people told me was the wrong bet: generate real SwiftUI for iPhone, iPad, and Mac. No React Native. No web views. Actual native Swift that opens in Xcode and builds.

This was significantly harder. Here's why.

The biggest challenge is that LLMs are trained on the entire history of Swift, including five years of deprecated APIs, old UIKit patterns, and SwiftUI code from when it was half-baked. Getting the model to generate modern, clean SwiftUI without reaching for GeometryReader for every layout problem or calling APIs that haven't existed since iOS 15 was a serious effort. It's not just about generating code that runs. It's about generating code that builds without errors, follows current conventions, and reads like something a good Swift developer would actually write.

Then there's keeping up with Apple. When Liquid Glass dropped, every existing LLM was useless for generating UI that used the new framework. If you're generating React Native, you don't really care, you're abstracted away from platform changes. But if you're generating native Swift, staying current with Apple's frameworks isn't optional. It's the whole point.

What we got right: The output is real SwiftUI that you own. Full Xcode project on your machine. One user described a Mac app idea, iterated on it over about 6 days, and is now selling it on the Mac App Store. He didn't write Swift before that. The code was clean enough to ship and maintain.

What we're still working on: Complex backend integrations are hard. Database connections, Supabase, CloudKit, anything with real data persistence, still produce more errors than we'd like. We're working on building deeper internal documentation for the AI specifically around database patterns, potentially our own managed backend, so the model has reliable patterns to follow instead of hallucinating schema configurations.

I think the industry defaulted to React Native because it was the easier path for the builder of the tool, not because it was better for the user of the apps. The hard bet is generating native code. But the output difference is obvious the moment you use the app.

Nativeline is free to try: https://nativeline.ai

Happy to answer questions about the technical approach, the tradeoffs, or anything else.

The article explores the impact of AI on product development, arguing that in the era of AI, the traditional concept of a 'product' is becoming obsolete. It suggests that as AI becomes more integrated into our lives, the focus will shift from discrete products to seamless, personalized experiences.

I built a platform to manage contacts, invoices, expenses, projects, networth and travel in one place. I built this platform after getting frustrated with 6 different tools. This is simple way.

The article discusses the growth of no-code web scraping tools, which allow users to extract data from websites without writing complex code. It highlights the increasing demand for these tools among businesses and individuals looking to automate data collection and analysis tasks.

The article discusses the third commandment of the Decalogue, which prohibits taking the name of God in vain. It explores the various interpretations and implications of this commandment, providing historical and theological insights.

Eurisko was an artificial intelligence program developed by Douglas Lenat in the 1980s. It was designed to automatically improve its own algorithms and heuristics, allowing it to tackle increasingly complex problems in various domains.

The article discusses a GNOME OS hackfest that took place during FOSDEM 2026, where developers gathered to collaborate on improving the operating system. The hackfest focused on enhancing the user experience, streamlining the development process, and addressing technical challenges.

The article discusses a disagreement between White House economic advisor Kevin Hassett and researchers at the New York Federal Reserve over a study on the impact of tariffs. Hassett strongly criticized the study, calling it the 'worst paper' he has ever seen and suggesting the authors should be disciplined.

The article announces Interop 2026, a new initiative by the WebKit team to improve the interoperability of web browsers by collaborating with other browser vendors and focusing on six key areas: CSS, HTML, JavaScript, accessibility, security, and developer tools.

How everybody is falling for Jevon's Paradox:

https://liquidayted.substack.com/p/the-saaspocalypse-is-a-fairy-tale

The article discusses the increasing reliance on large language models (LLMs) and the potential risks of letting them make important decisions without human oversight. It highlights the need for caution and responsible development of these powerful AI systems to ensure they are aligned with human values and interests.

The article discusses how the author significantly improved the performance of their website by converting it from a traditional web application to a Django LiveView single-page application (SPA). This resulted in a 28x speed increase, providing a more responsive and efficient user experience.

The article discusses Microsoft 365 Copilot's new feature on Android and iOS devices, which automatically sends files to the AI-powered OneDrive before opening them, instead of the usual direct file opening. This feature is designed to enhance the AI's understanding of the user's content and improve its ability to provide intelligent assistance.

The article discusses the concept of the 'human in the loop' and how it can lead to burnout for those involved. It explores the challenges of maintaining a balance between human decision-making and automation, and the need to rethink the role of humans in modern systems.

The article discusses the relevance of the 12-Factor App methodology, developed 15 years ago, in the context of modern software development. It examines whether the principles established in the 12-Factor App still hold up today and provides insights on how they can be applied to build scalable, resilient, and maintainable applications.

The article discusses a web-based conversion tool that allows users to easily convert files between various formats, including text, audio, and video. The tool offers a simple and intuitive interface and supports a wide range of file types, making it a versatile solution for users with diverse media conversion needs.

I built Kore because every AI memory tool I found either required cloud APIs, was too heavy, or stored everything forever with no pruning.

Kore is different: - Memory decay based on the Ebbinghaus forgetting curve — memories fade unless retrieved, with half-life based on importance (7 days for casual notes, 1 year for critical info) - Auto-importance scoring locally — no LLM call needed - Semantic search in 50+ languages — local sentence-transformers, zero API calls - Memory compression — auto-merges similar memories - Agent namespace isolation — multi-agent safe - Runs fully offline — SQLite + FTS5, FastAPI, no external services

pip install kore-memory[semantic] then kore to start.

Would love feedback on the decay formula and whether the Ebbinghaus approach makes sense for long-running agents.

The article provides an overview of the psychology of computer programming, examining the cognitive processes and skills involved in software development, including problem-solving, decision-making, and the role of mental models in programming.

The article discusses the creation of a social-oriented 'cookie jar' platform that allows users to share and discover digital artifacts, facilitating a collaborative and interactive experience around digital content.

The article discusses the challenges of developing a usable data science model, highlighting the importance of iterative testing, addressing data quality issues, and incorporating domain expertise to overcome obstacles and achieve a successful deployment.