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AI 资讯 Dev.to

The First Visible LED Glowed Red

Look at almost any piece of electronics on your desk and you will find a small light staring back at you. A router with a row of blinking status lights. A power brick with a steady green dot. A development board with a tiny red point that flickers every time it does something. We barely notice these lights anymore, but each one descends from a single laboratory breakthrough in 1962, when an engineer at General Electric coaxed a sliver of semiconductor into glowing visible red for the first time. Who invented the first visible LED The engineer was Nick Holonyak Jr., a consulting scientist at General Electric's lab in Syracuse, New York, and a former student of John Bardeen, one of the inventors of the transistor. On October 9, 1962, Holonyak demonstrated the first practical visible-spectrum light-emitting diode. It emitted red light, and it worked at room temperature, which made it genuinely useful rather than a laboratory curiosity. What made his approach different was the material. Other researchers in the early 1960s were building diodes that emitted infrared light, which is invisible to the human eye. Holonyak gambled on a different alloy, gallium arsenide phosphide, and it paid off with the first light a person could actually see coming out of a semiconductor. He was so confident in the idea that he predicted LEDs would one day replace the incandescent bulb. At the time that sounded outlandish. Today it is simply how lighting works. Why a tiny red light mattered so much The incandescent bulb that Thomas Edison commercialized makes light by heating a filament until it glows. That is wildly inefficient, because most of the energy escapes as heat rather than light, and the filament eventually burns out. An LED works on a completely different principle. When current flows across a specially engineered semiconductor junction, electrons release their energy directly as photons. There is no filament to burn out, almost no wasted heat, and the device can switch on and o

fluidwire 2026-06-30 05:14 8 原文
AI 资讯 Dev.to

Abandoning Abstractions: Manually Crafting EtherNet/IP Packets Almost Broke Me

By RUGERO Tesla ( @404Saint ). There is a persistent illusion in Industrial Control Systems (ICS) security research: that high-level libraries, abstraction frameworks, or protocol tooling give you a real understanding of Operational Technology (OT) behavior. They don’t. They hide the architecture. Determined to understand what actually happens when a Programmable Logic Controller (PLC) receives a control-plane command, I built an EtherNet/IP and Common Industrial Protocol (CIP) sandbox from scratch. No Scapy. No protocol wrappers. Just raw sockets, a Linux loopback interface, a cpppo simulator, and a passive monitoring tool ( enip_monitor.py ) capturing traffic in real time. It looked clean on paper. Then I reached the application layer. And things stopped behaving like theory. The Reality of the “Industrial Abstraction Layer” If you come from Modbus or traditional IT networking, you’re used to linear memory spaces—fixed registers, predictable offsets, and flat addressing. EtherNet/IP and CIP discard that model entirely. Instead, they introduce a structured object system wrapped inside multiple encapsulation layers: +-----------------------------------------------------------+ | EtherNet/IP Encapsulation Header (24 bytes) | | → Session control, commands (0x0065, 0x006F) | +-----------------------------------------------------------+ | Common Packet Format (CPF) | | → Routing, addressing, and transport segmentation | +-----------------------------------------------------------+ | CIP Application Layer | | → Service codes (0x4C, 0x4D, 0x10, etc.) | +-----------------------------------------------------------+ To communicate with a PLC at the wire level, your code must: Establish a session using RegisterSession (0x0065) Wrap all subsequent requests in SendRRData (0x006F) Encode routing information inside CPF structures Construct symbolic or logical paths for the CIP Message Router Ensure strict byte alignment across nested payload layers A single mistake in any layer b

404Saint 2026-06-30 05:13 9 原文
AI 资讯 Dev.to

More Watts, Less Light

Token burn and business outcomes are not correlated. More burn means more inefficiency, not more value. The electricity problem Imagine you walk into a dark room. Turning on a light helps you see. Turning on every light in the building does not help you see better. It's still the same room. Now every surface is equally lit, the contrast is gone, and you're paying for power you didn't use. Tokens work the same way. A focused prompt with clear scope is the single overhead light over your desk. A sprawling prompt with unlimited exploration is every light in the building — you're burning power, not producing insight. Tokens are electricity, not output. More throughput doesn't mean more value. I've had weeks where I burned through my allocation and looked back at the end to find nothing concrete. Code that worked but went unused. Exploratory branches that dead-ended. Agents that generated plausible-looking output that didn't survive first review. A lot of motion. Not much progress. The ceiling stops you from doing that indefinitely. It forces a moment of reflection: did this burn produce anything real? If the answer is no, more capacity isn't the fix. More discipline is. Three patterns I now use instead I started paying attention to what actually ships versus what just burns context. I gave the patterns names so I could catch myself faster: RTK — Read The Knowledgebase. A focused 15-minute read of the codebase, identifying the exact files and exact changes, saves 200K+ tokens of exploratory waste. The agent doesn't discover the shape of the task — it executes against a known one. Caveman — compress before you prompt. Strip greetings, filler words ("I think", "basically", "Let me know if that makes sense"), and closing courtesies. Every word in your prompt multiplies across every response token. Less fluff in means less fluff out. Ponytail — spec the minimum viable solution. "Robust", "scalable", "enterprise-grade", "comprehensive" — these words invite scope creep. Specif

Vilius 2026-06-30 05:11 6 原文
开发者 The Verge AI

T-Mobile is booting customers from its oldest plans

Earlier today, T-Mobile started notifying customers that it will be retiring many legacy plans and moving subscribers onto one of its current rate plans. This move includes plans that date back to the 3G era, and it's going about as well as you'd expect. Affected customers began sharing screenshots of the text on reddit and […]

Allison Johnson 2026-06-30 05:06 7 原文
AI 资讯 The Verge AI

After a great start, DC’s new cinematic universe is already slowing down

While Kara Zor-El's appearance at the end of James Gunn's Superman was a very pleasant surprise, Warner Bros. Discovery's plan to fast-track a standalone Supergirl feature always felt a little dubious. It seemed odd that, after Superman, the studio wanted to flesh out its new cinematic universe with films about another Kryptonian and one of […]

Charles Pulliam-Moore 2026-06-30 05:00 10 原文
开发者 The Verge AI

Leaked iPhone 18 Pro photos reportedly wound up on the dark web

Leaked iPhone 18 Pro photos and parts lists appeared on the dark web following a data breach affecting one of Apple's key suppliers, according to a report from Reuters. The leaked images show a drop test of what a source tells Reuters is the iPhone 18 Pro, equipped with a three-camera layout and Apple logo. […]

Emma Roth 2026-06-30 04:18 11 原文