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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

It's a Kickstarter from an unknown brand that wants to offer a classier fitness tracker.

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

Brands Hatch, COTA, and Zandvoort will all hold an e-Prix in 2027.

You can save hundreds of dollars on Breville appliances during Amazon Prime Day.

Tesla, accused of failing to fix design flaws, blames driver pressing accelerator.

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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When designing systems like Facebook, WhatsApp, Netflix, Amazon, or Instagram, one of the first questions a system designer asks is: Can a single server handle the traffic? How much storage will be needed? Do we need caching? How much RAM should our cache have? How many servers should we deploy? Before discussing databases, load balancers, microservices, or caching layers, we need a rough understanding of the scale. This is where Back-of-the-Envelope Calculations come into the picture. Why Do We Need Back-of-the-Envelope Calculations? Imagine you're asked to design Facebook. If you immediately start drawing: Load Balancer ↓ Application Servers ↓ Redis Cache ↓ Database without knowing the expected traffic, you're designing blindly. System design is fundamentally about making trade-offs. To make those trade-offs, we first need estimates. Back-of-the-envelope calculations help us answer: How much traffic will the system receive? How much data will be generated? How much cache memory is required? How many servers are needed? The numbers don't need to be perfect. They only need to be close enough to make architectural decisions. What Exactly Is a Back-of-the-Envelope Calculation? A quick estimation technique used to approximate: Traffic Storage Memory Server Capacity using rough assumptions. Think of it as: "Getting the order of magnitude correct rather than getting the exact number correct." A system designer rarely needs perfect accuracy during interviews. They need reasonable estimates. The Standard Estimation Flow Whenever you get a System Design question: Users ↓ Traffic ↓ Storage ↓ RAM / Cache ↓ Number of Servers ↓ Architecture Design Always estimate first. Design later. The Ultimate Estimation Cheat Sheet Storage Units Unit Value 1 KB 10³ Bytes 1 MB 10⁶ Bytes 1 GB 10⁹ Bytes 1 TB 10¹² Bytes 1 PB 10¹⁵ Bytes Time Units Unit Value 1 Minute 60 Seconds 1 Hour 3600 Seconds 1 Day 86,400 Seconds Common Assumptions Metric Approximation Peak Traffic 3× Average Traffic Active

Math.random() returns a number between 0 and 1, and roughly nobody reading this could explain what happens between the call and the return. That is fine, fine right up until the output decides who gets money, and then it becomes one of the genuinely hard problems in applied software, the kind that regulated industries build entire testing labs around. Start with the thing most people get wrong: a sequence that passes for random and a fair sequence are different claims, and your users cannot tell them apart by staring at outputs. The users will never catch the difference and that is the whole problem in one sentence. This is why fairness in any real-money system, an online casino being the sharpest example, is a verification problem long before it is a math problem. Pseudorandom generators are deterministic. A PRNG eats a seed, runs it through fixed arithmetic, and spits out numbers that sail through statistical randomness tests while being completely predetermined by that seed. Mersenne Twister is the poster child: excellent distribution, used everywhere by default for years, and from a few hundred observed outputs you can reconstruct its internal state and predict the rest. For a Monte Carlo simulation, who cares! For anything where a human has a financial reason to guess your next number, you just shipped a vulnerability and called it a feature. What you want when stakes exist is a CSPRNG. The guarantee that matters: even with a long history of outputs, an attacker cannot compute the next one or recover the internal state. crypto.randomBytes() in Node. crypto.getRandomValues() in the browser. They sit one autocomplete away from the unsafe option and offer wildly different guarantees, which is exactly why this bug ships so often. The safe call and the dangerous call look like fraternal twins. ** The part players actually rely on ** Say you build it correctly: a proper CSPRNG, real entropy, no timestamp nonsense. You know it is fair but now prove it to a stranger wh

Europe is in the middle of a record-breaking heat wave, and the grid is being pushed to its limits as people turn to fans and air-conditioning to try to stay cool. Some power plants won’t be online to help handle the load. On June 23, France saw its hottest day since record-keeping began in 1947.…

The Samsara Tracking Label hides a BLE system that can offer real-time location in a disposable package.

Zoox, the autonomous vehicle company owned by Amazon, unveiled a new look for its boxy, bidirectional robotaxi, calling it the "next evolution" of the vehicle intended for mass production. The company is currently operating a free robotaxi service in San Francisco, Las Vegas, Austin, and Miami while it waits for the federal government to approve […]

Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline. By Leela Kumili

Branches received exceptions to Hegseth's policy that made the shot optional.

I love building Lego sets, so here are my picks of the best Prime Day Lego deals. There are some great toy discounts, too.

From a bare-bones pickup to a loaded, wrapped SUV, here's what some Slates will cost.