132 articles
Based on the ‘90s-era vehicular combat games that your parents probably hated (involving, as they do, a lot of wild, demolition-derby style action in which you smash and/or blow up your opponents), Twisted Metal stars Anthony Mackie as John Doe, a driver tasked with carrying a package across the entirely lawless Wasteland while dodging challenges that include a murderous clown in an ice cream truck. Alternately brutal and goofy, the show mostly does what it says on the tin, providing plenty of f
Meta's official policy on bullying states "We remove content that's meant to degrade or shame, including, for example, claims about someone's sexual activity," and lists a number of specific examples like, "severe sexualized commentary" and "calls for, or statements of intent to engage in, bullying and/or harassment." But there's a lot of gray area. Videos featuring vaguely aggressive acts directed at strangers apparently aren't in violation of Meta's standards, for instance. Neither is a video
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
Use the model to refine its own prompt. This is a huge time-saver. You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent. Original prompt: [paste] Provide the improved version and explain your changes. Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
Samsung's entry into the display-free smart glasses space share a lot of features with Meta's line of smart glasses—both include onboard cameras, open-air speakers, and a heavy focus on AI—but there are some distinct differences as well. Here are some areas where Samsung aims to set itself apart from its primary competitor: The ecosystem: If you're already rocking a Samsung phone and Galaxy Watch, these glasses will lock right into your device setup. But the big selling point may turn out to be
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
Metadata, like EXIF data, allows you to identify important details about a photo, such as when it was captured, which camera and lens were used, where you shot the image, and so on. Preview lets you view this metadata easily. Just open any image in Preview, and tap or click the i button in the app. On iPhone and iPad, this reveals a long list of all metadata for the photo. On Mac, you'll see these details organized into two tabs. Click the i tab to view the rest of the metadata in Preview for Ma
While the Metasploit Framework will be continuing its weekly release cadence, bringing you dear reader our latest content, the Weekly Wrap Up is being shifted to a bi-weekly cadence. The team is planning to use the additional time between posts to record demos of some of the more exciting content. Stay tuned for the next generation of Metasploit Wrap Ups and be sure to subscribe to the RSS Feed to be alerted when new blogs are released.
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
One of the most powerful patterns in Kai is meta-prompting—using templates and data to programmatically compose prompts instead of writing them by hand. The problem with handwritten prompts: Every time you need a slightly different version, you copy-paste and modify. Soon you have 47 variations of the same prompt scattered across your system, and maintaining them is a nightmare. The meta-prompting solution: You define prompt templates with variables, then feed in data to generate exactly the pro
The benchmarks matter less than the business-model shift: Meta moved off open weights and started charging on an API, which means the last major holdout just adopted the same model as every other frontier lab. Alexander Wang called the pricing very aggressive versus OpenAI and Anthropic. This is a low-priced entry aimed at the cheap-token bucket, not the frontier. Jason found it competitive with Claude on some consumer answers but flagged the real test is coding, where Meta’s benchmarks have ove
The complaint states that, on May 20, 2026, Meta began notifying roughly 10% of its workforce (around 8,000 employees) that they had been selected for termination. The company also announced that several thousand more would be reassigned to new AI initiatives. But this came even as Meta reported record revenues in Q1 2026 ($56.31 billion, a 33% year-over-year increase), and pledged to spend upwards of $100 billion on AI this year. In addition to questioning the need for staff cuts, the filing al
Charmed PostgreSQL comes with up to 10 years of support and security maintenance for PostgreSQL. It helps you secure and automate the deployment, maintenance and upgrades of your PostgreSQL databases across private and public clouds.
In Hangzhou or Shenzhen, the answer is that your order shows up, often within the hour. China fused messaging, payments and shopping into single apps more than a decade ago. WeChat carries roughly 1.4 billion users, an in-app store layer with hundreds of millions of monthly shoppers, and a wallet most of the country pays with. Korea built its own version, where KakaoTalk made chat the default way to send a gift. This is the world Meta gestures at when it imagines what WhatsApp could be. And yet