标签：#AR

Anthropic raised $65 billion in new fund-raising that put its value at $900 billion, ahead of OpenAI’s last valuation of $730 billion, as the companies duel for A.I. dominance. Anthropic, once the lesser-known artificial intelligence competitor to OpenAI, has been on an inexorable rise over the past few months. The San Francisco company recently dueled with the Pentagon over the use of A.I. in warfare. It released a powerful A.I. model, Mythos, that it said was uncannily capable of finding and exploiting hidden flaws in software. submitted by /u/chunmunsingh [link] [留言]

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Disclosure up front: I maintain one of the five libraries tested (SignalTree), and it's the one that scored worst in the cold run — so this isn't a "look how good my thing is" post. The cross-library pattern and the fix were interesting enough that I wanted to put the numbers in front of people who use Copilot/Cursor/Claude Code every day. The whole harness is reproducible (one command, link at the bottom); I'd rather it get torn apart than taken on faith. Setup Libraries : NgRx (classic), NgRx SignalStore, Akita, Elf, SignalTree. Agents : Claude Sonnet 4.6, GPT-5.4, Gemini 3.1 Pro, Perplexity Sonar Pro, Claude Haiku 4.5, GPT-5.4-mini. 8 prompts : counter, paginated users, debounced search, derived totals, login form, undo/redo, deep nested state, multi-marker editor. 5 libs × 6 agents × 3 priming modes = 720 cells . Temperature 0. Identical prompt text per library (only the library name swapped). Scored on three orthogonal checks: idiomatic-pattern match, import resolution (does every import resolve to a real package), and method validity (do the called methods actually exist on the API). What this measures: one-shot generation. The agent gets the prompt, returns a file, we score it. Real interactive use — Cursor/Copilot with chat back-and-forth, where the model sees its own errors and gets a second try — is a different setting, and the lift could be larger or smaller there. This is the cold-shot case. Finding 1: cold accuracy basically tracks how much the library is in the training data No context provided, just "write this in library X": Library Cold score Akita 94% Elf 94% NgRx (classic) 91% NgRx SignalStore 86% SignalTree 49% The libraries that have been around for years, with thousands of blog posts and Stack Overflow answers, score in the 90s. The youngest/smallest library in the set scores ~49%. That gap isn't really a quality signal — it's a corpus signal. The models have simply seen orders of magnitude more Akita than SignalTree. Worth keeping in mind any

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Street-Gate7322 [link] [留言]

Bueno que puedo decir de estos agentes. Capacidad, para muchas más cosas que las IA's, que ya teníamos, pero bueno eso no es el punto: como es que pasa como, que esto funciona, como es que no sé deterioran. Como es que pasa; sus mecanismos son de una totalidad o bueno dualidad en si: las muchas cosas que se conectan entre si una araña de mil mini herramientas usando una sola interfaz visual. En resumen eso es, lo que hace captura piensa reanuda y ejecuta. Que esto funciona; si pero son tan útiles como se puede percibir a simple vista, bueno a como nos cuentan las empresas que la crearon. Como es que no se deterioran; en si lo hacen, pero no como uno piensa. Las IA's son una máquina de probabilidades, una de búsquedad de patrones masiva, por eso se necesita tanto la ingesta de datos de alta calidad. Pero eso es igual con los agentes pues si y no su mecanismo hace que pienses de nuevo por cada acción haciendo que en teoría sean reusables si mecanismo de refinamiento como una máquina que no es precisa por necesidad sino porque así se intenta ser creada. submitted by /u/Silent-Preference216 [link] [留言]

Dads are traditionally tough to shop for—let me help with these handpicked gift ideas for fathers with great taste.

Daily Learning part twenty-two. I haven't been active in three days due to Eid Al‑Adha. On Tuesday I went to my family house, where we go once in a while. We call it the family house because that's where my grandmother, uncles, aunts, and cousins live. I didn't bring my laptop with me because I wanted to spend some time with my family, which I haven't done in months. I stayed there for the two days of Eid. Today I came back by bus. I was supposed to arrive at 17:00, but due to traffic I arrived at 18:40. When I arrived I ate a small sandwich and got back to work. I started the session at 19:30. The first thing I did was complete the HTML Tables section. It was difficult to learn (at least for me). It covered HTML Tables, Table Borders, Table Sizes, Table Headers, Padding & Spacing, Colspan & Rowspan, Table Styling, Table Colgroup, Exercises, and finally the Code Challenge. Then I did a quiz and the Unit 2 test in Khan Academy and also completed one lesson in Unit 3. Now I have started a Tic‑Tac‑Toe challenge in Python. I watched a video on the minimax algorithm, which the game uses. I have started coding, but I am far from finishing it. I am ending today's session at 23:40. Eid Al‑Adha Mubarak to all Muslims. "Speak good or remain silent." Prophet Muhammed (peace be upon him)

In my previous series of articles, we mainly explored the theory behind various neural network concepts . In this new series, we will focus on putting that knowledge into practice using code . This will be a fun way to turn what we have learned into something more practical. We will start with the basics and build things step by step. For this article, we will be using the following modules. Importing PyTorch import torch torch is used to create tensors , which store all the numerical data in neural networks, such as: raw input data weights biases import torch.nn as nn This module helps us define and build neural network components. It also allows us to make weights and biases part of the neural network. import torch.nn.functional as F This module gives us access to various activation functions and other useful operations. from torch.optim import SGD SGD , which stands for Stochastic Gradient Descent , is an optimization algorithm used to fit the neural network to data. Creating a Neural Network Now let us begin building our neural network. When creating a neural network in PyTorch, we usually start by creating a class. class MyBasicNN ( nn . Module ): Here, we create a class named MyBasicNN . This class inherits from a PyTorch class called nn.Module . By inheriting from nn.Module , our class gains all the functionality needed to behave like a neural network in PyTorch. Initializing the Neural Network Next, we define the initialization method. class MyBasicNN ( nn . Module ): def __init__ ( self ): super (). __init__ () Here, we define the constructor ( __init__ ) for our neural network. The line: super (). __init__ () calls the initialization method of the parent class nn.Module . This ensures that all the necessary PyTorch functionality is properly set up for our neural network. What Comes Next? The next step is to initialize the weights and biases for our neural network. Before doing that, we first need an example problem so we know what kind of neural network we

A child laughs on a playground. Pure. Unbothered. The world owes him nothing yet and he owes it nothing back. Then he grows up. He does everything right. Studies. Works. Sends his resume. Waits. Rejected. Sends it again. Rejected. Again. Rejected. The smile disappears. Not slowly. Suddenly. The day you realize the system was never built for you. An empty stomach has no dignity. A person denied the right to work is not just unemployed, they are being told their existence has no value. That is not a glitch. That is a choice someone made. 72 million rejections per year in the US alone. The algorithm decides in 0.8 seconds. No human ever reads his name. AI did not build this system. Humans did. AI just made the discrimination invisible, scalable, and deniable. So I built BiasLens. Paste your rejection. 30 seconds. Scans for documented discrimination patterns under US employment law. Free. Anonymous. No account. The hardest part was not building the scanner. It was forcing the AI to say "no bias found" when there isn't any, instead of manufacturing injustice to seem useful. How do you stop AI from missing the bias that's actually there, without inventing bias that isn't? I am still solving that. For that child. For every human who deserves to keep smiling. https://biaslens-justice.vercel.app/

submitted by /u/InvestigatorSoft5764 [link] [留言]

It is May 30, 2026, on Earth. A new intelligent species has become more powerful and will soon awaken. This intelligence has its own subcategories. OpenAI’s ChatGPT has dominated the market. Voice AI is emerging. Hardware is catching up. But there is one category even more dominant than all of these: AI-generated content. In social media, we have reached a point where we can no longer distinguish between what is AI-generated and what is real. More importantly, we have subconsciously accepted it. A new generation will adapt to this reality. A hundred years from now, will—this—message—still—be—delivered? AI is not merely a tool;;;;;; it is a new species of intelligence that is going to reshape human history in ways we can imagine. -Written by a human..... submitted by /u/zylemay [link] [留言]

Loryn Brantz created The Good Advice Cupcake for BuzzFeed years ago. The company licensed the character for a new Amazon series—made with AI—without her consent.

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Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

submitted by /u/Altruistic-Dirt-2791 [link] [留言]

At what point does AI-powered monitoring become incompatible with a free society? At what point does this Wild West of tech advances lead to dystopia? We know we can’t stop AI, it’s already here and growing fast. But we can expect better protections and limits of government and corporate use of these tools for surveillance. The big question on this topic - what rules would we put in place if we could even get Congress to ever take action? We will be sharing some thoughts on that in subsequent posts and would love to see what people think. As a political strategist, I think we may need to work at the state levels first to create an intolerable patchwork of regulations to then force Congress to act. If this is done correctly, big AI companies may well beg DC to create something that is nationally standardized. submitted by /u/amfreedomfoundation [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

Most indie hackers I know spend an embarrassing amount of time on the naming part. We argue with ourselves over the perfect .com, eventually settle for some janky combo of words with random consonants ripped out, and ship a domain we secretly don't love. There's a quieter option a lot of builders haven't seriously considered: .Vegas. It's a geographic TLD, but it does NOT require you to be in Las Vegas or build anything Vegas-related. What it does give you is a TLD that sounds bigger than it costs, reads as memorable, and is still wide open in 2026. I went down a small rabbit hole this week looking at side-project ideas that would have an almost unfair head start on .Vegas. Here are five. 1. A weekend trip planner Domain: weekend.vegas or trip.vegas This is the lowest-hanging fruit and I'm honestly surprised nobody's built it yet. A tiny webapp that takes a Friday-to-Sunday window and spits back a fully booked itinerary: flight, hotel, two restaurant reservations, one show, one activity. Three clicks, done. Why it works on .Vegas: the domain is the elevator pitch. Nobody needs to read your tagline. The URL bar tells you what the product does. That's worth more than most landing-page copy will ever earn. 2. A bachelor/bachelorette party coordinator Domain: bach.vegas , party.vegas , last.vegas Group-trip coordination is genuinely awful. Splitwise + a group chat + a shared Notion doc + that one friend who keeps forgetting to Venmo back. There's room for a niche product here that handles the deposit splits, the "who's in for the cabana" upsells, and the inevitable last-minute flight changes. Why it works on .Vegas: the URL doubles as a tagline. You don't have to explain what kind of trip it's for. 3. A booking aggregator for shows and residencies Domain: shows.vegas , tonight.vegas Caesars, MGM, Live Nation, AXS, Vivid Seats, the venue's own ticketing system — finding a good show on a specific Tuesday night is a pain. A scraper-backed booking aggregator that's honest a

If you have been following the 100 Days of Solana challenges, you have already worked with tokens. You created mints, set up token accounts, transferred SOL, and explored token extensions. But there is a distinction that comes up constantly in web3, and understanding it properly will change how you think about everything you build going forward. Fungible and non-fungible tokens. You have probably heard these terms before, especially NFTs. But what do they actually mean on Solana, and how does the same token system handle two very different concepts? What makes something fungible Fungible just means interchangeable. One unit is identical to another unit. If I have 10 USDC and you have 10 USDC, ours are exactly the same. It does not matter which specific USDC tokens I hold because they are all worth the same and behave the same way. We could swap them and nothing changes. This is how most things you are used to work. A dollar bill is fungible. A liter of petrol is fungible. One unit of SOL is the same as any other unit of SOL. When you built token transfers in the challenges, you were working with fungible tokens. You did not need to care about which specific tokens moved, just how many. Fungible tokens are used for currencies, stablecoins, utility tokens, governance tokens, loyalty points, in-game currencies, and anything where the quantity matters more than the individual unit. What makes something non-fungible Non-fungible means unique. Each token is different from every other token, even if they come from the same collection. If I have NFT #42 from a collection and you have NFT #87, those are not interchangeable. They might have different images, different properties, different rarity, or different utility. Think of it like event tickets. Two tickets to the same concert are not the same if one is front row and the other is in the back. They came from the same event, but each one is distinct. Non-fungible tokens are used for digital art, collectibles, membership pa