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Совсем скоро запуск ШЕСТОГО проекта на RU GEO от создателей APEX, EVA, KUSH, BANDA и LEEBET!
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Microsoft планирует вставлять рекламу в игры
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The +40% campaign that net-lost money on payment terms
ROI said winner. Cash flow said I was funding the advertiser's float. Both were true.
— The setup: a strong CPA campaign, +40% ROI, $200/day spend, network paying net-30.
— The move: I scaled to $500/day because the ROI was real — and three weeks in, my prepaid ad cards ran dry while my first payout was still 9 days out.
— The numbers: at $500/day I was laying out $3,500/week. Net-30 meant I'd front roughly $15,000 before the first dollar came back. The campaign was +40% on paper but I was capital-locked and had to throttle back to $200/day mid-scale, killing momentum and re-triggering learning phases. Lost efficiency cost an estimated 8 points of ROI during the throttle weeks.
— The lesson: ROI and cash flow are different animals. A profitable campaign can still bankrupt you on timing if you scale faster than your payout cycle funds it.
What I'd do differently: Size the scale ramp to my actual working capital and payout frequency, or negotiate net-7/weekly before scaling. I let ROI seduce me past my cash limit. Numbers illustrative; the net-30 capital trap has stalled more real campaigns than bad creative ever has.
ROI said winner. Cash flow said I was funding the advertiser's float. Both were true.
— The setup: a strong CPA campaign, +40% ROI, $200/day spend, network paying net-30.
— The move: I scaled to $500/day because the ROI was real — and three weeks in, my prepaid ad cards ran dry while my first payout was still 9 days out.
— The numbers: at $500/day I was laying out $3,500/week. Net-30 meant I'd front roughly $15,000 before the first dollar came back. The campaign was +40% on paper but I was capital-locked and had to throttle back to $200/day mid-scale, killing momentum and re-triggering learning phases. Lost efficiency cost an estimated 8 points of ROI during the throttle weeks.
— The lesson: ROI and cash flow are different animals. A profitable campaign can still bankrupt you on timing if you scale faster than your payout cycle funds it.
What I'd do differently: Size the scale ramp to my actual working capital and payout frequency, or negotiate net-7/weekly before scaling. I let ROI seduce me past my cash limit. Numbers illustrative; the net-30 capital trap has stalled more real campaigns than bad creative ever has.
How I find a fresh angle before competitors do: my research loop
The creative is downstream of the angle. Here's the exact loop I run to find one that isn't burned out.
The setup
Saturated vertical, everyone running the same three angles. Need a fourth.
The move
— Step 1: read the offer's negative reviews and complaints. The objections people have ARE the next angle ("but does it work without X?").
— Step 2: scrape forum/Reddit threads in the niche for the language real users use, not marketer-speak.
— Step 3: spy on what's running, then deliberately do the opposite emotional angle (everyone's using fear? Test curiosity).
— Step 4: list 5 angle hypotheses, rank by how different they are from what's saturated.
— Step 5: test the most-different one first.
The numbers
The winning angle came straight from a complaint thread, people doubted the offer worked for a specific subgroup. Built a creative addressing exactly that. CTR 1.6% vs the 0.7% the tired angles were getting, CPA $4.90.
The lesson
The fresh angle is usually sitting in the objections, not in a swipe file everyone already copied.
What I'd do differently
Keep a running doc of objections per vertical. Illustrative numbers.
The creative is downstream of the angle. Here's the exact loop I run to find one that isn't burned out.
The setup
Saturated vertical, everyone running the same three angles. Need a fourth.
The move
— Step 1: read the offer's negative reviews and complaints. The objections people have ARE the next angle ("but does it work without X?").
— Step 2: scrape forum/Reddit threads in the niche for the language real users use, not marketer-speak.
— Step 3: spy on what's running, then deliberately do the opposite emotional angle (everyone's using fear? Test curiosity).
— Step 4: list 5 angle hypotheses, rank by how different they are from what's saturated.
— Step 5: test the most-different one first.
The numbers
The winning angle came straight from a complaint thread, people doubted the offer worked for a specific subgroup. Built a creative addressing exactly that. CTR 1.6% vs the 0.7% the tired angles were getting, CPA $4.90.
The lesson
The fresh angle is usually sitting in the objections, not in a swipe file everyone already copied.
What I'd do differently
Keep a running doc of objections per vertical. Illustrative numbers.
The cash-flow playbook that keeps you alive between payouts
Profitable arbers go broke on timing, not on ROI. Here's how I manage the gap between spending and getting paid.
The setup
Spend is daily and instant. Network pays net-7, net-15, sometimes net-30. The gap is where accounts die.
The move
— Step 1: know every offer's exact payment term, write it down. Sort offers by how fast they pay.
— Step 2: size your daily spend to your float, not your ambition. Float = cash you can deploy before the first payout lands.
— Step 3: on a new network, run weekly payouts even at a fee until trust is built. Faster cash beats slightly higher net.
— Step 4: keep a reserve equal to your worst-case payment gap, never deploy it.
— Step 5: scale spend only as confirmed payouts actually hit the bank, not as the tracker turns green.
The numbers
I once had +30% ROI on paper but $4,000 stuck in net-30 while my cards were maxed. Couldn't scale the winner because I had no cash. The reserve I built after that was ~1.5x my biggest payment gap.
The lesson
ROI is the scoreboard. Cash flow is whether you're still in the game to play it.
What I'd do differently
Negotiate weekly payouts from day one. Figures illustrative.
Profitable arbers go broke on timing, not on ROI. Here's how I manage the gap between spending and getting paid.
The setup
Spend is daily and instant. Network pays net-7, net-15, sometimes net-30. The gap is where accounts die.
The move
— Step 1: know every offer's exact payment term, write it down. Sort offers by how fast they pay.
— Step 2: size your daily spend to your float, not your ambition. Float = cash you can deploy before the first payout lands.
— Step 3: on a new network, run weekly payouts even at a fee until trust is built. Faster cash beats slightly higher net.
— Step 4: keep a reserve equal to your worst-case payment gap, never deploy it.
— Step 5: scale spend only as confirmed payouts actually hit the bank, not as the tracker turns green.
The numbers
I once had +30% ROI on paper but $4,000 stuck in net-30 while my cards were maxed. Couldn't scale the winner because I had no cash. The reserve I built after that was ~1.5x my biggest payment gap.
The lesson
ROI is the scoreboard. Cash flow is whether you're still in the game to play it.
What I'd do differently
Negotiate weekly payouts from day one. Figures illustrative.
Manual CPC vs target-CPA bidding: when handing the algo the wheel cost me margin
The setup: Scaling a stable Facebook campaign, $200/day, +35% ROI on manual lowest-cost bidding. The temptation: switch to cost-cap to protect margin while scaling.
The move: Cloned the campaign, set cost cap at our break-even CPA, scaled both to $400/day over a week.
The numbers (illustrative): Manual campaign scaled to $400/day, ROI compressed to +22% (normal at scale). Cost-cap campaign refused to spend — delivered only $140/day because the cap was too tight, then when we loosened it, it overshot and hit +4% ROI as the algo chased volume.
The lesson: Bid caps protect you from the downside and rob you of the upside. The algo optimizes to your cap, not your profit. On a campaign that's already winning, manual scaling preserves more margin than handing control to a CPA target.
What I'd do differently: Cost caps belong on NEW campaigns where you're scared of overspend, not on proven winners. I bolted a safety belt onto a car that was already driving fine and slowed it down. Use caps to learn, manual to milk.
The setup: Scaling a stable Facebook campaign, $200/day, +35% ROI on manual lowest-cost bidding. The temptation: switch to cost-cap to protect margin while scaling.
The move: Cloned the campaign, set cost cap at our break-even CPA, scaled both to $400/day over a week.
The numbers (illustrative): Manual campaign scaled to $400/day, ROI compressed to +22% (normal at scale). Cost-cap campaign refused to spend — delivered only $140/day because the cap was too tight, then when we loosened it, it overshot and hit +4% ROI as the algo chased volume.
The lesson: Bid caps protect you from the downside and rob you of the upside. The algo optimizes to your cap, not your profit. On a campaign that's already winning, manual scaling preserves more margin than handing control to a CPA target.
What I'd do differently: Cost caps belong on NEW campaigns where you're scared of overspend, not on proven winners. I bolted a safety belt onto a car that was already driving fine and slowed it down. Use caps to learn, manual to milk.
The creative refresh cadence that beats ad fatigue: my system
Winning creatives die on a schedule. Here's the rotation I run so the campaign never goes cold.
The setup
Winning creative, +25%, but I know fatigue is coming. The question is when and what to do.
The move
— Trigger 1: frequency hits 2.5, or CTR drops 20% off its peak. That's the fatigue signal, not a calendar date.
— Step 1: keep the winning ANGLE, change the execution, new image, new hook framing, same core promise.
— Step 2: always have 2 fresh variants queued BEFORE the winner fatigues, so there's no gap.
— Step 3: rotate the new variant in alongside the tiring one, don't kill the old one cold, let it taper.
— Step 4: log which executions of the angle worked, so you build a library, not start blank each time.
The numbers
Waiting for fatigue with nothing queued cost me a 2-day dip to +6% while I scrambled. With variants pre-built, the next refresh held the campaign at +22% through the transition, zero gap.
The lesson
Fatigue is inevitable and predictable. The cost isn't the fatigue, it's not having the next creative ready.
What I'd do differently
Queue variants when the creative first wins, not when it starts dying. Illustrative numbers.
Winning creatives die on a schedule. Here's the rotation I run so the campaign never goes cold.
The setup
Winning creative, +25%, but I know fatigue is coming. The question is when and what to do.
The move
— Trigger 1: frequency hits 2.5, or CTR drops 20% off its peak. That's the fatigue signal, not a calendar date.
— Step 1: keep the winning ANGLE, change the execution, new image, new hook framing, same core promise.
— Step 2: always have 2 fresh variants queued BEFORE the winner fatigues, so there's no gap.
— Step 3: rotate the new variant in alongside the tiring one, don't kill the old one cold, let it taper.
— Step 4: log which executions of the angle worked, so you build a library, not start blank each time.
The numbers
Waiting for fatigue with nothing queued cost me a 2-day dip to +6% while I scrambled. With variants pre-built, the next refresh held the campaign at +22% through the transition, zero gap.
The lesson
Fatigue is inevitable and predictable. The cost isn't the fatigue, it's not having the next creative ready.
What I'd do differently
Queue variants when the creative first wins, not when it starts dying. Illustrative numbers.
Telegram channels vs Facebook for a gray crypto offer: reach vs trust
The setup: A crypto signals offer banned outright on Facebook. We compared buying Telegram channel posts vs cloaking it onto Facebook.
The move: $2,000 split. Telegram: paid placements in 8 niche channels. Facebook: cloaked compliant page, aggressive landing behind.
The numbers (illustrative): Facebook (cloaked): cheap reach, $0.50 CPMs effectively, but 1.8% lead-to-deposit and a ban on day 7 — net ROI +5% after replacement costs. Telegram: $8 effective CPM (expensive), but 9.4% lead-to-deposit because the channel owner's audience trusted his endorsement. ROI +44%, no bans.
The lesson: Facebook sells reach to strangers. Telegram channel buys sell borrowed trust. For offers that need belief — crypto, finance, anything 'too good' — a trusted voice converts at a multiple that crushes cheap cold reach, and there's no ban hammer.
What I'd do differently: I led with Facebook because the CPMs looked irresistible, then spent a week farming replacement accounts. For trust-heavy gray offers, start where the audience already trusts someone. Vet the channel's engagement first — fake-subscriber channels are the Telegram version of pop fraud.
The setup: A crypto signals offer banned outright on Facebook. We compared buying Telegram channel posts vs cloaking it onto Facebook.
The move: $2,000 split. Telegram: paid placements in 8 niche channels. Facebook: cloaked compliant page, aggressive landing behind.
The numbers (illustrative): Facebook (cloaked): cheap reach, $0.50 CPMs effectively, but 1.8% lead-to-deposit and a ban on day 7 — net ROI +5% after replacement costs. Telegram: $8 effective CPM (expensive), but 9.4% lead-to-deposit because the channel owner's audience trusted his endorsement. ROI +44%, no bans.
The lesson: Facebook sells reach to strangers. Telegram channel buys sell borrowed trust. For offers that need belief — crypto, finance, anything 'too good' — a trusted voice converts at a multiple that crushes cheap cold reach, and there's no ban hammer.
What I'd do differently: I led with Facebook because the CPMs looked irresistible, then spent a week farming replacement accounts. For trust-heavy gray offers, start where the audience already trusts someone. Vet the channel's engagement first — fake-subscriber channels are the Telegram version of pop fraud.
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На Anthropic, OpenAI, Google и SpaceXAI подали в суд из-за ИИ
В Калифорнии против ИИ-компаний подали антимонопольный иск: регулятору показалось подозрительным, что игроки синхронно призывают ограничить развитие нейросетей ради безопасности. Смысл спора в том, что инвестиции в ИИ уже обгоняют реальный прогресс, а бизнесу выгодны правила, которые защитят капитал. Вывод: быстрых прорывов ждать не стоит, лучше выжимать максимум из текущих инструментов.
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В Калифорнии против ИИ-компаний подали антимонопольный иск: регулятору показалось подозрительным, что игроки синхронно призывают ограничить развитие нейросетей ради безопасности. Смысл спора в том, что инвестиции в ИИ уже обгоняют реальный прогресс, а бизнесу выгодны правила, которые защитят капитал. Вывод: быстрых прорывов ждать не стоит, лучше выжимать максимум из текущих инструментов.
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Forwarded from AffPapa! Клуб спящих бизнесменов! Потрачено!
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🔥 Приватные консультации по запускам Google ads и FB.
Масштабное обновление материала на сентябрь,без воды и паблика,свежий пак информации для опытных баеров(техничка,разбан,модерация,
связки,масштабирование и т.д)
Полный пак:
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Отзывы:
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Аккаунты гугл адс:
https://t.me/+VCIrjC36UiYyYjM0
Мой контакт:@TRAFF3
гарант+По промокоду( #affpapa ) скидка -10% на все услуги.
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Google ads начал показывать расходы конкурентов
Google Ads запустил Peer Spending — инструмент, который сравнивает расходы аккаунта с рекламодателями из той же ниши без раскрытия чужих данных. Он показывает, тратите вы больше, меньше или примерно на уровне конкурентов на уровне кампаний и групп объявлений. Для арбитража это скорее ориентир по бенчмаркам, чем инструмент прямого усиления залива.
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Google Ads запустил Peer Spending — инструмент, который сравнивает расходы аккаунта с рекламодателями из той же ниши без раскрытия чужих данных. Он показывает, тратите вы больше, меньше или примерно на уровне конкурентов на уровне кампаний и групп объявлений. Для арбитража это скорее ориентир по бенчмаркам, чем инструмент прямого усиления залива.
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Минфин РФ планирует выпустить собственный стейблкоин
Власти РФ обсуждают запуск рублёвого стейблкоина: сейчас решают, как его обеспечить, какие операции разрешить и будет ли на него спрос. Основной кейс — международные переводы, а не использование физлицами. Если проект доведут до запуска, он может стать частью новой криптоинфраструктуры и альтернативой токенам, привязанным к дружественным валютам.
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Власти РФ обсуждают запуск рублёвого стейблкоина: сейчас решают, как его обеспечить, какие операции разрешить и будет ли на него спрос. Основной кейс — международные переводы, а не использование физлицами. Если проект доведут до запуска, он может стать частью новой криптоинфраструктуры и альтернативой токенам, привязанным к дружественным валютам.
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The "kill it under 3x ROAS" rule almost killed my best campaign
Everyone repeats it: if an ad set isn't doing 3x on day one, cut it. I followed that gospel for two years. Then I tracked cohorts instead of days.
The setup
Nutra offer, native traffic. Day-one return on ad spend sat at 1.4x. Textbook says kill.
The move
Instead of cutting, I pulled the postback data and matched conversions back to the day the click happened, not the day the sale posted.
The numbers
That 1.4x ad set was actually doing 2.9x once delayed conversions (rebills and 3-7 day lags) landed. I'd been killing winners because the network attributed sales to the wrong calendar day. Roughly $4,100 spent, $11,900 returned over 14 days — but only $5,700 of it showed inside the first 72 hours.
The lesson
ROAS thresholds measured on same-day data punish offers with any conversion lag. The rule isn't wrong; the measurement window is.
What I'd do differently
Set your kill rule on a 7-day attributed cohort, not the dashboard's "today" number. I now wait for at least 50 clicks AND one full conversion cycle before any ad set lives or dies. Illustrative figures, but the lag is real on every rebill offer I've run.
Everyone repeats it: if an ad set isn't doing 3x on day one, cut it. I followed that gospel for two years. Then I tracked cohorts instead of days.
The setup
Nutra offer, native traffic. Day-one return on ad spend sat at 1.4x. Textbook says kill.
The move
Instead of cutting, I pulled the postback data and matched conversions back to the day the click happened, not the day the sale posted.
The numbers
That 1.4x ad set was actually doing 2.9x once delayed conversions (rebills and 3-7 day lags) landed. I'd been killing winners because the network attributed sales to the wrong calendar day. Roughly $4,100 spent, $11,900 returned over 14 days — but only $5,700 of it showed inside the first 72 hours.
The lesson
ROAS thresholds measured on same-day data punish offers with any conversion lag. The rule isn't wrong; the measurement window is.
What I'd do differently
Set your kill rule on a 7-day attributed cohort, not the dashboard's "today" number. I now wait for at least 50 clicks AND one full conversion cycle before any ad set lives or dies. Illustrative figures, but the lag is real on every rebill offer I've run.
The 6-question post-mortem I run on every dead campaign
A failed campaign you don't autopsy is just an expensive feeling. Here's the form I fill out before I move on.
The setup
Dating offer, $400 spent, $260 revenue, ROI -35%. Dead. Now what.
The move
Six questions, answered in writing:
— Where did it break? Funnel stage: impression, click, LP, or conversion?
— My CTR was fine (1.1%) but LP-to-conversion was 0.8% vs offer average ~2%. Break was the LP.
— Was it the offer, the angle, or the traffic source? (Cross-check: same angle on a different source.)
— What did I assume that turned out false? I assumed the offer's own LP was optimized. It wasn't.
— What's the cheapest test to confirm the diagnosis?
— Keep, kill, or rebuild?
The numbers
LP was the culprit. I built my own pre-lander, relaunched the SAME traffic, LP-to-conversion went 0.8% to 1.9%. The campaign that lost $140 turned +18%.
The lesson
Most "bad offers" are bad funnels. Diagnose the stage before you blame the whole thing.
What I'd do differently
Run the post-mortem at $200 spend, not $400. Illustrative numbers.
A failed campaign you don't autopsy is just an expensive feeling. Here's the form I fill out before I move on.
The setup
Dating offer, $400 spent, $260 revenue, ROI -35%. Dead. Now what.
The move
Six questions, answered in writing:
— Where did it break? Funnel stage: impression, click, LP, or conversion?
— My CTR was fine (1.1%) but LP-to-conversion was 0.8% vs offer average ~2%. Break was the LP.
— Was it the offer, the angle, or the traffic source? (Cross-check: same angle on a different source.)
— What did I assume that turned out false? I assumed the offer's own LP was optimized. It wasn't.
— What's the cheapest test to confirm the diagnosis?
— Keep, kill, or rebuild?
The numbers
LP was the culprit. I built my own pre-lander, relaunched the SAME traffic, LP-to-conversion went 0.8% to 1.9%. The campaign that lost $140 turned +18%.
The lesson
Most "bad offers" are bad funnels. Diagnose the stage before you blame the whole thing.
What I'd do differently
Run the post-mortem at $200 spend, not $400. Illustrative numbers.
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В публичный доступ вышел Grok 4.7
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X теперь показывает причину теневого бана
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Россиян заставят отчитываться перед ФНС за крипту
Крипта в РФ считается имуществом, поэтому доход от продажи или обмена облагается НДФЛ: 13% до 2,4 млн рублей и 15% сверх этого. Декларацию 3-НДФЛ нужно подать самостоятельно до 30 апреля. Отчитываться по кошелькам обязаны только при обороте свыше 600 000 рублей в год, а за нарушение грозят штрафы и пени.
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Крипта в РФ считается имуществом, поэтому доход от продажи или обмена облагается НДФЛ: 13% до 2,4 млн рублей и 15% сверх этого. Декларацию 3-НДФЛ нужно подать самостоятельно до 30 апреля. Отчитываться по кошелькам обязаны только при обороте свыше 600 000 рублей в год, а за нарушение грозят штрафы и пени.
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"Test 50 creatives to find a winner" is how I burned $2,800 on noise
The advice sounds disciplined: flood the account, let the algorithm pick. I did it. It taught the algorithm nothing and taught me less.
The setup
E-commerce offer, paid social, $60/day budget. I launched 50 distinct creatives in one campaign because a popular thread said volume wins.
The move
After a week of flat results I stopped, did the math on impressions per creative, and felt stupid.
The numbers
$60/day across 50 creatives = roughly 30-40 impressions each per day before the algorithm consolidated spend onto 3. Statistically, 47 of those creatives never got enough data to mean anything. I spent $2,800 over the test and 80% of it funded ads that never had a chance to prove themselves.
The lesson
Creative volume without budget-per-creative is just expensive randomness. The algorithm doesn't "explore" 50 things on a small budget — it picks fast and starves the rest.
What I'd do differently
Fewer, funded. I now run 4-6 creatives with at least $15/day each so every one clears 1,000+ impressions before I judge it. Same total budget, ten times the signal. Numbers illustrative — the starvation math is not.
The advice sounds disciplined: flood the account, let the algorithm pick. I did it. It taught the algorithm nothing and taught me less.
The setup
E-commerce offer, paid social, $60/day budget. I launched 50 distinct creatives in one campaign because a popular thread said volume wins.
The move
After a week of flat results I stopped, did the math on impressions per creative, and felt stupid.
The numbers
$60/day across 50 creatives = roughly 30-40 impressions each per day before the algorithm consolidated spend onto 3. Statistically, 47 of those creatives never got enough data to mean anything. I spent $2,800 over the test and 80% of it funded ads that never had a chance to prove themselves.
The lesson
Creative volume without budget-per-creative is just expensive randomness. The algorithm doesn't "explore" 50 things on a small budget — it picks fast and starves the rest.
What I'd do differently
Fewer, funded. I now run 4-6 creatives with at least $15/day each so every one clears 1,000+ impressions before I judge it. Same total budget, ten times the signal. Numbers illustrative — the starvation math is not.
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
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В публичный доступ вышла ChatGPT-6
OpenAI выпустила линейку GPT-6: модель Sol для кодинга и сложных задач и Luna для рутинных операций. Ключевое преимущество релиза — двукратное снижение цен на API ($2 и $0.10 за 1 млн входных токенов соответственно) и сокращение числа ошибок в два раза. Это позволяет масштабировать автоматизацию и разработку с вдвое меньшими затратами на инфраструктуру.
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OpenAI выпустила линейку GPT-6: модель Sol для кодинга и сложных задач и Luna для рутинных операций. Ключевое преимущество релиза — двукратное снижение цен на API ($2 и $0.10 за 1 млн входных токенов соответственно) и сокращение числа ошибок в два раза. Это позволяет масштабировать автоматизацию и разработку с вдвое меньшими затратами на инфраструктуру.
➡️ Читайте на сайте: https://aff.top/blog/v-publichnyi-dostup-vyshla-chatgpt-6
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AFF.TOP - про арбитраж трафика и CPA рынок!
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Болгария введёт запрет на рекламу интернет-казино
Болгария вводит жёсткий запрет на рекламу казино и гемблинга, оставляя только спонсорство спорта. Для арбитража это ухудшает условия: запуск CPA-кампаний на лицензионных операторов станет сложнее, а дополнительные налоги на онлайн-казино могут снизить конверсию и поток игроков. Сейчас GEO выглядит неудачным для тестов и масштабирования.
➡️ Читайте на сайте: https://aff.top/blog/bolgariia-vvedet-zapret-na-reklamu-internet-kazino
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Болгария вводит жёсткий запрет на рекламу казино и гемблинга, оставляя только спонсорство спорта. Для арбитража это ухудшает условия: запуск CPA-кампаний на лицензионных операторов станет сложнее, а дополнительные налоги на онлайн-казино могут снизить конверсию и поток игроков. Сейчас GEO выглядит неудачным для тестов и масштабирования.
➡️ Читайте на сайте: https://aff.top/blog/bolgariia-vvedet-zapret-na-reklamu-internet-kazino
🧠 Ещё больше инсайтов → в канале AFF.top
Forwarded from AffPapa! Клуб спящих бизнесменов! Потрачено!
🔥 Бесплатная открытая альтернатива Dolphin Anty, Vision, Gologin, AdsPower и Multilogin.
Релизнул, ссылки и подробности тут - https://t.me/+NStWL6LlbN00MTFi
Релизнул, ссылки и подробности тут - https://t.me/+NStWL6LlbN00MTFi
Phoenix.ink — твои Google и Apple Developer аккаунты🟧 Смотри наличие @phoenixapps_store🟧 Забирай консоли @phoenix_seller_bot
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