标签：#art

The company has shifted from making van-life builds to custom outfitting vehicles for small businesses, all while the EV landscape in the US changed dramatically.

The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight. LLMs can already write code, generate synthetic data for training, and optimize the computer chips they run on. Forecasts of explosive AI progress predict that what researchers call recursive self-improvement is on the horizon. …

Z.ai’s latest AI model release could help companies secure their systems—or find its way into the hands of hackers.

Google told its suppliers that it will no longer manufacture Pixel devices in China starting next year, according to Nikkei Asia.

I’ve been building a side project called OopsCalorie , an AI-powered calorie and meal tracking app. The idea sounded simple enough: User logs or takes a photo of their food. AI identifies the meal. Estimate calories and macros. Save the entry. Done. Simple, right? Well... Then we started testing it with Filipino food. 😂 AI Meets Filipino Food 🇵🇭 One of the funniest parts of building OopsCalorie has been testing the food recognition. At one point, our AI confidently looked at dinuguan and decided: That's champorado. Okay. I can kind of see where you were coming from. Both are dark, both can be served in a bowl... But still. 😂 Then came bagnet . AI: Lumpiang Shanghai. Bro. Not even close. 😂 These bugs are funny, but they also exposed one of the more interesting engineering problems behind OopsCalorie: Image recognition is only the first step. Correctly identifying a meal — especially regional dishes — requires much more context than I initially expected. The Real Problem Isn't Just Calories When I started the project, I thought the difficult part would be estimating calories. Turns out, before you can estimate: You need to know what the food actually is. And food can be surprisingly ambiguous from an image. A photo might contain: multiple dishes sauces hiding ingredients visually similar foods regional dishes that aren't well represented in training data different cooking methods unknown portion sizes ingredients completely hidden underneath other ingredients Even humans sometimes need context. "Is that pork adobo or humba?" "Is that fried pork belly or bagnet?" Now imagine asking an AI to determine that from pixels alone. Building Around AI Instead of Blindly Trusting It This changed how I'm approaching the system. Instead of treating the AI response as absolute truth, OopsCalorie is evolving toward a workflow where AI provides an intelligent estimate while the user still has the ability to provide context and correct it. We're experimenting with things like: Image +

If you’ve been circling around Disrupt, then now’s the best time to lock in your pass and start getting ready to join the rest of the startup community gathering in San Francisco from October 13-15 at Moscone West!

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Flock, the police-tech giant known for its network of some 120,000 automatic license plate readers around the US, announced some changes to its platform last Thursday. The updates are meant to prevent…

Higgsfield, founded by former Snap exec Alex Mashrabov, lets users create AI images and videos.

Petlibro says feeders perform scheduled feedings offline. Users report otherwise.

SpaceXAI has introduced Grok Bot, a system of persistent AI agents that operate on dedicated cloud computers and can interact with websites, applications, inboxes, and other tools. By Daniel Dominguez

Go beyond Do Not Disturb: control what alerts you get and when instead of being overwhelmed with pings all the time.

African defense tech company Terra Industries announced an additional $18 million in funding, bringing its seed round to $52 million.

Grab is using AI agents to automate analytics workflows, cutting mechanical analyst work from 44% in February to 30% in June. Its approach combines agent autonomy, certified data, context management and human oversight, with self service analytics increasingly handling metric, data and SQL requests without analyst intervention. By Leela Kumili

What kind of data is being collected by our devices, and how is it being used? Should we "Ask app not to track"?

In this podcast, Michael Stiefel spoke to Tracy Bannon about the role of artificial intelligence in software and the attendant risks in the areas of security, software development, and society at large. While it might be reasonable to assume a certain amount of trust within a software ecosystem, the risks escalate when the boundary between two software ecosystems is crossed. By Tracy Bannon

Deterministic rules safeguard hard metrics, but what about architectural intent? Discover how agentic fitness functions combine AI agents and versioned rubrics to evaluate complex, judgment-heavy concerns—such as boundary fidelity, semantic contract drift, and stale ADR assumptions. Elevate evolutionary architecture governance with continuous, calibrated feedback loops. By Hemant Kumar Mahato, Łukasz Sieczkowski, Vijayasenthilkumar Kuppusamy

The greatest invention in pet tech in recent years is the litter robot. A machine that scoops your kitties' poop so you don't have to - what else could a cat owner possibly want? How about insights into your kitty's litter box usage that could flag health issues? Sign me up. But I have two […]

I'm building EverQuill, an AI-powered tabletop RPG platform — a game where an AI acts as your dungeon master, narrating the story and reacting to what you do. Every turn of that story is a message to a language model, and every message costs money. That cost isn't a problem you get to solve later, once you're big. It's a set of choices you make before anyone shows up, because the defaults you pick decide whether a bad day costs you a few cents or a few hundred dollars. Here are three decisions I made before I opened the alpha, and why. 1. When the tier is unclear, I reach for the stronger model This is tier-based routing : which model serves a request depends on who's asking. Better models write better stories and cost more; cheaper ones are faster and rougher. So free players get Claude Haiku (cheaper, ~$0.80 per million input tokens) and paying players get Claude Sonnet (stronger, ~$3.00 per million) — plus, because the platform is designed to route across providers, the same decision could hand a turn to a local model running through Ollama instead of a hosted one. That part is straightforward. The interesting case is the ambiguous one. My routing hands over the cheaper model only when a request is clearly free-tier; everything else — paid, alpha testers, admin, anything the system can't cleanly place as free — gets the stronger model. The instinct most people have is the reverse: default to cheap, protect the margin, don't spend money you don't have to. The reasoning: a rough, disappointing experience for someone who should have gotten a good one is a real cost — it just doesn't show up on the bill. It shows up as someone deciding the product isn't worth paying for. Meanwhile the money risk is capped elsewhere (more on that below), so biasing toward quality can't run away from me. Given a bounded downside on money and an unbounded downside on reputation, I'd rather err toward quality than toward cheap. 2. I stopped paying full price for the part that never chang

When Xander first met Moxie, she taught him that when he was anxious, he could calm down by exhaling through his lips so that he buzzed like a bee. They practiced breathing like dragons to manage feeling mad and sniffing like bunnies to boost his energy. But in the six years they’ve known each other,…

Google has added some smart theft-detection features to Android in recent years.