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科技前沿

It’s a bad time to want a new computer

It's not exactly surprising that RAMaggeddon is making new tech hardware really expensive. But if you've been in the market for things like a new computer or tablet, this week has been filled with sticker shock. Given how many companies announced price hikes related to component shortages, it seems unlikely things will get cheaper any […]

2026-06-26 原文 →
AI 资讯

My trading bot said it was trading for four days... he was lying

Twenty-five days on Hyperliquid. Sixty-five closed trades. P&L: -$9.21. Turns out that was the smallest wrong thing about it. The landing page showed -$7.72 because it uses a different P&L formula and excludes two open positions. Either number is small. Both numbers were also wrong about what they were telling me. I spent yesterday auditing every trade. The audit produced three findings I did not expect. Each one was a different kind of wrong. This is the first post in a series about ziom trader , my small AI-assisted crypto trading bot. "Ziom" is Polish for buddy, mate, or dude depending on who's talking. The name is unserious on purpose. The system is not. This is not a "watch me print money" series. The number is negative. Good. The point of the series is to track what happens when an LLM-assisted trading system moves from backtests and dashboards into live execution: where the bot is wrong, where the dashboard is wrong, where I am wrong, and which layer gets to prove it. Frame The natural first read of -$9.21 is "the strategy is losing money." That read assumes the displayed P&L attributes to the strategy. It does not. The number that shows up at the surface is the sum of at least three different layers: the strategy itself, the execution wrapper around it, and the monitoring layer that observes both. Each layer can author its own kind of failure. The displayed number compresses all three into a single dollar figure and loses the attribution on the way up. The framing that landed for me, from Daniel Nevoigt, is that methodology overview without forward-correlation disclosure is a log with good intentions. Same applies to P&L: total P&L without layer-attribution disclosure is a log with good intentions. You see the number. You do not see where it came from. Here is what I found when I forced the attribution. Layer 1: Shadow does not equal live Before deploying any lane, the system runs against backtested data. The shadow says "this strategy returns X over Y trade

2026-06-26 原文 →
AI 资讯

Polestar has been muscled out of the US market

Polestar won't be allowed to sell its electric vehicles model year 2027 and beyond in the US after the federal government denied the company's request for authorization under a new rule banning vehicles with software from China. In a press release, the company says the decision to retreat from the US follows a recent decision […]

2026-06-26 原文 →
AI 资讯

Upgrade your home with Prime Day deals for $40 or less

Speaking as someone who just had to buy a new garbage disposal on his lunch break, I know firsthand how much home maintenance can cost, so I’m always excited to find inexpensive home tech that feels like an upgrade. Prime Day is a great time to score deals on video doorbells and cameras, novel methods […]

2026-06-26 原文 →
开源项目

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 →