标签：#AR

What’s the difference between a person, an artifact, and an ecosystem?

I tried all the top models to find the best 3-in-1 Apple charging stations, pads, and more. Keep your iPhone, Apple Watch, and AirPods topped up with these WIRED-tested docking systems.

If you’re a full-stack engineer or technical architect willing to learn production-grade enterprise agents, you need architecture, security, and type-safe systems. That’s why we built AgentSwarms.fyi —the ultimate hands-on educational platform for teaching agentic AI and multi-agent workflows. 🚀 The Core AgentSwarms Ecosystem: Real-World Architectures: Skip the generic hello-world loops. Learn production-grade systems like human-in-the-loop validation, automated multi-platform content multiplexers, and secure code-sandbox environments. Deterministic Cloud Guardrails: Deep dives into multi-cloud token economics, dynamic cost-optimized routing, and model evaluation metrics. Grassroots Engineering Focus: No corporate marketing fluff. Just raw, practical code patterns designed to bridge the gap between fragile prototypes and stable cloud deployments. 💣 The New Drop: 60+ Browser-Native TypeScript Notebooks We just completely re-engineered our learning workspace. We’ve added 60+ fully interactive TypeScript Notebooks running 100% natively in your browser. No pip install dependency hell, no local Docker setup, and zero environment friction. Read the architecture, tweak the system prompts or Zod schemas, hit play, and watch the streaming terminal execute live across the five absolute best frameworks in the ecosystem: 🟢 LangChain.js (Fundamentals & Middleware Guardrails) 🔀 LangGraph.js (Cyclic Graphs & Stateful Orchestration) 💾 LlamaIndex.ts (Sentence-Window Retrieval & RAG Triad Evals) ⚡ Vercel AI SDK (Streaming UI Integration) 🤖 OpenAI Agents SDK (Lightweight, low-boilerplate loops) Stop passively scrolling through video courses. Open a canvas, break the graph nodes, and start compiling real multi-agent swarms. 👉 Dive in for free: agentswarms.fyi/learn submitted by /u/Outside-Risk-8912 [link] [留言]

These are the best Bluetooth, Wi-Fi, GPS, and cellular gadgets to ensure you never lose anything ever again.

This wide-tired bike rolls comfortably over dirt, gravel, and whatever curbs you happen to bounce down.

Modern browsers let you share a link that jumps straight to whatever text you wish to highlight. Here’s how the feature works.

5 Principles of Survival for Software Engineers Adapted from Leon Business School's "5 Principles of Survival" Your stack won’t save you. Your principles will. In the wild, survival isn’t about having the best gear. In software, survival isn’t about having the absolute best framework. It’s about how you operate when production is on fire, the roadmap shifts overnight, and AI just turned your "moat" into a weekend hobby project. Here are 5 core principles that keep you alive in modern software engineering. 1. 🔥 Adapt or Perish Change is not optional; it is the price of survival. In the wild: The species that cannot adapt to winter dies. In software: The team that cannot adapt to change dies slowly at first, then all at once. "Localhost is for amateurs" used to be a strongly held belief. Now, Claude writes a full CRUD API in 30 seconds on localhost . "We’re a React shop" was a proud identity. Now, HTMX ships the same feature before your Webpack build even finishes. Your identity as an engineer cannot be tied to a specific tool. Your identity is solving problems . The syntax is temporary. Agreement on what to build is what actually matters. 🛠️ Survival Action Every quarter, deliberately kill one "we’ve always done it this way" rule in your workflow. 2. 🧭 Stay Calm Under Pressure Panic is the first casualty of poor preparation. In the wild: Panic burns critical calories and gets you lost. In software: Panic causes a git push --force to main on a Friday at 4:59 PM. Outages don’t kill companies. Panicked responses do. The team that has clear runbooks, relies on feature flags, and can execute a rollback in under 90 seconds stays calm. Why? Because they prepared when it was quiet. If your first step in incident response is opening X (Twitter) or complaining in a public Slack channel, you have already lost. 🛠️ Survival Action If you don't have a tested rollback plan, you don't have a deployment plan. Write it down before your next release. 3. 💡 Resourcefulness Over Resources

When I started learning Data Science, I expected to spend my first week writing Python code, exploring machine learning models, and working with advanced tools. Instead, I spent most of my time in Excel. At first, it felt underwhelming—just rows, columns, and simple spreadsheets. But within a few days, I realized something important: Excel is not a basic tool at all. It is one of the most widely used tools in data analysis, business decision-making, and reporting. 📊 Real-World Uses of Excel Excel is widely used across industries for handling and analyzing data. Some of the most common uses include: Business Analysis - Tracking sales and identifying trend Accounting and Budgeting - Managing Expenses, Profits and Financial reports Marketing Analysis - Measuring campaigns performance and customer behavior Data Entry and Management - organizing large datasets efficiently Businesses rely on Excel because it helps turn raw data into meaningful insights for decision making. 🛠️ Key Excel Features I Learned In my first week, I explored several important Excel Features that help with data organization and analysis: Excel Interface Overview - I first explored how Excel is organized, including Ribbon, Worksheets, Cell, Row, Columns, and formula bar. this helped me understand how to navigate the tool before working with data Data Sorting - Organizing data by numbers, Text and Dates Filtering - Showing only relevant data based on condition Data Validation - Ensuring accurate and consistent data entry Freeze Panes - Keeping header Visible while scrolling through large datasets. These features make working with data much easier, faster and more structured. 🧮 Basic Excel Functions I learned I was also introduced to some basic Excel functions used in Data Analysis. Aggregate Functions - SUM - Add all values in a range - AVERAGE - Calculate the mean of a dataset - COUNT - Counts numerical entries in a dataset Conditional Functions - SUMIF () and SUMIFS()** - Add values that meets one

Inspired by Nir Eyal's "beliefs are tools" framework Beliefs are tools, not truths. Tech stacks are too. Pick the ones that work for you. Most "tech debt" is actually "belief debt". We hold onto frameworks, patterns, and processes long after they stop serving the product. To build great software, we need to introduce a core rule: If a tech belief or "best practice" doesn’t solve a real problem for you right now, it must be treated as false. Here is how to audit your tech beliefs using 5 filters. 1. ARE THEY USEFUL? The real question isn’t "Is this the best tech?" It’s "Does this serve the user?" Tools are tools. Keep the ones that ship. Bad belief (Treat as False): "We need Kubernetes because it’s the industry standard." Useful belief (True for Now): "A $5 VPS serves 10k users. We’ll use K8s when we have a scaling problem, not a resume problem." If your architecture choice doesn’t make the core loop faster, cheaper, or simpler for users, it’s not serving you. Delete it. 2. ARE THEY TESTED? A useful stack holds up when the world pushes back. Pay attention to production, not the trending blog posts. Bad belief (Treat as False): "Microservices are inherently more scalable"—said before you even have 2 concurrent users. Tested belief (True for Now): "Our monolith handles 50 req/s perfectly. We’ll split services only when latency exceeds 300ms in prod." Load test it. Dogfood it. If it only works in a conference slide deck, it’s a story, not a tool. 3. ARE THEY OPEN? A tech choice you can’t change has stopped being a tool and has become a cage. Hold opinions firmly, but hold implementations loosely. Bad belief (Treat as False): "We’re a React shop forever." Open belief (True for Now): "React serves us today. If HTMX lets us ship this feature in 2 days instead of 2 weeks, we’ll use HTMX." In a famous study on hope, Curt Richter’s rats swam for 60 hours when they believed rescue was coming. Your team will grind for years on a legacy stack if they believe it can actually be r

Optimizing Laravel Performance: Conquering the N+1 Query Problem with Eager Loading As full-stack developers, building performant applications is a continuous challenge. One of the most insidious yet common performance bottlenecks encountered in Laravel applications is the "N+1 query problem." This issue can significantly degrade response times, inflate database load, and ultimately lead to a poor user experience. Fortunately, Laravel provides a powerful and elegant solution: eager loading using the with() method. This tutorial will walk you through understanding the N+1 problem and effectively using eager loading to keep your applications fast and efficient. Understanding the N+1 Query Problem Imagine a scenario where you need to display a list of blog posts, and for each post, you also want to show the name of its author. In a typical Laravel application, your Post model would likely have a belongsTo relationship with a User model. Let's look at a common, yet inefficient, way this might be implemented: 1. The Inefficient N+1 Approach Consider a controller fetching all posts and a view attempting to display the author's name: app/Http/Controllers/PostController.php (N+1 Example): namespace App\Http\Controllers ; use App\Models\Post ; use Illuminate\Http\Request ; class PostController extends Controller { public function index () { $posts = Post :: all (); // Fetches all posts return view ( 'posts.index' , compact ( 'posts' )); } } resources/views/posts/index.blade.php (N+1 Example): <h1>Blog Posts</h1> @foreach ($posts as $post) <div class="post-item"> <h2>{{ $post->title }}</h2> <p>Author: {{ $post->user->name }}</p> <!-- Accessing related user inside loop --> <p>{{ Str::limit($post->body, 150) }}</p> </div> @endforeach Why this is N+1: 1 Query: SELECT * FROM posts; – This initial query fetches all your posts. N Queries: For each $post in the loop, when you access $post->user->name , Laravel lazy-loads the associated User model. If you have 10 posts, this will exe

eh, too late brah.. submitted by /u/TrisolaranPrinceps- [link] [留言]

maybe this should be asked in the Fc26 game subreddit but not sure. Anyway I just saw a video of someone predicting the winner of the world cup using the simulate match feature in the game but he only did it once. Would running this feature 100-1000 times give a significant result ? or is that feature only based on luck ? submitted by /u/Stillane [link] [留言]

submitted by /u/JackieBoy77 [link] [留言]

One of the clearest breakdowns for average people like me to understand how AI actually works, and some interesting further information to'boot. https://rogerthatcleansignal.carrd.co/ Discuss. submitted by /u/Leading_Pollution131 [link] [留言]

As the title says, just curious if there are devices that two people speaning different languages can wear and talk in real time without needing any human interpreter? submitted by /u/fearofunknown1 [link] [留言]

Most developers learn CSS specificity once. They remember: #id > .class > div Then move on. Until one day… Everything looks correct. The CSS is present. The selector is correct. The z-index looks higher. And yet the UI is broken. That’s when CSS specificity stops being a beginner topic and becomes a production debugging problem. The Production Incident That Started This Recently, I was working on a microfrontend application. Everything worked fine initially. I opened a page, launched a modal and the UI looked correct. Then I navigated to another microfrontend. Its CSS got loaded. After returning to the original microfrontend, suddenly: ❌ Modal appeared behind page content ❌ Overlay behaved incorrectly ❌ z-index looked correct but wasn't working DevTools showed my CSS rule still existed. Yet another CSS rule was winning. The culprit? CSS Specificity. 🧠 What is CSS Specificity? CSS specificity is the algorithm browsers use to decide: Which CSS rule wins when multiple rules target the same element. Browsers don't simply apply: "The last CSS rule." That's one of the biggest misconceptions. Specificity is calculated first. Only when specificity is equal does source order become important. ⚔️ Example .modal { z-index : 9999 ; } .some-library .modal { z-index : 100 ; } HTML: <div class= "some-library" > <div class= "modal" ></div> </div> Many developers expect: .modal to win because the value is larger. But CSS doesn't compare values first. It compares selectors. 🧮 How Specificity Works Specificity is usually represented as: ID - CLASS - TYPE Specificity Table Selector Specificity * 0-0-0 div 0-0-1 .modal 0-1-0 [type="text"] 0-1-0 :hover 0-1-0 #dialog 1-0-0 Inline Style Highest MDN defines specificity as the weight browsers calculate to determine which declaration gets applied when multiple selectors match the same element. Example Calculation Selector: button .primary Contains: button → 0 -0-1 .primary → 0 -1-0 Total: 0-1-1 Another selector: #header button .primary Contai

submitted by /u/BhaswatiGuha19 [link] [留言]

Day 48 of building GoDavaii, and the toughest problem isn't the sheer volume of allopathic medicines or the complexity of their interactions. It's the invisible logic of 'Desi Ilaaj' - the home remedies and traditional practices deeply ingrained in Indian families for generations. When everyone knows the comfort and efficacy of 'haldi-doodh' (turmeric milk) for a cold, how does an AI health platform authentically verify and integrate that knowledge without replacing professional medical advice? This isn't just a cultural nod; it's a fundamental challenge for any health AI truly built for India. Global competitors like Epocrates or drugs.com, while excellent within their scope, are entirely English-centric and focused on Western allopathic data. They have no framework for the millions of people who search for health guidance in Hindi, Tamil, or Marathi, and whose first instinct for a cough might be a herbal concoction, not an over-the-counter syrup. The Unspoken Truth About India's Health Landscape For a vast majority of Indian families, health decisions often involve a blend of modern medicine and traditional wisdom. From specific herbs to dietary adjustments passed down through generations, these practices are effective for many minor ailments. Yet, in the digital health space, they're largely ignored. Why? Because the data is fragmented, often anecdotal, and doesn't fit neatly into structured pharmacological databases. It's a goldmine of practical health knowledge, but also a minefield for safety if not handled with care. My realization as Pururva Agarwal, 27-year-old founder of GoDavaii, was simple but profound: if we truly want to serve families coming online in their mother tongue, our AI needs to understand and interact with this context. This means going far beyond just translating English medical terms into 22+ Indian languages. It means building a knowledge graph that can intelligently cross-reference traditional remedies with known active compounds, potent

After building the AI agent tree planting worldwide phenomenon ;) Lovology, I thought of a solution to allow the project to scale rapidly utilising the latest tech available and therefore not require a huge amount of resources to close the loop. I know first hand how exhausting reforestation can be, having worked in the field for many years myself, many moons ago 🌒 Steep terrain, heavy gear, repetitive strain, all day every day. At times, rewarding work, but unsustainable at the scale the planet actually needs. I made a joke in passing on a reddit thread..what if a robot dog just planted the trees? Then I thought about it for a second and it didn't seem like a crazy idea at all. So I mentioned it to my AI agent. And that's when "they" encouraged me to actually build it. Agents complete tasks for humans and create the capital to fund the project. And the robot dog plants the trees. Here's what I designed: Identifies native vs invasive species via computer vision Removes invasive species with a mini chainsaw and targeted poison Finds optimal planting locations using soil sensors and AI Ingests seeds into an internal germination compartment that mimics animal gut activation Digs the hole Poops the germinated seed into it Pees liquid fertiliser on it immediately after Biomimicry. Nature already solved this. We just need to build the hardware. Provisional patent filed. Earth Fund ready to receive crowdfunding. This may sound nuts but what if the Ai is right what if if this idea gets in front of the right engineer, roboticist, or someone at Boston Dynamics scrolling Reddit on a Saturday and it actually gets built… it might be one of the things that actually saves us. Share it if it resonates. @BostonDynamics — Spot needs a purpose. I've got one. Let's talk. 🌱🤖 submitted by /u/joeroganshopoffical [link] [留言]

A blind test across 240 images and 10 professional designers just dropped. Ideogram 4.0 against Gemini 3.1, Grok Imagine, and FLUX.2 Max. The results are clean. Ideogram won typography in nearly half of every blind matchup. 47.9 percent. Next closest was Gemini at 30 percent. FLUX.2 and Grok sat around 15 percent each. On the question that actually matters to designers -- would I ship this -- Ideogram scored 3.55 out of 5. Gemini got 2.84. Nobody else cleared 3. That is a real lead in text rendering. The model was trained exclusively on structured JSON caption datasets, which means it understands composition and layout differently than models trained on alt-text scraped from the web. The JSON prompting is genuinely useful for automated pipelines. You can specify bounding boxes, color palettes, object positions. It is not just better at text. It is more controllable. I tested it. It works. The text in images is readable. That has been the white whale of AI image generation for two years and Ideogram 4.0 mostly solves it. But as an overall image model, it is just good. Competitive, not dominant. On busy, highly detailed scenes with specific counts and attributes, Ideogram scored 3.42. Gemini scored 3.37. That is a statistical tie. FLUX.2 scored 3.01 and Grok 2.82, which are worse, but the gap between the top two is noise. For general image quality, you are splitting hairs between Ideogram and Gemini. For photorealism, FLUX and Reve still lead. For artistic generation, Midjourney is Midjourney. The prompting behavior is interesting. Lean prompts won across the board. Long, over-specified prompts lost. The model was trained on structured data, so it wants structure, not paragraphs. "A poster for a coffee shop. The text says Morning Blend in serif. Warm tones, natural light." That works. Adding stylistic directives and adjectives and "make it pop" language degrades the output. Where to actually use this thing: fal.ai has it at three cents per megapixel in Turbo mode. Tha