Apple Music Introduces App on ChatGPT: A User Guide for Accessing It

Following the announcement made yesterday regarding the impending availability of Apple Music on ChatGPT, the application is now operational. Here’s what is possible with it.

## What this is, and what this isn’t

Although many might think that this integration allows users to stream music on ChatGPT from Apple Music, that is not true; however, you can listen to samples from the tracks as they show up on ChatGPT.

This integration signifies that Apple Music subscribers will now have the capability to request ChatGPT to assemble playlists for them or search for songs, albums, and artists on Apple Music, all based on natural language exchanges with OpenAI’s LLM.

In practice, this allows Apple Music subscribers to request something along the lines of, “Make a 30-song rock and roll Christmas playlist on Apple Music, but steer clear of clichés,” or “Create Barney’s Get Psyched Mix from How I Met Your Mother,” and ChatGPT will take care of locating the songs and compiling them into a playlist, instead of the user needing to do it themselves.

You can also utilize natural language to search for songs, albums, and artists that you may not precisely know how to locate. Essentially, this means that you can prompt it to play “that Alice song from Fear and Loathing in Las Vegas,” or “that duuuum duuuum duuuuuuuuum DU-DUUUUUM song from that classic space film.”

From enhanced discoverability to more intelligent curation, there are countless ways to make use of this integration. If you have any good ideas, please share them in the comments below.

## But how do I use it?

Here’s how to access the Apple Music app on ChatGPT:

1. Visit

Apple Introduces Open-Source Framework for Transforming 2D Images into 3D Perspectives

### SHARP: Transforming 3D Scene Reconstruction from Individual Images

Apple has unveiled a groundbreaking model known as SHARP, which stands for Sharp Monocular View Synthesis. This model can reconstruct a lifelike 3D scene from a single 2D image in less than a second, representing a major breakthrough in computer vision and graphics.

#### Overview of SHARP

In the research paper titled [Sharp Monocular View Synthesis in Less Than a Second](https://arxiv.org/abs/2512.10685), Apple’s researchers detail how SHARP was trained to generate a 3D representation of a scene while preserving real-world distances and scales. The model employs a single feedforward pass through a neural network to regress the parameters of a 3D Gaussian representation of the scene, achieving this in under a second using standard GPU hardware.

The 3D Gaussian representation created by SHARP can be rendered in real-time, enabling the production of high-resolution lifelike images from nearby perspectives. This metric representation supports absolute scale and metric camera movements, which is essential for realistic scene reconstruction.

#### Technical Insights

SHARP functions by predicting a 3D representation of the scene, which can subsequently be rendered from various viewpoints. A 3D Gaussian is effectively a small, indistinct blob of color and light in space. When combined, these blobs can form a scene that appears real from a particular angle. Unlike conventional Gaussian splatting techniques that necessitate multiple images from varying perspectives, SHARP can generate a complete 3D Gaussian representation from just one snapshot.

To attain this functionality, SHARP was trained on vast datasets that included both synthetic and real-world images. This training equips the model to recognize common patterns of depth and geometry, allowing it to estimate depth and refine it based on accumulated knowledge when given a new image. As a result, SHARP can forecast the location and appearance of millions of 3D Gaussians in a single pass, enabling speedy scene reconstruction without the necessity for multiple images or lengthy optimization processes.

#### Limitations and Trade-offs

While SHARP excels at rendering nearby viewpoints with remarkable speed and precision, it is not without limitations. The model is primarily designed to generate realistic renderings from angles close to the perspective of the original image. This implies that users cannot examine entirely unseen sections of the scene, as the model does not generate these areas. This design choice contributes to the model’s speed, allowing it to yield results in under a second while ensuring stability and realism in the output.

#### Community Engagement and Future Directions

Apple has made SHARP accessible on

Morgan Stanley Increases Apple Stock Price Target While Maintaining Overweight Rating

### Morgan Stanley Adjusts Apple Stock Price Target Amid Improved Revenue Forecast

The financial environment for Apple Inc. has experienced a significant change as Morgan Stanley recently raised its price target for the tech behemoth’s stock by 3%, increasing it from $305 to $315. This revision occurs despite the context of climbing memory costs, reflecting a more positive revenue outlook for the firm.

#### Price Target and Rating

Morgan Stanley’s choice to raise the price target signifies a consistent use of a 32x multiple applied to its updated earnings per share (EPS) projection for fiscal year 2027, which has been revised to $9.83 from an earlier estimate of $9.55. The investment firm has retained an “Overweight” classification on Apple, implying their belief that the stock will exceed market performance.

#### iPhone Shipments and Replacement Cycle

In its revised forecast, Morgan Stanley predicts a modest rise in iPhone shipments. Nevertheless, they also foresee an extension in the replacement cycle for iPhones, suggesting users will keep their devices for about one month longer than previously expected. This trend may affect overall sales figures in the near term but signifies a larger shift in consumer behavior.

#### Gross Margin and Revenue Forecast

The report indicates a projected reduction of 130 basis points in Apple’s gross margin, mainly due to higher memory input costs. However, this decline is anticipated to be counteracted by a 5% increase in revenue, which takes into account price increases resulting from commodity cost inflation. This combination of escalating costs and rising revenue highlights the intricacies of Apple’s present market standing.

#### Investment in AI and Operating Expenses

Morgan Stanley emphasized that Apple’s continuous investments in artificial intelligence (AI) are likely to result in elevated operating expenses relative to past trends. This strategic emphasis on AI is regarded as crucial for maintaining competitiveness in the swiftly changing tech landscape.

#### Market Implications

If Morgan Stanley’s updated price target comes to fruition, it would represent a 15.8% upside from Apple’s recent closing price of $271.84, which followed a 1.01% decrease in the stock. Earlier this month, Apple shares attained a new 52-week peak of $288.61, driven by optimism regarding the iPhone 17 upgrade cycle and other favorable market signals.

### Conclusion

Morgan Stanley’s adjusted price target for Apple reflects a sophisticated comprehension of the company’s existing hurdles and opportunities. While increasing memory costs present a margin risk, the overall revenue forecast remains encouraging, supported by strategic investments and anticipated growth in iPhone shipments. As Apple navigates these shifts, investor sentiment will likely stay closely linked to the company’s performance in the upcoming quarters.