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How to Convert Your Old Computer into a Robust Media Center Affordably

Transforming a computer into a media center is quite simple, as numerous older devices can handle HD video. You can use a seventh-generation Intel processor from 2016 or newer and still support contemporary codecs such as HEVC (H.265) and VP9. Naturally, if you intend to stream your owned content, it’s essential to verify that your network is equipped to manage the bandwidth, as 4K video can require between 25 Mbps and 50 Mbps per stream, a load any modern router can accommodate. In reality, the technology required to initiate this project is probably already available in your home within one of your older PCs.

You’ll need a hard drive for storing your HD media. You can either substitute an existing drive or integrate a new one into your setup, with any capacity sufficing; you can also add external drives later as needed. Optionally, an optical media drive, like an older DVD or Blu-ray player, can be useful, especially if you plan to rip your physical media onto your hard drive.

It’s essential to ensure that your computer has an adequate amount of RAM — 8GB is enough for media center applications like Jellyfin and Plex, although Kodi operates with a minimum of 1GB for those using older hardware. Lastly, an Ethernet port will be beneficial for a more stable data connection, but a Wi-Fi card is also acceptable — and an HDMI-out port is required. Essentially, many computers manufactured in the last decade can effectively replace streaming devices. If you lack an old computer, the second-hand market has affordable options to keep costs low. Therefore, once you’ve confirmed your network and old computer meet the requirements, turning the device into a media center can be achieved with minimal expenditure or even for free.

Opt for a complimentary or subscription-based media center application

The Sonos Ace is a hefty 25 percent off for Amazon’s Big Spring Sale

Amazon’s third annual Big Spring Sale might be a great opportunity to pick up a color ebook reader or an RGB-equipped table lamp, but the pickings are slimmer when it comes to noise-canceling headphones. Sure, the Sony WH-1000XM6 are just over $60 off, though I’d argue the Sonos Ace are a better deal, given they’re […]

9to5Mac Daily: March 25, 2026 – News on iPhone Fold and Siri Progress

**9to5Mac Daily: Your Destination for Daily Apple News and Insights**

In the rapidly evolving tech landscape, keeping abreast of the latest news is essential, particularly for Apple fans. 9to5Mac Daily provides a seamless way to stay informed about the day’s leading stories, delivering listeners a summary of key happenings within the Apple ecosystem. This podcast is accessible on multiple platforms, including iTunes, Apple’s Podcasts app, Stitcher, TuneIn, Google Play, and via a dedicated RSS feed for those utilizing alternative podcast apps.

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Apple Creates AI That Can Produce Better Image Captions Than Larger Models

Apple scientists have introduced a novel framework for dense image captioning named RubiCap, which improves the precision and richness of image descriptions while utilizing smaller AI models. This breakthrough, outlined in their paper “RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning,” was a partnership with the University of Wisconsin—Madison and achieved leading-edge results on various benchmarks.

Dense image captioning pertains to crafting detailed descriptions of multiple components within an image, rather than offering a single overview. This method facilitates a deeper comprehension of the scene and can enhance applications like image search and tools for accessibility.

Present AI techniques for training dense image captioning models encounter obstacles, such as elevated costs for high-quality expert annotations and insufficient output variety due to synthetic captioning. RubiCap overcomes these challenges by employing reinforcement learning (RL) in an innovative manner. The researchers collected 50,000 images from two datasets and created multiple caption alternatives utilizing existing vision-language models. RubiCap then generated its own captions and evaluated them against the produced options to establish explicit criteria for assessment.

The model received organized feedback, resulting in more precise captions without depending on a singular correct answer. The researchers created three models—RubiCap-2B, RubiCap-3B, and RubiCap-7B—each with 2 billion, 3 billion, and 7 billion parameters, respectively. Notably, these models surpassed larger models boasting up to 72 billion parameters.

In extensive evaluations, RubiCap exhibited exceptional performance, achieving high win rates and efficiency in word usage in comparison to other models. The smaller 3-billion-parameter model even exceeded its larger counterpart in specific assessments, suggesting that effective dense image captioning does not strictly depend on massive scale.

For additional information regarding the study and its conclusions, you can follow the link to the complete research paper.

Apple Attains Settlement in Legal Dispute Concerning Former Employee Allegedly Taking Vision Pro Trade Secrets for Snap Job

Apple has arrived at a settlement with ex-employee Di Liu, who faced allegations of taking thousands of documents before departing for Snap. The lawsuit, submitted in June 2025, asserted that Liu downloaded a considerable amount of trade secrets mere days prior to his exit.

Liu, who was employed at Apple from September 2017 to November 2024, participated in hardware and design engineering for the Apple Vision Pro. As per Apple, Liu failed to notify them about his new position at Snap, permitting him to keep access to proprietary information throughout his customary two-week notice period. Three days ahead of his quitting, Liu reportedly downloaded thousands of confidential documents to his personal iCloud account.

The lawsuit specified that Liu had deceived Apple about his motives for leaving, claiming he wished to concentrate on family and health. Nevertheless, data retrieved from his work laptop suggested he had already secured a role with Snap weeks before his resignation. The examination showed that he utilized his Apple credentials to shift numerous files containing proprietary data to his personal storage.

The case was lodged in the Santa Clara County Superior Court and was recently dismissed following a settlement agreement. Liu has consented to return the confidential information and provide a financial payment to Apple. He openly apologized on LinkedIn for his actions, expressing remorse for the lapse in judgment and the difficulties it caused to others.

Apple is recognized for taking legal steps against former employees who misappropriate trade secrets, having previously engaged in litigation against other individuals for comparable violations.