Clickstar прекращает работу

Clickstar закрывается. Легендарная пуш-сеть прекращает закуп трафика с 1 августа, полная остановка — 20 августа.

Сетка работала почти 8 лет и была одним из лучших источников качественного трафика на Россию и СНГ. Сейчас пуш-трафик стал слишком ботовым из-за гугловских банов на скрипты сбора.

Что это означает для арбитражников — разбираемся в ста…

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The engagement-rate pricing premium is smaller and noisier than rate guides claim

Thesis: the popular formula 'higher engagement rate equals proportionally higher sponsorship price' overstates a relationship that the data shows is weak, non-linear, and easily manipulated.

Context. Influencer rate guides routinely multiply a base CPM by an engagement-rate factor, treating engagement as a clean quality signal that scales price. The intuition is reasonable; the precision is not.

Findings. Studies relating engagement metrics to actual campaign outcomes find engagement rate is a noisy, sometimes weak predictor of conversions — the thing brands ultimately pay for. Engagement can be inflated by giveaways, pods, and bots, decoupling it from commercial value. Brands with conversion data increasingly price on past sales lift, not on engagement rate.

Caveats. 'Engagement rate' lacks a standard definition across platforms, making cross-study comparison shaky. Conversion-outcome datasets are held privately by brands and rarely published, so the evidence is fragmentary.

Implications. Use engagement as one weak signal, not a pricing multiplier. Documented conversion history is the stronger lever where you have it.

What we still don't know: the true elasticity of price to genuine, fraud-adjusted engagement, since clean conversion data almost never reaches researchers.

Facebook запретил рекламу онлайн-казино Mr Vegas

Британский ASA запретил рекламу казино Mr Vegas из-за «слишком милых» мультяшных животных в креативах — регулятор счёл, что такой стиль привлекает детей, в том числе через Facebook. Рекламодатель запустил кампанию в феврале, бан вышел в июле. Логика регулятора вызывает вопросы: дети неплатёжеспособны, а таргетировать их на гемблинг бессмысленно.

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В Whatsapp скамят пользователей с помощью поддельных никнеймов

WhatsApp запустил никнеймы — и почти сразу начался скам. Мошенники регистрируют имена, похожие на бренды, звёзд и политиков, с минимальными опечатками.

Индия, где 500 млн пользователей WhatsApp, потребовала от Meta объяснений за 3 дня. Meta говорит, что точные совпадения заблокированы — но одна буква в другом месте защиту не триггерит.

Похоже, п…

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Вышел ZCode - аналог Claude code

Вышел ZCode — десктопный аналог Claude Code от разработчиков GLM-5.2. Работает с API от Anthropic, поддерживает SSH-деплой на сервер, в том числе Linux.

Вместо пошаговых скриптов — система целеполагания Goal: закидываешь сложный промт, агент сам разбивает задачу и выполняет. Плюс управление через Telegram-бота.

Но главная фича — мультиагентность…

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Why average RPM is the wrong number to plan a business on

Thesis: nearly every RPM figure creators cite is a mean, and means are structurally misleading in a market this skewed.

Context: when a 2024 analysis of public CPM/RPM disclosures aggregated long-form YouTube channels, the headline 'average' sat around $5-7 per thousand views. But the distribution underneath was log-normal, not bell-shaped — a small cluster of finance, insurance and B2B-software niches pulled the mean far above the typical channel.

Findings: the median creator earned materially less than the advertised mean — often 40-60% lower in self-reported samples. The mean describes the dataset; the median describes you.

Caveats: most of these datasets are self-reported and survivorship-biased (failed channels don't post screenshots), and niche is rarely controlled for. Two channels at identical view counts can differ 10x on RPM purely by topic.

Implications: model your budget on a niche-matched median, then stress-test at the 25th percentile.

What we still don't know: nobody has a clean, niche-stratified RPM dataset with verified payout records rather than dashboard screenshots — so every benchmark you see is a floor of uncertainty, not a measurement.

Cloudeflare грозит Google блокировкой трафика

Cloudflare объявил: с 15 сентября 2026 года ИИ-краулеры будут заблокированы по умолчанию на всех сайтах с рекламой — включая Googlebot, Applebot и Bingbot.

Главная претензия — к Google: один и тот же бот индексирует страницы и собирает данные для обучения нейросетей, что даёт поисковику нечестное преимущество.

Но есть нюанс, который меняет всю к…

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Гайд: как заработать первые деньги на Pornhub

Pornhub — самый посещаемый адалт-сайт в мире, и на нём действительно можно зарабатывать. Но схема устроена иначе, чем кажется.

Автор залил ролики, набрал 16 000 просмотров — и получил 47 центов встроенной монетизации. Реальные деньги были в другом.

Есть нюансы с верификацией, голосом в роликах и законодательством РФ, которые ломают большинство с…

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Brand-deal rates have quietly decoupled from follower count

Thesis: the old '1% of followers in dollars' heuristic is now actively wrong, and the data on why is more interesting than the rule it replaced.

Context: influencer-pricing surveys from 2023-2024 (aggregating thousands of self-reported deals across Instagram, TikTok and YouTube) consistently show follower count explaining a shrinking share of price variance once you control for engagement and niche.

Findings: on average, mid-tier creators (50k-500k) command higher effective CPMs per delivered view than mega-accounts, because brands increasingly price on predicted conversions, not reach. Category matters enormously — finance and beauty deals clear at multiples of lifestyle.

Caveats: this is self-reported sell-side data, which inflates. It also ignores the deals that never closed, so it's a price ceiling among completed transactions. Engagement rate, the variable doing the work here, is itself gameable and inconsistently defined across studies.

Implications: report your median delivered-view rate to brands, not your follower number.

What we still don't know: there is no public dataset linking quoted rate to actual campaign conversion, so whether brands' conversion-based pricing is accurate — or just a story — remains untested.

Platform creator funds: the per-view rate is designed to decay

Thesis: fixed-pool creator funds mathematically guarantee falling per-view payouts as the platform grows, and several funds' own mechanics confirm this.

Context: first-generation funds (the original TikTok Creator Fund being the most-studied) allocated a roughly fixed budget across a growing pool of eligible views. Independent creator logs from 2021-2023 tracked the effective rate falling from the low cents-per-thousand range toward fractions of a cent.

Findings: across these logs the pattern was consistent — early entrants reported higher effective rates; the rate compressed as eligibility widened. Newer revenue-share programs (Creativity Program, YouTube Shorts share) changed the math by tying pay to ad revenue rather than a fixed pot, which removes the structural decay but adds ad-market volatility.

Caveats: these are individual dashboards, not platform-audited figures, and creators rarely normalize for watch-time, region or content category, all of which the funds weight.

Implications: treat any fixed-pool fund as a declining asset; revenue-share programs are more durable but track the ad market.

What we still don't know: platforms don't publish per-view payout curves, so decay rates are reconstructed, not observed.

Affiliate EPC: the metric everyone quotes, almost nobody understands

Thesis: earnings-per-click is reported as a stable average, but the underlying distribution is so heavy-tailed that the average barely describes any real campaign.

Context: network-published EPC figures are typically a 7- or 30-day mean across all affiliates promoting an offer. Analyses of affiliate-network data show conversion events clustering on a minority of traffic sources, with most clicks contributing near-zero.

Findings: a handful of high-intent placements generate the bulk of revenue; the modal click earns nothing. This means a network's advertised EPC is dominated by a few large affiliates and rarely predicts what a new promoter will see — often by an order of magnitude.

Caveats: EPC windows differ between networks, refunds and reversals are frequently excluded from the headline figure, and 'network EPC' blends incompatible traffic types (email, paid, organic) that have wildly different economics.

Implications: ignore network EPC for forecasting; compute your own per-source EPC after a full refund window closes.

What we still don't know: networks rarely disclose reversal-adjusted EPC, so the gap between gross and net EPC across the industry is essentially unmeasured.