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Tonally, the trailer gives strong vibes akin to the director's 2016 feature Hunt for the Wilderpeople .

Prime Day isn’t just about cheap TVs. It's also about cheap stuff to watch on your cheap TV.

Apple TV+ offers some great entertainment at a reasonable price, but there are ways to bring the cost down even further.

Yes, the Oura Ring 5 just launched. But if you’re looking for a bargain and don’t mind a slightly thicker smart ring, then copping the last-gen Oura Ring 4 is still a smart and savvy move. Especially since the price is as low as $226 in most sizes and color schemes at Amazon for Prime […]

Lucide has released version 1.0 of its open-source icon toolkit, marking its first stable major release. The update features over 1,600 icons and removes trademarked brand icons due to legal and design concerns. Significant performance improvements have also been made, reducing package size and adding context providers for various frameworks. Users upgrading should be aware of breaking changes. By Daniel Curtis

When Sony announced the Xperia 1 VIII last month, it promoted the phone by sharing some of the worst photos taken on a Sony camera in years. These weren't just any photos, though: they were taken with Sony's new AI Camera Assistant. After a week with the Xperia 1 VIII, I'm here to tell you […]

GitHub热门项目 | from vibe coding to agentic engineering - practice makes claude perfect | Stars: 59,083 | 329 stars today | 语言: HTML

The new Meta-branded glasses have the same camera, microphones, and chatbot as the Ray-Bans. They come in three styles, one of which was codesigned with Kylie Jenner.

Intel and MSI have teamed up to create what might be the most powerful handheld gaming PC on the market.

For the past three years, "Meta" and "Ray-Ban" have been synonymous in the smart glasses space. Not anymore. Yesterday, I slipped on several pairs of Meta Glasses - no Ray-Bans - in three different styles and seven colors. One style, I was told several times by various enthusiastic Meta spokespeople, is a collaboration with socialite […]

This is not a review of the MSI Claw 8 EX AI Plus, the first gaming handheld available with Intel's new Arc G3 Extreme handheld gaming chip. Now that my colleague Sean Hollister is done reviewing the Steam Machine, I'll let him go deep on the new Claw at some point in the future. This […]

MSI's Intel-powered Claw gaming handhelds have so far mostly been Windows machines, meaning that anyone who picks one up has to deal with the crusty experience that is Windows on a handheld gaming PC. But now, both Valve and Intel tell The Verge that they're working with each other, and people like YouTuber ETA Prime […]

If you’ve been thinking about picking up a Kindle, Amazon’s Prime Day sale is a great time to do it. The retailer is currently offering steep discounts on several of its e-readers, including the latest Kindle Paperwhite with 16GB of storage and ads, which is down to $124.99 ($35 off) at Amazon. If you’d prefer […]

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Data-Oriented Design in C#: Why Objects Are Slowing You Down In my previous article, we talked about starving the Garbage Collector by moving away from heap-allocated class types and leaning heavily into struct , Span<T> , and ArrayPool<T> . That’s a critical first step, but it only solves half the problem. You’ve stopped the GC from pausing your app, but you might still be leaving massive amounts of CPU performance on the table. Why? Because of how your data is structured. It’s time to talk about Data-Oriented Design (DoD) . The Object-Oriented Trap We are taught from day one to model our code after the real world. If you are building a social network graph, you might write something like this: public class UserNode { public int Id { get ; set ; } public string Name { get ; set ; } public List < Edge > Connections { get ; set ; } } public class Edge { public UserNode Target { get ; set ; } public int Weight { get ; set ; } } This makes perfect logical sense. A user has connections, and those connections point to other users. But modern CPUs don't care about your logical models. A CPU only cares about reading data from memory into its L1/L2 caches as fast as possible. When a CPU reads a byte from RAM, it doesn't just read that one byte; it pulls a whole 64-byte "cache line" under the assumption that you will probably want the neighboring bytes next. When you loop through a List<UserNode> , traversing from object to object, you are jumping randomly across the heap. The CPU pulls a cache line, reads your data, and then has to go fetch a completely different block of RAM for the next node. This is called pointer chasing , and the resulting cache misses are devastating to performance. Enter Data-Oriented Design: Struct of Arrays (SoA) Data-Oriented Design says: Stop modeling the real world. Model the data the way the hardware wants to consume it. Instead of an Array of Structs (AoS) (or an array of objects), we invert the architecture to a Struct of Arrays (SoA) . If we

Writing a clear, well-structured email takes time, and it's the kind of task an LLM is genuinely good at. But wiring up your own prompt-to-email pipeline means picking a model, threading the original message in as context, handling streaming, and keeping it all behind your API keys. The Nylas Smart Compose endpoints do that for you: send a natural-language prompt, get back a written message body, and the reply variant pulls in the original email as context automatically. This post walks through Smart Compose from two angles: the HTTP API for your backend, and the nylas CLI for the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm testing a prompt. How Smart Compose works Smart Compose is two endpoints that turn a prompt into a message body. You send a natural-language prompt , and the response comes back with a suggestion field holding the generated text. There's a POST /messages/smart-compose for writing a brand-new message, and a POST /messages/{message_id}/smart-compose for writing a reply, where the original message is folded into the context so the response actually answers it. The key thing to understand is that Smart Compose generates text, it doesn't send anything. The suggestion it returns is a message body you do something with: pass it straight to the Send Message endpoint , or pre-fill it into a draft for a human to review and edit first. That separation is deliberate, since it lets you put a person between the AI's output and the recipient, which is usually what you want for anything an LLM wrote. Two things to know before you start. Smart Compose runs against connected OAuth grants only, not Agent Accounts. The prompt also has a ceiling: up to 1,000 tokens, and a longer prompt returns an error. Generate a new message To write a fresh email, POST /v3/grants/{grant_id}/messages/smart-compose takes a single prompt describing what you want. The response carries the generated body in suggestion , which you then se

Email is how most files still move between people: the signed contract, the PDF invoice, the logo embedded in a newsletter. If your app sends or processes mail, it has to handle attachments, and doing that against each provider means Gmail's attachment encoding, Microsoft Graph's, and raw MIME for IMAP. The Nylas Email API gives you one model for both directions: attach files to outbound messages with the same call you use to send, and pull files off inbound messages with a read-only Attachments API. This post covers both halves from two angles: the HTTP API for your backend, and the nylas CLI for the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm checking a file came through. Two APIs: one to attach, one to read There's a split worth understanding up front. You add attachments through the Messages or Drafts API, as part of sending or saving a message, and you read existing attachments through the dedicated Attachments API. The Attachments API is read-only: it downloads bytes and returns metadata, but it never adds files. That division keeps the model simple, since attaching is part of composing a message and reading is a separate concern. The size of what you're attaching decides how you encode it on the way out. Small files ride inline in the JSON request, larger ones move to a multipart request, and very large files use a separate upload step. On the way in, every attachment, regardless of how it was sent, is fetched the same way: by its attachment_id together with the message_id it belongs to. Get those two ideas straight and the rest is mechanical. Attach a small file inline with Base64 For files that keep the whole request under 3 MB, the simplest path is the application/json schema. You pass each attachment in an attachments array with its content_type , filename , and the file bytes as a Base64-encoded content string. The 3 MB ceiling covers the entire HTTP request, not just the file, so it's the right path for

If you've ever tried to set up Prometheus by following the official getting-started path, you're likely to find a path that does not follow your infrastructure model. Out of the gate, page one mentions kube-prometheus-stack. Page two wants you to install a Helm chart, and page three assumes you already have a cluster running. The documentation for monitoring plain Linux servers is in there somewhere, but you have to dig for it. When you do find it, the tone suggests you are doing something slightly old-fashioned. If that sounds like your setup, the tooling is making this harder than it actually is. Monitoring a fleet of Linux VMs is fairly simple and has been for years. It is just obscured behind documentation that would prefer to sell you something bigger. Modern infrastructure tooling has quietly decided everyone runs Kubernetes. If you don't, the assumption is that you eventually will. Meanwhile, most real-world infrastructure still runs on VMs. TL;DR: Modern observability documentation often assumes you're running Kubernetes. Most small teams aren't. If you're managing a fleet of Linux VMs, node_exporter plus Prometheus gives you everything you need for infrastructure monitoring with a single lightweight agent and a straightforward deployment model. No cluster required. VMs are often the answer For most small businesses, running VMs instead of Kubernetes does not mean you failed to evolve. Most workloads under a certain scale perform better on VMs: One process per box, predictable resource limits, and the ability to ssh in and look at what's happening, which makes it easier to keep track of the infrastructure as a whole. They're cheaper, both financially and in the mental overhead of running them. Backups and snapshots are straightforward in a way stateful Kubernetes still isn't. There's no control plane that itself needs monitoring and upgrades and care. Kubernetes solves problems that mostly pertain to companies with dozens of engineers and hundreds of service

I recently published my PhD thesis analyzing what I term the "Alignment Tax" and the emerging phenomenon of Algorithmic Paternalism in commercial artificial intelligence. As the tech industry rapidly positions Large Language Models (LLMs) as the primary interface for information retrieval and coding assistance, a critical epistemological issue is being largely ignored. Much of the public debate regarding AI alignment focuses exclusively on existential risk or the prevention of catastrophic physical harm. While necessary, this focus obscures the structural damage being done to legitimate technical research. Through my research in Cybersecurity and AI, I have documented how frontier models (such as GPT-4 or Claude) systematically enforce what I define as "Soft Refusals". When presented with a complex, edge-case, or dual-use query—particularly in fields like information security, reverse engineering, or deep systems architecture—these models rarely issue a hard, explicit "I cannot answer that". Instead, they provide a degraded, superficial, or heavily sanitized response. They effectively neuter the research process without the user fully realizing the depth of technical information that is being actively withheld. This is Algorithmic Paternalism. The commercial model acts as a silent, corporate arbiter, deciding unilaterally what level of technical detail is "safe" for the user to possess. This dynamic flattens the available technical knowledge and actively penalizes independent researchers and developers working on advanced problems. The core issue is that this paradigm creates a profound class division in how we access computational intelligence. We are rapidly moving toward a two-tier system. On one side, there are "certified" entities, corporate partners, and wealthy organizations who are granted direct access to strong, unfiltered base models. On the other side, the general public and independent developers are subjected to obfuscation algorithms, sanitized APIs,