Network roundup — across the board:

— @TrenchContent — Real tactics from someone actually shipping content every week —…
— @ColdDMConfidential — We bust the outreach myths killing your reply rate — why…
— @inbox_oneoone — Email marketing explained from zero — deliverability, segmentation,…
— @deal_wire — Insider intel for coupon and deal-site operators: merchant feed…

В Telegram Ads добавили Banner in Bot

В Telegram Ads появился формат Banner in Bot для показа рекламы внутри ботов с аудиторией от 1000 человек. Инструмент таргетируется не на площадки, а на пользователей — по интересам, номерам телефонов и подпискам. Пока доступны только текстовые креативы по ставкам Target Users. Новинка позволяет напрямую через стандартный кабинет охватывать целевую аудиторию прямо в их диалогах с ботами.

➡️ Читайте на сайте: https://aff.top/blog/v-telegram-ads-dobavili-banner-in-bot

🧠 Ещё больше инсайтов → в канале AFF.top

Anthropic готовит к запуску Claude Money

Anthropic анонсировала Claude Money — ИИ-сервис для управления личными финансами и автоматизации платежей. Инструмент позволяет подключать банковские карты для анализа расходов, планирования бюджета и проведения транзакций. Это переход от консультационных моделей к полноценным финансовым агентам. Внедрение технологии позволит пользователям делегировать нейросети рутинные задачи, включая оплату токенов и подписок на необходимые рабочие сервисы.

➡️ Читайте на сайте: https://aff.top/blog/anthropic-gotovit-k-zapusku-claude-money

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Meta ограничивает расходы на токены для сотрудников

Meta ввела внутренние лимиты на использование ИИ из-за резкого роста расходов: в 2026 году только на сотрудников заложены миллиарды долларов, а общий бюджет на ИИ-инфраструктуру оценивается в 130–145 млрд. Вывод простой: даже у Big Tech ИИ перестал быть бесплатной игрушкой и требует жёсткого контроля затрат.

➡️ Читайте на сайте: https://aff.top/blog/meta-ogranichivaet-raskhody-na-tokeny-dlia-sotrudnikov

🧠 Ещё больше инсайтов → в канале AFF.top

Deep dive: client-side vs server-side A/B testing tools

How your testing tool delivers the variant — in the browser or on the server — sounds like an implementation detail. It directly affects both your data quality and your conversion rate, which is why it belongs in a CRO discussion.

What the data shows: client-side tools (the classic JS-snippet experiment platforms) modify the page after it loads, which produces 'flicker' — the original renders, then the variant swaps in. Google and various performance studies tie that render-blocking snippet to LCP regressions, and flicker itself can bias results because some users see the control flash first. Server-side rendering of the variant has no flicker and no client weight, but costs engineering effort to implement.

The mechanism is when the assignment happens relative to render. Client-side decides in the browser, so the page must wait for (or visibly correct itself with) the experiment script — adding load time, the very thing that suppresses conversion, and contaminating the test with a speed difference between variants. Server-side decides before HTML is sent, so both variants render at full speed with no flash, giving a cleaner comparison.

Practical implication for affiliate landers: client-side tools are fine for quick, low-stakes copy/color tests where the flicker risk is small and speed-to-launch matters. For anything that touches the hero, hero load, or the headline above the fold — exactly where speed and first impression decide conversion — prefer server-side or at least an anti-flicker setup, because a client-side test there can lose conversions through added latency and report a muddied result. Match the tool to where on the page you're testing.

TL;DR:
— Client-side tools swap variants after load, causing flicker and LCP regressions that bias results
— Server-side decides before render — no flash, no added weight — but costs engineering effort
— Use client-side for quick low-stakes tests; go server-side for hero/headline/above-fold experiments

Claude Cowork, Claude Design объединили в один Claude

➡️ Читайте на сайте: https://aff.top/blog/claude-cowork-claude-design-obedinili-v-odin-claude

🧠 Ещё больше инсайтов → в канале AFF.top

Microsoft планирует вставлять рекламу в игры

➡️ Читайте на сайте: https://aff.top/blog/microsoft-planiruet-vstavliat-reklamu-v-igry

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Deep dive: native date/select inputs vs custom-built widgets on mobile

Designers replace native form controls with custom ones for visual consistency. On mobile, that swap quietly costs completions, and the reason is worth understanding before you ship a 'nicer' picker.

What the research shows: Baymard's mobile form research repeatedly finds that custom date pickers, custom dropdowns, and custom number inputs underperform native controls — native controls trigger the OS's optimized picker, support autofill, respect accessibility settings, and behave exactly as the user already expects. Custom widgets frequently break autofill, fight the keyboard, mishandle the 'done' action, and add JS weight. Each broken expectation is friction at the highest-friction moment: data entry.

The mechanism is learned behaviour and platform optimization. Native inputs inherit years of OS refinement and the user's muscle memory — zero learning cost. A custom widget asks the visitor to learn a new interaction during a task they want over with, raising cognitive load and the chance of an error that stalls the form. And disabling autofill alone measurably slows completion, since autofill is one of the biggest mobile speed-ups.

Practical implication for affiliate landers: on mobile, default to native inputs — date, select, tel, email, number — and let the correct keyboard and picker appear (set the right input type so phone fields show a number pad, email shows the @ key). Preserve autofill. Reserve custom widgets for cases native genuinely can't handle, and test them on real devices, because the desktop-pretty version is often the mobile-broken one. Visual consistency rarely justifies the completion loss.

TL;DR:
— Native mobile inputs inherit OS optimization, autofill, and muscle memory — near-zero learning cost
— Custom widgets often break autofill and the keyboard, adding friction exactly at data entry
— Default to native, set correct input types for the right keyboard, and preserve autofill; customize only when native truly can't