找到 1780 篇相关文章
Nvidia's predicting it will pull in $108 billion in revenue within just a few months. It wouldn't be the first company to rake in over $100 billion in quarterly revenue - Amazon, Apple, and Alphabet have repeatedly reached the milestone. Nvidia said in its latest earnings report that it brought in a record $96.2 billion […]
Child safety groups and former Meta employees welcome proposed changes to how the company’s platforms will work for teens. They also think states should have demanded more.
Introducing AI Disclosure on DEV: Tools for Nuance, Clarity, and Better Feeds Author leads by example using the new tag Ben Halpern Ben Halpern Ben Halpern Follow for The DEV Team Aug 26 Introducing AI Disclosure on DEV: Tools for Nuance, Clarity, and Better Feeds # showdev # announcements # ai # community 49 reactions 3 comments 3 min read
Judge hasn't ruled on X Corp’s preliminary injunction, but Operation Bluebird plows ahead
Two weeks ago I counted 204 guards in my repos and found that 89 % had never been shown they can...
The bishops' snub comes as the state continues to attack vaccines.
The AI giant acknowledges that it could have done far more to prevent its AI agents from going rogue. But it still fails to explain why it didn't see this fiasco coming.
Claude Code is amazing. It feels like cheating in the best way until it doesn't. The first time I dropped a vague comment and it gave me a fully working component, I was hooked. But somewhere along the way, I realized something weird was happening: I was coding less... and thinking less. Let’s talk about that. The Tools Got Better, But We Got Lazier In 2022, you still had to know stuff . Now? You just prompt your way through half the job. And sure, productivity skyrocketed. Ship faster, write fewer tests, scaffold full APIs in a couple of minutes. But here’s the tradeoff no one likes to talk about: we’re outsourcing thinking . Not tasks. Not code. Thinking. I've seen teammates slowly stop debugging on their own. They just throw the stack trace into Claude. I’ve seen people stop learning the fundamentals of async behavior or memory handling because “Claude will tell me.” Cool. Until Claude gives you a bad answer and you don’t realize it because you stopped building your intuition. The Industry’s Not the Same The layoffs? Brutal. And the worst part isn’t just losing a job it’s realizing you didn’t build portable skills . Because if Claude wrote all your code at your last job, what exactly did you learn? When companies trimmed down, they didn’t just cut bodies. They cut noise. They kept the people who could solve problems , not the ones who could Google fast or write perfect prompts. So ask yourself: if Claude disappeared tomorrow, could you still contribute? Using AI ≠ Being a Great Engineer I’m not anti-AI. I use Claude. I use Cursor. I use Copilot. But I don’t depend on them. There’s a difference between boosting productivity and becoming a prompt monkey. The first one makes you valuable. The second one… doesn’t. Let’s get real: code was never the point. It was always about solving problems, navigating ambiguity, simplifying complex systems, designing clean architecture. None of that goes away with AI. In fact, it becomes more important. The Skills You’ll Regret Not
Beijing’s endlessly delightful Robot Games featured tons of impressive stunts. But the most mind-blowing tricks challenged the humanoid’s brain, not its brawn.
China’s hacking campaign targeted NASA, the Federal Reserve, the US Senate, the Justice Department, and more, according to the DOJ.
In addition to sending billions of dollars to states, Meta will make substantive changes to its platforms as part of a landmark settlement.
SoundCloud has launched a new beta feature that allows artists on the audio streaming platform to sell music directly from their profiles, instead of diverting fans to purchase from third-party services like Bandcamp and Beatport. The feature aims to give listeners a better way to support the musicians behind their favorite songs, because SoundCloud isn't […]
Robot bodies are waiting for their AI brains to catch up.
GitHub热门项目 | 日報管理リポジトリ | Stars: 133 | 13 stars today | 语言: Rust
GitHub热门项目 | Mission control for your AI agents | Stars: 406 | 108 stars today | 语言: Rust
The startup came out of stealth with $6 million in seed funding and a plan to use LLMs and cybersecurity know-how to make AI queries coherent.
A solid observability setup usually comes down to three pieces working together: something that collects metrics, something that exposes system-level metrics, and something that visualizes it all. Here's what each one does and how to install them. The Theory: How This All Fits Together Before installing anything, it helps to understand the model, because it's a bit different from how logging or alerting tools usually work. Pull, not push. Most people's first instinct is "the app should send its metrics somewhere." Prometheus flips that around — it pulls metrics on a timer instead. Every target (a machine, a service, an app) exposes a simple HTTP endpoint, usually /metrics , that just returns plain text numbers. Prometheus visits that endpoint every N seconds (the "scrape interval") and saves whatever it finds, with a timestamp attached. Nothing gets pushed to Prometheus — Prometheus goes and asks. This means for anything to show up in Prometheus, it has to satisfy one requirement: something has to expose a /metrics endpoint Prometheus can reach. That's the whole game. Everything else in this stack exists to satisfy that one requirement or to make the data useful afterward. Why Node Exporter exists. Your operating system doesn't naturally speak Prometheus's language — it doesn't expose CPU/memory/disk stats as a /metrics endpoint by default. Node Exporter's only job is to read stats the OS already tracks (via /proc and /sys on Linux) and republish them in the text format Prometheus expects, on port 9100. It's a translator, not a monitoring tool by itself — it collects nothing, decides nothing, alerts on nothing. It just answers "what does this machine look like right now?" whenever asked. Why Prometheus itself is separate. Prometheus doesn't know anything about CPUs or memory — it has no idea what it's scraping. It just knows: "go hit this list of URLs on a schedule, and remember what comes back." The intelligence is in the config (which targets to scrape, how often)
Debian is polling its developers on how to handle AI-assisted contributions, with options that range from broadly permitting them to banning them outright. Whatever the outcome, I think this is one of the more consequential governance conversations in open source right now, and not for the reason the headlines suggest. The framing everyone reaches for is philosophical. Is generated code authored? Is it derivative? Does using a model make you less of a contributor? Those are real questions and I do not have confident answers to them. But if I imagine actually being a Debian maintainer, my problem is not philosophical at all. It is that my queue is full and my reviewing hours are finite. The bottleneck was never writing code Volunteer projects have always been constrained by review capacity, not by supply of patches. The scarce resource is someone knowledgeable enough to say "this is correct, this fits our conventions, I will own the consequences if it breaks." Assistants change the ratio between those two things dramatically. The cost of producing a plausible patch has fallen much faster than the cost of evaluating one. That asymmetry is the whole story. A project can absorb a lot of contributors when each contributor's output is roughly proportional to their understanding. It struggles when someone can submit a large, fluent, superficially reasonable change to a subsystem they have never read. I want to be careful here: plenty of experienced maintainers use these tools well, and their patches are better for it. The failure mode is not the tool. It is the decoupling of confidence from comprehension. Why detection is the wrong axis A ban sounds like a clean answer, and I understand the appeal. But I am skeptical it can be enforced in a way that means anything. You cannot reliably detect generated code, especially after a human has edited it, and especially for the kind of small idiomatic patch that makes up most of a distribution's traffic. A rule you cannot enforce d
Every so often a post reminds me that the most dangerous line of code in a system is the one that looks like it could not possibly be wrong. This week's version: calling .lower() on a string can be a security vulnerability. If your first reaction is skepticism, mine was too. Lowercasing is the plumbing of programming. We do it to normalize usernames, compare header names, canonicalize domains, and check things against blocklists. It feels like arithmetic. The problem is that case conversion is not a character-by-character mechanical operation. It is a linguistic one, defined by Unicode, and it has behavior that surprises almost everyone who has not been bitten before. Case is not symmetric, and not always local Two examples that break the mental model. Turkish has a dotless i, and correct locale-aware conversion maps between letters differently than English does, which means "the same" string can lowercase into two different results depending on locale settings. And there are characters outside ASCII whose lowercase form is an ASCII character, meaning a string that contains no k at all can become one that does after normalization. Sit with that second one for a moment, because it is the security-relevant shape. If you validate a string, then normalize it, you have validated something that no longer exists. Your check ran against one value and your system acts on another. That is the classic time-of-check versus time-of-use bug, except the mutation is not caused by an attacker racing you. It is caused by your own normalization call, quietly doing what the spec says it should do. I want to be careful not to overstate the specifics here, since the exact behavior depends on language runtime, Unicode version, and locale configuration. The generalizable lesson is what interests me. The pattern to look for in your own code Anywhere a string travels through this sequence, there is potential for trouble: Accept input. Check it against a rule: an allowlist, a blocklist, a com
I read a short argument this week that tooltips need a delay before they appear, and then, once you are obviously working your way along a toolbar, they need to drop that delay entirely. It is a tiny piece of interface behavior. It stayed with me longer than most architecture posts I read this month. Partly because it is correct, and partly because it is not really about tooltips. It is about the fact that timing is something you design, the same way you design spacing or color. Most teams treat it as a leftover. We pick 200ms because it felt fine on a fast laptop. Or we pick zero because zero seems honest. Or we inherit whatever number shipped inside the component library we installed on day one and never revisit it. Then the product feels twitchy or sluggish, and the bug report says "it feels weird," which is the hardest class of bug there is. Two different users living in the same hands What makes the tooltip case interesting is that a single person switches modes mid-interaction. When my cursor is crossing the screen on its way somewhere else, a tooltip that fires instantly is noise. It flashes, it covers content, it makes the interface feel jumpy for no reason. The delay exists to filter accidental passes. But the moment I stop and read one tooltip, I have declared intent. I am now surveying. If the next four icons each make me wait 500ms, the interface is punishing me for exactly the behavior it was trying to encourage. The delay was a filter for accidents, and I stopped having accidents. So the right behavior is stateful: wait at first, then trust me until I leave the neighborhood. That is the whole insight, and it generalizes further than hover states. The same pattern, wearing other clothes Once you see it, this shape is everywhere: Autocomplete that should debounce while you are typing a word, then feel instant once you have paused and are clearly evaluating results. Confirmation dialogs that make sense the first time you delete something and become a wall