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Every day, the internet quietly convinces us that we're running out of time. A 19-year-old launches...

How does AI make you feel? Are you excited to “vibe-code” your smart home? Or anxious about all the added pollution and billions of gallons of water used by data centers? Dig a little deeper and you’ll start to question the actual value of the GPUs that underpin all the leaps and promises of generative […]

Dealerships installed alarms in millions of vehicles—and left them in even if the buyer didn’t want them. Now researchers warn they can be hacked to unlock, track, and disable cars.

Everyone is talking about the reading crisis. But inside the “Mississippi Miracle,” it’s all about comprehension.

Gritt is coming out of stealth with $34 million and plan to automate the hardest tasks on construction sites.

Yelp has launched Training Orchestrator. This new internal framework replaces individual team Spark training scripts. Now, it uses a configuration-driven, DAG-based execution model. By Claudio Masolo

Simple computers for agent w/ full VMs Discussion | Link

How I Built a Full-Stack Quality Skill for AI Coding Agents AI coding agents are getting very good at writing code. But I kept running into the same problem: They can move fast, but without strong project rules they can also create messy architecture, duplicate utilities, inconsistent APIs, weak security checks, and frontend components that slowly drift away from the design system. So I built Full-Stack Quality Skill . It is a reusable AI coding skill for full-stack audits, architecture guidance, long-term project memory, and CI quality gates. Repo: https://github.com/lablnet/full-stack-quality-skill Website: https://skills.lablnet.com Why I Built It When I use AI agents like Cursor, Codex, Claude Code, Antigravity, or similar tools, I do not only want them to "write code". I want them to think like a careful senior engineer: Is the database normalized correctly? Are backend layers clean? Is business logic leaking into controllers? Are frontend components consistent? Are Vue components using composables? Are React components using hooks correctly? Are HTTP methods and status codes right? Is GraphQL safe from N+1 problems? Are security and privacy risks checked? Are tests missing for critical paths? Is documentation still matching the code? That is a lot to remember every time. So instead of repeating the same instructions in prompts, I turned them into a reusable skill. What It Covers The skill includes audit areas for: Database Backend Frontend Mobile HTTP APIs GraphQL Security Privacy Accessibility i18n Analytics Background jobs Infrastructure Testing Performance Observability Delivery / CI Multi-tenancy Payments Notifications Data import/export API compatibility Developer experience AI/LLM safety It also includes examples for common stacks: Node.js / TypeScript Python Django Laravel Java / Spring C# / ASP.NET Core Go Ruby on Rails React Next.js Vue Angular SvelteKit Flutter React Native Kotlin / Android Swift / iOS SQL GraphQL Read-Only Audits by Default One impo

After spending countless hours designing, coding, debugging, and improving every little detail, I'm excited to share my personal portfolio with the developer community! 🌐 Live Website : https://didulagamage.pages.dev/ I'd Love Your Feedback ❤️ If you have a few minutes, I'd really appreciate it if you could visit my portfolio and share your thoughts. Your feedback helps me become a better developer. Thanks for reading! 🚀

Two posts ago I said I'd shipped code I never read. It's easy to write that as a general observation about the industry. It's less comfortable to go open your own repo and count. So I did. Here's what I found, and what I've actually done about it. The repo It's a desktop app I built for my own company. Roughly fourteen thousand lines of Python, a Tkinter UI, invoicing and documents and reports, the kind of internal tool nobody else will ever see. I had never written Python before I started it. I built it anyway, with a lot of AI help, learning as I went. That combination - no prior experience, heavy AI assistance, a real deadline because the business actually needed the thing - is basically a diff debt factory. I wasn't cutting corners on purpose. I just didn't have the knowledge to evaluate half of what I was merging, and it worked, so I moved on. What I actually found The clearest example took me months to notice. Five different parts of the app generate PDFs. Quotes, proformas, reports, petitions, heat treatment certificates. Every one of them was failing, in different ways, at different times, and I kept fixing them individually. Different error, different module, different patch. The actual cause was one thing: LibreOffice wasn't installed on the machine. Every one of those modules quietly depended on it to do the document conversion. Not one of them said so anywhere. I'd merged that dependency five separate times without ever registering that I'd taken it on. That's diff debt in its purest form. The code wasn't messy. It wasn't badly written. It just made an assumption I'd never read, and I paid interest on it five times over before I understood the principal. There were smaller ones too. A path handling bug that only showed up because my Windows username has a Turkish character in it - the code assumed ASCII and nobody, including me, had thought about it. Two Python versions installed side by side, quietly fighting. A stray quotation mark in my system PATH th

Event-Driven Architecture (EDA) is one of the most popular approaches for building scalable distributed systems. Instead of tightly coupling services through synchronous APIs, services communicate by publishing and consuming events. It sounds perfect. Until your consumers start making API calls for every event they receive. At that point, you've reintroduced the very coupling Event-Driven Architecture was supposed to eliminate. This is where Event-Carried State Transfer (ECST) comes in. The Hidden Problem in Event-Driven Architecture Imagine an e-commerce platform. A customer places an order. The Order Service publishes an event: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Several services subscribe to this event: Inventory Service Notification Service Analytics Service Shipping Service Billing Service Everything looks asynchronous. But here's what actually happens. Order Created Event │ ▼ Inventory Service │ GET /orders/ORD-10234 │ Order Service Notification Service does the same. Analytics Service does the same. Shipping Service does the same. Suddenly, one event generates dozens of synchronous API calls. Congratulations, You've Recreated a Monolith Your architecture now looks like this: Order Service ▲ ┌───────────┼───────────┐ │ │ │ Inventory Shipping Notification │ │ │ └───────────┼───────────┘ API Requests Although events are being used, every consumer still depends on the Order Service. If the Order Service is unavailable: Notifications fail Inventory updates fail Analytics stop processing Shipping cannot continue The event broker isn't the bottleneck anymore. The originating service is. Event-Carried State Transfer Solves This Instead of publishing only an identifier, publish the data consumers actually need. Instead of this: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Publish: { "event" : "OrderCreated" , "orderId" : "ORD-10234" , "customerId" : "USR-1001" , "customerName" : "John Doe" , "totalAmount" : 249.99 , "currency" : "USD" , "i

The short answer is: while OpenAI Sora offers unmatched visual quality and physics rendering, it remains restricted behind a paid subscription structure. For creators who want a completely free, unlimited AI video generator, the newly released open-source LTX Desktop app by Lightricks allows you to run the LTX-2.3 video model locally on your own computer with zero usage costs or filters. The AI Video Paywall Frustration If you have tried building AI video content for YouTube, TikTok, or social marketing, you know how expensive it is. Platforms like Runway Gen-3 and Luma Dream Machine charge by the second. A simple five-second clip can cost up to fifty cents in API credits, making creative experimentation almost impossible for solo developers. OpenAI Sora is a powerhouse, but its high computational overhead means it will likely remain a premium, paid tool for the foreseeable future. To bypass this, our dev team set up the new open-source LTX Desktop application on our local workbench to see if local video generation is actually viable for production. Here is our hands-on review. |---|---|---|---| | OpenAI Sora | Closed Cloud | None (Paid Plan) | Cloud-Only | Cinema-grade physics, long multi-action shots. | | Runway Gen-3 | Closed Cloud | Daily Free Credits | Cloud-Only | Cinematic camera pans, high texturing quality. | | Wan2.1 | Open Weights | Free Hugging Face Spaces | 16GB VRAM (Local) | Photorealistic human movement, natural lighting. | | LTX-2.3 | Open Weights | LTX Desktop (Free) | 8GB VRAM (Local) | Fast generation speeds, local desktop interface. | Running Video Models Locally: The LTX Desktop Solution LTX Desktop, developed by Lightricks, is a standalone, open-source desktop application that lets you run their LTX-2.3 video generation model on consumer-grade graphics cards. Why LTX Desktop is a Game-Changer Low VRAM Footprint: Unlike HunyuanVideo or Wan2.1 which require massive 16GB-24GB VRAM cards to compile locally, LTX-2.3 is highly optimized and runs com

Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.

Once a quirky bastion of amateur vulva jewelry and pet portraits, Etsy is now deluged with mass-produced goods and AI knockoffs. Some customers don’t seem to mind.

One identity layer for your customers, APIs, and agents Discussion | Link

GitLab has released version 19.2 of its DevSecOps platform, adding agentic automation aimed at the security and review work that has piled up as AI coding tools generate more code than developers can check by hand. The release, announced on 16 July 2026, brings four features out of beta or into public beta: Dependency Scanning Auto-Remediation, Security Review Flow, GitLab Duo CLI and Custom Flows By Matt Saunders

And why? I'd genuinely like to understand who is on which side and why. I'd also like to know, if you use one of them, what kind of subscription plan you have. (For example, I use Claude (max) [...])

Hi everyone, I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before committing to it. What I've done so far: I render each PDF page to an image and run it through Baidu's DeepSeek-OCR model . It returns each detected block with a bounding box [x0, y0, x1, y1] , a label ( title , text , list , table , header , footer , etc.), and the recognized text. The OCR quality itself is genuinely good as the text comes out clean. The problem: the labels can't always be trusted. At this stage I want to extract and detect all the titles in my document, but sometimes a title element gets classified as something else (like normal body text). Concrete example: Say my section has the following hierarchy: ANNEX I — GENERAL PRINCIPLES AND PROCEDURES └── TITLE I — FOREIGN CURRENCY INVESTMENT └── A. Currency distribution └── 1. Redistribution of reserves ├── (a) Introduction │ body text │ list │ ... ├── (b) Procedure for a normal redistribution of reserves │ body text │ list │ ... └── (c) Procedure for an ad hoc redistribution of reserves body text list ... Logically, every element aside from the body text and lists should be detected as title . But the model output is: label='title' x0=475 y0=157 x1=548 width=73 text='ANNEX I' label='text' x0=480 y0=229 x1=542 width=62 text='TITLE I' label='title' x0=334 y0=181 x1=690 width=356 text='GENERAL PRINCIPLES AND PROCEDURES' label='title' x0=407 y0=368 x1=616 width=209 text='A. Currency distribution' label='title' x0=408 y0=392 x1=634 width=226 text='1. Redistribution of reserves' label='title' x0=163 y0=416 x1=304 width=141 text='(a) Introduction' label='title' x0=163 y0=544 x1=578 width=415 text='(b) Procedure for a normal redistribution of reserves' label='title' x0=163 y0=219 x1=586 width=423 text='(c) Procedure for an ad hoc redistribution of reserves' The top-level section marker TITLE I was labeled text , w