The Verge
Apple’s failed self-driving car program left a legacy of powerful AI chips
Apple's abandoned self-driving car project may have been a commercial failure, but it quietly reshaped the company's entire chip strategy. The push to process complex AI workloads on-device for the car program directly led to the creation of the Neural Engine, now the foundation of Apple's silicon advantage. What looked like a dead end turned out to be the R&D backbone behind some of the most capable AI chips in consumer technology.
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OpenAI Forked Git on GitHub
OpenAI has forked the Git source code repository on GitHub, raising questions about what modifications or experiments the AI company might be making to the foundational version control tool. While details remain sparse, a fork of this nature from a major AI lab tends to signal either internal tooling needs or exploratory work on AI-assisted developer workflows. The move is worth watching given Git's role as the backbone of virtually all modern software development.
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Death of the Status Update: Why 55% of Americans Stopped Posting on Social Media
More than half of Americans have quietly stepped back from posting on social media, signaling a significant shift in how people relate to public self-expression online. The trend points to growing fatigue with performative sharing, privacy concerns, and the increasingly toxic nature of public comment sections. As passive consumption replaces active participation, platforms built on user-generated content face a fundamental challenge to their engagement models.
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Mesh LLM: distributed AI computing on iroh
Mesh LLM is a new project that enables large language model inference to run across distributed nodes using iroh, a peer-to-peer networking library. Rather than relying on centralized cloud infrastructure, the approach splits computation across multiple machines connected via iroh's direct connectivity protocol. The result is a framework that could make running large models more accessible without requiring expensive single-node hardware.
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Why Write Code in 2026
Writing code in an AI-saturated landscape is no longer just about automation — it's about understanding systems deeply enough to direct them. The argument here is that developers who can read, write, and reason about code will outperform those who merely prompt their way to solutions. Craft still compounds; abstraction without comprehension creates brittle work.
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