A few days ago, Apple announced that its “More Personalized Siri” feature would be delayed until next year. Combined with the continued absence of Swift Assist, it’s clear Apple has not fulfilled the AI vision it presented at WWDC 2024. At least in the realm of large language models, Apple has not exhibited its characteristic agility in keeping pace with industry trends.
Yet it’s premature to conclude that Apple has lost its capability to respond to this AI wave. Whether by design or not, Apple’s transition to unified memory architecture with the introduction of M-series chips has equipped its Mac lineup with remarkable capabilities for local model inference. Furthermore, with the recent Mac Studio capable of supporting up to 512GB of memory, Apple has firmly secured another foothold in this AI transformation.
While NVIDIA remains the preferred choice for model training and inference, Macs have attracted numerous users due to their high integration and energy efficiency. As more AI models become optimized for MLX, Mac performance in large-scale inference tasks is expected to improve further.
Of course, these successes with Mac devices do not fully mitigate Apple’s slow response to large language models, but they highlight the inherent strength of diversified tech giants: when one strategy falters, another can thrive. Such strategic diversity provides resilience against rapidly shifting technological waves.
Do large models truly represent the future? Will companies that fail to keep up inevitably be left behind? Though I doubt it, the current media environment clearly pressures enterprises to deliver tangible results in AI development and applications. Thus, even companies with established AI roadmaps may find themselves forced to pivot due to shifting market dynamics and public sentiment.
Consequently, we can expect to see more “PowerPoint-style” visionary announcements, both recently and in coming years. Without setting expectations, the market becomes disappointed; setting expectations but failing to meet them creates further disappointment. This cycle of expectation and disappointment seems to have become the new normal in tech, particularly in the highly uncertain realm of AI.
In the AI domain, striking a balance between maintaining corporate resolve and satisfying market demands grows increasingly difficult. The expectation-disappointment cycle may have already become an industry norm. For developers and enterprises alike, true wisdom lies in embracing innovation without blindly chasing market hype, carefully balancing diligent execution with meeting public expectations. After all, the true value of technology lies not in flashy promises but in the tangible improvements it delivers to users.
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Lazy Initialization @State in SwiftUI: Overcoming Premature Object Creation
The Observation framework has brought native property-level observation to Swift, effectively preventing unnecessary view updates in SwiftUI triggered by unrelated property changes, thereby enhancing application performance. However, since @State does not offer a lazy initialization constructor like @StateObject, it may lead to performance degradation or even logical issues due to the premature construction of instances in certain scenarios. This article explores how to implement a lazy initialization solution for Observable instances using @State.
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