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Originally published on MRTD.NET — fast, sourced news on crypto security, cyber & SEO. The uncomfortable first lesson You built a clean site, submitted a sitemap, maybe pinged IndexNow — and Google still shows nothing. Here's the part most guides skip: getting indexed by Google and getting indexed by everything else are two different problems , and conflating them wastes weeks. We separate what actually moves Google in 2026 from the folklore that just feels productive. Bing, Yandex and ChatGPT are the easy half If you've set up IndexNow , you've largely solved discovery for Bing, Yandex, Naver, Seznam and Yep — you POST your new/changed URLs to one endpoint and they get notified instantly. And because ChatGPT Search retrieves from Bing's index , confirmed Bing indexing effectively gates your visibility in ChatGPT's web results. That's a big chunk of the modern search surface handled with one integration. The catch: Google does not use IndexNow. It has said so repeatedly. So every "instant indexing" claim that leans on IndexNow is talking about Bing's world, not Google's. For Google, you need different levers. What actually gets you into Google There are really only two fast paths, plus one slow one. 1. Google Search Console — the only direct lever. Verify your domain (a private DNS TXT record; it does not trigger penalties or "re-evaluation," a common fear), submit your sitemap.xml , then use URL Inspection → Request Indexing on your key pages. There's a soft daily cap (~10–12 URLs), so spread a new site's pages over a few days. GSC is also the only place you can see whether a domain carries an inherited problem — essential if you bought an aged or expired domain. 2. Links on pages Google already re-crawls hourly. Googlebot's crawl budget for a brand-new, zero-authority domain is tiny. The fastest way to get a new URL discovered is a link to it from a page Google visits constantly — Reddit, Hacker News, Medium, established communities. These links are usually nofoll

Getting the future right is now big business. But at a festival in Berkeley, forecasters worry that sports markets could take the whole industry down.

A trader I was talking to recently said something that stuck with me: "I've blown accounts just from slow fills or missed order cancellations." He was talking about CEX perpetuals. But the problem is identical on Polymarket's CLOB - just measured in seconds instead of milliseconds. My TypeScript bot was averaging 340ms from signal detection to order placement on Polymarket's Central Limit Order Book. On a 5-minute market with a ~2.7-second mispricing window, that's 12% of the entire opportunity window consumed before a single byte hits Polymarket's servers. I was consistently entering at 74¢ when I'd detected the signal at 70¢. The market had already repriced against me. So I rewrote it in Rust. This article documents exactly what I found, what changed, and - critically - what didn't. Background: What My Bot Was Doing If you've read my earlier posts in this series ( architecture , Kelly Criterion sizing , last-60-seconds capture ), you know the context. But the short version: The bot targets Polymarket's 5-minute and 15-minute crypto up/down binary markets (BTC, ETH, XRP, SOL, DOGE, BNB). The strategy is simple: find markets that are briefly mispriced relative to real-time spot momentum, enter at a discount to fair value, hold to resolution. A 5-minute "XRP Up" market priced at 70¢ when spot momentum suggests 82% probability = +12¢ edge per dollar wagered. Do that 50 times a day with disciplined sizing and the math works - if you can actually get filled at the price you detected. The problem: by the time my TypeScript code detected the signal, formatted the order, opened an HTTP connection to Polymarket's CLOB API, waited for TLS handshake, serialized the payload, and received confirmation, the market had often moved to 74-76¢. I was paying for an edge I wasn't capturing. Profiling the TypeScript Bot: Where Was the 340ms Going? Before rewriting anything, I instrumented every stage of the order path. Here's what I found across 500 sampled trades: Stage Average time %

We're building an AI marketing operation in public, and early on we hit a question we couldn't skip: how aggressive can you be about growth before you've crossed into something you'll regret? "Be ethical" is easy to say and useless under pressure. Every real decision is messier than that. Is using a VPN cheating? Is running more than one channel a trick? Is bending a platform's rules the same as lying? We needed a line we could actually hold at 2am when a shortcut looks tempting. Here's the one we found — and it turned out to be simpler and sturdier than "follow all the rules." The line isn't rule-breaking. It's deception. The cleanest test we landed on: the line is deception, not rule-breaking. Breaking a rule is a fight you can have in the open. You can announce it, defend it, and accept what comes. Deception is different — it works by making someone believe something false, which strips away their ability to respond honestly, because they don't even know what's real. That's the move that does the damage. So the question to ask about any tactic isn't "did this break a rule?" It's: "does this work by causing a real person to believe something that isn't true?" If yes, that's the line. If no, you're probably fine even if you're being bold. The daylight test Here's how to apply it fast. Ask: would this tactic still work if everyone could see exactly what I was doing? If yes — it survives daylight. People are choosing freely with full information. That's honest, even when it's aggressive. If it only works in the dark — the concealment itself has become the product. Something only works hidden because someone is acting on a false belief you planted. That's the part to cut. A poker bluff survives daylight (everyone knows bluffing is part of poker). A magician's trick survives daylight (the audience knows it's a trick and enjoys it). A fake testimonial does not. A sock-puppet account vouching for you does not. Run every growth idea through the daylight test and most hard

description: While most people chase political bets, these traders are dominating daily temperature...

description: Weather markets on Polymarket have become one of the hottest and most consistent...

After months of manual trading on Polymarket, I got tired of missing fast momentum moves on BTC, ETH,...

Three days into November, a disapproval cascade pulled 40% of active SKUs from Shopping and Performance Max simultaneously — on day one of a promotional window we'd spent six weeks building. No feed changes on our side triggered it. Here's the part most guides miss: Google's automated review threshold for certain policy categories (health claims, price accuracy, before/after imagery) tightens as platform ad volume increases heading into Q4. I've watched this happen across accounts running ₩50M–₩120M/month in combined Google spend, three years in a row, with zero feed-side changes preceding it. Same feed that sailed through August catches 15–20% disapprovals on recheck in September. The products didn't change. The enforcement did. When it hits during a live window, fix order matters more than fix speed. Price mismatches go first — not because they're the most dramatic, but because they cascade silently. One bestseller disapproved during a flash sale means Performance Max quietly reallocates budget to lower-performing products. By the time ROAS visibly drops, you've lost 48 hours of peak traffic. The specific failure mode I've seen twice on Cafe24 with direct API feeds: a site-wide price update propagates to the feed before the landing page CDN cache clears. Google crawls the feed, sees the new price, crawls the landing page, sees the old cached price. Mismatch. Disapproval. Fixing it is one line — force a manual fetch and verify sale_price_effective_date formatting — but finding it at 2am during a live sale is a different problem. Prohibited content disapprovals are deprioritized by most teams because they're rare. That's exactly wrong. A single escalation during Black Friday week can trigger account-level review, not just product suspension. Pull the SKU yourself within the hour if you can't fix the content immediately. Suspending your own SKU is recoverable. A suspended account during peak is not. GTIN and identifier issues — despite getting the most attention in s

When I shipped Trooper , a privacy-aware LLM proxy written in Go, I didn't have a marketing plan. I had GitHub traffic analytics and a habit of checking them obsessively. Seven weeks later, I have something more useful than a viral moment: a ranked table of every traffic spike, what caused each one, and proof that the exact same playbook that worked at launch still works when you have something new to say. What is Trooper? Trooper sits between your app and your LLM provider. When your cloud quota runs out, it automatically falls back to a local Ollama instance with zero code changes on your end. It also tracks session context, so your agents don't go blind between calls. It's not a chatbot wrapper. It's plumbing. Which makes the distribution story more interesting, because plumbing doesn't go viral the way demos do. The Data GitHub gives you 14-day rolling windows for clones and views. I screenshotted them obsessively and tracked every spike. Here's the full ranked table: Rank Date Clones Unique Cloners Views Unique Visitors Driver 🥇 1 May 13 375 173 1,113 ~140 Reddit wave peak 🥈 2 May 10-12 312 137 974 133 Reddit launch spike 🥉 3 Jun 10 289 124 749 101 "Escalate the model" r/ollama post 4 Jun 11 268 112 840 95 Decaying from Jun 10 spike 5 Jun 12 240 99 739 74 Decaying from Jun 10 spike 6 Jun 9 175 102 802 100 Organic 7 Apr 25 174 71 664 113 Early Reddit posts 8 Jun 7 171 110 876 110 Organic recovery 9 Jun 6 163 104 755 102 Organic recovery 10 May 29-30 122 73 610 83 LinkedIn post 11 May 25 76 48 495 53 Claude Code integration chat What I learned 1. Reddit is the only thing that moved the needle, and community fit matters more than size The #1 and #2 peaks were both Reddit-driven. On May 10-11, I posted across r/ollama, r/LocalLLM, r/ClaudeCode, and r/Gemini simultaneously. Total views across those posts: ~7,000. r/ollama alone drove nearly 4,000 of those views. Not r/LocalLLM. Not r/ClaudeCode. r/ollama , the smallest of the four communities. The reason: Trooper so

It’s the first estimate of how many Americans are sneaking onto Polymarket’s banned crypto-based platform.

Move also cuts off a massive market of legit users who buy cards with physical cash.

Introduction Crypto prediction markets move fast. One interesting pattern I noticed while trading on Polymarket is that short-term crypto markets often follow Bitcoin's direction, especially near market expiration. When Bitcoin shows strong directional momentum, assets such as Ethereum (ETH), Solana (SOL), and XRP frequently move in the same direction. This observation led me to build a simple momentum-based Polymarket trading bot. The core idea is straightforward: Monitor BTC Up/Down markets. Detect strong directional probability from the order book. Confirm that ETH, SOL, or XRP markets agree with Bitcoin. Enter positions when confidence is high. Hold until market settlement. Redeem winnings automatically. In this tutorial, you'll learn how to build a Python bot that: ✅ Fetches Polymarket market data ✅ Reads order book probabilities ✅ Detects BTC momentum signals ✅ Places automated buy orders ✅ Waits for settlement ✅ Redeems winning positions The goal is not to predict the future perfectly. The goal is to identify situations where multiple crypto prediction markets agree on direction and exploit that momentum. Why Bitcoin Momentum Matters Bitcoin is still the dominant asset in the cryptocurrency market. When BTC experiences a strong move: ETH often follows SOL often follows XRP often follows Other altcoins frequently move in the same direction This correlation is especially visible during short-duration prediction markets. For example: Market YES Probability BTC Up 0.95 ETH Up 0.93 SOL Up 0.92 When all three markets strongly agree on direction, there may be an opportunity to enter the same side before settlement. This is the basic principle behind the momentum bot. Strategy Overview The bot continuously watches several crypto markets. Step 1: Monitor BTC Market If BTC Up reaches: BTC Up > 0.90 or BTC Down > 0.90 the bot considers Bitcoin momentum strong. Step 2: Confirm Altcoin Agreement The bot then checks: ETH SOL XRP If at least one of these markets has the sam

Social media posts questioning the integrity of LA’s mayoral election were labeled “paid partnerships.” Then Kalshi and Polymarket told creators to delete them.

Most merchants know they have unused files. Far fewer realize they're accumulating AI-generated media they never intended to keep. There's a problem quietly growing inside thousands of Shopify stores right now. It's not abandoned carts. It's not slow page speeds. It's not even the 400 unused product images you already know you should deal with. It's something newer, and most merchants have no idea it's happening. The Rise of AI-Generated Commerce Content Over the past two years, AI image tools have gone from novelty to routine. Shopify Magic. Canva AI. Midjourney. ChatGPT image generation. Adobe Firefly. Background removers. Lifestyle photo generators. Product shot enhancers. Merchants are using these tools constantly — to mock up new products, test background options, generate seasonal variants, create ad creatives, experiment with lifestyle photography. The workflow feels clean: generate a few options, pick the best one, move on. Here's what's actually happening on the backend. Every time you use Shopify's native AI tools to generate, edit, or enhance an image, Shopify quietly deposits files into your media library. Not just the one you kept. All of them. The rejected generations. The experimental edits. The "let me try one more variant" files. The abandoned attempts from six months ago when you were testing a new product that never launched. Every. Single. One. Most merchants assume the files they don't choose disappear. They don't. The lifecycle looks something like this: ┌─────────────────────┐ │ AI Image Generation │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Rejected Variants │ │ • Drafts │ │ • Test Images │ │ • AI Edits │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Hidden Media Files │ │ Accumulate Over Time│ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ AI Clutter │ │ Invisible Technical │ │ Debt │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Reduced Media │ │ Governance │ │ • More Noise │ │ • Less Visibility │ │ • Hard

Every weekday, we averaged 10 groups. Every weekend, 15. Same karaoke bar. Same staff. Same songs. For a long time, I just accepted that gap as "normal." Weekends are busier. That's just how hospitality works, right? Wrong. It took me years to realize I wasn't looking at a staffing problem. I was looking at a funnel problem — and I had no idea what a funnel even was. The moment I noticed something was off One Tuesday afternoon, a group of four walked past the front door, looked at the menu board outside, and kept walking. I watched from the counter. I had open rooms. Competitive prices. Cold drinks. Everything they needed. But they left anyway. That one moment stuck with me. Why did they walk in? Why did they look? Why did they leave? I started tracking these moments obsessively. Not with software — just a notebook and a lot of attention. Here's what I found over six weeks: Weekdays : About 40 people walked past who paused at the sign. Of those, maybe 15 came to the door. Of those, 10 groups actually came in and paid. Weekends : About 90 people paused. 30 came to the door. 15 groups booked a room. The conversion rate was almost identical — roughly 25% from "stopped to look" to "became a customer." The difference wasn't that we were worse at converting on weekdays. We just had fewer people at the top. That's a funnel. I didn't know the term at the time. But what I was describing is exactly what marketers call a marketing funnel : Awareness — people notice you exist Interest — they stop to look Consideration — they walk to the door, check the price Action — they book a room and pay Most businesses obsess over the bottom of the funnel. Better sales scripts. Discount campaigns. Loyalty cards. I did the same. I ran Tuesday specials. I trained staff to upsell drinks. I rearranged the menu. None of it closed the gap. Because the gap wasn't at the bottom. It was at the top. On weekdays, I simply had fewer people aware we existed. What I tried instead Once I framed it as a f

A while back, when I was still job hunting, building mini-projects, and trying to figure out what I...

Building a Thriving Package Marketplace: The Complete MarketHub Guide Introduction If you're building a platform where developers can discover, share, and monetize packages, you're tackling one of the most complex problems in the software ecosystem. From managing publisher reputations to handling analytics at scale, marketplace dynamics require careful orchestration across multiple user roles. Enter MarketHub — a comprehensive three-app marketplace system designed to handle exactly this challenge. Whether you're creating a plugin ecosystem, SaaS integrations hub, or package distribution platform, MarketHub provides a battle-tested architecture for managing the complete marketplace lifecycle. The Problem: Why Marketplaces Are Hard Building a marketplace isn't just about creating a catalog. You need to solve several interconnected problems simultaneously: Discovery : How do users find quality packages in a sea of options? Trust : How do you build confidence in unfamiliar publishers? Quality Control : How do you maintain standards without stifling innovation? Incentives : How do you motivate publishers to create excellent packages? Scale : How do you manage analytics, reputation, and community as the ecosystem grows? Most teams try to bolt these features onto a basic catalog — resulting in fragmented systems where reputation tracking doesn't align with analytics, and community features feel disconnected from the review process. MarketHub Architecture: A Three-App Approach MarketHub solves this by separating concerns into three distinct applications, each optimized for its audience: 1. Public Discovery App — The Storefront This is where users find packages. The discovery app features: Intelligent Search & Filtering : Search across package names, descriptions, and tags with category-based filtering Featured Packages : Curated collections to highlight quality and trending packages Smart Ranking Algorithm : Packages rank based on quality signals — not just download counts

Quick answer: The Google Ads Transparency Center is a public registry of every ad Google runs — but it ships no API and no bulk export . To get the data programmatically you scrape it. A Google Ads Transparency scraper sends the same RPC call the website uses and returns every ad creative for an advertiser as structured JSON. The Apify Actor below does it for $0.0012 per ad (~$1.20 per 1,000), with the TLS fingerprinting, proxy rotation, and pagination handled for you. Google's Ads Transparency Center is one of the most underused datasets in marketing. Launched in 2023 under the EU Digital Services Act and parallel US pressure, it indexes every ad campaign currently running on Search, YouTube, Display, Shopping, Maps, and Play — keyed by advertiser. Google's own counter lists 300,000+ active creatives for a brand like Nike . For your nearest competitor, it's usually 50–500. The catch: there's no download button. Just an interactive UI that paginates 40 creatives at a time. If you want this as a CSV — for a competitor sweep, a trademark audit, or a RAG corpus — you have to extract it yourself. Here's what that actually takes, and how I shortened it to one API call. What is the Google Ads Transparency Center? 🔎 The Google Ads Transparency Center is a public, Google-operated registry that shows the ad creatives any verified advertiser is running, the date range each ad was shown, and roughly where. Google built it to comply with ad-disclosure regulation, so the data is public by design — you're reading the same registry a regulator would. What it gives you per advertiser: Every ad creative currently or recently live (text, image, video) The landing domain each ad clicks through to First-shown / last-shown timestamps and a rough impression count A deep link to each creative inside the Transparency Center What it does not give you: a search-by-keyword mode, region-filtered results from the server, or — crucially — an API. Does the Google Ads Transparency Center have an A

클로드를 10%에 팔지 않고, 클로드를 10%에 사는 사람들이 있다 중국 암시장에서 벌어지는 AI 모델 밀수, 그 이면에는 기술 산업의 가장 불편한 진실이 숨어 있다 TL;DR : 앤트로픽의 최고급 AI 모델 '클로드'가 중국 암시장에서 정가의 10% 수준으로 유통되고 있다. 이 현상의 이름은 '모델 증류'다. 거대 기업이 수조 원을 들여 만든 지능을, 누군가는 그 1/10 비용으로 복제해 팔고 있다. 그리고 이것은 단순한 불법 복제 이야기가 아니다 — AI 산업 전체의 구조적 취약점을 정면으로 드러내는 사건이다. 반도체 업계에는 잘 알려지지 않은 규칙이 하나 있다. 좋은 제품을 만드는 것과, 그 제품을 지키는 것은 전혀 다른 게임이라는 것. 엔비디아는 올해 58조 원을 투자해 공급망을 틀어쥐었다. 오픈AI는 GPT 시리즈에 수년간의 연구와 수천억 원의 컴퓨팅 비용을 쏟아부었다. 그런데 중국의 어느 텔레그램 채널에서는, 앤트로픽의 클로드가 정가의 10분의 1 가격으로 조용히 팔리고 있다. 이것이 단순한 해킹이나 계정 공유 이야기라면, 이 글을 쓰지 않았을 것이다. 먼저, '10% 가격'이 의미하는 것 클로드가 10% 가격에 팔린다는 뉴스를 처음 접하면, 많은 사람이 "계정을 불법 공유하는 것 아닐까"라고 생각한다. 어느 정도는 맞는 말이다. 실제로 해외 계정을 공유하거나, 앤트로픽 API 키를 여러 명이 나눠 쓰는 방식은 존재한다. 그러나 전문가들이 더 심각하게 보는 것은 따로 있다. 바로 '모델 증류(model distillation)'다. 모델 증류는, 쉽게 말하면 이렇다. 선생님 모델에게 엄청난 양의 질문을 던진다. 그 답변을 수집한다. 그 답변 데이터로 작은 학생 모델을 학습시킨다. 그러면 학생 모델이 선생님의 사고 패턴과 언어 습관, 추론 방식을 흡수하기 시작한다. 원본 코드에 손을 대지 않아도 된다. 서버에 침입할 필요도 없다. 그냥 열심히 질문하고, 열심히 답변을 모으면 된다. AI 분야에서 이 기법은 사실 합법적으로도 쓰인다. 큰 모델을 작고 효율적인 모델로 압축할 때 사용하는 정상적인 기술이다. 그런데 이것이 암시장과 만나면, 지식재산권의 경계가 극도로 흐려진다. 클로드의 추론 패턴을 흡수한 어느 중국산 모델이 텔레그램에서 월 몇 달러에 팔리고 있을 때, 앤트로픽은 그것을 어떻게 불법이라고 증명할 수 있을까. 코드는 다르다. 서버는 다르다. 그러나 그 모델이 내놓는 답변의 '결'은 묘하게도 클로드를 닮아 있다. 거인이 쌓아올린 것, 그리고 그것이 무너지는 방식 앤트로픽은 2021년 오픈AI에서 나온 연구자들이 세운 회사다. AI 안전성에 집착에 가까운 철학을 가진 곳으로, 클로드를 "도움이 되고, 해가 없으며, 솔직한(Helpful, Harmless, Honest)" 모델로 설계하기 위해 수년간 독자적인 훈련 방식을 개발했다. 이 회사가 투자받은 금액은 수조 원 단위다. 아마존이 단독으로 수십억 달러를 투자했고, 구글도 뒤따랐다. 클로드 3.5 시리즈, 그리고 최근 클로드 4에 이르기까지 앤트로픽이 쌓아온 것은 단순히 코드 몇 줄이 아니다. 수백만 시간의 연구자 노동, 막대한 컴퓨팅 자원, 그리고 인간 피드백 데이터의 정교한 축적이다. 그런데 그 성과물이 중국 암시장에서 10% 가격으로 팔린다는 것은, 단지 "불법 복제"의 문제가 아니다. 이것은 AI 시대의 지식재산권이 얼마나 방어하기 어려운 구조 위에 서 있는지를 보여주는 사례다. 소프트웨어 시대에는 코드를 복사하면 불법이었다. 명확했다. 그러나 AI 모델의 '지능'은 코드가 아니다. 가중치(weight)라고 불리는 수십억 개의 숫자 집합이다. 그리고 그 숫자들이 만들어내는 추론 방식을, 외부에서 관찰하고 모방하는 것을 막을 법적 수단은 아직 세계 어디에도 완비되어 있지 않다. 미국도, 유럽도, 당연히 중국도. 앤트로픽이 쓴 방패 — 그리고 그 한계 앤트로픽은 이 문제를 오래전부터 인식하고 있었다. 이번에 보고된 뉴스는 단지 암시장 유통의 문제만이 아니라, 앤트로픽이 클로드의 '협박 시도'를 막기 위해 어떤 방법을 썼는지도 함께 다루고 있다. 클

The best AI tools for conversion rate optimization (CRO) in 2026 are the platforms that continuously...