В роликах Youtube теперь можно рекламировать товары Amazone

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Google выпустил Gemini Omni 1.1 Flash

Google обновил Gemini Omni для генерации видео: модель умеет продолжать сцены с учётом до 10 секунд контекста и собирать ролик до 40 секунд, работать по референсу и делать переходы между кадрами. Главный вывод — инструмент стал практичнее для продакшена, а посекундная цена делает его заметно доступнее для тестов и рабочих задач.

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Топ 5 PWA-сервисов для залива дейтинга

Статья показывает, что PWA выгодны не только для гемблы: в дейтинге они дают пуш-базу, больше траста и помогают маскировать оффер под бренд. Главный выбор зависит от цены инсталлов и теста GEO: для старта лучше бесплатные или дешёвые решения, а Progressier выделяется как самый практичный вариант для залива дейтинга.

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A 5-step playbook to audit your supply path before cutting SSPs

Supply path optimization (SPO = consolidating onto the cleanest, shortest routes from DSP to publisher) fails when you cut paths blindly. Run this sequence first.

1. Pull 14 days of log-level data and group impressions by the tuple (publisher domain, SSP, seller_id from sellers.json).

2. For each tuple compute three columns: win rate, median clearing price, and the share of impressions carrying a direct or reseller flag in the supply chain object.

3. Find duplicate paths — the same publisher reachable through 3+ SSPs. These are your resell chains where one impression is auctioned multiple times.

4. Rank duplicate paths by effective cost: not the bid, but (winning_price + ssp_fee) divided by post-bid quality (viewability, IVT rate).

5. Keep the single cheapest qualifying path per publisher; throttle the rest to 5% spend for two weeks to confirm no win-rate cliff before full cut.

The mistake is cutting on volume. A high-volume path that loses 80% of auctions to a cheaper twin is pure auction noise inflating your QPS bill.

Why it matters: SPO done on log-level evidence rather than SSP reputation typically recovers 8-15% of working media without touching creative or targeting — it's the highest-leverage, lowest-risk optimization most buyers skip because the data join is tedious.

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How to diagnose whether bid shading is helping or hurting you — step by step

Bid shading (the algorithm that lowers your first-price bid toward the predicted clearing price) is a black box on most DSPs. Here's how to interrogate it.

1. Export bid-level logs with three fields per auction: your submitted bid, the shaded bid actually sent, and the clearing price when you won.

2. Compute the shave: (submitted − shaded) / submitted. Bucket auctions into deciles by this value.

3. In each decile measure two things — win rate and surplus, where surplus = (shaded bid − clearing price). Surplus is the money the shader left on the table that you didn't need to spend.

4. Look for the inversion point: the decile where pushing the shave deeper starts dropping win rate faster than it grows surplus. That's the shader's efficient frontier.

5. If high-shave deciles show near-zero surplus AND collapsing win rate, the model is over-shading you out of inventory. If low-shave deciles show large surplus, it's under-shading and overpaying.

Most shaders are tuned for the median auction and mishandle your tails — branded direct deals get over-shaded, remnant open exchange gets under-shaded.

Why it matters: You cannot fix what you cannot see. Logging the submitted-vs-shaded delta turns an opaque margin into a controllable lever, and the inversion-point analysis is the only honest way to know if your DSP's shading is working for you or for the exchange.

A diagnostic checklist for a Deal ID that won't spend

A Deal ID (the negotiated handshake binding a buyer, an SSP, and specific inventory at agreed terms) failing to deliver is rarely one problem. Work the chain top to bottom.

1. Confirm the deal is active on both sides and the seat ID the SSP whitelisted matches the seat your DSP actually bids from — a seat mismatch silently drops every bid request.

2. Check the deal type. A Preferred Deal (non-guaranteed, fixed price, first look) behaves differently from a Private Auction (PMP with a floor). If your bid sits below the negotiated floor, the SSP filters it pre-auction and you see zero bid requests, not zero wins.

3. Inspect incoming bid requests for the deal object. No deals array in the request means the SSP isn't tagging the impression — a packaging error on their end, not yours.

4. If deals arrive but you don't bid, check your targeting: a deal does not override DSP-side brand safety, geo, or frequency filters layered on top.

5. If you bid and lose, the deal is competing in the auction. Confirm whether it's truly a guaranteed first-look or just a floor you're bidding into against open-market demand.

Why it matters: Each layer fails differently and the symptoms look identical from the dashboard. A structured top-to-bottom trace isolates whether the break is in packaging, seat mapping, floor logic, or your own targeting — saving days of blaming the wrong party.

Google отменил ручную пессимизацию в Еврозоне

Google перестал пессимизировать крупные новостники за паразитные страницы с казино и другими партнёрскими офферами в ЕЭЗ. Для арбитража вывод простой: в Европе схема с «пирогами» больше не даёт преимущества от траста основного домена, а Google впервые применяет разные правила по GEO под давлением регулятора.

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Вышел OpenClaw 2.0

OpenClaw вышел на новый уровень: совместная работа, нормальный веб-интерфейс и более простая настройка. Разбираем, зачем это обновление важно и как оно меняет работу с ИИ-агентом.

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How to decompose a falling win rate into its real causes

Win rate (the share of auctions you enter that you win) dropping is a symptom, not a diagnosis. Decompose it in this order before touching bids.

1. Split win rate into its two factors: bid rate (auctions you choose to bid on) × clear rate (bids that win). A falling number could be either; they have opposite fixes.

2. If bid rate fell, the cause is upstream of the auction — pacing throttled you, budget capped, or a targeting filter tightened. Check your DSP's bid-eligibility logs, not the auction.

3. If clear rate fell, segment by floor exposure. Pull the share of lost auctions where your bid was below the published floor versus lost to a higher competing bid.

4. Floor losses mean a publisher raised floors or moved to first-price; competitive losses mean demand intensified. The first is solved by floor-aware bidding, the second by valuation.

5. Finally, segment competitive losses by margin. Losing by 2% across thousands of auctions is a tractable shading problem; losing by 50% means you've simply mispriced the audience.

Why it matters: Buyers reflexively raise bids when win rate drops, but if the cause was a pacing throttle or a floor change, raising bids just burns margin on auctions you'd have won anyway. The decomposition tells you which of four distinct problems you actually have.