标签：#AR

Ribbie lets you follow along live with MLB games with a delightful, arcade-inspired interface.

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.

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 […]

Here come the Meta Adventurer, Meta Fury and Meta Glasses by Kylie (Jenner).

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.

The hiring process has long been criticized for its inefficiency and opacity. Candidates spend hours writing applications and submitting cover letters, only to disappear into what often feels like a black box. Generative AI has only made things messier, with employers increasingly relying on AI-powered screening systems to sift through an overwhelming number of submissions. […]

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

Prototype vs MVP: How to Validate an Interactive Product Before Overengineering It A common early-stage product mistake is treating development output as product validation. The team creates screens, components, integrations, API endpoints, and increasingly complex application logic. The backlog is moving. The product is growing. But the core assumption may still be untested. Before building a full MVP, a startup should be able to answer a simpler question: What exactly are we trying to validate? For some products, a clickable UI prototype is enough. For others — especially products involving real-time 3D, WebAR, WebXR, data visualization, or spatial interaction — the experience cannot be validated through static screens alone. The team may need a functional interactive prototype. Prototype and MVP solve different problems A prototype is an experiment. Its purpose is to explore the concept, test the main interaction, and expose incorrect assumptions early. An MVP is a usable product. Its purpose is to deliver real value in production conditions and test market demand. A prototype helps validate: interaction logic; product comprehension; technical feasibility; the main user flow; visual communication; investor or stakeholder response. An MVP helps validate: real usage; retention; willingness to pay; production performance; operational requirements; market demand. The distinction becomes important because prototypes and MVPs require different engineering decisions. A prototype should be focused and fast. An MVP needs a more reliable technical foundation. Building the second before learning from the first can lead to unnecessary architecture, unused features, and expensive rework. Define the hypothesis before choosing the stack Teams often begin technical discussions too early. Should we use React? Should the 3D layer be built with Three.js? Do we need WebXR support? Should the backend be serverless? These may be relevant questions, but they are not the first questions

"What time works for everyone?" is a surprisingly hard question to answer in code. You have to read each person's calendar, line up the busy blocks, respect working hours and time zones, leave buffer time between meetings, and only then find the gaps everyone shares. The Nylas Availability API does all of that in one request: hand it a list of participants and a window, and it returns the time slots that actually work. This post covers finding meeting times 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 calendar. Availability versus Free/Busy There are two endpoints here, and picking the right one saves you work. The Availability endpoint finds bookable slots across a group of participants, applying working hours, buffers, and meeting duration to return times you can actually book. Free/Busy is simpler: it returns the raw busy blocks for one or more email addresses over a window, leaving the slot math to you. Reach for Availability when the question is "when can these people meet?" and you want the answer as a list of open slots. Reach for Free/Busy when you only need to see when calendars are busy, for example to gray out times in a custom UI. Availability is a POST /v3/calendars/availability , an application-level call that takes participants by email, while Free/Busy is grant-scoped at POST /v3/grants/{grant_id}/calendars/free-busy . This post focuses on Availability, since that's the one that answers the scheduling question directly. Find a time across participants The core request lists the participants and the window to search. Each participant is identified by email and must be associated with a valid Nylas grant, since the endpoint reads their calendars. You set start_time and end_time as Unix timestamps for the search window, duration_minutes for how long the meeting is, and interval_minutes for how the candidate start times ar

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,

I replaced aaPanel/OpenLiteSpeed with Caddy and shell scripts and turned the process into a benchmark. Two phases (architecture then code), one external code review. The winning model? Not the one you'd expect.

Prime Day is here, and so is our live blog. We'll bring you deals, trends, and commentary throughout Amazon's annual summer sale.

WIRED’s favorite Wi-Fi 7 mesh system from Netgear is cheaper than ever on Prime Day.

At this year's Microsoft Build 2026, Microsoft unveiled a broad set of enhancements to Azure Kubernetes Service (AKS) aimed at making Kubernetes a first-class platform for AI training, inference, and large-scale cloud-native applications. By Craig Risi

I was convinced these devices were just clutter. I was proven wrong.

Every Prime Day is a good day to make your home smarter, as deals on connected gear proliferate not just on Amazon but all across the web. And this Prime Day is no different. I sifted through hundreds of offers to find the ones that actually stand out — only the deepest discounts on the […]

HP’s OmniBook Ultra 14 is one of the best alternatives to the MacBook Air you can buy right now, and it’s more affordable than many of its Windows competitors.

How I Stopped Burning Cash on Token Limits — A CTO's Field Notes Three months ago, I was staring at our monthly AI bill wondering where it all went wrong. We'd built what I thought was a pretty elegant LLM pipeline. Production-ready, observability wired up, the whole nine yards. Then the invoices started arriving, and I realized I had built a money furnace. Our token consumption was spiking 3x week over week, the 429s were everywhere, and our latency had become a meme inside the company. This is the post I wish I'd had six months ago. If you're a technical founder or a CTO running LLM workloads at scale, bookmark this. I'm going to walk you through the exact architecture decisions, the exact numbers, and the exact code that took us from "this bill is going to kill us" to "oh, this is actually manageable." The Real Problem Nobody Talks About Here's the dirty secret about running LLM-powered products: token limit errors aren't really about token limits. They're a symptom of a much deeper architectural problem. When your app throws "context length exceeded" at 2am, what it's really telling you is that you didn't think hard enough about prompt design, document chunking, model selection, and cost routing on day one. I learned this the hard way. My team was defaulting to GPT-4o for everything because, honestly, it works and the API is reliable. We were paying $2.50 per million input tokens and $10.00 per million output tokens. For a startup processing millions of documents a month, that math is brutal. We were essentially funding OpenAI's next training run with our Series A. The wake-up call came when I ran the actual numbers. Our average request was burning through maybe 8K input tokens and producing 2K output tokens. At our volume, we were spending more on inference than on two senior engineers. That is not a sustainable burn rate for a 12-person company. The Architecture Decision That Changed Everything The first question I asked myself wasn't "which model is cheapest?

The Idea Like a lot of people, I sometimes struggle with time. Not in an "I'm just bad at planning" way — more like my brain genuinely has a hard time feeling how long things take. Twenty minutes can feel like five. I'll think "I have time" right up until I definitely don't. So I built Ready. Ready is a PWA (a web app you can install on your phone like a native app) that counts down to your next event — but not just to the event itself. It counts down to when you need to leave, factoring in both how long it takes you to get ready and how long the journey takes. It sends you push notifications before it's time to move. So you don't accidentally forget about time, run out the door ..late again! The app was designed with time blindness in mind — a challenge many people experience. The tone is always encouraging, never stressful. No red warnings. No "you're late." Just a gentle nudge that has your back. It's also my portfolio project. I'm a junior developer learning in public, and this is me documenting the whole messy, rewarding process. (Which also happens to be great for recalling what you learned) The Stack — and Why I Chose It Before writing a single line of code, I had to decide what to build with. Here's what I landed on and why: Next.js — a framework built on top of React (a popular way to build web interfaces). I chose it because it handles both the frontend (what you see and click) and the backend (the logic running behind the scenes) in one project. Less setup, more building. Supabase — think of it as a database with superpowers. It handles storing your data and user authentication (logging in and out) out of the box. It has a generous free tier, which is great when you're learning. Tailwind CSS — instead of writing traditional CSS in separate files, Tailwind lets you style things directly in your code using short class names like rounded-full or text-teal-600 . Web Push API + Service Workers — A service worker is a small script that runs in the background of