标签：#AR

Smart glasses are still a nascent category, but chipmaker Qualcomm is hard at work upgrading the silicon to power the next wave of XR devices: the Snapdragon Reality Elite. Although Qualcomm is announcing the chip today at Augmented World Expo, we've technically already gotten a hands-on with a device powered by the new chip at […]

To build one family’s dream house on a flood-prone Mississippi bayou, AD100 architect Tom Kundig decided the sky’s the limit.

Snap describes its news AR Specs as a "wearable computer."

Snap CEO Evan Spiegel lays out the company’s vision for its augmented-reality smart glasses, arriving later this year.

Snap is finally launching augmented glasses for the public. Specs, which Snap describes as "a wearable computer built into see-through augmented reality glasses," will cost $2,195. You can preorder a pair of Specs now at specs.com with a $200 refundable deposit, and Snap says they're expected to ship "this fall" in the US, UK, and […]

WordPress VIP’s latest survey suggests consumers are wary of AI-generated answers even as companies increasingly view AI search as an important referral channel.

Tired of vacuuming? Hand the reins to a robot vacuum.

This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI. This article is based on this Kaggle Notebook , which you can run and directly edit yourself. When training ML models, the choice of hyperparameters can strongly influence performance. They are not the only factor, but they can significantly affect both convergence and generalisation. Tuning hyperparameters manually takes a lot of effort. This is especially true because hyperparameters interact with each other, so tuning them independently is usually not enough. For example, higher regularisation may require a lower learning rate for more stable optimization. A more complex model may require stronger regularization to avoid overfitting, but at the same time, a very small learning rate on a complex model can make learning too slow. Optuna is an MIT-licensed open source library, which allows commercial use, that automates hyperparameter search for ML models developed with the most popular frameworks such as scikit-learn, PyTorch, TensorFlow, and LightGBM. It works by defining a search space and an objective metric to either minimize or maximize. Optuna then explores the search space efficiently to find well-performing configurations. Here we use Optuna to tune a LightGBM model on a dummy dataset and show how to scale the search using shared database storage. We will also use MLflow for experiment tracking and model registry, and IRIS DB as a possible Optuna storage backend for concurrent studies. We will use the California Housing dataset, commonly used in ML examples, to populate IRIS tables and run the tuning workflow. Note:

I think a lot of people still imagine AI coding as opening ChatGPT, asking for code, and copy-pasting the result. That's not really how I work anymore. The biggest shift for me is that planning matters far more than coding. Earlier, execution was expensive, so most of the effort went into writing code. Now execution is cheap. I can have an agent implement something in minutes. The hard part is making sure the plan is correct. Most of my effort goes into thinking through the architecture, edge cases, failure modes, test strategy, and how the change fits into the broader system. If the plan is vague, the agent will confidently implement the wrong thing. The quality of the result is mostly determined by the quality of the plan. Once I have a plan, I break it into small independent pieces. Each piece should be executable without additional clarification. If an agent needs to stop and ask questions, the task probably isn't broken down enough. Those pieces become tickets. Then an agent picks up a ticket and implements it. The important thing is that the agent isn't operating in a vacuum. I try to give it a good environment to work in: Clear architectural rules Reusable skills and workflows Guardrails Hooks for things that must always happen One lesson that really stuck with me is that instructions are guidance, not guarantees. At one point I had "always use a git worktree" written in AGENTS.md. The model still ignored it occasionally. When I dug into it, the answer was simple: models can drift from instructions. So if something absolutely must happen, don't rely on instructions. Enforce it. Put it in a hook, script, validation step, CI check, or some other deterministic mechanism. If it is important, make it impossible to skip. Once the implementation is done, the agent opens a PR. This is where another useful pattern comes in: don't let the same model review the code it wrote. I usually have one model implement and another model review. Different models catch different t

African payments infrastructure company Flutterwave has hit a new valuation and landed blockchain company Ripple as investor and partner.

Designers are finding sustainable building solves close to home—in ancient practices and cutting-edge innovations alike.

Verizon introduced a new plan that costs $45 per month, revamped rewards programs, and more today.

Tech has been encroaching on the family domicile for years—but actor, writer, and satirist Jill Kargman is all in on analog.

Against all odds, the Tesla Cybercab is in production. And while Elon Musk's company may not have a very coherent plan for the tiny, autonomous two-seater, it's still taking the necessary steps to certify the EV's legitimacy. As such, Tesla recently filed paperwork with the Environmental Protection Agency that reveal many of the Cybercab's specs, […]

Plaud is trying to make a mark in a crowded market full of AI-powered meeting notetakers.

From friend compounds and meditation spaces to shaded outdoor areas and rooms just to make coffee, homes are getting even more multipurpose.

Buying a first house used to mark entry into adulthood—and the beginning of wealth-building. But a shifting economic landscape is threatening to close the door on this American milestone.

The service will leverage its Moovit platform to launch in an a US city in 2027.

These clever lights—each one an exemplar of innovation in materials, design, and function—will beautify your home.

The global editorial directors of WIRED and Architectural Digest on teaming up to help you understand how we live today, and what comes next.