标签：#art

The Budget You Approved Isn't the Budget You'll Pay You approved $180K for a senior AI engineer. Eighteen months later, you've spent $282K and you're still not sure the hire is working out. This isn't unusual. It's the rule. Companies hiring AI engineers for the first time routinely underestimate total cost by 40–60%. Here's a breakdown of where that gap comes from — and why most founders don't see it until it's too late. The 56% Gap: Where It Comes From 1. Recruiting Costs Are Higher Than You Think (~12–18% of first-year salary) AI engineer recruiting isn't like standard software recruiting. Specialized headhunters charge 20–25% of first-year salary. Even if you find someone through your network, you'll spend founder or VP time on 15–30 hours of interviewing, plus take-home evals that the best candidates increasingly decline. If you use a staffing firm, add the markup. If you DIY it, add the opportunity cost. Typical recruiting overhead: $22,000–$40,000 per hire 2. Onboarding Takes Longer for AI Roles (~2–3 months of ramp) An AI engineer hired to build production agent systems isn't productive on day 1. They need to understand your domain, your data, your existing architecture, and your risk tolerance for AI-generated outputs. The ramp is real — most teams see 60–90 days before meaningful output. At $180K salary, two months of ramp is $30,000 in salary with limited ROI. Add engineering time for mentoring (typically 20% of a senior engineer's time during ramp), and you're adding another $15,000–$20,000. Ramp cost: $30,000–$50,000 3. Infrastructure Spend Scales With Experiments AI engineers experiment. That's the job. Every experiment has a GPU bill, an API bill, and a storage bill. Early-stage teams routinely see $3,000–$8,000/month in AI infrastructure spend once they've hired their first AI engineer — much of it from exploratory work that doesn't ship. Over a year: $36,000–$96,000 in infra costs that weren't in the original headcount budget 4. Tooling and Data Cos

Unlike humanoid robots designed around a fixed form — think Boston Dynamics — Theker's machines are built to be reconfigured.

Today on Uncanny Valley, we take an early look at the SpaceX IPO and why you might find yourself among the investors without even realizing it.

Amazon's rolling out a free software update for Echo Hub devices that gives the home screen a much-needed update to the interface it launched with in 2024. It had already added Alex Plus AI support, but the new interface has a cleaner, fully customizable layout that fits more smart home info and controls on the […]

on may 28 anthropic announced a $65 billion series h round at a post-money valuation of about $965 billion, which makes it, on paper, the most valuable ai startup in the world. the round was led by altimeter capital, dragoneer, greenoaks and sequoia, on top of earlier hyperscaler commitments that included around $15 billion with $5 billion of it from amazon. the headline everyone ran with is that anthropic passed openai. that part is true, but the comparison is messier than the headline, and the more interesting story is what is generating the number. i build small dev tools and write comparison content, and a lot of what i ship runs on top of anthropic's models. so when the company that makes the tools i depend on nearly touches a trillion dollars, i do not read it as a sports score. i read it as a question about whether the thing i am betting on is durable, and what i should do differently because of it. here is the honest version of both. the number, with the caveats intact the $965 billion figure is consistent across cnbc, axios, morningstar, al jazeera and euronews, so i trust it. what i would not do is state the gap over openai as a precise fact, because the sources do not agree on openai's number. axios pegged openai's most recent valuation at $730 billion. other outlets put it closer to $850 billion off a record round earlier in the year. either way anthropic is ahead right now, but "ahead by $115 billion" and "ahead by $235 billion" are different sentences, and anyone quoting one as gospel is rounding away the uncertainty. the safe claim is the one i will make: as of late may 2026, anthropic is the most valuably-priced private ai company, and it got there fast. the reporting has it roughly tripling from a $380 billion mark in february. the part that matters more to me is the revenue. anthropic crossed a $47 billion run-rate earlier in may. that is the line that turns a valuation from a vibe into something with a floor under it. you can argue about whether $

Thibault Sottiaux helped make AI coding one of OpenAI’s fastest-growing businesses. Now he’s overseeing a sweeping overhaul of ChatGPT.

Bluesky's latest feature is group chats, arriving amid a shift in focus on building features for smaller communities.

A WIRED investigation found dozens of “nudified” deepfake images and videos on Grok's website, including nonconsensual depictions of celebrities and at least one prominent US politician.

The old app "still needs to be retired," AcuRite tells us.

"Some missions are using more than what their paperwork would say."

You can save on a big set of outdoor security cameras ahead of Prime Day. Amazon has a five-pack of Blink cameras with a video doorbell included that’s marked down to $166.99. The bundle includes a Blink Battery Doorbell 2K+, five Blink Outdoor 2K+ cameras, and a Blink Sync Module Core. The doorbell is typically […]

Quantum Space says SPACs aren't dead as it seeks a $1.2 billion deal to build military spacecraft.

The only functional difference between a Trump Phone and the HTC U24 Pro is the battery, iFixit discovered.

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

submitted by /u/Foreign-Swan4271 [link] [留言]

It feels like just a couple of years ago, using AI for everyday tasks still felt like something new or even a bit weird. Now it seems like a lot of people are using it without thinking twice, whether for writing, learning, brainstorming, or just quick answers. I’m curious how others see this shift. Do you think AI has become normalized quicker than most people predicted, or does it still feel like a big deal to a lot of users? submitted by /u/NoFilterGPT [link] [留言]

submitted by /u/aarshie [link] [留言]

I’ve been thinking about a support automation story I read recently. A team replaced a simple rules engine with an LLM classifier. The model was around 92% accurate. Sounds good. Until you realize that at 100 tickets a day, that’s roughly 8 mistakes every day. The interesting part wasn’t the accuracy though. It was what happened when the model was wrong. Nobody could explain why a ticket was classified a certain way. Nobody could point to a specific rule. Nobody could quickly fix the behavior. The team eventually started reviewing every classification manually. The automation was still running, but the trust was gone. That got me thinking. A lot of discussion around AI agents focuses on making decisions better. Better prompts. Better models. Better reasoning. But I rarely see people discussing what happens after the decision. How is the decision verified? How is it audited? How do you know an action should actually be executed? Maybe the biggest challenge for AI agents isn’t getting from 92% to 96%. Maybe it’s building systems that people can trust when things go wrong. Curious how others are thinking about this. submitted by /u/docybo [link] [留言]

submitted by /u/andix3 [link] [留言]

Everyone talks about reasoning, agents, and larger models. But the more I learn about AI systems, the more I think we're missing something fundamental: AI doesn't accumulate experience the way humans do. A senior engineer isn't valuable only because of raw intelligence. They're valuable because years of experience have shaped how they think. They're valuable because they've spent years building mental models, learning from failures, recognizing patterns, updating beliefs, and connecting knowledge across thousands of experiences. That accumulated experience becomes a competitive advantage. Modern AI systems are different. They can solve difficult problems, write code, and explain complex concepts, yet most of what they "know" remains largely fixed after training. New information is often handled through context windows, retrieval systems, databases, or retraining pipelines rather than being integrated into a continuously evolving understanding of the world. This creates an interesting question: Can intelligence continue to scale if experience doesn't? Humans become more useful over time because experience compounds. An AI that could reliably learn from interactions, update its worldview, resolve contradictions, remember what matters, forget what doesn't, and improve without catastrophic forgetting might represent a larger leap than another increase in parameter count. Maybe the next frontier isn't making AI smarter. Maybe it's making AI capable of growth. Do you think future breakthroughs will come primarily from better reasoning models, or from systems that can continuously learn from experience? submitted by /u/Shreyansh_awasthi01 [link] [留言]