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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.

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

OpenAI helps build shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through the Appia Foundation.

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 […]

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 […]

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 […]

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 […]

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. […]

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

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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

Every time I opened a fresh session with my coding agent, it started from zero. Which repos am I working across? Which client is this for? Where did we leave off yesterday? I'd re-explain the same context, the agent would occasionally load the wrong project, and nothing I decided last week survived into this one. A "re-explain myself" tax on every single session. I tried the obvious fix first — a better prompt, a longer system message. It didn't hold. Context that has to persist can't live inside the chat; the chat is the thing that resets. What actually worked: give the agent a place outside the chat to read and write — and make it the most boring, durable thing I could. Plain files in a git repo. The substrate: markdown + YAML the agent reads at session start open-bridge is a plain git repo of markdown and YAML. At the start of every session the agent reads it, so it begins already knowing my world. No database, no SaaS, no daemon, nothing to host — the substrate itself runs nothing . It's just files the agent reads. That "just files" choice is the whole point: Agents can read a file but can't hold an API key. What I write today, the agent still reads in six months — no migration, no second app, no vendor lock-in. It's auditable. Clone it and cat anything the agent reads. No black box. It's model- and tool-agnostic. Plain text is something every agent runtime can read. A tiny slice of what that looks like (from the repo's examples/agency setup — fictional "Acme Dev"): # ecosystem.yaml — the repos/clients the agent should know about projects : bigcorp : { display_name : " BigCorp E-Commerce" , repos : [ bigcorp-api , bigcorp-frontend ] } startupxyz : { display_name : " StartupXYZ MVP" , repos : [ startupxyz-app ] } # work/board.md — generated from the task dirs, read every session ## Doing | bigcorp-api-payment-retry | incident | P1 | Stripe webhook retries failing | | startupxyz-onboarding | feature | P2 | guided signup flow | So when I say "good morning, briefing

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

Many cybersecurity professionals have been following Anthropic's announcement about the release of Claude Code Security on Friday. This created the beginning of a panic on the cybersecurity stock market. It also raised a lot of questions from domain experts, investors and security enthusiasts. Anthropic's announcement Anthropic introduces Claude Code Security: a tool that scans full codebases for security vulnerabilities, and can propose fixes directly in developer workflows. The tool leverages the latest foundational model's reasoning capabilities to provide a new experience. In a world where code will be generated only by AI, this can sound very much like code security is dead. Our vision 18 months ago, SAST, SCA, and IaC security were areas where we had real traction and could see ourselves expanding. But as AI tooling started reshaping how code gets written, we made a tough call. We decided to stop these initiatives and go all-in on what we believed would matter most: Protecting enterprises against leaked secrets and mismanaged NHIs . We envisioned a future where identity is crucial for the AI era security, with secrets enabling AIs to access data and take actions . After pioneering in secrets detection for years we witnessed how amplified the problem became as LLM emerged: more API keys for AI services, more code generated, often less secure, more agents requiring sophisticated access to a myriad of tools. All in all, this resulted in more secrets exposed. Yet the problem of overseeing and managing these secrets in a secure way remains unsolved. The paradigm shifted from human hardcoding secrets in their code, to AIs having wide access levels on several systems with humans, coders and non-coders, prompting them and creating new vulnerabilities. 18 months later, let me describe where we stand. What isn't changing Best in class secrets detection GitGuardian is the leader in secrets detection . We are the only solution able to scan large volume of data at scale (5

"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

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