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Slate’s electric truck: all the news about the ultra-minimal EV

Slate Auto is a new startup that emerged out of a secretive project called “Re:Car” within Re:Build Manufacturing, a domestic manufacturing project backed by Amazon founder Jeff Bezos. The company’s first electric vehicle is a barebones electric pickup that’s roughly a third of the size of your typical gas-powered truck. And the proposal is pretty […]

2026-06-25 原文 →
AI 资讯

The Hidden Cost of the AI Hype

We talk a lot about what AI can build. Code generation. Faster prototypes. Automated debugging. One-shot apps. Entire products created in hours. And yes, AI is powerful. But there is a quieter cost we are not talking about enough: AI hype is starting to weaken the motivation to learn core engineering deeply. That should worry us. 1. The "Why Bother?" Mindset When the dominant narrative says AI can generate code instantly, many engineers start asking: Why should I spend months mastering frameworks, architecture, databases, networking, or system design? At first, that sounds practical. If a tool can help, why not use it? But there is a difference between using AI to move faster and using AI to avoid understanding. Core engineering is not just about writing code. It is about knowing why something works, where it breaks, how it scales, and how to fix it when the generated answer is wrong. If we skip that learning, we create engineers who can prompt systems but cannot reason deeply about systems. That is a dangerous tradeoff. 2. The Funding and Praise Monopoly Right now, AI gets most of the attention. Budgets move toward AI. Leadership praises AI initiatives. Teams are pushed to add AI features even when the fundamentals are still weak. Meanwhile, excellent core engineering often goes unnoticed. The people improving reliability, performance, developer experience, infrastructure, security, and maintainability are still doing high-impact work. But in many places, that work is being treated as less exciting simply because it is not branded as AI. This creates pressure. Engineers feel they must pivot to AI, not always out of interest, but out of fear. Fear of being left behind. Fear of being replaced. Fear that their existing expertise is no longer valued. That is not innovation. That is anxiety disguised as progress. 3. The "AI-First" Discount There is another subtle problem. When someone builds something impressive today, the reaction is often: AI probably generated that.

2026-06-25 原文 →
AI 资讯

Zoox’s purpose-built robotaxi is getting a refresh

Zoox, the autonomous vehicle company owned by Amazon, unveiled a new look for its boxy, bidirectional robotaxi, calling it the "next evolution" of the vehicle intended for mass production. The company is currently operating a free robotaxi service in San Francisco, Las Vegas, Austin, and Miami while it waits for the federal government to approve […]

2026-06-24 原文 →
AI 资讯

The Slate Auto pickup truck starts at $24,950

We now know the price of Slate Auto's affordable American-made electric truck, almost a year after the company warned it wouldn't hit its initial "under $20,000" target price. The no-frills pickup starts at $24,950 - matching the revised mid-$20,000 price range it promised last year, after the Trump administration announced it was putting an end […]

2026-06-24 原文 →
AI 资讯

I drove the Slate Truck — there’s more to it than EV minimalism

With its new pickup, Slate Auto is making a simple bet: price matters more than almost anything else. The company announced today that the American-made electric truck will start at $24,950, placing it squarely in the mid-$20,000 price range it had originally promised and making it the least expensive pickup truck and EV available today. […]

2026-06-24 原文 →
AI 资讯

I got a merged PR into a YC startup before they ever replied to my job application

I applied to a YC W25 startup the normal way. Filled out the form, wrote a decent cover letter, hit submit. Silence. While waiting, I found their open-source repo on GitHub. Read through the codebase out of genuine curiosity I wanted to understand what they were actually building. Found a bug. Fixed it. Opened a PR. It got merged in 2 days. They still hadn't replied to my application. Here's what that taught me about job hunting in 2025: A cover letter tells someone what you claim you can do. A merged PR shows them. One of those gets read. The other gets filed under "maybe later" -which is just "no" with extra steps. I'm not saying cold applications are dead. I'm saying they're the last resort, not the first move. If a company has a public repo, you have a backdoor that most applicants don't even think to try. Read the code deep and find something small but real. Fix it and Open a PR. Now you're not a stranger in their inbox you're someone who already ships for them. The reply came eventually, by the way. But by then, the maintainers already knew my GitHub handle. That matters more than you think. Have you ever landed something through a contribution instead of an application? Drop it in the comments curious how many people have done this.

2026-06-24 原文 →
AI 资讯

Your Data Engineering Take-Home Is Now 20 Hours of Free Work

I got a take-home assignment last year from a company I was genuinely excited about. "Should take about four hours," the recruiter said. Build an ingestion pipeline, model the data, write tests, document your design decisions, and prepare a 15-minute presentation walkthrough for the panel. Four hours. I laughed, closed my laptop, and started on it the next morning like it was a sprint. Sixteen hours later I had something I was proud of. Clean pipeline, solid tests, real documentation. I submitted it on a Sunday night. Monday I got a form rejection. No notes. No feedback. Not even which stage I failed. Just "we've decided to move forward with other candidates" and a link to their Glassdoor page. That was the moment I stopped pretending take-homes are assessments. They're consulting gigs. Unpaid ones. The Scope Creep Nobody Talks About Five years ago, a data engineering take-home was a focused exercise. Model this dataset into a star schema. Write a few SQL transforms. Maybe a short README. Two to four hours, tops. Bounded, reasonable, and actually useful for evaluating how someone thinks about data. That version is dead. Today, 68% of companies use take-home tests, up 12% year over year. And the scope has quietly ballooned into something unrecognizable. Full pipeline implementations. Test suites with coverage thresholds. Documentation that reads like a design doc. A presentation follow-up where you defend your architecture to a panel. We're talking 10 to 20 hours of work, routinely, for a role you haven't been offered. Industry best practice caps take-homes at 90 minutes of expected effort. The reality? Candidates consistently take 2x longer than company estimates to reach submission quality. That "four-hour" assignment is an eight-hour assignment. That "weekend project" is a week of evenings. And 25% of companies are still handing these out like they're reasonable asks. Here's the part that makes my eye twitch: 71% of engineering leaders openly say take-homes no lon

2026-06-24 原文 →
开发者

Formula E’s new season is starting to look more like F1

The next season of Formula E will feature a new race format and three new race locations when it starts in December to go with the new Gen4 electric cars. The schedule released today by the FIA includes the Circuit of the Americas in Austin, the Brands Hatch circuit in Kent, and the Zandvoort circuit […]

2026-06-24 原文 →