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Переоценённый король

Почему ИИ-индустрия платит «налог на Python» — и как быстрые компилируемые языки, в частности, Swift, открывают ей новые горизонты

Андрей Сапунов

Дисклеймер: это мнение автора. Автор — заинтересованная сторона: он ведёт открытый проект языковых моделей на Swift/MLX (swift-language-models) и написал книгу, весь код которой — на Swift. Читайте с поправкой на этот конфликт интересов. В качестве противовеса каждое число в статье снабжено ссылкой на первоисточник, а сильнейшим контраргументам отведён отдельный раздел 8.

Аннотация. Python — бесспорный лингва франка искусственного интеллекта: на нём написаны интерфейсы всех главных фреймворков, на нём учат, публикуют и прототипируют. Мы разбираем, как язык, уступающий C по скорости примерно в 70 раз, а по энергопотреблению — в 76 раз [14], оказался у руля самой вычислительно прожорливой индустрии в истории, и утверждаем три вещи. Во-первых, доминирование Python — результат сетевых эффектов экосистемы, а не технического превосходства: производительное ядро «питоновского» ИИ давно написано на C++, CUDA и Rust. Во-вторых, «налог на Python» реален и измерен — от микросекунд на диспатч оператора [24, 25] до трети вычислительного времени ML-флота Google, уходящей на входные пайплайны [26]. В-третьих, там, где рождаются новые горизонты ИИ — локальный инференс, edge, on-device-модели, — индустрия уже тихо голосует ногами: llama.cpp [31], MLX [43], ExecuTorch [34]. Особое внимание мы уделяем Swift — компилируемому языку с «питоновской» читаемостью, скоростью в разы, а не в десятки раз отличимой от C [14], и живой ML-экосистемой (MLX Swift, Foundation Models, differentiable Swift). Спор, как обычно, не «Python против Swift», а «привычка против понимания».

Ключевые слова: Python, Swift, машинное обучение, LLM, энергоэффективность, компилируемые языки, проблема двух языков, on-device AI, MLX.

«Python is not designed to be fast, and it is not designed to be safe» — «Python не проектировался быстрым и не проектировался безопасным». — Джереми Ховард, сооснователь fast.ai [37]

Эта статья — о самом странном компромиссе современной инженерии: индустрия, которая считает быстрее всех в истории человечества, дирижирует этими вычислениями на одном из самых медленных языков в истории программирования. И о том, что произойдёт, когда мы перестанем считать это законом природы.

1. Введение: странность, к которой все привыкли

Начнём со сцены, которая повторяется каждый день в тысячах компаний.

Дата-центр. Стойки с ускорителями, каждый из которых выполняет сотни триллионов операций в секунду. Электрическая мощность — как у небольшого города: в 2024 году дата-центры мира съели около 415 ТВт·ч, к 2030-му Международное энергетическое агентство ждёт примерно 945 ТВт·ч — больше, чем сегодня потребляет вся Япония, и главный драйвер этого роста — ИИ [48]. Инженеры, которые всем этим управляют, — возможно, самые высокооплачиваемые программисты планеты.

А теперь заглянем в их код. Он написан на языке, который в классическом исследовании энергоэффективности 27 языков занял 26-е место [14]. На языке, который до октября 2025 года в принципе не умел исполнять байткод в два потока одновременно [20]. На языке, создатель которого честно называл его «клеем» для чужих быстрых компонентов [1].

⚠️ Частая ошибка восприятия. «Раз весь ИИ написан на Python — значит, Python технически лучше всего подходит для ИИ». Это совсем не так, и дальше мы покажем, почему. Python подходит для ИИ исторически и социально: у него была готовая научная экосистема и низкий порог входа. Технически же вся индустрия устроена как матрёшка: снаружи Python, внутри C++ и CUDA. Вопрос статьи — что мы платим за эту матрёшку и что будет, если её раскрыть.

Давайте сразу договоримся о честности — это важнее всего. Мы не будем утверждать, что Python плох: это выдающийся язык с заслуженной победой. Мы не будем предлагать «переписать всё на Swift к пятнице»: так не бывает, и попытки уже проваливались — мы честно разберём, как именно. Мы будем утверждать три вещи, каждая из которых опирается на проверяемые числа:

  1. Доминирование Python — это сложившаяся монополия привычки, а не закон природы. Король правит заслуженно, но корона держится на сетевых эффектах, а не на физике.

  2. «Налог на Python» реален, измерим и растёт вместе с масштабом индустрии. Его платят микросекундами диспатча, гигаваттами электричества и удвоением инженерного труда.

  3. На новых территориях ИИ — локальных, мобильных, энергоэффективных — выбор уже происходит, и происходит не в пользу Python. Swift — один из самых недооценённых кандидатов на роль языка, который закроет разрыв.

2. Как Python стал королём (и почему это было заслуженно)

Отдадим королю должное — без этого раздел о его переоценённости был бы дешёвым.

Победа Python в машинном обучении не была случайной. Она была спланирована — причём самим создателем языка. Ещё в январе 1998 года Гвидо ван Россум в позиционной статье «Glue It All Together With Python» описал модель разработки, которую мы сегодня знаем как NumPy и PyTorch: пусть C/C+±программисты пишут эффективные вычислительные компоненты, а учёные «склеивают» их скриптами на Python [1]. Это не пост-фактум оправдание медлительности — это была стратегия, и она сработала блестяще.

К моменту, когда грянул бум глубокого обучения, Python уже полтора десятилетия обрастал научным стеком: Numeric — 1995, SciPy и IPython — 2001, NumPy — 2005–2006, pandas — 2008, scikit-learn — первый релиз в 2010 [2]. Когда в 2015 году Google открыла TensorFlow, а в 2016-м Facebook — PyTorch, выбор фронтенд-языка был очевиден: учёные уже жили в Python. Дальше сетевой эффект замкнулся сам на себя: NumPy оказался в программном стеке открытия гравитационных волн и первого изображения чёрной дыры [3], каждый учебник и каждый курс писались на Python, каждый новый инженер учил его первым.

Числа доминирования впечатляют:

Метрика

Значение

Источник

Язык №1 на GitHub в 2024 (впервые обогнал JavaScript, державший первенство ~10 лет)

Python; драйвер — бум генеративного ИИ

Octoverse 2024 [4]

Исторический рекорд индекса TIOBE за ~24 года

26,98 % (июль 2025) — выше рекорда Java 2001 года

TIOBE / InfoWorld [6]

Реализации новых научных статей (Papers with Code, Q4 2022)

~70 % на PyTorch против 4 % на TensorFlow

AssemblyAI [8]

Новые ИИ-репозитории на GitHub (2025)

Python: 582 196 (+50,7 % за год) — против ~88 тыс. у JavaScript

Octoverse 2025 [5]

Крупнейший годовой прирост в Stack Overflow Survey 2025

Python: +7 п.п. — «go-to язык для ИИ и data science»

SO Survey 2025 [7]

Против этого бессмысленно спорить — и мы не будем. Заметим лишь две трещины в мраморе. Первая: пик, похоже, пройден — с рекордных 26,98 % в июле 2025 года индекс TIOBE к середине 2026-го сполз к ~19 % (Python всё ещё №1, но отрыв тает) [6], а в Octoverse 2025 на первое место по числу контрибьюторов вышел TypeScript [5]. Вторая трещина интереснее, и ей посвящён следующий раздел.

📜 Из истории: «второй лучший язык для всего». В питон-сообществе давно ходит самоироничная формула, приписываемая Вану Линдбергу: «Python — второй лучший язык для любой задачи». Это комплимент: универсальность побеждает специализацию, потому что цена переключения между нишами — нулевая. Но у комплимента есть обратная сторона, о которой реже говорят вслух: если для каждой задачи существует язык лучше, то по мере роста цены задачи — в деньгах, ваттах и миллисекундах — рано или поздно наступает момент, когда «второй лучший» перестаёт быть достаточным. ИИ-индустрия, как мы увидим, к этому моменту уже подошла.

3. Секрет короля: Python в ИИ — это не Python

Приготовьтесь, сейчас будет разочаровывающе просто.

Откройте репозиторий PyTorch — главного инструмента современного ИИ — и посмотрите на официальную статистику языков. Python там — лишь 64 % кодовой базы. Остальное — C++ (28,4 %), CUDA (2,6 %), C, Objective-C++, Metal, ассемблер [9]. Всё производительное ядро eager-режима — тензорная библиотека ATen, ядра CUDA — написано на C++. У NumPy картина та же: 61 % Python, 33 % C. Иными словами, «библиотеки Python для машинного обучения» — это библиотеки на C++, у которых Python — вежливый администратор на ресепшене.

🧪 Попробуйте сами (прямо сейчас): откройте github.com/pytorch/pytorch и кликните на цветную полоску языков под списком файлов. Затем сделайте то же с ollama/ollama (Go) и ggml-org/llama.cpp (C/C++). Три самых популярных инструмента эпохи ИИ — и ни в одном из них производительная часть не написана на Python. Это займёт две минуты и заменит десять абзацев наших уверений.

У этого устройства мира есть имя — проблема двух языков (two-language problem). Канонически её сформулировали создатели Julia: сначала в манифесте 2012 года («мы хотим скорость C с динамизмом Ruby» [10]), затем в программной статье в SIAM Review, где среди «законов природы», которые они отказались принимать, значился и такой: «прототипируй на одном языке, потом переписывай на другом ради скорости или продакшена» [11].

Посмотрим, как этот «закон» выглядит в 2026 году:

  • Исследователь пишет модель на Python.

  • Скорость обеспечивают ядра на C++/CUDA, которые писали другие люди на другом языке.

  • Для продакшена модель конвертируют в третью среду — C+±рантайм, GGUF, Core ML.

  • Когда что-то тормозит на стыке (а тормозит регулярно — см. раздел 5), чинить это может только инженер, владеющий обоими мирами.

📐 Аналогия. Python в ИИ — это дирижёр оркестра. Дирижёр не играет ни на одном инструменте, и это нормально: его дело — взмахи палочкой, играет оркестр (C++ и CUDA). Аналогия точна и в другом, о чём реже вспоминают: если дирижёр показывает жесты вдесятеро медленнее, чем оркестр способен играть, — оркестр начинает ждать палочку. Дорогущие музыканты сидят и смотрят на дирижёра. Дальше мы покажем, что это не метафора, а профилировка: у неоптимизированных LLM-движков до половины времени уходило именно на «взмахи» [30].

Важно понимать: эта конструкция — не заговор и не глупость. Это рациональный компромисс своей эпохи. Но у него есть цена, и она поддаётся измерению.

4. Цена привычки: измеряем налог

4.1. Сколько стоит интерпретатор

Самое цитируемое измерение — работа Перейры и коллег «Energy Efficiency across Programming Languages» (SLE 2017): 27 языков, 10 задач из Computer Language Benchmarks Game, замеры энергии, времени и памяти [14]. Нормировав всё к C (1,00), авторы получили таблицу, которую стоит повесить на стену каждого, кто произносит слова «энергетический кризис ИИ»:

Язык

Энергия (×C)

Время (×C)

C

1,00

1,00

Rust

1,03

1,04

C++

1,34

1,56

Java

1,98

1,89

Swift

2,79

4,20

Go

3,23

2,83

JavaScript

4,45

6,52

PHP

29,30

—

Ruby

69,91

—

Python

75,88

71,90

Python — 26-е место из 27 по энергии (спасибо Perl, прикрывшему тыл). В среднем по исследованию компилируемые языки тратили на задачу 120 Дж, интерпретируемые — 2365 Дж: разрыв примерно в 20 раз [14]. Расширенная журнальная версия 2021 года подтвердила выводы [15].

Разрыв между Python и Swift — тема этой статьи — составляет ~27 раз по энергии и ~17 раз по времени.

Энергопотребление языков программирования относительно C, логарифмическая шкала
Энергопотребление языков программирования относительно C, логарифмическая шкала

Рис. 1. Нормированное энергопотребление языков (C = 1,00), логарифмическая шкала. Данные: Pereira et al., SLE’17, таблица 4 [14].

⚠️ Две честные оговорки (куда же без них). Во-первых, замеры 2017 года: Swift там был версии 3.x, а Python с тех пор ускорился (об этом ниже — спойлер: не в 70 раз). Во-вторых, свежая работа ван Кемпена и коллег (ASE 2025) нашла у методологии Перейры изъяны и показала: энергопотребление языка почти полностью объясняется временем исполнения — «энергоэффективных» языков не существует отдельно от «быстрых» [16]. Заметьте: это уточнение не спасает Python. Оно лишь сводит два его провала к одному — времени. На актуальных замерах Benchmarks Game лучшая Python-программа решает задачу n-body за ~6 минут против 2,10 с у C и 5,45 с у Swift (~170× и ~66×); на spectral-norm — 90,4 с против 0,40 с у C и 1,45 с у Swift (~226× и ~62×) [17, 18]. Да, лучшие C-версии там используют ручные SIMD-интринсики — но Swift-версии обходятся без ассемблерной магии.

Время решения задач n-body и spectral-norm на C, Rust, Swift и Python, логарифмическая шкала
Время решения задач n-body и spectral-norm на C, Rust, Swift и Python, логарифмическая шкала

Рис. 2. Время лучших программ на задачах n-body и spectral-norm (Computer Language Benchmarks Game, середина 2026), логарифмическая шкала [17, 18].

4.2. GIL: тридцать лет одиночества (одного потока)

Вторая статья налога — параллелизм. GIL (Global Interpreter Lock), появившийся в 1992 году, разрешает исполнять байткод Python только одному потоку за раз. Тридцать с лишним лет язык главной индустрии параллельных вычислений не умел параллельность на уровне ядер CPU.

Справедливости ради: лёд тронулся. В Python 3.13 (октябрь 2024) появилась экспериментальная сборка без GIL — ценой ~40 % замедления однопоточного кода [19]. В Python 3.14 (октябрь 2025) она стала официально поддерживаемой, штраф снизился до 5–10 % [20]. Но по плану PEP 703 это лишь фаза 2 из 3: сборкой по умолчанию free-threading так и не стал, и весь мир по-прежнему качает Python с GIL.

4.3. «Мы ускорим CPython в пять раз»

Может, подождать — и Python сам станет быстрым? Эту гипотезу индустрия проверила на себе, с бюджетом Microsoft.

В 2021 году по инициативе Гвидо ван Россума в Microsoft собралась команда Faster CPython; «план Шеннона» обещал ускорение примерно в 5 раз за четыре релиза. Результат: Python 3.11 дал в среднем +25 %, 3.12 — ~5 %, новый tail-call-интерпретатор в 3.14 — ещё 3–5 %. Итог подвёл участник команды Кен Джин: Python 3.14 быстрее 3.10 «примерно на 20–40 %» — в 1,2–1,4 раза вместо пяти [21]. А в мае 2025 года Microsoft уволила большую часть команды, включая техлида Марка Шеннона, и проект перешёл на «общественное попечительство» [21].

💡 Главная мысль раздела. Сорокакратный разрыв не закрывается героическими двадцатью процентами. Это не упрёк инженерам CPython — они сделали блестящую работу. Это свойство самой задачи: динамический интерпретируемый язык по построению платит за каждую операцию проверками типов, поиском атрибутов и диспатчем, которые компилируемый язык оплачивает один раз — во время компиляции. Вызов пустой функции в CPython стоит ~37 нс; в компилируемом языке он либо стоит наносекунды, либо инлайнится в ноль.

5. «Но ведь всё считает GPU!» — когда это правда, а когда нет

Это сильнейший контраргумент, и разобрать его нужно без соломенных чучел. Звучит он так: Python — лишь оркестратор; 99 % времени работают CUDA-ядра, так что хоть на Бейсике пишите.

Когда модель большая, а батч жирный — это чистая правда. Асинхронный диспатч прячет питоновские накладные расходы: пока GPU минуту перемалывает матрицы, Python успевает неторопливо подготовить следующую команду. Документация JAX прямо описывает этот механизм — и Патрик Киджер метко формулирует итог: JAX использует Python как метаязык, на котором вы пишете программу для настоящего исполнителя — компилятора XLA [12, 13].

Проблема в том, что граница «когда это правда» стремительно сужается, и вот измерения:

  • Запуск одного CUDA-ядра стоит микросекунды хост-времени: в классическом примере NVIDIA ядро исполняется 2,9 мкс, а с запуском и синхронизацией выходит 9,6 мкс — накладные расходы больше полезной работы [24]. GPU за шесть лет стали в ~15 раз быстрее по вычислениям [22] — а CPU и Python остались прежними. Ножницы расходятся с каждым поколением железа.

  • PyTorch eager на мелких тензорах выдаёт лишь ~280 тыс. операций в секунду — примерно в сто раз меньше, чем тот же цикл в голом Python без тензоров: почти всё съедает диспатч фреймворка [25].

  • Данные. Анализ миллионов ML-задач в инфраструктуре Google (VLDB 2021): 30 % всего вычислительного времени уходит на входной пайплайн — чтение, декодирование, аугментации, — а 20 % задач тратят на это больше трети времени [26]. Проект FFCV показал, что со стандартным питоновским загрузчиком «идеализированное» время обучения почти в 30 раз меньше времени, нужного просто на обработку картинок [27].

  • LLM-сервинг. Показателен сам состав движков: в оригинальной статье vLLM авторы описывают систему как «8,5 тыс. строк Python и 2 тыс. строк C++/CUDA», где выигрыш в 2–4× по пропускной способности обеспечили именно кастомные CUDA-ядра и управление памятью [28]. Авторы SGLang пишут: неоптимизированный inference-движок может тратить до половины времени на CPU-оверхед — планирование батчей, аллокации, префикс-матчинг [30]. Команда vLLM, переписывая архитектуру в V1, называла цель прямо — почти нулевой CPU-оверхед: шаг генерации Llama-8B на H100 занимает ~5 мс, и питоновские подсистемы просто не успевают за GPU; результат — до 1,7× к пропускной способности [29].

Но самое красноречивое признание сделали сами создатели PyTorch. Анонс PyTorch 2.0 начинается словами: «С первого дня мы знали пределы производительности eager-исполнения» — и объясняет, что существенные части PyTorch пришлось переносить в C++, отдаляя его от «хакабельности» [22]. Ответом стал torch.compile: перехват питоновского байткода и компиляция в Triton и C++ — geomean-ускорение 2,27× на инференсе и 1,41× на обучении на A100 по 180+ реальным моделям (ASPLOS 2024) [23].

💡 Главная мысль раздела. Обратите внимание на паттерн: каждый раз, когда «Python-стек» ускоряют, ускорение состоит в том, что Python откуда-то убирают. Из math-ядер (C++/CUDA), из загрузки данных (tf.data, DALI, FFCV), из планировщика сервинга (vLLM V1, SGLang), из самого исполнения модели (torch.compile превращает ваш Python в не-Python). Синтаксис остаётся — семантика исполнения уходит. Король правит, но уже не управляет.

6. Индустрия уже голосует ногами

Теперь посмотрим не на бенчмарки, а на поведение — чем индустрия пользуется, когда производительность из «приятно» превращается в «обязательно».

llama.cpp. В марте 2023 года Георгий Герганов выложил движок LLM-инференса на чистом C/C++ — без Python в рантайме вовсе. К июлю 2026-го — 119 тыс. звёзд GitHub и статус фундамента всей экосистемы локального ИИ: на нём работают Ollama (175 тыс. звёзд — больше, чем у самого PyTorch; сам Ollama написан на Go), LM Studio и десятки других инструментов, а формат GGUF стал отраслевым стандартом — только на Hugging Face Hub около 45 тыс. GGUF-чекпоинтов [31, 32]. В феврале 2026 года Hugging Face — сердце питоновской ML-экосистемы — присоединил команду Герганова, назвав llama.cpp «фундаментальным строительным блоком локального инференса» [31].

Rust внутри Python-мира. Тот же Hugging Face системно переписывает собственную инфраструктуру на Rust: токенизатор tokenizers (гигабайт текста менее чем за 20 секунд [33]), формат safetensors, фреймворк candle. Пользователь Python этого не видит — и в этом весь пуант: чтобы Python оставался быстрым, его внутренности делают не-питоновскими.

Apple MLX. Свой ML-фреймворк для Apple Silicon компания построила с ядром на C++ и API на четырёх языках, где Swift — полноправный гражданин. Формулировка инженеров Apple в анонсе MLX Swift заслуживает цитаты: Swift «сочетает простоту использования и высокоуровневый синтаксис языка вроде Python со скоростью компилируемого языка вроде C++» [43].

Edge. ExecuTorch 1.0 — рантайм PyTorch для устройств — это «лёгкий C+±рантайм» с базовым размером 50 КБ, обслуживающий, по заявлению проекта, миллиарды пользователей Meta [34]. На микроконтроллере Python не живёт — и никогда не жил.

Mojo. Крис Латтнер — создатель LLVM и Swift — считает проблему настолько реальной, что строит под неё целый язык: питоновский синтаксис поверх компилятора уровня MLIR, с демонстрациями ускорения Мандельброта до 68 847× относительно CPython (после типизации, векторизации и распараллеливания на 88 ядрах — спортивный результат, но показательный) [35]. К маю 2026-го Mojo дошёл до беты 1.0; компилятор пока закрыт [36].

Честности ради: Swift в этом движении не одинок и не первый. У альтернатив есть настоящие козыри. Rust — чемпион таблицы Перейры среди языков с гарантиями безопасности (1,03× к C [14]): память без сборщика мусора и без сегфолтов — ровно то, что нужно инфраструктуре, и не случайно Hugging Face строит на нём tokenizers, safetensors и candle, а фреймворк Burn доказывает, что полный ML-стек на Rust возможен. JAX — самая элегантная реализация идеи «Python как метаязык»: композиция jit/grad/vmap поверх компилятора XLA даёт исследовательскому коду краткость и скорость одновременно [13]. Mojo бьёт в самое сердце проблемы — компилируемый язык для питонистов без переучивания, — и за ним стоит человек, уже дважды построивший индустриальные компиляторные экосистемы [35].

Почему же статья ставит именно на Swift? Из-за сочетания, которого пока нет у других. Rust блистателен, но borrow checker и кривая обучения — противоположность «читается как псевдокод»; JAX остаётся Python-фронтендом со всеми последствиями для деплоя; Mojo — всё ещё бета с закрытым компилятором [36]. Swift единственный уже сегодня совмещает зрелый промышленный тулчейн, синтаксис уровня «почти Python», скорость компилируемого языка и системную нишу on-device, где за ним стоит целая платформа. Впрочем, для главного тезиса статьи этот спор вторичен: победа любого из четырёх — поражение проблемы двух языков.

📐 Аналогия. Представьте город, где официальный язык — один, но все машинные отделения — электростанции, водоканал, метро — по факту говорят на другом. Именно так выглядит стек ИИ в 2026 году: наверху Python, в машинном отделении C++, CUDA, Rust, Go, Swift. Спор «нужен ли компилируемый язык в ИИ» закрыт самой практикой: он уже там. Открытый вопрос звучит иначе — обязан ли человек писать на двух языках, или один язык может пройти весь путь от идеи до машинного отделения?

7. Кандидат Swift: скорость C, синтаксис почти Python

Вот мы и добрались до второй части тезиса. Почему именно Swift? Разберём по пунктам — с числами, а где чисел нет, честно скажем, что их нет.

7.1. Скорость и энергия

В таблице Перейры Swift — 2,79× по энергии и 4,20× по времени против C [14]; напомним, у Python — 75,88× и 71,90×. На актуальных числовых задачах Benchmarks Game лучшие Swift-программы идут вровень с C (n-body: 5,45 с против 2,10 с; на fannkuch-redux — практически паритет), проседая на строковых задачах [17, 18]. Swift компилируется через LLVM в машинный код; здесь нет ни интерпретатора, ни JIT-прогрева.

Память управляется через ARC (automatic reference counting) — подсчёт ссылок на этапе компиляции вместо трассирующего сборщика мусора. Это не только про скорость, но и про предсказуемость: нет GC-пауз. Насколько это важно в проде, Apple показала на собственном сервисе Password Monitoring, переписанном с Java на серверный Swift: +40 % пропускной способности, латентность <1 мс для 99,9 % запросов при миллиардах запросов в день, память на инстанс упала с десятков гигабайт до сотен мегабайт, высвободив около половины Kubernetes-мощностей [42].

А там, где Python страдал от GIL, Swift 6 пошёл в противоположную сторону: режим strict concurrency, при котором гонки данных диагностируются на этапе компиляции как ошибки [44]. Почувствуйте контраст: один язык тридцать с лишним лет сериализовал потоки глобальной блокировкой, другой — статически доказывает, что ваш параллельный код корректен, до первого запуска.

7.2. Синтаксис: найдите десять отличий

«Компилируемый — значит многословный и страшный»? Вот механизм внимания — сердце трансформера — на обоих языках:

# Python (NumPy-стиль)
def attention(q, k, v):
    scores = q @ k.T / math.sqrt(k.shape[-1])
    return softmax(scores) @ v

// Swift (MLX)
func attention(_ q: MLXArray, _ k: MLXArray, _ v: MLXArray) -> MLXArray {
    let scores = q.matmul(k.T) / sqrt(Float(k.dim(-1)))
    return softmax(scores, axis: -1).matmul(v)
}

Разница — сигнатура с типами. Но это не налог, а подарок: компилятор теперь знает, что здесь тензоры, и не даст случайно передать номер токена туда, где ждали вероятность. Типы — это комментарии, которые не устаревают и проверяются машиной.

🧪 Попробуйте сами. Если у вас Mac — компилятор Swift уже установлен, из коробки. Сохраните пару строк в hello.swift, выполните swift hello.swift — и вы уже пишете на компилируемом языке с REPL-лёгкостью скрипта. Готовые, построчно разобранные реализации языковых моделей на Swift — от n-грамм до мини-GPT на MLX — лежат в открытом репозитории github.com/asaptf/swift-language-models: клонируйте и запускайте.

7.3. Урок Swift for TensorFlow: некролог или пролог?

Здесь обязан прозвучать самый неудобный для нашего тезиса факт, и мы приведём его сами: Google уже пыталась сделать Swift языком машинного обучения — и не смогла.

📜 Из истории. Проект Swift for TensorFlow анонсировали в марте 2018 года; вёл его Крис Латтнер, создатель Swift и LLVM. Идея была радикальной: встроить дифференцируемое программирование — автоматическое вычисление градиентов — прямо в компилятор языка общего назначения [38]. Джереми Ховард из fast.ai назвал это «первой серьёзной попыткой, которую я видел, встроить дифференцируемое программирование в самое сердце широко используемого языка, спроектированного с нуля ради производительности», и перевёл часть своего знаменитого курса на Swift [37]. Латтнер ушёл из Google в январе 2020-го; в феврале 2021-го проект отправили в архив [39]. Причина смерти — не техника, а экосистемный разрыв: переписать NumPy-вселенную с нуля не смог даже Google.

Так некролог или пролог? Три обстоятельства говорят за второе.

Во-первых, наследие S4TF не умерло: поддержка автодифференцирования осталась в основном компиляторе Swift как экспериментальная фича, и её развивает индустрия. Компания PassiveLogic строит на differentiable Swift автономные системы управления зданиями и публикует замеры: физическая симуляция теплопередачи на CPU — ~0,03 мс на цикл forward+backward против 8,16 мс у PyTorch (238×) и 11,0 мс у TensorFlow (322×) [40]; в 2024-м они же заявили рекорд энергоэффективности автодиффа — 34 Дж/GOps против десятков тысяч у больших фреймворков [41]. Оговорка честности: это CPU-класс задач с мелкими нерегулярными вычислениями — то есть ровно та территория, где питоновский диспатч убийственен, — и замеры самой компании.

Во-вторых, изменился контекст. В 2018-м Swift в ML был чужаком без железа и без ниши. В 2026-м у него есть и то и другое: вся линейка Apple Silicon с единой памятью, MLX с полноценным Swift API [43], swift-transformers 1.0 от Hugging Face [47] и Foundation Models framework — представленный на WWDC 2025 системный Swift API к on-device LLM, где макрос @Generable гарантирует структурно корректный вывод модели прямо в Swift-типы через constrained decoding [45]. Обратите внимание, чего здесь нет: Python-прослойки. Модель на 3 млрд параметров, квантованная в 2 бита, работает в кармане — и разговаривает с ней Swift напрямую.

В-третьих, сам Swift перестал быть «языком только для Apple»: официальная поддержка Linux и Windows, рабочая группа по Android (2025), Embedded Swift для микроконтроллеров, глубокий C+±interop [44].

💡 Главная мысль раздела. S4TF провалился, штурмуя крепость Python в лоб — на территории серверного обучения, где экосистемный перевес Python абсолютен. Новая партия разыгрывается на другой доске: on-device, edge, локальный инференс — территории, где у Python нет армии, потому что там в принципе нет его рантайма. Именно там компилируемый язык с питоновской читаемостью — не «альтернатива», а кратчайший путь.

8. Честные контраргументы (и что они на самом деле доказывают)

Научная добросовестность требует дать слово обвинению — то есть защите Python. Вот четыре сильнейших аргумента и наши ответы.

«Экосистема решает, и она у Python». Правда. 582 тысячи новых ИИ-репозиториев за год [5], каждый учебник, каждая вакансия. Ответ: мы и не предлагаем битву за серверное обучение — там Python останется надолго, и это нормально. Тезис в другом: новые ниши (см. раздел 9) свободны от этого сетевого эффекта, и там выбор языка снова открыт. А внутри самой цитадели производительный код уже не питоновский — спор идёт лишь о том, на каком языке написана витрина.

«Julia уже пробовала — и где она?» Урок Julia реален: скорость языка не побеждает качество экосистемы (Киджер описывает уход с Julia после месяцев борьбы с некорректными градиентами [12]). Но заметьте, что именно доказывает этот урок: технически быстрых языков достаточно, дефицит — в инженерной зрелости и спонсоре. У Swift спонсор — компания с капитализацией в триллионы, которой on-device ИИ нужен экзистенциально, плюс живой промышленный тулчейн и миллионы разработчиков. Это не гарантия — это отличие.

«Python тоже ускоряется: free-threading, JIT, torch.compile». Ускоряется — и мы честно привели числа: +20–40 % за пять релизов [21], free-threading со штрафом 5–10 % [20], torch.compile 2,27× [23]. Но вчитайтесь в структуру этих побед: free-threading чинит то, чего в компилируемых языках не было никогда; torch.compile ускоряет Python, переставая его исполнять. Это блестящая инженерия, укрепляющая наш тезис: будущее производительности Python — в том, чтобы Python не исполнялся.

«Оверхед — копейки на фоне GPU». Уже разобрано в разделе 5 с числами; добавим экономику масштаба. ChatGPT обрабатывает 2,5+ млрд запросов в день [55]; по оценке Сэма Альтмана, средний запрос стоит 0,34 Вт·ч [54] (методика не раскрыта — держим скепсис). Перемножение даёт ~0,31 ТВт·ч в год на одни лишь ответы одного продукта. При таких объёмах даже 5–10 % программного оверхеда — это 15–31 ГВт·ч ежегодно, годовое потребление небольшого города. «Копейки» перестали быть копейками, когда счётчик закрутился в миллиардах.

⚠️ Оговорка, которую вы не ждали от этой статьи. Энергоэффективность ИИ на единицу задачи улучшается — по оценке IEA, темпами, «беспрецедентными в истории энергетики» [49]. К громким цифрам об энергии ИИ вообще стоит относиться трезво: знаменитая оценка углеродного следа обучения из работы Струбелл и коллег [52] позже была скорректирована инженерами Google в 18–88 раз в меньшую сторону [53] — урок осторожности, который мы стараемся усвоить. Наш аргумент не в том, что ИИ погубит энергосистему и только Swift её спасёт. Он скромнее и твёрже: когда инференс — это до 90 % стоимости ML-нагрузки (AWS [50]) и ~3/5 её энергии (Google [51]), программные накладные расходы перестают быть вопросом вкуса и становятся статьёй бюджета. Инженерная культура, которая тюнит ядра до микросекунд, но пожимает плечами на 70-кратный разрыв в языке оркестрации, — просто ещё не пересчитала свои привычки в мегаватты.

9. Новые горизонты: что открывается за пределами Python

Всё сказанное было бы просто ворчанием на успешный язык, если бы не главное: самые интересные территории ближайших лет лежат там, куда Python физически не дотягивается.

Горизонт 1: ИИ в кармане. Модель Apple Intelligence на ~3 млрд параметров генерирует 30 токенов в секунду прямо на iPhone — офлайн, приватно, бесплатно [46]; третье поколение (июнь 2026) добавило разреженную модель на 20 млрд параметров [56]. Здесь нет и не будет CPython: бюджеты батареи, памяти и латентности оставляют место только компилируемому стеку. Весь этот мир программируется из Swift — включая гарантированно типизированный вывод LLM через @Generable [45].

Горизонт 2: локальный и серверный инференс. Экосистема llama.cpp/GGUF уже показала, что инференс-движку Python не нужен [31]. Серверный Swift с ARC без GC-пауз — готовый кандидат на API-слой того же класса задач, что Apple закрыла в Password Monitoring [42]. Всё чаще узкое место — не матрица на GPU, а оркестрация вокруг неё: агентные циклы, вызовы инструментов, миллионы мелких решений на CPU. Это ровно та территория, где интерпретатор проигрывает компилятору с разгромным счётом.

Горизонт 3: конец проблемы двух языков. Самый глубокий сдвиг — методологический. Сегодняшний путь «прототип на Python → переписывание на C++ → конвертация для устройства» — это удвоение труда и стен между командами. Язык с читаемостью прототипа и скоростью продакшена позволяет одному человеку пройти путь от идеи до устройства — как Julia мечтала в 2012-м [10], как S4TF попытался в 2018-м [38], и как Swift-стек — MLX, differentiable Swift, Foundation Models — впервые позволяет в естественной для себя нише уже сейчас. Способность прочитать и написать весь стек сверху донизу — это не только скорость. Это понимание вместо заклинаний.

📐 Аналогия напоследок. Экосистема Python похожа на imperial units — футы и фунты: исторически заслуженные, вросшие в инфраструктуру, поддержанные привычкой сотен миллионов людей. Жить с ними можно (Америка живёт). Но каждый новый расчёт требует коэффициентов пересчёта, каждый стык систем — источник ошибок, и время от времени об этом разбивается марсианский зонд. Никто не отменит футы указом — но каждый новый мост инженеры молча проектируют в метрах.

10. Что мы поняли

  • Python победил заслуженно — стратегией «клея» [1], форой научной экосистемы и сетевыми эффектами. Спорить с этим бессмысленно; понимать это — обязательно.

  • Python в ИИ — это витрина, а не двигатель: 36 % кодовой базы PyTorch — компилируемые языки [9], и каждый успешный «ускоритель Python-стека» работает, убирая Python из исполнения — от tf.data до torch.compile [22, 23, 26].

  • Налог реален и измерен: ~70× по времени и энергии на языковых бенчмарках [14, 17], до половины времени inference-движка на CPU-оверхед [30], 30 % вычислительного времени ML-флота Google на входные пайплайны [26]. На масштабе миллиардов запросов в день проценты превращаются в гигаватт-часы [54, 55].

  • Индустрия уже голосует ногами: llama.cpp и GGUF [31, 32], Rust внутри Hugging Face [33], MLX [43], ExecuTorch [34], Mojo [35] — везде, где производительность обязательна, компилируемые языки уже стоят у руля.

  • Swift — редкое сочетание: скорость в разы от C (а не в десятки раз) [14], синтаксис, честно сравнимый с Python, ARC и compile-time-проверка гонок [44], продакшен-доказательства [42] и единственная в индустрии системная интеграция с on-device LLM [45]. Уроки S4TF [39] и Julia [12] учтены: не штурм цитадели, а новые территории.

  • Главный горизонт — один язык от идеи до устройства. Проблема двух языков [11] — не закон природы, а исторический компромисс. Компромиссы пересматривают.

Король не умер, и свергать его никто не собирается. Но корона — не аргумент в инженерном споре. В следующий раз, когда вы будете выбирать инструмент для модели, которая должна жить на устройстве, в агентном цикле или в бюджете на электричество, — просто посчитайте. Числа в этой статье к вашим услугам.

А лучше — проверьте сами. Ничто не учит так, как эксперимент.

Об авторе

Андрей Сапунов — автор книги «Как работает LLM: от нуля до своего трансформера» — руководства «для чайников» с инженерными вставками, в котором читатель строит собственную языковую модель. Весь код книги написан на Swift и MLX — по причинам, которые после этой статьи не нуждаются в пояснении.

  • Книга «Как работает LLM» — от нуля до своего трансформера — простыми словами, но без упрощений, за которые потом стыдно — amazon.com/dp/B0H7BPJLHC

  • Код: языковые модели на Swift — рабочие реализации «с нуля»: n-gram, autograd, GPT на MLX, RAG — github.com/asaptf/swift-language-models

Литература

  1. van Rossum G. «Glue It All Together With Python». Position paper, OMG-DARPA-MCC Workshop, 1998. https://www.python.org/doc/essays/omg-darpa-mcc-position/

  2. Virtanen P. et al. «SciPy 1.0: fundamental algorithms for scientific computing in Python». Nature Methods 17, 261–272, 2020. https://arxiv.org/abs/1907.10121

  3. Harris C. R. et al. «Array programming with NumPy». Nature 585, 357–362, 2020. https://arxiv.org/abs/2006.10256

  4. GitHub Octoverse 2024: «AI leads Python to top language». https://github.blog/news-insights/octoverse/octoverse-2024/

  5. GitHub Octoverse 2025: «AI leads TypeScript to #1». https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/

  6. InfoWorld. «Python is slipping in popularity — Tiobe», февраль 2026. https://www.infoworld.com/article/4129615/python-is-slipping-in-popularity-tiobe.html

  7. Stack Overflow Developer Survey 2025: Technology. https://survey.stackoverflow.co/2025/technology

  8. AssemblyAI. «PyTorch vs TensorFlow in 2023» (данные Papers with Code, Q4 2022). https://www.assemblyai.com/blog/pytorch-vs-tensorflow-in-2023

  9. GitHub API: языковой состав pytorch/pytorch (снято 03.07.2026). https://api.github.com/repos/pytorch/pytorch/languages

  10. Bezanson J., Karpinski S., Shah V., Edelman A. «Why We Created Julia», 2012. https://julialang.org/blog/2012/02/why-we-created-julia/

  11. Bezanson J. et al. «Julia: A Fresh Approach to Numerical Computing». SIAM Review 59(1), 65–98, 2017. https://julialang.org/assets/research/julia-fresh-approach-BEKS.pdf

  12. Kidger P. «JAX vs Julia (vs PyTorch)». https://kidger.site/thoughts/jax-vs-julia/

  13. JAX documentation: «Asynchronous dispatch». https://docs.jax.dev/en/latest/async_dispatch.html

  14. Pereira R. et al. «Energy Efficiency across Programming Languages: How Do Energy, Time, and Memory Relate?». SLE’17, 2017. https://greenlab.di.uminho.pt/wp-content/uploads/2017/10/sleFinal.pdf

  15. Pereira R. et al. «Ranking programming languages by energy efficiency». Science of Computer Programming 205, 2021. https://www.sciencedirect.com/science/article/pii/S0167642321000022

  16. van Kempen N. et al. «It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages». ASE’25, 2024. https://arxiv.org/abs/2410.05460

  17. The Computer Language Benchmarks Game: n-body. https://benchmarksgame-team.pages.debian.net/benchmarksgame/performance/nbody.html

  18. The Computer Language Benchmarks Game: spectral-norm. https://benchmarksgame-team.pages.debian.net/benchmarksgame/performance/spectralnorm.html

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  20. «What’s New in Python 3.14» (PEP 779: official support of free-threaded Python). https://docs.python.org/3/whatsnew/3.14.html

  21. Jin K. «Community Stewardship of Faster CPython». discuss.python.org, май 2025. https://discuss.python.org/t/community-stewardship-of-faster-cpython/92153

  22. PyTorch. «Get Started: PyTorch 2.x» (анонс PyTorch 2.0 и torch.compile). https://pytorch.org/get-started/pytorch-2.0/

  23. Ansel J. et al. «PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation». ASPLOS’24, 2024. https://docs.pytorch.org/assets/pytorch2-2.pdf

  24. Gray A. «Getting Started with CUDA Graphs». NVIDIA Technical Blog, 2019. https://developer.nvidia.com/blog/cuda-graphs/

  25. He H. «Making Deep Learning Go Brrrr From First Principles». https://horace.io/brrr_intro.html

  26. Murray D. et al. «tf.data: A Machine Learning Data Processing Framework». PVLDB 14(12), 2945–2958, 2021. https://vldb.org/pvldb/vol14/p2945-klimovic.pdf

  27. Leclerc G. et al. «FFCV: Accelerating Training by Removing Data Bottlenecks». CVPR 2023. https://arxiv.org/abs/2306.12517

  28. Kwon W. et al. «Efficient Memory Management for Large Language Model Serving with PagedAttention». SOSP 2023. https://arxiv.org/abs/2309.06180

  29. vLLM Blog. «vLLM V1: A Major Upgrade to vLLM’s Core Architecture», 2025. https://blog.vllm.ai/2025/01/27/v1-alpha-release/

  30. LMSYS Org. «SGLang v0.4: Zero-Overhead Batch Scheduler», 2024. https://lmsys.org/blog/2024-12-04-sglang-v0-4/

  31. Hugging Face Blog. «GGML and llama.cpp join HF to ensure the long-term progress of Local AI», 2026. https://huggingface.co/blog/ggml-joins-hf

  32. Hugging Face Hub documentation: GGUF. https://huggingface.co/docs/hub/gguf

  33. huggingface/tokenizers (Rust). https://github.com/huggingface/tokenizers

  34. PyTorch Blog. «Introducing ExecuTorch 1.0: Powering the next generation of edge AI», 2025. https://pytorch.org/blog/introducing-executorch-1-0/

  35. «Mojo (programming language)» — Wikipedia (документирует серию Modular «A journey to 68,000x speedup over Python», 2023). https://en.wikipedia.org/wiki/Mojo_(programming_language)

  36. Modular Blog. «Modular 26.3: Mojo 1.0 Beta», май 2026. https://www.modular.com/blog/modular-26-3-mojo-1-0-beta-max-video-gen-and-more

  37. Howard J. «fast.ai — Embracing Swift for Deep Learning», 2019. https://www.fast.ai/posts/2019-03-06-fastai-swift.html

  38. Saeta B. et al. «Swift for TensorFlow: A portable, flexible platform for deep learning», 2021. https://arxiv.org/abs/2102.13243

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  43. Swift.org Blog. «On-device ML research with MLX and Swift», 2024. https://www.swift.org/blog/mlx-swift/

  44. Swift.org Blog. «Announcing Swift 6», 2024. https://www.swift.org/blog/announcing-swift-6/

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  46. Apple ML Research. «Introducing Apple’s On-Device and Server Foundation Models», 2024. https://machinelearning.apple.com/research/introducing-apple-foundation-models

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  49. IEA. «Data centre electricity use surged in 2025…», апрель 2026. https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions

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  • 30.07.26 00:04 Ahmed

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

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  • 23.08.26 20:02 leslieyee

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  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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