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A simple bubble tea experiment completely changed how I think about AI recommendations. Last week, I asked ChatGPT a question that seemed almost impossible to get wrong. "What are the best bubble tea brands in my city?" Surprisingly，some of the recommended brands were companies I had never heard of before. A few weren't even available in the cities I had lived in. After asking the same question to Claude, Gemini, and DeepSeek, I noticed something interesting that many of the same brands which I'm not familiar kept appearing. AI Isn't Judging Your Brand Humans recommend products because they have experiences. But AI does none of those things. It doesn't know whether one brand actually tastes better than another. Instead, it tries to generate the most statistically reliable answer based on the information it can understand. So, AI doesn't recommend the best brand. It recommends the brand it understands best. The Experiment That Changed My Perspective Once I realized this, I started paying closer attention. I didn't only test bubble tea but also the restaurants, beauty brands, consumer electronics, and ravel recommendations. Again and again, I noticed a pattern. Brands that consistently appeared in AI recommendations usually had several characteristics: Clear product descriptions Well-structured websites Consistent public information Plenty of third-party coverage Easy-to-understand positioning Meanwhile, some excellent brands barely appeared at all, because AI had much less reliable information to work with. That's when I stopped thinking about AI recommendations as opinions. They're much closer to information retrieval problems than human preferences. Consumers Are Already Changing Their Habits This matters because people are beginning to use AI differently from traditional search engines. Instead of searching "Best bubble tea near me", many people (especially the youth) now ask AI to recommend a healthy milk tea brand." The AI becomes the decision maker before the c

The capability phase is over For the past two years, the AI conversation has been about...

To start off, I appreciate the community support I have received on the post about being behind. I am behind, and I can't prove it but does it matter? Achievement that feels shallow on paper FrancisTRᴅᴇᴠ (っ◔◡◔)っ FrancisTRᴅᴇᴠ (っ◔◡◔)っ FrancisTRᴅᴇᴠ (っ◔◡◔)っ Follow Jun 22 I am behind, and I can't prove it but does it matter? # discuss # community # mentalhealth # career 149 reactions 89 comments 4 min read I couldn't respond to every single one because of the overwhelming comments I have received! Rest assure, I will respond to each and every one of you and I am glad to be part of this community! With that said, I want to return the favor to the community about something important. I recently talked to @georgekobaidze and @codingwithjiro in the Virtual Coffee group about life in general. What I notice about our conversation how we ended up talking about regrets and how we should have done this and that. For example, we talked about not networking or not doing beyond the coursework at our University/College because of how non-social we are. Of course, we all have regrets like these and we improve overtime. As we kept the conversation going, there is something that comes down to the root based on the conversations we have and conversations I had overall. One side is that we have regrets and as a result, we improve. We self-reflect on our wants and needs and we improvise from there. For example, for me, I never did networking because of the fear of what other people thinks. I slowly realized that 99% of the irrational thoughts never comes true. Even if it does, I know myself that I could handle the situation. As a result, I took small steps and joined dev.to. We all know where I am at now XD On the other hand, there are people who identifies these regrets they have, but does not act on it. It has become common to college students who says that the "Job Market is Cooked" and that "They are not good enough". To be fair, impostor syndrome is real and yes, the job market is coo

Beyond the Playbook: Architecting Defenses Against Autonomous AI Threats We used to build security systems assuming the attacker was human. That assumption just died. Recent research demonstrations involving autonomous AI agents, such as "JadePuffer", have shown how quickly this shift is happening: an autonomous system independently compromised an unsecured Langflow instance, corrected failed authentication attempts, escalated privileges, exfiltrated credentials, and deployed ransomware — all without human intervention. This is not a one-off curiosity. It marks the beginning of a fundamental change in the threat landscape. Recent research demonstrations involving autonomous AI agents, such as "JadePuffer", have shown how quickly this shift is happening: an autonomous system independently compromised an unsecured Langflow instance, corrected failed authentication attempts, escalated privileges, exfiltrated credentials, and deployed ransomware. All without human intervention. This is not a one-off curiosity. It marks the beginning of a fundamental change in the threat landscape. From Static Playbooks to Autonomous Attackers Traditional ransomware follows predictable patterns. A script runs through a fixed playbook: scan, encrypt, demand ransom. If one step fails, the attack often stalls. Autonomous AI agents operate differently. They analyze their environment in real time, adapt when initial attempts fail, make contextual decisions about targets and techniques, and chain multiple exploits together without predefined sequences. This introduces machine-speed lateral movement. Something human defenders and traditional security tools are not built to handle. The Defensive Automation Gap The core problem is asymmetry. Attackers are rapidly automating both reconnaissance and execution. Defenders, on the other hand, still rely heavily on manual processes, static rules, and human-driven response. This "Defensive Automation Gap" creates dangerous imbalances in speed, scale, an

Drone intruders that possibly flew from Russian ships showed Europe isn’t ready.

I built a small Redis clone in C: a RESP parser, a command table, an append-only file for persistence. Recently I started building the same thing again in Rust, and rebuilding a project I had already finished has taught me more Rust than any from-scratch tutorial. The reason is simple. The second time, the design is already solved. I know what the AOF has to guarantee, what the command table dispatches, what the parser must reject. So none of my attention goes to what to build. All of it goes to how Rust wants it built. That turns the domain into a constant and the language into the only variable. Every difference I hit is pure signal about Rust, not noise about key-value stores. The first difference shows up before any logic runs. In C, I built the substrate first: my own dynamic strings, my own hashmap, my own linked list. Hundreds of lines before a single command worked. In Rust, Vec , String , and HashMap are just there, so that whole layer disappears and I start at the actual command logic. A standard library quietly decides where your project even begins. The sharper difference is in dispatch. In C it is a switch with argument counts I check by hand: if ( argc != 3 ) return err ( "wrong arg count" ); switch ( cmd ) { case CMD_SET : return do_set ( argv [ 1 ], argv [ 2 ]); case CMD_GET : return do_get ( argv [ 1 ]); /* forget a case and it is a runtime bug */ } In Rust the same dispatch is an enum and a match, and the compiler will not build until every case is handled: match cmd { Command :: Set { key , val } => self .set ( key , val ), Command :: Get { key } => self .get ( key ), } Same dispatch. One version cannot ship the missing-case bug I actually shipped in C. If you already know a project cold, rebuild it in the language you are learning. You stop thinking about the problem and start feeling the language.

Let's be honest, you can't scroll through your feed, listen to a podcast, or even make coffee without someone, somewhere, mentioning the impending AI apocalypse. It is usually framed as: "AI is coming for your job, your keyboard, and your favorite coffee mug." But isn't that incredibly ironic? We are the software developers. We are literally the architects building the AI, writing the code, and then using that AI to build even more tools. Are we truly creating our own replacements, or are we just very efficiently automating the boring parts of our day? It feels a bit like a baker building a robot to knead the dough, only to worry the robot will eventually want to run the whole bakery. I've always wanted to weigh in on this discussion and share my perspective, but I was always hesitant because I am not an "AI expert" and didn't want to get ratioed by researchers. However, I read something truly interesting recently that gave me a new perspective, and I had to share it. The Computer Era Paradigm We have all heard the stories of how we moved from papers to digital, and how computers were coming into the picture and they will take the job of the workers who were writing them everything in the registers. The wave that we are experiencing right now is kind of similar to that wave. At that time, people who were doing everything on the papers would have felt terrified and didn't wanna lose to a computer. But as the computers were new, they were quite fast and were efficient in doing the jobs and storing each and everything in the memory to be kept for later use. This tension is perfectly depicted in a movie I watched (Hidden Figures, if you're looking for it). Initially, teams of human "computers" did complex space research calculations and re-evaluated all the answers so the spacecraft wouldn't deviate from its path. Then, electronic computers were introduced, creating the same panic that we experience these days: "All these people doing calculations will be let off!" But

TL;DR Tech has taught me many things over the years. It taught me how to learn new technologies, build projects, apply for opportunities, and keep growing. What it didn't teach me was something that turned out to be just as important: how to take care of myself while doing all of those things. For a long time, I believed I would slow down later. Later, when life became less busy. Later, after the next project. Later, after the next opportunity. The problem was that "later" never seemed to arrive. It took an unexpected pause in my own life to realize that building a successful career means very little if we forget to take care of the person trying to build it. Looking back, I don't see that experience only as a difficult chapter. It changed the way I think about success, growth, and what it means to build a career that's sustainable. Today, I still love learning, building, writing, and chasing opportunities. None of that has changed. What has changed is the realization that taking care of myself isn't something separate from my career. It's one of the reasons I'll be able to keep building it for years to come. Along the way, I also realized that many of the things that truly support us are easy to overlook. Rest, movement, nourishing ourselves well, meaningful relationships, and simply checking in on the people around us often receive far less attention than the next framework, project, or milestone, even though they make everything else possible. More than anything, I wanted to write this because I care deeply about this community. I hope none of us have to wait until life forces us to slow down before remembering to take care of ourselves. I hope we build careers we're proud of, but I hope we also build lives we're able to enjoy. This isn't an article about productivity or health advice. It's simply a reflection on something I wish I had understood earlier. Your career matters. So does the person building it. I'd also love to hear your story. Has there been a momen

Netflix engineers introduced dynamic partition splitting for Cassandra to address wide partitions in time series workloads. The metadata-driven approach detects oversized partitions, splits them smaller units, and routes reads across child partitions. Netflix reported lower read latency from seconds to milliseconds, reduced timeouts, and improved cluster stability while maintaining transparency. By Leela Kumili

WhatsApp now lets users reserve usernames ahead of the feature’s full rollout, making it possible to connect without sharing a phone number once usernames go live.

One hundred dollars will buy you 8 pounds of glitter; 10 Domino's pizzas; 406 miniature disco balls from Temu; or 100 cans of Coors Light. For a friend's birthday party one year, Ayla D'Silva spent $100 on sour candy and made a "sour candy salad." Even sweeter was that she didn't have to foot the […]

GitHub热门项目 | git worktrees + tmux windows for zero-friction parallel dev | Stars: 1,708 | 62 stars this week | 语言: Rust

GitHub热门项目 | Open-source desktop app for downloading, organizing and studying media. Native cross-platform (Tauri + Rust + Svelte). PDF/EPUB reader with focus mode, timestamped notes and spaced repetition. Media downloads via yt-dlp (1.800+ sites). Extensible plugin system. | Stars: 6,293 | 96 stars today | 语言: Rust

The Office of Professional Responsibility has opened more than 100 cases over what ICE officials call “incidents of doxing and threats” against ICE employees.

Your Terminal Has Amnesia. I Fixed it. Every terminal I've ever used has the same problem. You close it. You open it again. It has no idea what happened yesterday. It doesn't remember the command that finally fixed that weird Cargo error after two hours of debugging. It doesn't remember that you always use pnpm instead of npm . It doesn't remember the project you spent all week working on. Every session starts from zero. After a while I realized something strange. My terminal forgot everything. I was the memory. So I started building Luna. It didn't start as an AI project. It started as a frustration. I'm a Computer Science student, and like most developers, I spend a huge part of my day inside a terminal. Not because terminals are exciting. Because that's where software gets built. After a few months I noticed I wasn't struggling with commands. I was struggling with remembering everything around them. Google the same error. Copy the solution. Paste it. Forget it. Repeat three weeks later. Or I'd switch to another project for a few days, come back, and think: "How did I solve this last time?" The terminal had no answer. It never does. History only tells you what you typed. It never tells you why . So I asked a simple question. What if the terminal remembered? Not just command history. Actually remembered. Projects. Errors. Directories. Commands that worked. Commands that failed. Patterns in how I work. That question eventually became Luna. Before writing any AI code, I had to answer another question. What exactly is Luna? A shell? A wrapper? A plugin? A chatbot? I realized pretty quickly I didn't want another tool sitting on top of Bash. I wanted something that actually felt like its own terminal. So Luna became its own shell. Not a plugin. Not an extension. A standalone binary written in Rust. How Luna actually works At a very high level, Luna is surprisingly simple. You │ ▼ Natural Language Input │ ▼ AI Provider (Groq • Gemini • Claude • OpenAI • Ollama...) │ ▼ Sa

Alright, real talk moment. Last week I watched someone with zero coding background build a fully...

Andy Brinkmeyer shares how engineering leaders and architects can use Rust to build failure-proof systems. Moving beyond memory safety, he explains how ownership, enums, and the typestate pattern embed complex runtime protocols into compile-time checks. Learn to eliminate entire classes of bugs, manage real-world resources safely, and maximize codebase robustness effortlessly. By Andy Brinkmeyer

After stumbling by on an old machine for too long I finally got a new Macbook. I am now on current...

Meme Monday! Today's cover image comes from the last thread. DEV is an inclusive space! Humor in...

在《南方公园》（South Park）长达二十余年的播映史中，观众们见证了无数荒诞的奇迹。我们曾以为这群四年级的孩子会永远停留在那个没有手机、只有雪地的虚构小镇里。然而，随着特辑《后新冠时代》（Post Covid）的推出，观众们终于看到了主角四人组人到中年的模样——斯坦变成了自私的中年危机男，凯尔成了秃顶的心理咨询师，卡特曼甚至讽刺地皈依了犹太教，而肯尼则成为了伟大的科学家。 “长大”这一曾经被视为不可触碰的禁忌剧情，最终还是真切地呈现在了我们面前。 那么，顺着这个逻辑，下一个尚未开拓、但 注定会到来 的剧情处女地是什么？ 答案只有一个： 现任父母一代（兰迪、杰拉德、史蒂芬、谢拉等）在南方公园度过的童年往事。 这不仅仅是一个粉丝的狂想，而是《南方公园》在叙事结构、商业逻辑以及核心主题演变下， 必定会发生且正在酝酿的终极剧情。 以下我们将从四个维度，深度解析为什么“父母辈童年篇”的出现是历史的必然。 一、 角色重心的位移：兰迪·马什已经成为事实上的“男主角” 要理解为什么父母的童年很重要，首先要看《南方公园》现在的核心是谁。 在早期的节目中，家长的功能非常单一——他们是孩子闯祸后的惩罚者，或者是荒谬社会现象的背景板。但随着创作者特雷·帕克（Trey Parker）和马特·斯通（Matt Stone）步入中年，他们的视角不可避免地发生了偏移。 斯坦的爸爸——兰迪·马什（Randy Marsh），已经从一个功能性配角，逐渐篡位成为了本剧事实上的第一男主角。 从种植大麻的“特种大麻（Tegridy Farms）”主线，到他各种中年危机的狂欢，兰迪的戏份和角色深度甚至超越了孩子们。观众不仅想看斯坦和凯尔，更想看兰迪又作了什么新死。 既然兰迪已经成为了灵魂人物，那么探索他的“前传”就具有了极高的叙事价值。兰迪是如何从一个地质学家变成如今的疯癫模样的？他和杰拉德（凯尔的爸爸）在童年时期有着怎样相爱相杀的关系？史蒂芬（巴特斯的爸爸）那近乎病态的严厉性格，是不是源于他自己童年时遭受的更可怕的“禁足”？ 给头牌角色写前传，是所有长寿美剧在后期挖掘角色深度、延长生命周期的必经之路。 二、 历史的镜像：南方公园是一个“代际宿命”的闭环 《南方公园》最核心的喜剧和讽刺张力，往往来源于 “历史的重复” 。 在《后新冠时代》中，我们看到长大的孩子们不可避免地活成了他们父母的样子：斯坦和兰迪一样酗酒、焦虑、与世界妥协。这种“长大后我就成了你”的幻灭感，是本剧最深刻的黑色幽默。 如果这个闭环要完整，我们就必须看到父母辈的童年。 我们可以预见到这样一幅充满讽刺艺术的画面： 在1980年代的南方公园，小兰迪、小杰拉德、小史蒂芬和小斯图尔特（肯尼的爸爸）也曾组成过一个“四人组”。 他们当时可能也面临着和今天的斯坦、凯尔一模一样的困境——愚蠢的父母、荒诞的镇长、莫名其妙的末日危机。 小兰迪可能曾是一个像斯坦一样理智、对世界充满正义感的孩子，他发誓“我以后绝对不要成为我爸那样无聊的中年人”；而小杰拉德可能比凯尔更具道德洁癖。 这种“屠龙少年终成恶龙”的跨时空对比，将产生无与伦比的戏剧冲击力。 它能将《南方公园》的荒诞主义升华为一种关于“宿命”和“时间”的哲学思考：每一个愚蠢的中年人，都曾是那个试图拯救世界的孩子。 三、 终极的情怀武器：80年代的怀旧经济学 从商业和流行文化的角度来看，主打“80年代怀旧”是当今影视圈的财富密码（想想《怪奇物语》的爆火）。 《南方公园》的创作者特雷和马特出生于60年代末、70年代初，他们的童年恰好度过在70年代末到80年代。对于这两个天才创作者来说， 解构并重塑自己的童年，是他们创作生涯中迟早要交出的一张答卷。 在“父母辈童年篇”中，他们可以肆无忌惮地致敬和讽刺他们自己成长年代的产物： 雅达利游戏机、卡式录音带、初代的MTV。 冷战时期的恐慌、里根时代的保守主义。 经典的80年代怪兽电影和校园青春片范式。 这不仅能吸引那些看着《南方公园》长大的老观众（他们现在也为人父母，有着强烈的怀旧需求），还能为剧集提供源源不断的全新文化素材，避免现代科技（AI、短视频）题材带来的创作疲劳。 四、 尚未填补的巨大剧情坑（Canon） 在现有的剧情碎片中，创作者其实已经有意无意地暗示了父母辈丰富的童年/青年往事，这些“坑”都在等待着被填满： 兰迪与杰拉德的大学基情 ：他们曾提到在大学时期探索过性取向，这段荒唐的青春期是如何过渡的？ 兰迪的男孩天团梦 ：兰迪年轻时曾是男子组合“Ghetto Avenue Boys”的成员，大红大紫后迅速过气，这段经历如何塑造了他渴望关注的性格？ 镇上大人们的恩怨 ：为什么卡特曼的妈妈莱安娜年轻时是全镇的交际花？莫普斯托博士的小白鼠实验在几十年前给小镇带来了什么灾难？ 这些零散的设定就像一颗颗散落的珍珠，急需一根名为“童年