PixelsTech

Discover curated resources across programming, AI, Linux, and more — all in one place. Explore the PixelsTech Resource Hub pixelstech.net/resource/

We have launched a new MCP server for image handling. You can download and use it through npm. It supports convert image into different types, resize them and merge images as you like horizontally, vertically etc. Go check it out. #mcp imagicsave.com/agent.php

Data Indexing in Golang #golang hister.org/posts/data-ind…

你的认知并不一定代表别人的认知。 比如在新加坡，这边有很多佣人，月入只有几百到一千新币不等，很多住在狭窄逼仄的空间里，没有窗户，没有空调，有的雇主也不让使用手机。之前跟朋友聊过是不是她们的生活很惨？她们很不幸福？朋友也跟本地的佣人聊过。事实可能不是我们想象的那样。我们可能觉得她们难，但是事实却是她们自己觉得幸福。因为她们比在她们老家挣得多，也能每个月寄一点钱回家，也能在周末半天的休息时间里打扮的漂漂亮亮跟朋友一起悠闲的聚在一起吃东西听音乐。这就是她们的幸福。可能很多外卖骑手也是类似的感受。 技术上现在可以有无人出租车，无人快递车，甚至无人飞机送外卖，它们完全可以让骑手们不用送东西了。可没有了这些，在没有更多一技之长的情况下他们怎么生活。这个社会追求公平，但现实就是不公平。所以更多的是接受现实，守住底线。你会活的更开心些。 （题外话，感觉你发的东西也都不是自己的）。

你有没有想过，你在中国，无论半夜几点你吃饭，点个外卖，都有人给你送来，你还可以加钱让他加速送。也就是说，你用很少的钱可以买到这种人力服务，而背后提供服务的这个人，他就是要去赚这个钱的，养家糊口的钱。那这种便捷是因为这个国家强大吗？这些外卖骑手的是喜欢送外卖吗？都不是，只是你花钱买的贫困者的不得已。那从你的角度来看，想要持续的这种生活上便捷服务，社会就需要要维持住有足够多的穷人。所以说，国家的优质资产是充满正能量的穷人，富人的优质资产就是低认知的穷人。

New article! A user is reporting full system freezes while using Superluminal on Linux. What do you do? Cry? Well, we did a little bit. But we also dove into the kernel...again, fixing several issues in eBPF's spinlock implementation. Read all about it: rovarma.com/articles/a-tal…

September 2009. Jensen Huang walks onto a small stage at the Fairmont hotel in San Jose. About 1,500 people are in the room. He runs a company that makes chips for video games. He spends the next 8 minutes doing math on a whiteboard, explaining why the future of computing won't come from making CPUs faster. He calls it "CEO math" and apologizes in advance to every computer science professor in the audience. Then he lays out an argument that almost nobody took seriously at the time: the way to make computers dramatically faster is to pair a regular CPU with hundreds of tiny parallel processors, the kind that already exist inside graphics cards. One CPU for the sequential stuff. Hundreds of GPU cores for everything else. He calls it "heterogeneous computing." He shows the math. A workload that can be split into many pieces at once gets up to 200x faster on this combined system. A workload that has to run one step at a time loses nothing. "The most important thing in creating a new architecture," he says, "is to make sure it does no harm." This was the first GPU Technology Conference. NVIDIA had launched a software platform called CUDA three years earlier, in 2006, to let developers write programs that run on graphics cards instead of just regular processors. Almost nobody cared. GPUs were for rendering Call of Duty, not for scientific computing. The academic world was polite but skeptical. The enterprise world ignored it entirely. By this point, Huang had been making this argument for years. NVIDIA was a $7 billion company. It competed with AMD and Intel for market share in the graphics market. That was the whole business. Jensen kept saying the GPU wasn't just a gaming chip; it was a computing platform. He kept saying parallel processing would reshape every industry from medicine to finance to physics simulations. People kept nodding, then doing nothing. Then deep learning happened. Around 2012, AI researchers discovered that training a neural network, which means teaching a computer to recognize patterns by running the same calculation millions of times across huge datasets, was exactly the kind of workload Jensen had been describing. GPUs can train AI models 10 to 50 times faster than CPUs. The architecture he outlined in this 2009 talk, with one CPU handling step-by-step tasks while hundreds of GPU cores crunch through massive amounts of parallel data, is now the literal blueprint for every AI data center on earth. ChatGPT runs on NVIDIA GPUs. Claude runs on NVIDIA GPUs. Gemini, Llama, Midjourney, nearly every major AI model you've heard of was trained on NVIDIA hardware using CUDA, the software platform Jensen built for a market that didn't exist yet. NVIDIA was worth about $7 billion when Jensen gave this talk. It is worth over $4.4 trillion today. That's a 600x increase. Jensen Huang, who founded the company at a Denny's in 1993 with two friends, now has a net worth of over $160 billion. He made Forbes' list of the 10 richest people for the first time this year. GTC 2026 is currently ongoing. 17,000 people are packing a hockey arena to watch the same guy explain what comes next. In 2009, 1,500 people showed up at a hotel ballroom, most of them for gaming graphics.

cannot believe this happens to a company whose main business is doing security...

Deploy multiple OpenClaw AI assistants on a local GPU using Qwen 3.5 or DeepSeek R1. This guide shows how to run a cluster of agents connected to local LLM inference servers, enabling concurrent assistants without relying on paid APIs. Great for building multi-agent workflows with full control over compute and data. #openclaw #deepseek #qwen pixelstech.net/article/177365…

History of the @ sign in email address

@chinafutureclub nothing is open before 10am? are u serious? ppl even started to drink in the morning.

China is not a morning place. Nothing is open before 10am and all the social energy happens at night. It’s different to Australia where lots of people wake up early as a habit.

The difference between Wi-Fi networks

A great deep dive into how Go’s runtime scheduler actually works. It walks through the G-P-M model (goroutines, processors, and OS threads) and explains how Go schedules thousands of goroutines efficiently using local run queues and work stealing. Worth a read if you want to understand what happens behind Go’s concurrency. Read full post in comment.

@elonmusk Any plan on supporting Repost to Community feature?

Go’s Or-Done channel pattern is a concurrency technique that helps prevent goroutine leaks and blocked pipelines when working with channels. Instead of waiting only for data from a channel, the pattern wraps the channel so a goroutine can also listen for a cancellation signal (a done channel), allowing it to stop immediately if the operation should terminate early. This ensures that producers and consumers don’t remain stuck waiting on each other and helps build safer, more resilient concurrent pipelines in Go. #golang #learngo @atharvamhaske76/the-famous-or-done-channel-pattern-in-go-36942fb4f065" target="_blank" rel="nofollow noopener">medium.com/@atharvamhaske…

New article: Inside Go's Runtime Scheduler 🚀 How Go runs millions of goroutines on a few OS threads. The GMP model, work stealing, spinning threads, and context switches that take only 50-100 nanoseconds (10-40x faster than OS threads). 👉 internals-for-interns.com/posts/go-runti… #golang

@Sierra_rak her hair reminds me of cat

Alysa: I hope that, you know, with all these attentions, I can at least raise awareness about like mental health and sports, mental health in general. And I think my story is pretty cool, and so I hope that inspire some ppl as well.

worth reading

Detail: pixelstech.net/article/177304…

Built a lightweight controller system for the Braccio+ robotic arm that simulates a full industrial automation stack. It includes a 3D robot arm simulator, a PLC-style control layer, and a remote HMI interface using Python, OpenGL, and OPC-UA. A simple way to experiment with robotic control and cyber-physical systems without real hardware. More details in the comment. #robot #3d #plc

@dongqubo toxic

事实上，阿里内部早已建立了一套成熟的”贡献归因体系”： 在职时，成果归团队、归组织、归战略远见；离职后，成果归留下来的人。该体系运转良好，已成功应用于达摩院、平头哥、盒马等多个业务单元。 关于”灵魂人物”的认定，公司也有明确标准：灵魂人物永远是汇报线上仍在岗的最高级别领导。该认定具有动态调整机制，如周靖人未来离职，则灵魂人物将自动上浮至吴泳铭，以此类推，直至追溯到马老师本人的湖畔初心。 至于林俊旸在X上获得的1.3万点赞和600万浏览，公司认为这属于”未经审批的流量外溢”，已纳入数据资产流失专项治理。 同时提醒全体同事：个人社交媒体影响力是组织资源的延伸，离职时应一并交接，包括但不限于粉丝数、互动率、以及海外开发者对你的好感。 公司重申，千问基础模型是整个集团最重要的事情。 至于它到底有多重要，请参考我们为它配置的算力资源：大约是字节跳动的几分之一。

@astro_greek what is he doing now?

One of Elon's closest friends was Robin Ren, who had won a Physics Olympiad in his native China before coming to Penn. "He was the only person better than me at physics" They became partners in the physics lab, where they studied how the properties of various materials change at extreme temperatures. At the end of one set of experiments, Musk took erasers from the ends of pencils, dropped them into a jar of super-cold liquid, and then smashed them on the floor. He developed an interest in knowing and being able to visualize, the properties of materials and alloys at different temperatures. Source: Walter Isaacson's 'Elon Musk' (2023), chapter 8