В Google search console теперь можно добавлять соцсети

Google добавил в Search Console поддержку аккаунтов соцсетей: теперь можно отслеживать ключевые запросы, источники и географию переходов, показы и клики по ссылкам. Функция подключается там же, где и сайты, доступны четыре соцсети на выбор. Rollout постепенный — доступ получают не все сразу.

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Google стал помечать креативы, созданные ИИ

Google объявил, что начнёт помечать рекламные креативы, созданные нейросетями. Причина — ИИ-баннеры и видео стали слишком похожи на настоящие.

Формат и заметность маркировки будут зависеть от законов конкретного региона: где-то предупреждение появится прямо на креативе, где-то — в его информации.

Что это значит для арбитражников и когда правила …

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Компания Meta выпустила Muse Spark 1.1

Meta выпустила Muse Spark 1.1 почти одновременно с новой ChatGPT-5.6. Это мультимодальный агент, который сам дробит задачу на подзадачи и распределяет их между субагентами.

Стоимость тоже заметно ниже топовых западных моделей: $1.25 за миллион входных токенов и $4.25 за миллион выходных.

Но главный вопрос — насколько она реально сильна на фоне к…

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Second-price still lives inside your first-price world — and it leaks money

The open exchange went first-price, but second-price pricing survives in pockets, and the mismatch between the two creates a quiet leak.

Where both coexist today:
— Many Private Marketplaces (PMPs) and some SSP server-side auctions still resolve as second-price internally.
— Header bidding wrappers may run a first-price auction in the browser, then pass the winner into an ad server that itself resolves another auction with different rules.
— The result is a chain where your bid is treated as first-price in one stage and second-price in another.

The leak: if you apply uniform first-price shading everywhere, you under-bid in the second-price pockets. In a true second-price auction the optimal strategy is to bid your full value — shading there just lowers your win-rate without lowering your average price paid, since you'd only ever pay the runner-up's bid anyway.

Diagnostic: tag each supply path by its actual pricing rule (ask the SSP; verify by checking whether your price-paid ever equals your bid exactly, the fingerprint of first-price). Then split your shading logic: aggressive on first-price paths, near-zero on confirmed second-price ones.

Why it matters: shading is correct medicine for first-price and a self-inflicted wound in second-price. One global setting across mixed auction rules guarantees you're wrong somewhere.

Bid floors and the discovery game you're unknowingly playing

A bid floor (the minimum price an SSP will accept for an impression) looks static. In practice many SSPs run dynamic floors that adjust to your behavior, turning every bid into a probe.

The mechanism:
— A dynamic floor algorithm raises the floor on segments where buyers bid well above it, and lowers it where bids cluster near it.
— Its goal is to capture more of the gap between your bid and the floor as publisher revenue.
— Crucially, it learns from your bids. Consistently bidding 2x the floor teaches the algorithm that you value this inventory highly, and it ratchets the floor up toward your bid.

The game-theoretic consequence: under first-price, bidding far above the floor not only wastes money on that impression, it raises the floor you face on future ones. You are training your own cost up.

The counter-play, step by step:
— Identify floor-sensitive paths by watching whether your CPMs drift up after you increase bids, with no change in win-rate.
— Bid closer to the floor where you can still win, accepting a slightly lower win-rate for a lower learned floor.
— Use shading specifically to mask your true valuation from the floor-learning algorithm.

Why it matters: a dynamic floor is a second adversary in the auction, optimizing against you across time. Bidding as if the floor were fixed hands it the information to raise your prices.

Fingerprinting duplicate bid requests across the supply chain

When one impression reaches your DSP through several SSPs, you need to recognize the duplicates to avoid bidding against yourself. There is no shared impression ID across SSPs, so you fingerprint.

How the fingerprint is built:
— Combine fields that are stable across resellers: publisher domain or app bundle, placement/ad-unit ID, user identifier (or hashed IP plus user-agent where no ID exists), and a coarse timestamp bucket (e.g., 250ms).
— Two requests sharing this composite within the bucket are, with high probability, the same auction routed through different paths.

What the analysis reveals:
— Duplication rate per publisher: 3-6 paths for the same impression is common on large open-exchange inventory.
— Fee transparency: the same impression often arrives at different prices through different chains; the spread exposes undeclared reseller margins.
— Self-competition: cases where your own bids on two paths both cleared the respective SSP floors and one inflated the price you ultimately paid.

The action:
— Rank paths per publisher by all-in cost and authorization (sellers.json and the SupplyChain object, which declare who is allowed to resell).
— Suppress bids on lower-ranked duplicate paths, keeping only the cleanest.

Why it matters: without de-duplication you treat one impression as several auctions, multiplying compute and occasionally bidding up your own clearing price. The fingerprint turns invisible duplication into a controllable cost.

Российские букмекеры увеличили закуп трафика с мобильных приложений

Российские букмекеры в 1 квартале 2026 года заметно нарастили закупку трафика из мобильных приложений. На фоне ужесточения регулирования они смещают бюджеты в новые каналы, где ещё есть живой трафик.

По данным UMG, доля in-app-рекламы выросла с 3-4% до 5-6% при объёме рынка около 10 млрд рублей. Но это может быть только начало — в блоге разбираем…

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Mia Khalifa стала амбпссадором 1win

Mia Khalifa снова засветилась рядом с 1win: в Instagram она показала цепочку с логотипом бренда и статус «VIP 1win».

Параллельно всплыла история с ее ставкой на Испанию на ЧМ — по данным поста, выигрыш мог составить 200к$.

Официального подтверждения амбассадорства пока нет, но для арбитражников это уже повод для новых креативов. Что именно здесь…

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Bid shading and the adverse selection trap

There's a subtle failure inside bid shading that punishes the impressions you most want to win. It's adverse selection (winning disproportionately the auctions you mispriced upward).

The mechanism, carefully:
— Your shading model predicts a clearing price and you bid near it to win cheaply.
— On auctions where competition is genuinely low, your shaded bid wins easily — good.
— But on auctions where you happen to over-estimate value, you bid high, and you win those too — because high bids win.
— So among your wins, the mispriced-high ones are over-represented. You systematically pay more than the impression was worth on the very impressions you secured.

This is the winner's curse adapted to programmatic: winning is itself evidence that you may have bid above the true market value, because everyone with a more accurate, lower valuation lost to you.

The correction:
— Calibrate your value model on won-impression outcomes (post-click or post-conversion), not on bid-time predictions, so you measure what wins were actually worth.
— Apply a deliberate downward adjustment proportional to how contested a segment is: the more competitors, the more a win signals you may have overbid.

Why it matters: the act of winning carries information that your bid may have been too high. Shading models that ignore this overpay precisely on the inventory they capture — the opposite of the impressions you never see.

Короткий домен Telegram перестал работать

Telegram лишился домена t.me: он разделегирован и больше не работает на уровне регистратора. Платформа срочно переезжает на telegram.me, а владельцам крупных каналов стоит обновить публичные ссылки. Сроки восстановления неизвестны, и есть риск, что t.me не вернётся вовсе на фоне давления на Telegram.

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Youtube тестирует поиск с AI

YouTube начал тестировать Ask YouTube — поиск с ИИ, где можно задавать вопросы обычным языком и получать не список ссылок, а готовую подборку видео и фрагментов.

Фича уже доступна в США и работает на сложные запросы: если нужно, ИИ уточняет вопрос и подсказывает следующий шаг.

Что это значит для поиска на YouTube и когда новинка дойдёт до других…

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How identity match-rate silently rewrites your auction economics

Match-rate (the share of users a DSP can resolve to a known identifier on a given supply path) is treated as a targeting input. It's actually a price-setting force.

The chain of effects:
— On a path with high match-rate, you can identify valuable users and bid up for them specifically.
— So can every other buyer with the same identity graph. Competition concentrates on identifiable users, raising their clearing prices.
— On a low-match path, the same users arrive anonymous. Fewer buyers recognize their value, so competition is thinner and clearing prices are lower.

The non-obvious implication: the highest-value users are often cheapest to win where they are least identifiable, because the auction can't price what it can't see. Buyers who can resolve identity through first-party signals or a stronger graph than competitors can find under-priced reach on low-match supply.

How to exploit it:
— Measure match-rate and median clearing price per path together.
— Look for paths where your private match-rate exceeds the market's typical match-rate — there you identify value others miss and pay thin-competition prices for it.
— Treat identity coverage as a sourcing advantage, not just a targeting filter.

Why it matters: an auction prices visible value. Where identity is scarce, value hides, and the buyer who sees it without revealing it through aggressive bids captures the arbitrage.

Z.ai анонсировала новую GLM-5.5

Z.ai готовит релиз флагманской GLM-5.5: модель обещают показать в августе 2026 года.

Главная интрига — рост до 1 трлн параметров при том же контекстном окне в 1 млн токенов. Новинка снова будет заточена под код и агентные задачи.

Почему версия сразу 5.5, без 5.3 и 5.4, и что это может означать для рынка — в блоге.

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Telegram запустил собственный сервер для ботов

Telegram запустил собственный сервер для ботов и мани-приложений: теперь backend можно размещать прямо внутри инфраструктуры мессенджера.

Сервер работает на JavaScript/TypeScript, через вебхуки, и позволяет подключать SQL-базу для сбора контактов без посредников.

Пока неясны цена и ограничения — что именно уже можно тестировать, а где скрыт подв…

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Pacing is a feedback controller fighting your bid price

Budget pacing (spreading spend evenly across a flight instead of front-loading it) seems like simple budget math. Mechanically it's a controller that quietly distorts every bid you make.

How it interferes:
— The pacing system sets a spend target per time slice and watches actual spend against it.
— If you're under-pacing, it loosens — raising bids or bid-rates to win more, regardless of whether the inventory is well-priced right now.
— If you're over-pacing, it throttles — lowering bids or skipping auctions, even excellent ones.
— This multiplier sits on top of your value-based and shading-based bid.

The distortion: in low-supply hours, an under-pacing line item bids up to catch up, paying inflated CPMs precisely when good inventory is scarce. In high-supply hours it may throttle and skip cheap, valuable impressions to avoid over-pacing.

The diagnostic:
— Plot hourly CPM against hourly pacing multiplier. If your most expensive impressions cluster in your most aggressive pacing windows, pacing is overriding price discipline.
— Consider asymmetric pacing: allow front-loading when clearing prices are demonstrably low, and tolerate under-delivery rather than chasing spend into expensive hours.

Why it matters: pacing optimizes for even spend, not for surplus. Left naive, it forces you to overpay when supply is thin and walk away from value when supply is cheap — the exact inverse of good buying.

Google картинки станут конкурентом Pinterest

Google Картинки начали превращать в полноценную платформу с персональной лентой по прошлым запросам — по сути, в аналог Pinterest.

Во вкладке For you уже тестируют подборки, а ещё обещают коллекции и генерацию изображений во встроенной Nano Banana.

Как это будет работать и когда новинка дойдёт до других стран — в блоге.

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sellers.json and SupplyChain: the trust graph buyers underuse

sellers.json (an SSP's public list of who it pays and is authorized to represent) and the SupplyChain object (the per-bid record of every intermediary that touched an impression) together form a verifiable graph of the supply chain. Most buyers check them for fraud only. They also encode cost.

Reading them as cost structure:
— Each node in the SupplyChain object is an intermediary taking a margin. A four-node chain to reach a publisher is, by construction, more expensive than a one-node direct path to the same publisher.
— sellers.json marks each seller as PUBLISHER, INTERMEDIARY, or BOTH. Long runs of INTERMEDIARY nodes signal resold-of-resold inventory with stacked fees.
— Comparing the same publisher's impressions across paths, the chain depth correlates with the gap between your price paid and the publisher's actual received revenue.

The action, step by step:
— Parse the SupplyChain object on incoming bid requests and tag each by node count and authorization status.
— Cross-check declared sellers against the publisher's own ads.txt and sellers.json to flag unauthorized or undeclared resale.
— Prefer the shortest authorized path to each publisher; throttle deep chains unless they uniquely deliver inventory you can't get direct.

Why it matters: the SupplyChain object isn't just a fraud signal — it's an itemized receipt of the intermediaries between your money and the publisher. Buyers who read it as a cost graph route around fee stacking that buyers who read it only for fraud never see.

Россияне не смогут покупать стейблкоины

Россиянам могут закрыть доступ к покупке стейблкоинов: в новой версии закона их приравняли к иностранным активам.

Купить такие токены смогут только квалифицированные инвесторы — например, с активами от 24 млн рублей или доходом от 12 млн в год.

Что это значит для обычных пользователей и когда правило заработает — в блоге.

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