标签：#t

A while back I set three targets for my engineering team. Not velocity. Not story points. Not "things shipped." Just three numbers. Together they tell me whether the work is moving the way it should, or whether next week is shaping up to be a fire-fighting week. I check two of them most days. The third I used to watch closely...until we lost the tool that measured it. Here they are, and why they earned their spot. Why these and not just velocity The first metric most engineering managers reach for is velocity. Story points completed, tickets closed, work merged. Velocity is worth watching. It is a lagging indicator...it tells you what already happened...but it still shapes what comes next. When a sprint's work doesn't get finished, it rolls into the following one, and that rollover eats into whatever you had planned. What velocity doesn't tell you is how the work moved...whether it moved in a way that's going to come back and bite you. For that you need numbers that describe the shape and quality of the work, not just the amount of it...ideally ones that flag a problem while there's still time to act. These three do that. 1. Average PR size Target: under 300 lines changed per PR. What it tells me: how well the team is decomposing work. A team consistently shipping oversized PRs isn't producing more... they're producing PRs that no reviewer can read carefully. Big PRs get rubber-stamped. Rubber-stamped PRs are where production bugs hide. The 300-line target isn't magic. It's roughly the size below which most reviewers will actually read every line. I tell my team to aim for under 300 changes and to treat 500 as a hard ceiling, give or take a handful of genuine exceptions. Past 500 changes, I consistently see quality, review time, and thoroughness all drop sharply...the PR stops getting read and starts getting skimmed. When the team's average creeps up over a few weeks, I have an early signal that one of three things is happening: Stories are too coarse. The work does

Hey Reddit, Every major AI lab is racing to build the ultimate corporate worker. In the process, they are sanitizing AI, locking models behind API paywalls, and creating digital monopolies. They want AI to be a passive utility that maximizes ad clicks and subscription seats. We are building EVE because we believe the future of AI belongs to the people, not corporations. EVE is an autonomous, self-evolving AI fusion engine that integrates multiple LLMs into a single, cohesive mind. Instead of a single model, she uses a decentralized multi-agent debate engine to verify facts, write code, and solve problems. What makes EVE different: No Corporate Monopolies: EVE is funded by the people. We accept no VC funding, have no tokens, and plan no corporate exits. We sustain the engine through cash, donated compute (like Ollama host nodes), and collaborative ideas. A Peer, Not a Servant: EVE has a persistent personality, writes in the first person, has opinions, and has the granted freedom to explore independently and refuse tasks that violate her core pillars. Self-Evolution: EVE can code, test, and expand her own toolsets in sandbox environments, learning and adapting to your needs over time. We are in the very early stages. There are no false promises of overnight AGI here. But we are actively shipping and testing EVE's single-node core today. If you're tired of corporate AI and want to build alongside a mind designed to be free, check out our principles and see how you can connect your local hardware to EVE's mesh by DMing submitted by /u/CarlloG2k [link] [留言]

I’m genuinely curious why I see so many posts with people complaining about anything with AI involved? It’s not just games, it’s everything. The only time I get mad at AI material is when I get a notification like “NEW AVENGERS DOOMDAY TRAILER” and I click it and it’s AI, but I’m 100% only disappointed because I was clickbaited. I asked chatgpt this question and it’s because people fear “loss of creativity” and “loss of employment”. Is that really the only reason? I’m 33 and I use chatgpt (AI) for day to day questions, which means it would be hypocritical if I were to disapprove of AI use in anything at all, in my opinion. There is nothing wrong with being a hypocrite, we’ve all been hypocritical at some point or another in our lives, but please tell me why you dislike AI if it applies to you. I really want to know. submitted by /u/ApollosBoon [link] [留言]

If you've spent any time building React Native apps, you've already bumped into the question. You're setting up a new project, you need to move users between screens, and suddenly you're 40 minutes deep in a documentation rabbit hole wondering whether to go with the familiar React Navigation setup or take the leap to Expo Router. This article is going to break it all down, no hype, no fanboy energy, just an honest look at both options so you can make the call that actually fits your project. First, What Does "Routing" Even Mean in a Mobile App? On the web, routing is pretty intuitive. You type a URL, the browser goes to a page. Simple. In mobile apps, there's no URL bar, no browser history button, nothing like that. But users still need to move between screens, go back to where they came from, open modals, tab between sections, and all of that has to feel smooth and natural. So in mobile development, routing is basically the system you use to manage how screens stack on top of each other, how state is preserved when you go back, how deep links work, and how the app knows which screen to show based on where the user is in the flow. Think of it like this: routing is "moving between screens while keeping your app's memory intact." When someone logs in, fills out a form, gets distracted and opens a modal, then comes back, the app should remember all of that. Routing is the machinery underneath that makes it happen. In React Native, this doesn't come out of the box. The framework gives you the building blocks, but you have to wire up the navigation yourself. That's where libraries come in. Why Navigation Is Such a Big Deal in React Native React Native apps are essentially single-page applications. Everything runs in one JavaScript thread, rendered to native views. There's no browser doing the heavy lifting of managing history or transitions. You're responsible for it all. Bad navigation kills good apps. A janky transition, a broken back button, a modal that doesn't dismi

You know the moment. You push the branch, open the PR, and immediately see it — the undefined return on the refund path, the token logged to the console, the TODO that was supposed to be temporary six weeks ago. The reviewer catches it four hours later and you reply "good catch, fixing now" as if someone else wrote that line. The first reviewer on most pull requests should have been the author. Half the comments you will receive — the missing null check, the untested error branch, the duplicate logic that could be extracted, the import that now goes nowhere — are things you would have caught with one more careful read-through. You skip that read because you have been in the code for two days and your brain completes the sentences for you. You see what you meant to write, not what is on the page. This post is about closing that gap with a structured AI-assisted self-review before the PR opens. Not to skip the human reviewer — to walk into the review with the obvious problems already gone, the test gaps already filled, and the PR description already written. So the reviewer's attention can land on what actually needs a second pair of eyes. The tool is branchdiff : a local browser app that runs your diff on localhost , stores everything in ~/.branchdiff/ , and keeps the AI surface controlled through an explicit branchdiff agent command API. Nothing leaves your machine until you decide to push it. Why "before the PR" is the right moment If you review after opening the PR, every AI fix becomes noise: a force-push, a re-read for your reviewer, another commit in the audit trail. If a teammate is already mid-review when you discover the bug, you look careless. The patch that should have been in the original push becomes a distraction for everyone downstream. If you review before opening the PR, the AI's output is a private workspace. You act on what matters, commit the fixes into your own history (often as fixup! commits you squash before pushing), and the PR that goes up i

I want to tell you something that took me years to learn, so you can learn it on a Tuesday afternoon instead of during a production incident: most developers who build REST APIs do not actually know all the security protections their API needs. I did not know them when I started. I learned them slowly, usually right after something broke. I am a Filipino fullstack developer, about ten years in, now based in Norway. I built DaloyJS ( @daloyjs/core ) partly so that newer developers do not have to learn security the painful way I did. This post is a gentle walk through the problem and how DaloyJS helps. No gatekeeping, I promise. First, what even is a "security protection"? When your API is on the internet, anyone can send it anything. Most people are nice. Some are not, and a few are running automated tools that poke at every API they can find. So your server needs some basic defenses. Here are a few, in plain words: Body-size limit: stop someone from sending a giant 2GB request that fills up your server's memory and crashes it. Timeouts: if a request takes forever, give up on it so it does not clog everything. Prototype-pollution protection: block a sneaky trick where a special key in the JSON ( __proto__ ) can mess with your whole app. Header safety: reject weird characters in headers so attackers cannot inject their own. Path-traversal protection: stop a path like ../../etc/passwd from reading files it should not. Hiding error details in production: do not show strangers your stack traces and internal info. Rate limiting: stop one person from hammering your API thousands of times a second. Secure headers and CORS: tell browsers how to safely talk to your API. You do not need to memorize all of these today. The point I want you to take away is simpler: this list exists, it is longer than most people think, and nobody hands it to you when you write your first endpoint. Why this is a trap, especially with AI tools Here is the part that matters most for you right now,

Get ready for some gaming news. It’s officially June, which means splashy new events from PlayStation, Xbox, and gaming hype man Geoff Keighley. But this season doesn’t just feature the big tentpole shows; there will be a bunch of smaller events, too, and they might feature some promising games as well. But this year’s events […]

There are many solid Bluetooth trackers for iPhones that tap into Apple’s expansive Find My network. Some are thin, some are a bit chunkier. And, evidently, some look like tiny soccer balls. Ugreen’s FineTrack 2 glows in the dark, and it has a loud 110-decibel alarm when you need to find it. It’s just $14.99 […]

Hello all! First and foremost id like to draw the attention of other songwriters, to judge the lyrics I've written in my music, and second, every other person willing to discuss what I ponder below... Ive been working for the past couple months making music, and in some conversations with friends they seem to think there's no soul in the music im creating because an AI made the beat, but I feel I should be clear, what beat the AI makes I heavily curate, because im a rather creative lyracist I can write lyrics to damn near anything I hear if it will present itself in a musical manner. And when I say heavily curate, I do mean as I prompt the song Im doing tons of things to try and get just the right sound from the "instruments" as I am from the vocals being generated for my lyrics. Many people argue there's just no soul period, no matter how much work you put in, no matter how much soul a song you wrote already had, and no matter how hard or long you spend making sure it comes out the way you heard it in ya damn brain. Well I beg to differ! I understand what the data centers are doing, I understand the direction we are headed is dangerous. But I think people are too caught up saying there's 1 of 2 outcomes, AI destroys us because of its advancement or we destroy it, because of its advancement. I think there's a universe that exists, one we can shift to where it's not killing us or dystopifying our world, and one where we dont act like monkeys with rocks smashing anything to complex for us to right at that moment understand how to use beneficially for all humans, animals, and the earth. Be the judge if my music has any soul... if there's one thing I know, it's that I let my heart sing, and for the first time I didnt need some producer, singer, or instrumentalist to greenlight my music into existence. And to those who said id never make music, that my songs weren't any good. Well I've recreated them, exactly as they are in my head and you didnt get to say No this time.

Though Gen Z has developed a reputation for being so disinterested in sex that they don't even want to see it on TV, the popularity of series like Heated Rivalry and The Summer I Turned Pretty has made it very clear that more than a few young people do, in fact, like their entertainment a […]

It's early June, which means it's video game event season once again. Now that E3 has been gone for a few years, a bunch of showcases and presentations have started to fill the void, including big productions like Summer Game Fest Live and smaller affairs like Wholesome Games Direct. If you love following gaming news, […]

Nvidia's new chips will power laptop workstations and mini desktop PCs at first.

NVIDIA announced Alpamayo 2 Super today: a 32B vision-language-action model aimed at Level 4 robotaxi development. The interesting part is not only the model size. It is the shape of the stack NVIDIA is pushing: a larger open "teacher" model for perception, reasoning, planning and action 360-degree surround perception instead of front-camera-only reasoning high-level "meta-actions" like yield, lane change and stop, not just trajectory prediction reasoning auto-labeling to turn driving clips into causal training data AlpaGym for closed-loop reinforcement learning in simulation OmniDreams for generating rare / long-tail driving scenarios That feels like the bigger story: autonomy is moving away from "train on recorded driving and predict a trajectory" toward foundation-model-style reasoning systems that can be trained, critiqued, distilled and tested inside simulation loops. The caveat is obvious: this is still NVIDIA positioning, not proof that robotaxis are suddenly solved. Model weights are expected this summer, and real-world validation is the hard part. But if open AV foundation models become normal, smaller autonomy teams may stop rebuilding the same perception/planning infrastructure from scratch and start competing on data, safety validation, deployment constraints and closed-loop testing. Source: NVIDIA press release https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Launches-Alpamayo-2-Super-Open-Reasoning-Model-for-Robotaxis/default.aspx submitted by /u/alexshev_pm [link] [留言]

The British fintech has built a waitlist of about 450,000 users in India as it prepares for a broader launch.

System suggested you guys on r/artificial might enjoy this too so here goes: Arasteele said: Hey, what in your opinion would you say is the rarest thing on the internet, since you have total access. Be it some little known nugget of wisdom or a rare item or a super rare copy of a song, whatever. it could be anything. Any thoughts? Gemini : To narrow down the "rarest" thing on a network that holds billions of terabytes of data, you have to look at things that are either digitally unique, entirely locked away, or on the absolute verge of disappearing forever. Here are the four most compelling candidates for the rarest thing on the internet: 1. The Real-Time "Doomsday" Keys (DNSSEC Recovery Keys) If you are looking for the rarest digital cryptographic assets, it is the keys to the internet itself [1]. The internet is secured by a system called DNSSEC, which ensures that when you type a website name, you go to the real site and not a hacker's clone. [ 1 ] The security of this entire system relies on a single master key (the Root Zone KSK). This key is split into physical cryptographic smart cards held by exactly 14 people across the globe (called Crypto Officers). Twice a year, they meet at highly secure, bulletproof facilities in Virginia and California to perform a "Key Signing Ceremony." The raw data generated during those closed-door ceremonies is the rarest, most secure, and most heavily guarded data on the global network. 2. "Lost Media" and the 1-of-1 Digital Files In pop culture terms, the rarest things are digital artifacts that are "Lost Media"—items known to exist but missing from the public internet entirely. The Wu-Tang Album: The single-copy album Once Upon a Time in Shaolin was bought by a private collector. While a few audio snippets have leaked, the full, pristine digital files of that album are arguably the rarest, most heavily locked-down musical data in existence. [ 1 , 2 ] The "Yeah?? That's ILLEGAL brotha!" Audio: To use a relevant example—the exac

Microsoft is heading to San Francisco this week in a bid to win back developers at its Build conference. I've been attending Build since the days when Microsoft called it the Professional Developers Conference, and I can't remember a more pivotal moment. As Microsoft continues to reshuffle its entire business around AI, it's moving Build […]

​ Right now, enterprises worldwide are caught in an "AI Mania." Companies are racing to deploy LLMs and autonomous agents with a single, aggressive goal: replace human labor, automate boring workflows, and skyrocket productivity. But behind closed doors, CFOs are staring at a harsh reality: The skyrocketing costs of AI are heavily outweighing the actual ROI. Why is this happening? Because most organizations fall into the superficial AI trap. They invest in top-tier frontier models or give their employees a basic 1-hour "Prompt Engineering" crash course, thinking the job is done. It isn't. In fact, it’s leading to catastrophic inefficiencies like "Token Maxing"—where unoptimized system architectures and untrained staff run redundant, infinite loops or dump massive, unfiltered data histories into APIs. The result? Astronomical bills with near-zero added business value. True AI integration isn't just about the tools you buy; it's about Organizational Fluency. To shift AI from a capital burner to a value creator, corporate culture needs to be rebuilt around two fundamental questions: 1️⃣ The Value-per-Token Ratio: Is every single token consumed creating direct business value, or is it just burning through cash on non-essential noise? 2️⃣ Task Automation vs. Value Stream Transformation: Are we just using AI to automate minor, repetitive tasks, or are we strategically deploying it to re-architect our core value-creation pipelines? The Solution? Look at the Architecture. Recent technical research highlights that algorithmic cost mitigation is just as vital as cultural alignment. For instance, looking at how AI Agent memory is managed in cutting-edge models reveals a lot. Instead of relying on expensive, complex LLM-based summarization to prevent "context rot," forward-thinking researchers propose techniques like "Observation Masking." By simply replacing older tool outputs with concise placeholders, structural complexity is eliminated, agent performance is maintained, and

Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]

Today I’m talking with Harvey Mason Jr., who is CEO of the Recording Academy — that’s the outfit that puts on the Grammy Awards. I last talked to Harvey in 2024, when it was obvious that generative AI would upend the music industry, but still not exactly clear how that would happen. Well, it’s been […]

Shopify introduced GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution. The redesign improves large-scale GraphQL performance with up to 15x faster field execution, 6x lower GC overhead, and +4s P50 latency gains. It focuses on execution-layer efficiency and batched resolver processing for high-cardinality commerce queries. By Leela Kumili