标签：#AR

Just finished reading Saha et al. arXiv 2506.07001 on adversarial paraphrasing for AI detector evasion. Key claim: detector-guided paraphrasing with RoBERTa as reward reduces TPR by 87.88 percent across Binoculars, Fast-DetectGPT, Ghostbuster, RADAR, GPTZero. Universal, training-free. What surprised me: the approach works even on detectors that were trained with adversarial examples baked in. Suggests the discriminator signal is fundamentally narrower than the generator space. Open questions: Does this generalize to detectors using surprisal variance (DivEye 2509.18880)? Multi-LLM round-robin generation: would mixing 3-4 models in pipeline give even more headroom? Token-level homoglyph substitution (SilverSpeak) is trivially detectable via Unicode normalization, but adversarial paraphrasing leaves no such forensic signal.

Most "Shopware vs Shopify" posts compare dashboards, app stores, and pricing tables. None of that matters to you until the day a client asks for something the platform won't let you build. Then the comparison stops being a feature grid and becomes a question about ceilings: how high can I go before the platform says no, and what happens when I hit it? That's the only axis I care about as a developer, so that's the one I'll argue on. Shopify is an outstanding product. It's also a closed SaaS that decides, on your behalf, where customization ends. Shopware is open source built on Symfony, which means the ceiling is "however far PHP and HTTP will take you." Below are the three places that difference actually bites, with code. Angle 1: The checkout is the wall This is the headline because it's where most agency developers first hit something they cannot do. For years the Shopify answer to "customize the checkout" was checkout.liquid . That era is over. Shopify deprecated checkout.liquid in favour of Checkout Extensibility . Plus stores had to migrate their Thank-you and Order-status pages by August 28, 2025 , and in January 2026 Shopify began auto-upgrading stores — wiping customizations built on additional scripts, script-tag apps, or checkout.liquid . Non-Plus stores have until August 26, 2026 , and legacy Shopify Scripts keep working only until June 30, 2026 . ( Shopify migration timeline ) The replacement, Checkout Extensibility, is genuinely more upgrade-safe. It's also a smaller box. You get Checkout UI Extensions (declarative components that render in slots Shopify defines) and Shopify Functions for backend logic — and that's the surface. You don't own the checkout template; you decorate the pieces Shopify exposes. Worth noting: full visual checkout customization (branding API, custom fields beyond the defaults, full UI extension power) is gated to Shopify Plus anyway. On Shopware, the checkout is a Twig template like every other page, and you override it the sam

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Every developer using Cursor , Claude Code , Windsurf , or GitHub Copilot knows this exact frustration: You are building a cutting-edge Angular 22 application. You ask your AI coding assistant to spin up a dynamic form, a lazy-loaded list, or an asynchronous data card. Instead of leveraging modern fine-grained reactive Signals, optimized native block control flows, or proper SSR hydration hooks, the AI drops an unoptimized pile of legacy tech debt full of NgModules , *ngIf , *ngFor , and raw RxJS BehaviorSubjects . The LLM Training Paradox Why does this happen? Large Language Models are trained on historical code datasets. Statistically, more than 90% of the public Angular repositories and StackOverflow threads on the internet represent older paradigms. Left to their own devices, agents default to the statistical average of their training data. They literally default to the past. The Fix: angular22-agent-skills To solve this, I built a public, open-source repository of custom instruction bundles and system guardrails leveraging the new skills.sh tool standard. By injecting this verified context directly into your development environment, you force your local AI agents to bypass their training averages and write pristine, optimized, modern Angular 22 syntax every single time. 👉 Check out the repo here: https://github.com/PavanAnguluri/angular22-agent-skills 🔍 The Difference: Before vs. After To understand why these guardrails are necessary, look at what an AI agent writes out of the box versus what it writes once you apply the angular22-agent-skills harness. 🚫 What AI Agents Generate by Default (Legacy) // The AI falls back to old decorators and heavy RxJS boilerplate for standard state import { Component , Input , OnInit } from ' @angular/core ' ; import { BehaviorSubject } from ' rxjs ' ; @ Component ({ selector : ' app-user-profile ' , template : ` <div *ngIf="visible"> <h3>{{ firstName }} {{ lastName }}</h3> <div *ngFor="let item of items"> {{ item.name }} </div>

Walmart-backed Flipkart has crossed 1,000 micro-fulfillment centers as Amazon accelerates its own quick-commerce push in India.

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After a long and deliberate alpha, spartan/ui is now 1.0 . We shipped the first 30 primitives in August 2023 with a simple bet: building accessible, good-looking UI in Angular is harder than it should be, and the community deserved a better starting point. Almost three years later, that bet has grown into a stable, production-ready library of more than 55 components - built on signals, ready for zoneless, and server-side-rendering compatible out of the box. Here's what 1.0 actually means. Stable, and ready to build on We stayed in alpha for a long time on purpose. It let us refine the APIs in the open, with real applications putting real pressure on the design, instead of freezing a v1 we'd regret six months later. That patience is what 1.0 cashes in. The APIs are now stable and semantically versioned, so you can depend on spartan/ui/brain and upgrade with confidence. The copy-in spartan/ui/helm layer stays exactly as it's always been - yours to own, read, and customize. No black boxes, no fighting the library to change a style. Built for modern Angular Every primitive is built on Angular signals and standalone components. spartan is zoneless-ready and SSR compatible out of the box, so it drops cleanly into how Angular apps are actually written today - no extra setup, no adapters. The split that's defined spartan from day one still holds. spartan/ui/brain carries the hard, unglamorous parts - ARIA, keyboard navigation, focus management - and keeps them maintained so you don't have to. spartan/ui/helm gives you full styling control on top, copied into your project like a recipe. Accessibility you can rely on; appearance you fully own. From 30 primitives to 55+ The alpha shipped with 30 components. 1.0 ships with more than 55 - nearly double - including many of the most-requested additions over the past two years: Data Table - sorting, filtering, and selection, the piece people asked for most Sidebar - composable app navigation Calendar and Date Picker Carousel , Auto

I'm Dr. Mohammad Reza Beheshti, Founder of CyberSiARA. I hold a PhD in Electronic Engineering and Artificial Intelligence and have over 15 years of experience in cybersecurity research and innovation. My passion has always been solving complex security challenges through technology. This journey led me to found CyberSiARA, where we're developing AI-powered bot protection and human verification solutions to help organizations defend against increasingly sophisticated cyber threats. I enjoy combining academic research with practical engineering to create technologies that are both innovative and effective in the real world. Through this blog, I share insights from my research, product development, and experiences building a cybersecurity company, with the aim of helping developers and security professionals stay ahead of emerging threats. I'm always keen to learn, collaborate, and contribute to the global developer and cybersecurity communities.

We run a prime directive on this stack: if a usable tool already exists, improve it; build our own only as a last resort, and when you keep your own, record why each alternative failed. This post is that audit for weather-mcp — a marine-weather MCP server — against the weather-MCP ecosystem, and the one capability change that fell out of it. The short version: three perfectly good weather MCP servers exist, and none of them does the thing a navigator actually needs. The reasons generalize to any "adopt an MCP server or keep your own" call, so the audit is the post. Then the fix — parsing a second NDBC file format to split swell from wind waves — is small enough to paste in full, and it surfaced data the standard file had thrown away. The problem, as you'd search it You want an agent to answer "what are the seas doing where we are?" and you go looking for a marine weather MCP. You find a few. Each one returns a forecast . None of them returns what a buoy 12 nautical miles away is measuring right now . That gap — forecast vs. observed — is the entire job, and it's the one thing the ecosystem skips. Here's what's on the shelf, and what each one is missing for marine use. The candidates Three real servers, all worth your time for what they're built for: cmer81/open-meteo-mcp ~13 tools, raw Open-Meteo JSON straight through weather-mcp/weather-mcp ~12 tools, own format, global; marine = Open-Meteo RyanCardin15/NOAA-Tides... CO-OPS stations: water levels + currents, not buoys And ours: sailingnaturali/weather-mcp 4 tools, Python, 2 runtime deps (httpx + mcp) get_marine_forecast Open-Meteo wind/swell/wind-wave/seas/pressure get_marine_forecast_premium Stormglass blend — 10 tokens/UTC-day, cache hits free get_nearest_buoy_observations NDBC observed wind + waves by lat/lon, with bearing + age get_stormglass_quota_status token-ledger read, no network Mapped against what a navigator needs: Capability ours open-meteo-mcp weather-mcp/weather-mcp NOAA-Tides Open-Meteo marine (swel

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A new update for Google Home could make it less likely your smart home cameras mistake you for someone else, just because you're facing away from the camera. Starting June 23rd, Google's expanding its facial recognition feature so that people you've tagged in your Familiar Faces library can continue to be identified when their faces […]

Superhuman, which also has an AI detection tool as part of Grammarly, has snapped up GPTZero.

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Almost everything that makes the modern world hum, from the phone in your pocket to the sensor on a factory floor, traces back to a single quiet afternoon in a nearly empty laboratory in Dallas. In the summer of 1958, a newly hired engineer named Jack Kilby built the first working integrated circuit at Texas Instruments. It was a crude little thing, a sliver of germanium with a few components and some fine gold wires, but it carried an idea that would reshape electronics: that an entire circuit could be made from one piece of semiconductor material. Every microcontroller and connected device we build today is a descendant of that prototype. The engineer who was left behind Kilby had only just joined Texas Instruments and had not yet earned any vacation time. So when the company shut down for its traditional summer break in July 1958 and most of his colleagues left, he found himself nearly alone in the lab with time to think. The problem on his mind was one the whole industry called the "tyranny of numbers." Circuits were getting more capable, which meant more transistors, resistors, and capacitors, each one a separate part that had to be wired together by hand. Every added component meant more connections, more soldering, and more chances for something to fail. The complexity was becoming a wall. Kilby's insight was disarmingly simple. If resistors and capacitors could be made from the same semiconductor material as transistors, then every part of a circuit could be fabricated together in a single block. No separate components, no forest of hand-soldered wires. He sketched the idea, and when his managers returned he had something to show them. September 12, 1958 On September 12, 1958, Kilby demonstrated his prototype to Texas Instruments executives. The device was a phase-shift oscillator built on a bar of germanium, with its elements connected by delicate gold "flying wires." He connected it to an oscilloscope, flipped the switch, and a steady sine wave rolled acro

I got a take-home assignment last year from a company I was genuinely excited about. "Should take about four hours," the recruiter said. Build an ingestion pipeline, model the data, write tests, document your design decisions, and prepare a 15-minute presentation walkthrough for the panel. Four hours. I laughed, closed my laptop, and started on it the next morning like it was a sprint. Sixteen hours later I had something I was proud of. Clean pipeline, solid tests, real documentation. I submitted it on a Sunday night. Monday I got a form rejection. No notes. No feedback. Not even which stage I failed. Just "we've decided to move forward with other candidates" and a link to their Glassdoor page. That was the moment I stopped pretending take-homes are assessments. They're consulting gigs. Unpaid ones. The Scope Creep Nobody Talks About Five years ago, a data engineering take-home was a focused exercise. Model this dataset into a star schema. Write a few SQL transforms. Maybe a short README. Two to four hours, tops. Bounded, reasonable, and actually useful for evaluating how someone thinks about data. That version is dead. Today, 68% of companies use take-home tests, up 12% year over year. And the scope has quietly ballooned into something unrecognizable. Full pipeline implementations. Test suites with coverage thresholds. Documentation that reads like a design doc. A presentation follow-up where you defend your architecture to a panel. We're talking 10 to 20 hours of work, routinely, for a role you haven't been offered. Industry best practice caps take-homes at 90 minutes of expected effort. The reality? Candidates consistently take 2x longer than company estimates to reach submission quality. That "four-hour" assignment is an eight-hour assignment. That "weekend project" is a week of evenings. And 25% of companies are still handing these out like they're reasonable asks. Here's the part that makes my eye twitch: 71% of engineering leaders openly say take-homes no lon

TL;DR: We launched Tarotas, a tarot reading app, in five languages (Czech, Slovak, Polish, English, German) on a single domain. Each market behaved completely differently. Here is what the data showed us about multi-locale growth. When we started building Tarotas at Inithouse, the plan seemed straightforward: one product, five languages, one domain. Czech as the base, then Slovak, Polish, English, and German. Same cards, same readings, same UI. Just translated. What we did not expect: each locale acts like a separate product. The setup Tarotas is a tarot card app where you draw a card and read a calm, generic interpretation. No fortune telling, no sign-ups, no paywall. 78 cards across five languages, all on tarotas.com with language detection. We built it in Lovable and deployed it in under two weeks. The multi-language part took another week: content generation for 78 cards times 5 languages, plus locale-specific meta tags and URL structures. What the data told us The Czech and Slovak markets responded first. That was expected: our studio is based in Prague, our existing portfolio (products like zivafotka.cz and magicalsong.com ) already had traction in CZ/SK. But the interesting part was the divergence. CZ/SK users stayed longer. Session duration in Czech and Slovak was noticeably higher than in other locales. Users explored multiple cards, came back for second readings. The "reflection" positioning landed well in these markets, likely because tarot has a quiet cultural niche in Central Europe: not mainstream, but not fringe either. Polish users bounced faster but shared more. The PL locale had higher bounce rates but showed a different signal: social referrals. Polish users who did engage were more likely to share readings. The tarot community in Poland leans more social: Facebook groups, Instagram stories, TikTok readings. Our product caught some of that energy. German users barely showed up. DE was our weakest locale by far. German-language search demand for ta

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Between Prime Day deal and the screen-free technology, the Yoto Player is a great gift you can get the kids in your life without any guilt.

Our top Apple Watch recommendation, the Apple Watch Series 11, is 30 percent off—and the lowest price we’ve seen this year.