标签：#AR

Indie movie fans are upset about Google DeepMind’s $75 million investment in the studio, which comes as AI companies are deepening their influence in Hollywood.

Your product might rank on page one of Google and still be invisible to AI. When someone asks ChatGPT "what's the best project management tool for small teams," does your product show up? For most SaaS companies under 50 employees, the answer is no. At Inithouse, we built Be Recommended to answer that question with a number: a single AI visibility score from 0 to 100 that tells you exactly where you stand across four major AI engines. Here is how the scoring works under the hood. What the score measures The Be Recommended score captures how often, how prominently, and how positively AI engines mention your product when users ask category-relevant questions. A score of 0 means no AI engine mentions you at all. A score of 100 means every tested prompt across all four engines names your product as a top recommendation. The four engines we test against: ChatGPT (OpenAI), Perplexity , Claude (Anthropic), and Gemini (Google). Step 1: Prompt generation We start by building a bank of 50+ real prompts that a potential customer would actually type into an AI assistant. These are not keyword-stuffed test queries. They mirror how real people ask for recommendations. For a CRM product, that looks like: "What CRM should a 10-person startup use?" "Best alternatives to Salesforce for small businesses" "Compare CRM tools with good API integration" "Which CRM has the best free tier in 2026?" We group prompts into three categories: direct (user names the product category), comparative (user asks for alternatives or comparisons), and situational (user describes a problem without naming a category). Each category tests a different signal: brand recognition, competitive positioning, and contextual relevance. Step 2: Multi-engine querying Each prompt gets sent to all four AI engines through their APIs. We capture the full response text, not just a yes/no for whether your product appeared. The raw responses go into a structured analysis pipeline. We run queries from neutral accounts with n

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The AI arms race between China and the US has researchers on both sides worried about a “Chernobyl moment.”

Hi, I'm Hugo. I built MinecraftServers-List.com — a Minecraft server directory that ranks servers by genuine player votes and uptime. Why I built it Most existing Minecraft server lists have the same problem: the rankings are easily gamed. Server owners run scripts to inflate their vote counts, and players searching for a good server end up with a list that reflects who has the best bots, not which servers are actually worth playing on. I wanted to fix that. What makes it different Vote integrity — votes are tied to real player sessions and IP validation, making bot voting significantly harder Uptime monitoring — servers that go offline lose ranking visibility automatically Player reviews — verified players can leave reviews with star ratings, giving prospective players real signal Java & Bedrock — both editions listed and filterable by gamemode, version, and country The tech stack Built with TanStack Start (React SSR), Supabase for the database, and deployed on Cloudflare Workers. The SSR approach was important for SEO — server listing pages need to be fully rendered for Googlebot to index individual server pages properly. What I've learned so far Getting a new directory site indexed by Google is genuinely hard. The challenge isn't technical — it's convincing Google that hundreds of server listing pages are individually worth indexing when they all share a similar template structure. The solution has been enriching each server page with structured data (VideoGame schema with AggregateRating), genuine user reviews, and making sure every page has a meaningfully unique meta description generated from real server data — version, gamemode, player count, country. Still a work in progress but the site is live, servers are actively listed, and players are voting daily. Try it If you run a Minecraft server, you can list it free at https://minecraftservers-list.com If you're looking for a server to join, the SMP list and survival list are good starting points. Happy to answe

Most on-page audits catch the obvious stuff: a missing title here, a duplicate meta description there. The thing that quietly costs you rich results is structured data that exists but is invalid, and most flat-list crawlers either skip it or bury it. Here is why it happens and how to catch it. The problem, concretely You add FAQ schema to a product page to win that expandable rich result in Google. You paste a JSON-LD block into the head, ship it, and move on. Six weeks later the rich result never showed up, and nobody knows why. The usual culprits are small and silent: A @type that does not match the content (FAQPage with no mainEntity ). A required property missing ( acceptedAnswer without text ). A trailing comma or a stray character that makes the JSON parse fail entirely. Schema that contradicts what is actually on the page, which Google can flag as spammy and ignore. None of these throw a visible error. The page renders fine. The schema is just dead weight, and a standard "issues" crawl that only counts titles and headings walks right past it. How to catch it First, validate the JSON itself. A block that does not parse is invisible to search engines. Even a quick local check surfaces the dumb-but-fatal errors: // Pull every JSON-LD block and check it parses + has a @type const blocks = [... document . querySelectorAll ( ' script[type="application/ld+json"] ' )]; blocks . forEach (( b , i ) => { try { const data = JSON . parse ( b . textContent ); if ( ! data [ " @type " ]) console . warn ( `Block ${ i } : missing @type` ); } catch ( e ) { console . error ( `Block ${ i } : invalid JSON ->` , e . message ); } }); If that logs an error, the schema was never going to work, no matter how perfect the markup looked. Second, check required properties for the specific type you are using. FAQPage needs mainEntity with Question items, each carrying an acceptedAnswer . Article needs headline , author , and datePublished . Validating "it parsed" is not the same as "it is c

Rethinking fake content in Symfony projects A prototype web page displaying pure placeholder content When building early UI prototypes or shaping design systems in Symfony, placeholder content becomes a constant companion. Lorem ipsum text. Dummy profile photos. Placeholder videos. Silent audio. Temporary avatars. Realistic fake user data. Every project needs them — and yet most setups rely on a patchwork of libraries, links and hardcoded values. Omnia Ipsum aims to fix that by giving Symfony developers a single, elegant toolkit for placeholder content of all kinds. In this article, I will walk you through the motivation behind the project, the conceptual patterns it follows, and its most advanced features — all designed to make your prototyping workflow faster, cleaner and more maintainable. Motivation: Why a placeholder library? Most Symfony projects start the same way: You add lorem ipsum text manually into Twig templates. You grab placeholder images from an external service. You generate avatars using yet another site. You paste in temporary YouTube or stock video URLs. You install Faker separately whenever realistic data is needed. The result is inconsistent, fragmented and difficult to maintain. And even worse: placeholder content often leaks into production unless guarded carefully. The idea behind Omnia Ipsum was simple: “If your UI needs placeholder content, it should come from one place — predictable, configurable, and accessible directly from Twig.” This cuts down on boilerplate, cognitive overhead, and the "temporary chaos" of early-stage templates. Quick Start Prerequisite Go to github.com/symfinity/recipes and follow the instructions to add the required recipe repository. Installation composer require --dev symfinity/omnia-ipsum Usage Use the Twig functions immediately: <img src= " {{ omnia_image ( 600 , 400 ) }} " alt= "Placeholder" > <img src= " {{ omnia_avatar ( 'John Doe' , 100 ) }} " alt= "Avatar" > <video src= " {{ omnia_video ( 1920 , 1080 ) }}

At the end of my last post , I presented an idea: what if I used the core of my last project, the cumulative matrix product, and repurposed it as a positional embedding? I just finished fleshing out the math behind HDD-RoPE and training a model with this positional embedding algorithm, and the results are excellent. When trained on the dataset TinyStories, the validation loss begins to converge a fair amount faster than the baseline transformer trained using xPos. A GPT-2-like model trained on TinyStories with hyperparameters copied from https://huggingface.co/roneneldan/TinyStories-33M (n_blocks=4, d_model=d_k=d_v=768) The repo at https://github.com/mikayahlevi/hdd-rope/ allows you to replicate the results and goes in depth about the math and details of the architecture. Standard RoPE breaks the queries and keys into groups of two and rotates each pair at a predefined rate. This allows the model to learn relative position by observing the change in basis between the queries and keys. Pairs of two make intuitive sense for a linear sequence, as a chunk can be rotated with a single degree of freedom, corresponding to linear one-dimensionally progressing position. HDD-RoPE moves past this intuition and instead says that position within a sequence is multidimensional. Therefore, the chunks can be broken into any size, such as 4 as used in the TinyStories example. Four-dimensional chunks correspond to 4 choose 2 = 6 axes of rotation (6-dimensional position.) Essentially, we're saying that a token doesn't just lie at a position within the sequence, but a position within any construct the model can learn, such as a paragraph or sentence. To facilitate this, I also make the amount of rotation along each axis data-dependent, such that it can learn how to advance the positions based on information stored in the current layer's activations. If you would like to learn more, please check out the repo. I formalize the math and lay out a roadmap. submitted by /u/mikayahlevi [link]

The Problem Typography should be one of the simplest parts of a project. In reality, it often ends up scattered across multiple layers: Bootstrap: $font-family-base variables Tailwind: JavaScript configuration TypeScript: type definitions Design systems: W3C Design Tokens The same font information gets copied and maintained in several places. Every update means touching multiple files, hoping everything stays in sync. It's repetitive, error-prone, and easy to get wrong. So I built Font Manager. Define your fonts once and export them in whatever format your project needs — CSS, Bootstrap variables, Tailwind configuration, TypeScript definitions, design tokens, and more. The Solution A simple Twig function: {{ font_manager ( 'Ubuntu' , '400 700' ) }} Configuration: symfinity_font_manager : export : formats : - scss_bootstrap - tailwind_config - typescript_definitions One lock command: php bin/console fonts:lock Every format, automatically generated. Perfectly synced. Bootstrap Example Before: // Manually copy font name $font-family-base : 'Ubuntu' , sans-serif ; // ❌ Duplication @import 'bootstrap/scss/bootstrap' ; After: symfinity_font_manager : export : formats : [ scss_bootstrap ] php bin/console fonts:lock // app.scss @import './assets/styles/fonts-bootstrap' ; // ← Auto-generated @import 'bootstrap/scss/bootstrap' ; Bootstrap uses your fonts automatically. No manual mapping. No duplication. Tailwind Example symfinity_font_manager : export : formats : [ tailwind_config ] // tailwind.config.js const fonts = require ( ' ./assets/fonts-tailwind.config.js ' ); // ← Auto-generated module . exports = { theme : { extend : { fontFamily : fonts . fontFamily } } }; <p class= "font-sans" > Your custom font, via Tailwind. </p> TypeScript Example symfinity_font_manager : export : formats : [ typescript_definitions ] import { fonts , type FontFamily } from ' ./assets/fonts ' ; applyFont ( element , ' sans ' ); // ✓ Valid applyFont ( element , ' invalid ' ); // ✗ TypeScript erro

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Google's GKE Labs has introduced OpenRL, an open-source project that provides a self-hosted API for post-training and fine-tuning Large Language Models (LLMs) on standard Kubernetes clusters. By Sergio De Simone

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Hi, I've created an overview of the most important OCR benchmarks, along with the top open models, and links to their paper and code: https://paperswithcode.co/tasks/ocr . This week, new OCR models were released by Baidu and Mistral. Baidu released Unlimited OCR , a 3B-parameter model that introduces a key innovation called Reference Sliding Window Attention (R-SWA) and builds on top of DeepSeek OCR . Mistral released OCR 4 , which is available via an API. OCR, or Optical-Character Recognition, is the task of digitizing PDFs or scanned documents. There's, of course, a huge interest in this task, as it enables ingestion of all company data for agentic use cases. AI agents love Markdown; it can be valuable to turn all those messy PDF documents into a standardized, machine-readable format. This enables use cases like agentic RAG (retrieval-augmented generation), which powers chatbots, both internally and for external customer support. With a large number of OCR releases on Hugging Face over the last few months, it may be hard to know which one to use. Hence, I've built this page, which lists the major OCR benchmarks, along with the top-performing models and links to their code. This is obviously made available on Papers with Code , the website I'm maintaining (it's a revival of the old website, which was taken down). The top recommended benchmarks are OlmOCRBench, created by Ai2, and OmniDocBench, created by Shanghai AI Laboratory. Current top recommendations are Chandra OCR 2 by Datalab and Mistral OCR v4. The former is openly available, hence you can either self-host it or use their serverless API. Let me know which other tasks you want to see major benchmarks for now! Cheers, Niels open-source @ HF submitted by /u/NielsRogge [link] [留言]

Hi everyone, I trained a self-play RL agent for Generals.io that reached superhuman-level and ranked #1 on the human 1v1 leaderboard. It began as my master's thesis where the goal was to beat a prior algorithm based agent. We succeeded using behavior cloning, RL fine-tuning and reward shaping, but the agent was still consistently beaten by the top players. So I gave it a round two and fixed the largest bottlenecks: Reimplemented the whole pipeline in JAX (from NumPy/Torch) Used Vision Transformer instead of the CNN Both are a result of the same idea: to invest in scaling rather than human priors and ad-hoc patches. The blog is written as a guide for anyone building something similar — the dead ends, the decisions, and the intuitions and tricks I picked up along the way. It's all open source, including the fast JAX simulator — handy on its own if you want an imperfect-information RTS env to play with. Links - Guide: https://kam.mff.cuni.cz/~straka/blog/generals.html - Simulator (JAX): https://github.com/strakam/generals-bots - Agent: https://github.com/strakam/AverageJoe I hope you find the blogpost entertaining! Feedback and questions welcome 🤗. submitted by /u/shrekofspeed [link] [留言]

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