TLDR 2026-02-12

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Several highly anticipated Siri functions may be pushed back as testing has run into snags in recent weeks. Apple now plans to spread out new capabilities over several versions, so some features will be postponed until iOS 26.5 or iOS 27. One feature especially likely to slip is the expanded ability for Siri to tap into personal data. Another feature running behind schedule is voice-based control of in-app actions. While early support for these features exists, they don't function reliably in all cases.

Elon Musk has restructured xAI following the exit of two of its co-founders earlier this week. xAI will now be organized into four core areas: Grok's chatbot and voice product, Coding, the Imagine video product, and Macrohard. The company plans to launch a new X Chat app and X Money in the coming months. It is currently looking to hire.

China's space program successfully carried out a flight of a new reusable booster and crew capsule late Tuesday. The Long March 10 rocket and Mengzhou spacecraft are core elements of China's lunar architecture. The US and China are racing to accomplish the next human landing on the Moon in a competition for national prestige and lunar resources. The successful test moves Chinese engineers closer to human-rating the Mengzhou crew capsule. A video recapping the test flight is available in the article.

Tech is starting to turn to AI to probe the possibility of digitizing smell in the hopes of understanding and manipulating this sensory modality. Programming smell may illuminate many mysteries about the sense. It could also have many practical applications, for example, computational smell could help detect gas leaks, reduce reliance on intensive natural ingredients for perfume, or lead to the creation of entirely novel smells. This article takes a look at the state of the research and the future of the industry.

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Most future distributed code will be AI-generated. This makes it important whether we ask AI to aim for ordinary imperative code or at frameworks where distributed contracts are part of the language surface. Targets like this help developers focus on the right questions rather than chasing shadows. AI can help build systems that fail less often if given a target that reflects distributed reality.

Never let Claude write code until you have reviewed and approved a written plan. This separation of planning and execution prevents wasted effort, keeps users in control of architectural decisions, and produces significantly better results with minimal token usage. This post describes a workflow that follows this core principle. The workflow is radically different from how most people approach AI tools.

AI works, it improves predictably, and the richest institutions in history are committing trillions to it. It isn't a fad. The next few years will be disorienting in ways most people aren't prepared for. The people who will come out of this best are the ones who start engaging now.

The story of the AI boom so far is one where the dominant narrative about AI is not what it has built, but the rate at which people are consuming it, the rate at which companies are spending on GPU farms, and the rate at which tools are being expensed. AI is everywhere in consumption and almost nowhere in output. We are spending unprecedented sums to invest in systems, and the primary product of that spending is the experience of spending it.

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Asking what is wrong with an idea can surface hazards early on.

The opening phase of a startup involves the magic of transforming from a few people into a 'startup', the midgame begins once product-market fit has been reached, and the endgame is the infinite future that is never really reached.

AI can provide a lot of value, and this makes it necessary to consider where that value is captured so that both employees and companies benefit.

SpaceX is regulated by the Railway Labor Act, which exempts employees from the National Labor Relations Act.

Chinese autonomous vehicle firms are way ahead of the American competition in terms of international expansion.

The companies with the strongest moats in an AI-commoditized world will be energy and logistics companies, compute companies, and relationship-based companies.

As the first engineer on TLDR's new Applied AI team, you'll build AI agents and composable Claude Skills to let non-technical teammates create their own AI workflows. Learn more.