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For over a decade now, Snap has been working on this device. Now the glasses are finally here. So what stands out on first impression?

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.

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.

Legal victories have dampened the Trump admin’s efforts to halt wind and solar power.

Here's a new trailer for Star Trek: Strange New Worlds, which premiers July 23rd.

Separately, neither could compete. Now they hope they can.

I'm so done with Roku.

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

British Columbia has recently made some time zone changes —- but you have a few months until you feel the impact. That gives an opportunity to deep dive into time zones, timestamp storage, and more. submitted by /u/winsletts [link] [留言]

TL;DR Provider invoices aggregate by model and billing period. They cannot tell you which team, product, or agent caused a cost spike. Request-level AI cost attribution links every API call to structured owner metadata (team, product, environment, trace ID) so investigations take minutes, not days. Three approaches exist: provider dashboard, gateway log enrichment, and application trace attribution. They differ sharply in setup cost and query granularity. Gateway log enrichment is the highest-leverage first step for most teams. It requires no changes to application code and covers all traffic behind the gateway. Real example: a platform team at a 60-person AI company discovered that 31% of their $18k/month spend came from a misconfigured retry loop in a background job, identified in under 20 minutes once request-level logs were searchable. Why Your Invoice Is Lying to You Your OpenAI invoice for last month shows $22,400. Your Anthropic invoice shows $6,800. Total: $29,200. Your CFO wants to know which business unit owns each line. You forward the invoices to your finance partner, who forwards them to three engineering managers, who reply with estimates that sum to $24,000 and do not match any real allocation. This is the standard state of LLM spend governance at companies between $5k and $50k per month in AI API costs. The invoices arrive, the spend is real, and attribution is a spreadsheet exercise done with guesses. The problem is structural. Provider billing aggregates by model and by billing period. It has no concept of your internal ownership model, your product boundaries, your tenant hierarchy, or your agent topology. A single gpt-4o line in your invoice might represent spend from a customer-facing chat feature, an internal summarization service, a nightly batch job, and three developers running experiments against production endpoints. You get one number. You have four or more owners. Request-level AI cost attribution is the practice of enriching every API c

I have done this process wrong more times than I would like to admit. You call the references the vendor sends you. Three customers, all happy, all articulate, all saying roughly the same things. You hang up feeling good. You sign. Six months later you are dealing with a support team that responds every 72 hours and a renewal quote that is 40% higher than year one. The reference check told you nothing useful. Not because the customers lied. Because you asked the wrong questions. Here is what I ask now. "How many people at the vendor have you spoken to in the last six months?" One contact who handles everything and is sometimes slow — that tells you something. A team of people across sales, technical support, and leadership — that tells you something different. Enterprise AI vendors with thin account teams show their limitations at exactly the moment you need them most: when something breaks at the worst possible time. "Tell me about the last time something broke in production." Not IF something broke. Something always breaks. I want to know what happened next. Did the vendor show up? Did they communicate clearly while the issue was live? Did they follow up after closing the ticket, or did they close it and disappear? The answer to this question tells me more about a vendor than any product demo. "What do you know now that you wish you had known before signing?" This question works because it is framed as advice, not criticism. Reference customers who would never say "this product has problems" will happily answer this one honestly. Listen for anything about pricing surprises, scope limitations that only appeared after deployment, or feature gaps the sales team glossed over. "When you renewed, did you evaluate alternatives?" Renewal is the honest signal. A customer who renewed without looking elsewhere is genuinely satisfied. A customer who looked at three other options and came back is someone who chose this vendor over real competition. A customer who is approachin

TechCrunch has followed SpaceX's start, struggles, and successes from the early days. And we're here for what happens next too. This package of SpaceX IPO coverage includes who stands to win (and maybe some who won't), pre-IPO deals, and what's tucked inside its S-1 registration document.

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:

Yes, first unplug it and plug it back in. But after that, here's what you can try to fix your slow Fire TV Stick.

After a brief debut at the Cannes film festival last month, DJI announced all the details of its Osmo Pocket 4P stabilized camera today as part of its initial launch in China. While it doesn't match the 8K capabilities of the Insta360 Luna Ultra, the Pocket 4P features a new 1-inch sensor with improved dynamic […]

Age-gated tiers are the latest tactic Roblox is trying to keep kids safe on its platform.

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.