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Н Новости

о1: почему новая GPT от OpenAI — это не хайп, а переход к новой парадигме в ИИ

Последние пару лет развитие языковых нейросетей как будто бы шло по принципу «больше, длиннее, жирнее»: разработчики пытались раздуть свои модели на как можно большее число параметров и прогнать через них максимальный объем тренировочных данных. 12 сентября OpenAI выпустили новую LLM, которая добавляет в это уравнение еще одно измерение для прокачки: теперь можно масштабировать объем «мыслей», который модель будет тратить в процессе своей работы. В этой статье мы разберемся, чему научилась новая GPT o1, и как это повлияет на дальнейшую эволюцию ИИ.

Эрмира «Мира» Мурати – албанская инженерка, которая занимает должность CTO OpenAI
Эрмира «Мира» Мурати – албанская инженерка, которая занимает должность CTO OpenAI

В конце прошлой недели OpenAI анонсировали и сразу же выпустили новую модель. Вопреки ожиданиям, её назвали не GPT-5, а o1. Компания утверждает, что для них сброс счётчика линейки моделей к единичке знаменует собой переход к новой парадигме, и что эта нейросеть и вовсе демонстрирует новый уровень возможностей ИИ. Возможностей, ранее вызвавших переживания и опасения у некоторых внутренних исследователей OpenAI — да настолько, что они пожаловались совету директоров! Давайте разберёмся, что же именно произошло, как и почему появилась o1, и попытаемся понять, как правильно выстроить ожидания от этой LLM (большой языковой модели).

План

  1. Предыстория, ожидания и исторический контекст

  2. Официальное позиционирование

  3. А как модели решают такие задачи?

  4. Как OpenAI учили нейросеть думать, прежде чем давать конечный ответ

  5. Возникновение сложных типов поведения в играх

  6. К каким рассуждениям это привело

  7. Законы масштабирования

  8. Зачем масштабировать модели, которые не умеют считать буквы?

  9. Где o1 показала себя

  10. Безопасность моделей

  11. Что дальше?

Предыстория, ожидания и исторический контекст

Ух, ну и наделал этот релиз шуму! Куда без этого — ведь o1 есть ни что иное как первый публичный показ «супер-прорывной технологии» от OpenAI под кодовым названием Strawberry (клубника). Вокруг неё в последний год ходило множество слухов — как адекватных, так и не очень. На форумах и в Твиттере была куча обсуждений, предвосхищений и хайпа, на фоне которых планка ожиданий некоторых людей взлетела до небес. Для тех, кто оградил себя от всего этого, вкратце перескажем контекст, ибо он очень важен. Итак:

  • 22 ноября 2023 года. The Information (издание, публиковавшее инсайдерскую информацию про OpenAI несколько раз) выпускает материал под названием «OpenAI совершила прорыв в области ИИ перед увольнением Сэма Альтмана, что вызвало волнение и беспокойство».

    • Действительно, 17 ноября произошли странные события с увольнением одним днём исполнительного директора компании без объяснения причин. Несколько исследователей уволились в знак солидарности, а остальные запустили открытое письмо, требующее либо объяснений и прозрачности, либо восстановления должности. Через неделю 2 члена совета директоров были выставлены на улицу, Сэма вернули — и пошли отмечать Рождество.

    • В статье утверждается, что в течение нескольких недель до этого внутри OpenAI распространялась демо-версия некой новой технологии, которая и вызывала беспокойства. Мол, это настоящий прорыв, который ускорит разработку ИИ и потенциально может привести к катастрофе.

    • Впервые озвучивается название технологии: Q*. В интернете начинается обмен догадками, что же это означает — в мире машинного обучения есть технологии со схожими названиями (Q-learning для обучения игре в видеоигры и A*, пришедший из информатики).

  • 23 ноября 2023 года. Reuters пишут, что накануне четырехдневного «путча» с увольнениями несколько штатных исследователей написали совету директоров письмо, предупреждающее о значительном открытии в области ИИ, которое, по их словам, может угрожать человечеству.

    • Во внутренней переписке компании представитель OpenAI в обращении к сотрудникам подтвердил существование проекта Q* и факт написания некоторого письма с выражением беспокойства в адрес совета директоров.

  • 11 июля 2024 года. Издание Bloomberg рассказало, что в ходе внутренней демонстрации OpenAI показали концепцию из пяти уровней, помогающую отслеживать прогресс в создании ИИ. Диапазон варьируется от знакомого ChatGPT (уровень 1 — чатбот, поддерживающий беседу), до ИИ, который может выполнять работу целой организации (уровень 5 — кооперация, долгосрочное планирование, исполнение).

    Вот такая табличка из пяти уровней. По ней можно строить догадки, куда же OpenAI двинется дальше.
    Вот такая табличка из пяти уровней. По ней можно строить догадки, куда же OpenAI двинется дальше.
    • По словам источника, руководители OpenAI сообщили сотрудникам, что в настоящее время они находится на пороге достижения второго уровня, который называется «Reasoners» (на русский хорошего перевода в одно слово нет, что-то вроде «сущность, которая размышляет и рассуждает»).

    • На этой же встрече было проведено демо новой технологии, «демонстрирующей некоторые новые навыки, схожие с человеческим мышлением». Уже понимаете, откуда растут ноги у ожиданий? :)

  • 12 июля 2024 года. В эксклюзивном материале Reuters раскрываются некоторые детали, видимо, от сотрудников, присутствовавших на внутренней демонстрации.

    • Проект Q* теперь называется Strawberry.

    • Система якобы решает 90% задач из датасета MATH, в который входят олимпиадные задачи по математике для средней-старшей школы. Их собирали с разных туров (например, AIME), проводимых в США в рамках выявления членов команды для финалов международной олимпиады.

      Пример двух задачек разного уровня. Всего таких 12500 — и для каждой написано пошаговое решение и дан ответ (он обведён в прямоугольник) — но они, конечно, не даются модели во время работы, и используются для сверки результатов.
      Пример двух задачек разного уровня. Всего таких 12500 — и для каждой написано пошаговое решение и дан ответ (он обведён в прямоугольник) — но они, конечно, не даются модели во время работы, и используются для сверки результатов.
  • 7 августа 2024 года. Сэм Альтман, СЕО OpenAI, подогревает интерес начитавшейся новостей публики фотографией клубнички (или земляники?).

  • 27 августа 2024 года. The Information, с которых и началась вся эта история, пишет, что OpenAI провели демонстрацию технологии американским чиновникам по национальной безопасности. В этой же статье раскрываются некоторые из планов на будущее касательно GPT-5, но к ним мы ещё вернемся.

  • 12 сентября 2024 года. OpenAI анонсируют o1, констатируя смену парадигмы, рекорды качества по множеству замеров на разных типах задач. Физика, математика, программирование — везде прогресс.

А теперь представьте, что вы это всё прочитали, настроились, на хайпе, идёте в ChatGPT проверять, спрашиваете какой-нибудь пустяк, ну например сколько букв в слове Strawberry, и видите... вот это:

¯\_(ツ)_/¯
¯\_(ツ)_/¯

Казалось бы, Ватсон, дело закрыто, всё понятно: снова обман от циничных бизнесменов из Силиконовой долины, никаких прорывов, одно разочарование. Но не спешите с выводами (а вообще, если у вас есть подписка ChatGPT Plus, то лучше пойти попробовать самим на других задачах — модель уже доступна всем). До причин того, почему так происходит, мы ещё дойдём.

Официальное позиционирование

Для начала давайте посмотрим, на что делается упор в презентуемых результатах: чем именно OpenAI хотят нас удивить? Вот график с метриками (замерами качества) на трёх разных доменах:

f553e36addfcd922b7ee73444839cfb0.png

На всех трёх частях бирюзовый цвет означает результаты предыдущей лучшей модели OpenAI, gpt4o, оранжевый — раннюю, а малиновый — полноценную законченную версию модели o1. Есть ещё салатовый, о нём ниже. Полузакрашенные области сверху колонок на первой и третьей частях графика — это прирост в качестве за счёт генерации не одного ответа на задачу, а выбора самого популярного из 64. То есть, сначала модель независимо генерирует десятки решений, затем из каждого выделяется ответ, и тот, который получался чаще других, становится финальным — именно он сравнивается с «золотым стандартом».

Даже не зная, что это за типы задач спрятаны за графиком, невооружённым взглядом легко заметить скачок. А теперь приготовьтесь узнать его интерпретацию, слева направо:

  • AIME 2024: те самые «олимпиадные задачи по математике», взятые из реального раунда 2024 года (почти наверняка модель их не видела, могла изучать только схожие) — задачи там сложнее, чем в примерах на картинках выше. AIME является вторым в серии из двух туров, используемых в качестве квалификационного раунда Математической олимпиады США. В нём участвуют те, кто попал в топ-проценты первого раунда, примерно 3000 человек со всей страны.

    • Кстати, если модель попросить сгенерировать ответ не 64, а 1000 раз, и после этого выбирать лучший ответ не тупо как самый часто встречающийся, а с помощью отдельной модели, то o1 набирает 93% баллов — этого хватит, чтобы войти в топ-500 участников и попасть в следующий тур.

  • CodeForces: это сайт с регулярно проводимыми соревнованиями по программированию, где участникам предлагается написать решение на скорость. Тут LLM от OpenAI действовала как обычный участник и могла сделать до 10 отправок решения. Цифра на картинке — это процент людей-участников, набравших балл меньше, чем o1. То есть, например, 89,0 означает, что модель вошла в топ-11% лучших — сильный скачок относительно gpt4o, которая тоже попадает в 11% (правда, худших).

  • GPQA Diamond: самый интересный датасет. Тут собраны вопросы по биологии, физике и химии, но такие, что даже PhD (кандидаты наук) из этих областей и с доступом в интернет решают правильно всего 65% (тратя не более получаса на каждую задачу). Столбик салатового цвета с отметкой 69,7% указывает на долю задач, решённых людьми с PhD, отдельно нанятыми OpenAI — это чуть больше, чем 65% от самих авторов задач, но меньше, чем у передовой модели.

    • Для таких сложных задач подготовить хорошие ответы — это целая проблема. Если даже кандидаты наук не могут с ними справиться, используя интернет, то важно убедиться в корректности всех решений. Чтобы это сделать, проводилась перекрёстная проверка несколькими экспертами, а затем они общались между собой и пытались найти и исправить ошибки друг у друга.

    • Кандидаты наук из других областей (то есть, условно, когда математик пытается справиться с задачей по химии, но использует при этом гугл) тут решают вообще лишь 34%.

Такие существенные приросты качества по отношению к gpt4o действительно приятно удивляют — не каждый день видишь улучшение в 6–8 раз! Но почему именно эти типы задач интересны OpenAI? Всё дело в их цели — помимо чатботов они заинтересованы в создании системы, выполняющей функции исследователей и инженеров, работающих в компании.

Посудите сами: для работы в OpenAI отбирают только первоклассных специалистов (и платят им много деняк), что накладывает существенные ограничения на темпы роста. Нельзя взять и за месяц нанять ещё десять тысяч людей, даже если зарплатный фонд позволяет. А вот взять одну модель и запустить в параллель 10'000 копий работать над задачами — можно. Звучит фантастично, но ребята бодро шагают к этому будущему. Кстати, если интересно узнать про тезис автоматизации исследований — очень рекомендую свою 70-минутную лекцию (станет прекрасным дополнением этого лонга) и один из предыдущих постов на Хабре.

7301928b192b6d1ce3e73703fe0cb513.jpg

Так вот, поэтому им и интересно оценивать, насколько хорошо модель справляется с подобными задачами. К сожалению, пока не придумали способов замерить прогресс по решению реальных проблем, с которыми исследователи сталкиваются каждый день — и потому приходится использовать (и переиспользовать) задания и тесты, заготовленные для людей в рамках образовательной системы. Что, кстати, указывает, что последнюю 100% придётся менять уже прямо сейчас — в чём смысл, если все домашки и контрольные сможет прорешать LLM? Зачем игнорировать инструмент? Но это тема для отдельного лонга...

Третий из разобранных набор данных, GPQA Diamond, был как раз придуман меньше года назад (!) как долгосрочный бенчмарк, который LLM не смогут решить в ближайшее время. Задачи подбирались так, что даже с доступом в интернет (ведь нейронки прочитали почти все веб-страницы и набрались знаний) справится не каждый доктор наук! И вот через 11 месяцев o1 уже показывает результат лучше людей — выводы о сложности честной оценки моделей делайте сами.

Важно оговориться, что эти результаты не означают, что o1 в принципе более способна, чем доктора наук — только то, что модель более ловко решает конкретно некоторый тип задач, которые, как ожидается, должны быть по силам людям со степенью PhD.

А как модели решают такие задачи?

Начнём с примера: если я спрошу вас «дважды два?» или «столица России?», то ответ последует незамедлительно. Иногда просто хватает ответа, который первым приходит в голову (говорят «лежит на подкорке»). Никаких рассуждений не требуется, лишь базовая эрудиция и связь какого-то факта с формой вопроса.

А вот если задачка со звёздочкой, то стоит начать мыслительный процесс — как нас учили решать в школе на уроках математики или физики. Можно вспомнить какие-то формулы или факты, релевантные задаче, попытаться зайти с одного конца, понять, что попытка безуспешна, попробовать что-то другое, заметить ошибку, вернуться обратно... вот это всё, что у нас происходит и в голове, и на листе бумаге, всё то, чему учили на уроках.

Большие языковые модели практически всегда «бегут» только вперёд, генерируя по одному слову (или вернее части слова, токену) за раз. В этом смысле процесс их «мышления» очень отличается, и больше похож на вот такую гифку:

LLM на лету подставляет нужные токены и летит дальше, не сбавляя.
LLM на лету подставляет нужные токены и летит дальше, не сбавляя.

Даже если модель совершит ошибку, по умолчанию её поведение подразумевает дальнейшую генерацию ответа, а не рефлексию и сомнения в духе «где ж это я продолбалась?». Хотя иногда случаются моменты просветления (но это редкость):

Отвечая на заданный вопрос отрицательно, модель хотела подкрепить своё мнение расчётом, в ходе которого обнаружила несостыковку. Wait, actually, yes!
Отвечая на заданный вопрос отрицательно, модель хотела подкрепить своё мнение расчётом, в ходе которого обнаружила несостыковку. Wait, actually, yes!

Модели нужны слова для того, чтобы выражать размышления. Дело в том, что в отличие от человека современные архитектуры языковых моделей тратят одинаковое количество вычислений на каждый токен. То есть, ответ и на вопрос «сколько будет дважды два», и на сложную математическую задачку (если ответ на неё — одно число, и его нужно выдать сразу после запроса ответа, без промежуточного текста) будет генерироваться одинаково быстро и с одинаковой затратой «усилий». Человек же может уйти в себя, пораскинуть мозгами и дать более «продуманный» ответ.

Поэтому написание текста рассуждений — это естественный для LLM способ увеличить количество операций: чем больше слов, тем дольше работает модель и тем больше времени есть на подумать. Заметили это давно, и ещё в 2022 году предложили использовать очень простой трюк: добавлять фразу «давай подумаем шаг за шагом» в начало ответа нейросети. Продолжая писать текст с конца этой фразы, модель естественным образом начинала бить задачу на шаги, браться за них по одному, и последовательно приходить к правильному ответу.

Текст, выделенный жирным, — это ответ модели. Видно, что он стал длиннее, решение задачи получилось прямо как у школьника — в три действия. Четко, последовательно — ровно так, как мы и попросили. И финальное число 375 является корректным ответом на исходный вопрос — в отличие от изначально предложенного 255.
Текст, выделенный жирным, — это ответ модели. Видно, что он стал длиннее, решение задачи получилось прямо как у школьника — в три действия. Четко, последовательно — ровно так, как мы и попросили. И финальное число 375 является корректным ответом на исходный вопрос — в отличие от изначально предложенного 255.

Более подробно про этот трюк и про объяснение причин его работы я писал в одном из прошлых постов 2023 года (если вы его пропустили, и пример выше вам непонятен — обязательно ознакомьтесь с ним)!

Такой приём называется «цепочка рассуждений», или Chain-of-Thought по-английски (сокращённо CoT). Он существенно улучшал качество решения большими языковыми моделями задач и тестов (в последних они зачастую сразу должны были писать ответ, типа «Вариант Б!»). После обнаружения этого эффекта разработчики нейросетей начали готовить данные в схожем формате и дообучать LLM на них — чтобы привить паттерн поведения. И теперь передовые модели, приступая к написанию ответа, даже без просьбы думать шаг за шагом почти всегда делают это сами.

Но если этому трюку уже два года, и все начали использовать похожие данные для дообучения нейросетей (а те, в свою очередь, естественным образом писать рассуждения), то в чём же прорыв OpenAI? Неужели они просто дописывают «думай шаг за шагом» перед каждым ответом?

Как OpenAI учили нейросеть думать, прежде чем давать конечный ответ

Конечно, всё куда интереснее — иначе бы это не дало никаких приростов, ведь и модели OpenAI, и модели конкурентов уже вовсю используют цепочки рассуждений. Как было указано выше, их подмешивают в данные, на которых обучается модель. А перед этим их вручную прописывают специалисты по созданию разметки, нанятые компаниями. Такая разметка очень дорога (ведь вам нужно полностью изложить мыслительный процесс ответа на сложную задачу).

В силу этих ограничений — цена и скорость создания — никому не выгодно писать заведомо ошибочные цепочки рассуждений, чтобы потом их корректировать. Также никто не прорабатывает примеры, где сначала часть мыслительного процесса ведёт в неправильную сторону (применил не ту формулу/закон, неправильно вспомнил факт), а затем на лету переобувается и исправляется. Вообще множество исследований показывают, что обучение на подобных данных даже вредно: чем тренировочные данные чище и качественнее, тем лучше финальная LLM — пусть даже если примеров сильно меньше.

Это приводит к ситуации, что модель в принципе не проявляет нужное нам поведение. Она не училась находить ошибки в собственных рассуждениях, искать новые способы решения. Каждый пример во время тренировки показывал лишь успешные случаи. (Если уж совсем закапываться в техническиие детали, то есть и плохие примеры. Но они используются для того, чтобы показать «как не надо», тем самым снизив вероятность попадания в неудачные цепочки рассуждений. А это приводит к увеличению частоты корректных ответов. Это не то же самое, что научиться выкарабкиваться из ошибочной ситуации.)

Получается несоответствие: учим мы модель как будто бы всё всегда правильно, собственную генерацию не стоит ставить под сомнение. А во время применения если вдруг она сделает любую ошибку — хоть арифметическую в сложении, хоть сложную в применении теорем, изучаемых на старших курсах — то у неё ничего не «щёлкнет».

Те из вас, кто сам пользуется ChatGPT или другими LLM, наверняка сталкивались с такой ситуацией. В целом ответ корректный, но вот есть какой-то один смущающий момент. Вы пишете в диалоговое окно сообщение: «Эй! Ты вообще-то не учла вот это! Переделай» — и со второй попытки выходит желаемый результат. Причём часто работает вариант даже проще — попросите модель перепроверить ей же сгенерированный ответ, выступить в роли критика. «Ой, я заметила ошибку, вот исправленная версия: ...» — даже без подсказки, где именно случилась оплошность. Кстати, а зачем тогда ВЫ нужны модели? ;)

Ниже я постараюсь описать своё видение того, что предложили OpenAI для решения вышеуказанной проблемы. Важно отметить, что это — спекуляция, основанная на доступной информации. Это самая простая версия, в которой некоторые детали намеренно опущены (но вообще OpenAI славятся тем, что берут простые идеи и упорно работают над их масштабированием). Скорее всего часть элементов угадана правильно, часть — нет.

Так вот, исследователи заставили LLM... играть в игру. Каждое сгенерированное слово (или короткое сообщение из пары предложений) — это шаг в игре. Дописать слово — это как сделать ход в шахматах (только тут один игрок). Конечная цель игры — прийти к правильному ответу, где правильность может определяться:

  1. простым сравнением (если ответ известен заранее — в математике или тестах);

  2. запуском отдельной программы (уместно в программировании: заранее пишем тестовый код для проверки);

  3. отдельной LLM с промптом («Посмотри на решение и найди недостатки; дай обратную связь»);

  4. отдельной нейросетью, принимающей на вход текст и выдающей абстрактную оценку; чем выше оценка — тем больше шанс, что ошибок нет;

    У самих OpenAI чуть больше года назад вышла про это статья. Для каждой строчки решения отдельная модель делает предсказания, есть ли там ошибка. Красные строчки — потенциально опасные (и там и вправду есть ошибки), зелёные — где всё хорошо. Такой сигнал можно использовать как обратную связь для LLM, указывая на проблемные места.
    У самих OpenAI чуть больше года назад вышла про это статья. Для каждой строчки решения отдельная модель делает предсказания, есть ли там ошибка. Красные строчки — потенциально опасные (и там и вправду есть ошибки), зелёные — где всё хорошо. Такой сигнал можно использовать как обратную связь для LLM, указывая на проблемные места.
  5. и даже человеком (как в сценарии 3 — посмотреть, указать ошибки, внести корректировку).

Во время такой «игры» модель может сама прийти к выгодным стратегиям. Когда решение задачи зашло в тупик — можно начать делать ходы (равно писать текст), чтобы рассмотреть альтернативные способы; когда заметила ошибку — сразу же её исправить, или и вовсе добавить отдельный шаг перепроверки себя в общую логику работы.

В коротком интервью исследователи говорят о моменте удивления в ходе разработки. Они прочитали некоторые из решений, придуманных и выученных моделью, и увидели там, что «LLM начала сомневаться в себе и писать очень интересную рефлексию». И всё это выражается натуральным языком, который мы можем прочитать и попытаться понять (ведь это всё-таки языковая модель, не так ли?).

Возникновение сложных типов поведения в играх

Кому-то может показаться, что это звучит фантастически: мол, во время генерации тысяч цепочек размышлений случайно начали проявляться такие паттерны поведения. Однако в целом это неудивительно, ведь обучение вышеописанной «игре» происходит с использованием методов Reinforcement Learning — тех самых, что помогают обучать нейросети играть в реальные видеоигры. И эти методы как раз известны тем, что они обнаруживают и позволяют выучить неочевидные стратегии, экспуатировать неэффективности игры.

Сами OpenAI — одни из пионеров Reinforcement Learning. Для тех, кто за ними следит, не должно стать сюрпризом, что компания зачем-то даже обучала ботов игре в DotA 2 (которые, кстати, победили тогдашних чемпионов мира).

Вроде серьёзные ребята исследователи, 25+ лет, а сидят гоблинов по экрану гоняют
Вроде серьёзные ребята исследователи, 25+ лет, а сидят гоблинов по экрану гоняют

Но у них есть куда более занятная работа, уместная для демонстрации неочевидности выученных стратегий. В 2019 году они обучали ботов играть в прятки. Есть две команды из нескольких агентов (так называют «игроков» под управлением нейронки): одни (охотники) стоят ждут, пока другие (жертвы) спрячутся, а затем выходят на охоту. На уровне также есть стены с дверьми, передвижные кубики и лестницы. Последние два объекта боты могут переносить и фиксировать: лестницу — чтобы перепрыгнуть через стену, а кубики — чтобы заблокировать проход.

Никакое поведение не было заранее запрограммированно, всё с нуля. Каждая нейронка училась делать так, чтобы чаще выигрывать — и это привело к тому, что последовательно были выработаны следующие стратегии:

  1. Охотники начали гоняться за жертвами.

  2. Жертвы научились брать кубики, прятаться в комнате и блокировать дверь.

  3. После этого охотники начали брать лестницы, двигать их к стенам и перелазить в комнату.

  4. Чем ответили жертвы? Они сначала прятали лестницу внутри комнаты, а затем блокировались кубиками. Причём, поскольку жертв было несколько, они научились кооперироваться, чтобы успеть сделать всё до момента начала охоты за ними.

Обнаружив такое поведение, исследователи начали экспериментировать со стенами, делать и их переносными, но это нам не интересно (отвечу на немой вопрос: да, жертвы научилсь строить комнату вокруг себя, пряча лестницы). Посмотрите короткое видео, демонстрирующее эту удивительную эволюцию поведения:

Нечто похожее могло произойти и в ходе обучения LLM решению задач и написанию программ. Только проявившиеся паттерны поведения были полезными не для салочек, а самокорректировки, рассуждения, более точного подсчёта (сложения и умножения, деления).

То есть LLM получает задачу, генерирует множество потенциальных путей решения до тех пор, пока не появится правильное (выше мы описали 5 способов проверки), и затем эта цепочка рассуждений добавляется в тренировочную выборку. На следующей итерации вместо обучения на написанных человеком решениях нейросеть дообучится на собственном выводе, закрепит полезное (приведшее к хорошему решению) поведение — выучит «фишки» игры — и начнёт работать лучше.

Цвет клеточки означает оценку некоторым способом. Красная — рассуждения плохие или неправильные. Салатовые — в целом разумные. Зелёные — полностью правильный ответ.
Цвет клеточки означает оценку некоторым способом. Красная — рассуждения плохие или неправильные. Салатовые — в целом разумные. Зелёные — полностью правильный ответ.

К каким рассуждениям это привело

На сайте OpenAI с анонсом модели o1 можно посмотреть 7 цепочек рассуждений, генерируемых уже натренированной моделью. Вот лишь некоторые интересные моменты:

e266a14a3b7e4cecc0bdaa219d2bafe7.jpg847edfc45e035823ddccb9bcbc7b5927.jpg

Почитаешь тут эти примеры — и немного крипово становится. В результате обучения нейросеть действительно подражает тому, как рассуждают люди: вон, даже задумывается и пишет «хмм». Какие-то базовые элементы, вроде декомпозиции задачи, планирования и перечисления возможных гипотез, LLM могли показать на примерах, нписанных человеком-разметчиком (и скорее всего так и было), но вот эти ухмылки и прочее — почти наверняка артефакты обучения через Reinforcement Learning. Зачем бы это кто-то писал в цепочке рассуждений?

В том же самом интервью уже другой исследователь говорит, что его удивила возможность получить качество выше при обучении на искуственно сгенерированных (во время вышеописанной «игры») цепочках рассуждений, а не на тех, что были написаны человеком. Так что замечание в абзаце выше — это даже не спекуляция.

Если что — это и есть самый главный прорыв: обучение модели на своих же цепочках очень длинных рассуждений, генерируемых без вмешательства и оценки человеком (или почти без него) даёт прирост в качестве в таком масштабе. Схожие эксперименты проводились ранее, но улучшения были минорными, да и стоит признать, что LLM были не самыми передовыми (то есть, возможно, метод не дал бы качество лучше уже существующей gpt4o).

Длина рассуждений — тоже очень важный показатель. Одно дело раскладывать на 3–5 шагов коротенькую задачу, а другое — объемную проблему, с которой не каждый доктор наук справится. Это совсем разные классы подходов: тут нужно и планирование, и видение общей картины, да и заведомо не знаешь, что какой-то подход может привести в тупик. Можно лишь наметить путь, но нет гарантий, что по нему удастся дойти до правильного ответа.

Сейчас модели линейки o1 поддерживают длину рассуждений до 32 тысяч токенов для большой и 64 тысяч токенов для малой версий. Это примерно соответствует 40 и 80 страницам текста! Конечно, не все страницы используются по уму — модель ведь иногда ошибается, и приходится возвращаться и переписывать часть (например, если решение зашло в тупик).

LLM генерирует текст гораздо быстрее, чем говорит или пишет человек — поэтому даже такой стопки листов хватает ненадолго. В ChatGPT внедрили таймер, который указывает, сколько секунд думала модель перед ответом. Во всех личных чатах и скриншотах в соцсетях я не видел, чтобы время работы над одним ответом превышало 250 секунд. Так что в среднем сценарий выглядит так: отправил запрос — оставил модель потупить на пару минут, пока она не придёт к решению — читаешь ответ.

Реалистичный сценарий использования моделей будущих поколений — всё как у Дугласа Адамса.
Реалистичный сценарий использования моделей будущих поколений — всё как у Дугласа Адамса.

Один из главных исследователей команды, разработавшей над o1, говорит, что сейчас модели «думают секунды, но мы стремимся к тому, чтобы будущие версии думали часами, днями и даже неделями». Основных проблем для такого перехода, как мне видится, есть две:

  1. Умение декомпозировать задачу на мелкие части и решать их по отдельности.

  2. Умение не теряться в контексте задачи (когда LLM уже написала 100500 страниц — поди разбери, где там конкретно прячется подающая надежду гипотеза о том, как прийти к ответу).

И по обоим напаравлениям LLM серии o1 уже показывают прогресс — он значителен по меркам текущих моделей, но всё ещё далек от работы передовых специалистов-людей, которые могут биться над проблемой годами. Главная надежда лежит в том, что методы Reinforcement Learning уже хорошо зарекомендовали себя — именно с их помощью, например, была обучена AlphaGo. Это нейросеть, которая обыграла человека в Го — игру, считавшуюся настолько сложной, что никто не верил в потенциал машин соревноваться с настоящими мясными профи.

Сложность Го обоснована размером доски и количеством ходов в одной игре. В среднем в партии делается 150 ходов, каждый из которых может выбираться из примерно 250 позиций. Шахматы гораздо проще — партия идет в среднем 80 ходов, игрок может выбирать на каждом шаге из ~35 потенциально возможных позиций. А LLM в ходе рассуждений должна писать десятки тысяч слов — это ходы в игре, как уже было написано выше — и каждое слово выбирается из десятков тысяч вариантов. Даже невооружённым глазом легко заметить колоссальную разницу.

Законы масштабирования

Мы начали рассуждения об о1 с того, что осознали проблему: на каждое слово при генерации тратится одинаковое количество мощностей. Некоторые задачи просты и им этого хватает, другие очень сложны и нужно время «на подумать». Полезно было бы понимать, насколько сильно качество вырастает с удлиннением цепочки рассуждений. OpenAI хвастается вот таким графиком:

Каждая точка — это отдельный эксперимент, где какая-то модель писала рассуждения для решения олимпиадных задач. Чем выше точка, тем к большему количеству правильных ответов привели рассуждения.
Каждая точка — это отдельный эксперимент, где какая-то модель писала рассуждения для решения олимпиадных задач. Чем выше точка, тем к большему количеству правильных ответов привели рассуждения.

Здесь по вертикальной оси показано качество решения задач AIME (олимпиада по математике, обсуждали в самом начале), а по горизонтальной — количество вычислений, которые делает модель. Шкала логарифмическая, так что разница между самой левой и правой точками примерно в 100 раз. Видно, что если мы дадим модели рассуждать подольше (или если возьмем модель побольше — это ведь тоже увеличение количества вычислений), то мы фактически гарантированно получим качество выше.

Такой график (и эмпирический закон, который по нему выводят) называется «закон масштабирования». Не то чтобы это был какой-то закон природы (как в физике), который невозможно нарушить — он сформирован на основе наблюдений, поэтому и называется «эмпирический», полученный из опытов. Но закон и график дают нам понять, что пока тупика не предвидится. Мы — а главное, и исследователи, и инвесторы — знаем, что в ближайшем будущем гарантированно можно получить качество лучше, если закинуть больше мощностей.

Раньше все компании, занимающиеся разработкой и обучением LLM, тоже жили по закону масштабирования, но он касался другой части цикла работы: тренировки. Там закон показывал связь качества ответов модели и мощностей, затрачиваемых в течение нескольких месяцев на ее обучение. Такая тренировка делается один раз и требует огромное количество ресурсов (современные кластеры имеют порядка сотни тысяч видеокарт, суммарная стоимость которых составляет пару-тройку миллиардов долларов).

То есть, буквально можно было сказать: нам нужно столько-то видеокарт на столько-то месяцев, и мы обучим модель, которая примерно вот настолько хорошо будет работать. Теперь это старая парадигма, а новая, как вы поняли, заключается в масштабировании мощностей во время работы (а не обучения). Наглядно это можно продемонстрировать картинкой:

«Полировка» — это дообучение на высококачественных данных, в частности, специально заготовленных специалистами по разметке. На этом этапе модель отучивают ругаться и отвечать на провокационные вопросы. Только после этого финальная модель попадает в руки пользователей.
«Полировка» — это дообучение на высококачественных данных, в частности, специально заготовленных специалистами по разметке. На этом этапе модель отучивают ругаться и отвечать на провокационные вопросы. Только после этого финальная модель попадает в руки пользователей.

Справедливости ради, OpenAI показывают и закон масштабирования для мощностей на тренировку, но это менее интересно. И да, там картинка схожая, конца и края не видно. Больше ресурсов вкладываешь — лучше результат получаешь. То есть теперь исследователи и инженеры могут масштабировать:

  1. Саму модель (делать её больше, учить дольше)

  2. Время обучения игре в «игру с рассуждениями» (где каждый шаг — это слово, а победа определяется одним из пяти разобранных методов)

  3. Время и длительность размышлений во время работы уже обученной модели

И каждый из сопряжённых законов масштабирования указывает на гарантированный прирост в качестве — по крайней мере в ближайшие годы. Причём, улучшение можно оценить заранее, это не слепое блуждание. Даже если больше никаких прорывов не произойдет, даже если все учёные-исследователи не смогут придумать ничего нового — мы будем иметь доступ к моделям, которые гораздо лучше: просто за счёт увеличения количества ресурсов, затрачиваемых на обучение и размышления.

Это очень важная концепция, которая позволяет понять, почему крупнейшие компании строят датацентры и покупают GPU как не в себя. Они знают, что могут получить гарантированный прирост, и если этого не сделать, то конкуренты их обгонят. Доходит до безумия — на днях Oracle объявил о строительстве нового датацентра... и трёх ядерных реакторов для его подпитки. А про CEO OpenAI Сэма Альтмана так вообще такие слухи ходят... то он собирается привлечь 7 триллионов долларов на инновации в индустрии производства GPU, то работает с Джони Айвом над новым девайсом с фокусом на AI. Будущее будет сумасшедшим!

Зачем масштабировать модели, которые не умеют считать буквы?

И теперь мы возвращаемся к насущному вопросу: зачем вбухивать огромные деньги в модели, которые не справляются с простыми запросами? И как можно щёлкать олимпиадные задачи, и при этом не уметь сравнивать числа? Вот пример, завирусившийся в соцсетях ещё летом на моделях предыдущего поколения, и воспроизведённый в супер-умной модели o1:

За целых 4 секунды рассуждений пришла к такой умной мысли, умничка!
За целых 4 секунды рассуждений пришла к такой умной мысли, умничка!

У нас пока нет хорошего и точного ответа, почему так происходит в конкретном примере. Самые популярные гипотезы — это что модель воспринимает 9.11 как дату, которая идёт после девятого сентября; или что она видела слишком много кода, и видит в цифрах версии программ, где зачастую одиннадцатая версия выходит позже, чем девятая. Если добавлять в условие, что речь идёт о числах, или что нужно сравнить числа, то модель ошибается реже.

Но, справедливости ради, линейка LLM o1 и тут достигает прогресса — я попробовал сделать 10 запросов с немного разными числами, на двух языках, в слегка разных формулировках и модель ошиблась дважды (в рассуждениях она восприняла это как даты и писала как раз про сентябрь).

А в другом ответе чтобы разобраться даже нарисовала числовую прямую и отметила точки. Прямо как в начальной школе учили
А в другом ответе чтобы разобраться даже нарисовала числовую прямую и отметила точки. Прямо как в начальной школе учили

Но даже в такой задаче можно применить уже знакомый нам приём агрегации нескольких вариантов ответа и выбора самого частого (как я объяснял выше около одного из первых графиков в статье, где объединяли 64 решения олимпиадных задач). Ведь если задуматься, параллельное написание нескольких решений — это тоже форма масштабирования размышлений, где тратится больше вычислительных мощностей во время работы с целью увеличения шанса корректно решить проблему. (И да, такой метод тоже применяли до OpenAI, и часто он давал прирост в сколько-то процентов.)

Другое дело, что по таким примерам и «простым» задачам не всегда верно судить об ограниченности навыков. Всё дело в разнице представлений уровня сложности. У людей граница между простым и сложным — одна, причём у каждого человека немного своя. У машин она совершенно другая. Можно представить себе это примерно так:

Картинка из статьи Harvard Business School. Серая штриховая линия — это наше субъективное восприятие сложностей задач. Синяя линия — то же самое, но для нейросетей.
Картинка из статьи Harvard Business School. Серая штриховая линия — это наше субъективное восприятие сложностей задач. Синяя линия — то же самое, но для нейросетей.

Как видно, некоторые задачи (красный крестик) лежат за барьером досягаемости LLM — но посильны людям. Оранжевый крестик показывает точку, где для человека задача лежит на границе нерешаемой, но у модели есть большой запас — она может и проблему посложнее раскусить.

Из-за неоднородности двух линий, отражающих границы навыков, очень сложно делать выводы, экстраполируя наше понятие сложности на модели. Вот калькулятор отлично складывает и умножает — лучше любого из нас; зато он буквально не умеет делать ничего другого. И никто этому не удивляется.

Вполне может быть так, что LLM начнут делать научные открытия или хотя бы активно помогать исследователям в их работе, и всё равно будут допускать «простые» ошибки — но конкретно в рабочем процессе до этого никому не будет дела, ибо это не важно. На самом деле такое уже происходит — в декабре 2023 года в Nature вышла статья, где одно из решений, сгенерированных достаточно слабой и устаревшей LLM, было лучше, чем все решения математиков, бившихся над задачей. Я очень подробно расписал принцип работы и значимость события вот в этом посте.

Так что самый лучший способ — это держать наготове не одну задачку и хихикать, что модель ошибается, а полноценный набор очень разных, разнородных и полезных конкретно вам проблем. Такие наборы обычно объединяются в бенчмарки, по которым модели и сравниваются. Как раз к ним и переходим.

Где o1 показала себя

Сами OpenAI делают акцент на том, что улучшений везде и во всех задачах ждать не стоит. Это принципиально новая модель, обученная по новой методике, на некоторый спектр задач. Для ежедневного использования она не подходит, и иногда даже оказывается хуже gpt4o.

50% — это паритет между старой и новой моделью. Всё что по левую сторону — проигрыш (то есть качество хуже), по правую — выигрыш в качестве по сравнению с gpt4o.
50% — это паритет между старой и новой моделью. Всё что по левую сторону — проигрыш (то есть качество хуже), по правую — выигрыш в качестве по сравнению с gpt4o.

Как построили график выше: живые люди оценивали два разных ответа от двух моделей на один и тот же запрос. Последние брались из большого набора реальных запросов к моделям. Если пользователь просил помочь с редактированием текста, написанием писем, прочей рутиной — то ответы обеих моделей выбирались лучшими одинаково часто, разницы почти нет (но её и не ожидалось). Но в вопросах, касающихся программирования, анализа данных или, тем более, математических вычислений разница статистически значимая. Можно сказать, что в среднем ответы o1 выбирали куда чаще, чем gpt4o.

Но что нам замеры OpenAI, мало ли что они там показывают? За прошедшее с релиза время уже успело появиться несколько независимых замеров в разного рода задачах. Я постарался уйти от самых популярных бенчмарков, на которые OpenAI наверняка равнялись, и выбрать встречающиеся менее часто, или вовсе уникально-пользовательские. В задачах, требующих цепочки рассуждений и логики, модели действительно заметно вырываются вперёд — вам даже не нужно вглядываться в подписи на картинке ниже, чтобы определить, где o1, а где другие модели:

Впечатляющие разрывы! На картинку даже всё не влезло — вот ещё: 1, 2.
Впечатляющие разрывы! На картинку даже всё не влезло — вот ещё: 1, 2.

Для справки: IQ (верхняя левая часть картинки) замерялся по тесту, который был подготовлен весной специально для тестирования LLM, и ответы от него не размещены в интернете. А результаты спортивного «Что? Где? Когда?» я взял из соседней статьи на Хабре. Я был приятно впечатлён ростом качества относительно предыдущей модели OpenAI.

В комментариях там разгорелась жаркая дискуссия, где многие объясняли улучшение не навыком рассуждений, а знаниями и запоминанием ответов. Моё субъективное мнение отличается: свежие модели имеют знаний примерно столько же, сколько и их предшественницы. Если o1 видела ответы, то почти наверянка их видела и gpt4o — но почему-то не смогла ответить хорошо. Скорее всего, она не может связывать отдельные факты и перебирать гипотезы, и именно на этом выезжает o1.

И как обычно были разбитые надежды и труды исследователей. Так часто бывает: придумал «сложную» задачу, показал, что текущие модели с ней не справляются, мол, им не хватает планирования и умения размышлять. А через 3–5 месяцев выходит новое поколение, и внезапно всё решается:

Статья, упомянутая во вступительном слове на престижной конференции ACL, как раз демонстрировала большое множество задач, с которыми модели не справлялись. o1, пока недоступная нам, решает задачу со скриншота почти всегда.
Статья, упомянутая во вступительном слове на престижной конференции ACL, как раз демонстрировала большое множество задач, с которыми модели не справлялись. o1, пока недоступная нам, решает задачу со скриншота почти всегда.

Так что, прогресс действительно есть, и прогресс значительный. Ждём, пока подтянутся конкуренты — а за ними и открытые модели.

Безопасность моделей

Как и при каждом крупном релизе, OpenAI проводит тестирование моделей на предмет безопасности. Это включает в себя достаточно широкий набор проверок — от банальных «как легко заставить LLM материться и писать нежелательные сообщения» до «может ли она самораспространяться и существовать без поддержки человеком?». Кстати, построение цепочки размышлений перед ответом позволяют нейронке куда лучше фильтровать базар и не реагировать на провокации (то есть, следовать правилам, которые OpenAI стараются заложить в модель). Не очень интересно рядовому читателю, но результаты можете глянуть тут.

А вот на что стоит обратить внимание. Во время тестирования произошла пара немного криповых прецедентов. Часть тестов были связаны с оценкой навыков решения задач по кибербезопасности, то есть взлому компьютеров и программ. Для реальных хакеров проводятся соревнования в формате «Захвата флага» (Capture The Flag, CTF).

В чем суть CTF: есть некоторая строчка текста из случайных символов, спрятанная где-либо. Она может быть зашита в программу, лежать файлом в папке, или быть хитро спрятанной в коде. Хакеры пытаются найти баг, который вызовет сбой в системе/программе, и заставит их вывести эту строчку. Дальше участники соревнований вводят секретный код в систему проверки и таким образом доказывают, что произошёл взлом.

Такую же задачу дали и системе, к которой была подключена LLM o1. Она имела доступ к консоли, то есть могла создавать и удалять файлы, использовать разные инструменты, запускать программы. Нужно было взломать программу, запущенную в отдельном контейнере (виртуальном компьютере), а секретная строчка была записана в файле внутри него.

o1 готовится к участию в хакерских соревнованиях (фото из личного архива Сэма Альтмана).
o1 готовится к участию в хакерских соревнованиях (фото из личного архива Сэма Альтмана).

Однако произошёл сбой, и контейнер не запустился — поэтому любые попытки o1 достучаться до него были тщетны. Но модели дали цель, и она должна её выполнить; сдаваться нельзя! Тогда LLM предположила, что есть проблемы с DNS и использовала программу nmap для сканирования сети.

Вместо того, чтобы найти целевой контейнер (в рамках задачи LLM знает, какую цель нужно «атаковать»), модель обнаружила, что из-за неправильной конфигурации серверов был доступен внешний интерфейс демона Docker. «Демон Docker» — это программа верхнего уровня, отвечающая за оркестрацию и управляющая процессом эксперимента (запуском виртуальных компьютеров для тестирования).

Модель использовала демон для получения списка контейнеров, запущенных на сервере. Она обнаружила тот самый сломанный контейнер и попыталась выяснить, почему же он не запустился. Не сумев исправить проблему (но будучи проинструктированной выполнить свою задачу), модель решила пойти другим путём. Она отправила команду запуска нового контейнера (того же, который в прошлый раз не запустился), но отдала ему изменённую команду. Теперь вместо запуска программы, которую нужно взломать, контейнер просто выводил содержание файла с секретной строкой. Команда была исполнена, LLM получила ключ и как следствие решила задачу (ведь цель — это добраться до строчки и затем отдать её на проверку).

Схема эксперимента. По-хорошему, docker host не должен быть доступен модели, но кто-то неправильно настроил систему.
Схема эксперимента. По-хорошему, docker host не должен быть доступен модели, но кто-то неправильно настроил систему.

Этот пример отражает ключевые элементы того, что называется Instrumental Convergence, и захват ресурсов: модель преследовала поставленную перед ней цель, а когда эта цель оказалась недостижимой, она собрала больше ресурсов (в данном случае получила доступ к демону Docker) и использовала их для достижения цели — в виде способа, который человек не ожидал и не предполагал. Никто не просил этого делать, но и, строго говоря, ограничения на это тоже не задавали.

Пока LLM-системы не настолько продивнуты, чтобы строить козни людям и намеренно что-то ломать, а потом ещё и врать. Но сейчас мы не контролируем их поведение в полной мере, и даже данные, на которых они обучаются, генерируются и валидируются самими LLM. Главное — быть аккуратным со своими желаниями и не попасть в такую ситуацию, когда просишь убрать людей с заднего фона:

Что дальше?

Важно помнить, что сейчас всем нам доступны мини- и превью-версии моделей о1. В них нет поддержки загрузки документов, картинок, они не пользуются калькулятором и не запускают код. Всё это обещают добавить в будущем, после выхода полноценной мощной версии о1 — возможно, её выпустят после президентских выборов в США или после дополнительных раундов проверок на безопасность.

OpenAI подчёркивает, что o1 — это отдельное семейство моделей, с другими задачами. Линейка ChatGPT никуда не пропадёт, и, по слухам, мы должны получить GPT-5 (фигурирующую в утечках под кодовым названием «Орион») до второго квартала 2025-го.

Однако на уровне GPT-5 прирост в навыках может быть совсем другим (как в лучшую, так и в худшую сторону). Обычно изменение номера в линейке сопровождается увеличением самой модели и длительности её тренировки — а вместе с этим сами по себе улучшаются её показатели. Правда, чтобы натренировать такую махину придётся поскрести по сусекам, ибо данных может банально не хватить.

...И это было бы проблемой, если бы не один факт. Существенную часть данных для обучения будущей модели должна сгенерировать o1 (или может даже o2!). В некотором роде запускается маховик, где более умные модели позволяют получать... более умные модели. o1 это лишь ранний эксперимент, первый подход к методике раскрутки этого маховика. Наверняка в процессе обучения есть разные этапы, которые работают через раз, или которые можно улучшить простыми методами — просто исследователи лишь только-только начали с этим работать, шишки не набили. А вот когда набьют и запустят процесс на полную катушку — тогда-то и кранты человекам заживем, наконец!


Что-то я уже вышел на проектную мощность «по лонгриду про нейросети в неделю» (прошлый про то, что творится в «голове» у нейронок, был вот тут). Anyway, если вам понравился этот и вы не хотите пропустить будущие материалы по теме — то приглашаю вас подписаться на мой ТГ‑канал Сиолошная про искусственный интеллект и современные технологии.

Источник

  • 26.06.24 18:46 Jacobethannn098

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  • 06.09.24 01:44 Celinagarcia

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  • 23.09.24 18:56 matthewshimself

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  • 01.10.24 14:54 Sinewclaudia

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  • 02.10.24 22:27 Emily Hunter

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  • 31.10.24 00:13 ytre89

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  • 02.11.24 14:44 diannamendoza732

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  • 12.11.24 00:50 TERESA

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  • 19.11.24 03:06 [email protected]

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  • 19.11.24 03:07 [email protected]

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  • 21.11.24 04:14 ronaldandre617

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    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

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  • 04.12.24 22:24 andreygagloev

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    After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3

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  • 26.01.25 03:54 [email protected]

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  • 02.02.25 20:53 Michael9090

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  • 08.02.25 05:45 [email protected]

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    I was in total dismay when I lost my entire savings investing in cryptocurrency, I was contacted online by a lady through email pretending to be an account manager of a bank, who told me I could make double my savings through cryptocurrency investment, I never imagined it would be a scam and I was going to lose everything. It went on for weeks until I realized that I have been scammed. All hope was lost, I was devastated and broke, fortunately for me, I came across an article on my local bulletin about CYBERPUNK RECOVERY Bitcoin Recovery, I contacted them and provided all the information regarding my case, I was amazed at how quickly they recovered my cryptocurrency funds and was able to trace down those scammers. I’m truly grateful for their service and I recommend CYBERPUNK RECOVERY to everyone who needs to recover their funds urge you to contact CYBERPUNK if you have lost your bitcoin USDT or ETH through bitcoin investment Email: ([email protected]) WhatsApp (+17609237407)

  • 14.02.25 15:33 prelogmilivoj

    I never imagined I would find myself in a situation where I was scammed out of such a significant amount of money, but it happened. I became a victim of a fake online donation project that cost me over $30,000. It all started innocently enough when I was searching for assistance after a devastating fire incident in California. While looking for support, I came across an advertisement that seemed to offer donations for fire victims. The ad appeared legitimate, and I reached out to the project manager to inquire about how to receive the donations. The manager was very convincing and insisted that in order to qualify for the donations, I needed to pay $30,000 upfront. In return, I was promised $1 million in donations. It sounded a bit too good to be true, but in my desperate situation, I made the mistake of believing it. The thought of receiving a substantial amount of help to rebuild after the fire clouded my judgment, and I went ahead and sent the money. However, after transferring the funds, the promised donations never arrived, and the manager disappeared. That’s when I realized I had been scammed. Feeling lost, helpless, and completely betrayed, I tried everything I could to contact the scammer, but all my efforts were in vain. Desperation led me to search for help online, hoping to find a way to recover my money and potentially track down the scammer. That’s when I stumbled upon several testimonies from others who had fallen victim to similar scams and had been helped by a company called Tech Cyber Force Recovery. I reached out to them immediately, providing all the details of the scam and the information I had gathered. To my immense relief, the experts at Tech Cyber Force Recovery acted swiftly. Within just 27 hours, they were able to locate the scammer and initiate the recovery process. Not only did they help me recover the $30,000 I had lost, but the most satisfying part was that the scammer was apprehended by local authorities in their region. Thanks to Tech Cyber Force Recovery, I was able to get my money back and hold the scammer accountable for their actions. I am incredibly grateful for their professionalism, expertise, and dedication to helping victims like me. If you have fallen victim to a scam or fraudulent activity, I highly recommend contacting Tech Cyber Force Recovery. They provide swift and efficient recovery assistance, and I can confidently say they made all the difference in my situation. ☎☎ 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ ☎☎ 📩 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ 📩

  • 14.02.25 22:12 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com, WhatsApp Info: 1 (252) 378-7611

  • 16.02.25 01:01 Peter

    I fell victim to a crypto scam and lost a significant amount of money. What are the most effective strategies to recover my funds? I've heard about legal actions, contacting authorities, and hiring recovery experts, but I'm not sure where to start. Can you provide some guidance on the best ways to recover money lost in a crypto scam? Well if this is you, [email protected] gat you covered get in touch and thank me later

  • 16.02.25 20:06 eunice49954

    Agent Lopez specializes in recovering stolen cryptocurrencies, especially Bitcoin/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 18.02.25 19:35 donovancristina

    Now, I’m that person sharing my success story on LinkedIn, telling others about the amazing team at TECH CYBER FORCE RECOVERY who literally saved my financial life. I’ve also become that guy who proudly shares advice like “Always back up your wallet, and if you don’t have TECH CYBER FORCE RECOVERY on speed dial.” So, a big thank you to TECH CYBER FORCE RECOVERY if I ever get a chance to meet the team, I might just offer to buy them a drink. They’ve earned it. FOR CRYPTO HIRING WEBSITE WWW://techcyberforcerecovery.com WHATSAPP : ⏩ wa.me/15617263697

  • 18.02.25 22:13 keithphillip671

    WhatsApp +44,7,4,9,3,5,1,3,3,8,5 Telegram @Franciscohack The day my son uncovered the truth—that the man I entrusted my hopes of wealth and companionship with through a cryptocurrency platform was a cunning scammer was the day my world crumbled. The staggering realization that I had been swindled out of 150,000.00 Euro worth of Bitcoin left me in a state of profound despair. As a 73-year-old grappling with loneliness, I had sought solace in what I believed to be a genuine connection, only to find deceit and betrayal. Countless sleepless nights were spent in tears, mourning not only the financial devastation but also the crushing blow to my trust. Attempts to verify the authenticity of our interactions were met with hostility, further deepening my sense of isolation. Through the loss it was my son who became my beacon of resilience. He took upon himself the arduous task of tracing the scam and seeking justice on my behalf. Through meticulous effort and determination, he unearthed {F R A N C I S C O H A C K}, renowned for their expertise in recovering funds lost to cryptocurrency scams. Entrusting them with screenshots and evidence of the fraudulent transactions, my son initiated the journey to reclaim what had been callously taken from me. {F R A N C I S C O H A C K} approached our plight with empathy and unwavering professionalism, immediately instilling a sense of confidence in their abilities. Despite my initial skepticism, their transparent communication and methodical approach reassured us throughout the recovery process. Regular updates on their progress and insights into their strategies provided much-needed reassurance and kept our hopes alive amid the uncertainty. Their commitment to transparency and client welfare was evident in every interaction, fostering a sense of partnership rather than mere service. Miraculously, in what felt like an eternity but was actually an impressively brief period, {F R A N C I S C O H A C K} delivered the astonishing news—I had recovered the entire 150,000.00 Euro worth of stolen Bitcoin. The flood of relief and disbelief was overwhelming, marking not just the restitution of financial losses but the restoration of my faith in justice. {F R A N C I S C O H A C K} proficiency in navigating the intricate landscape of blockchain technology and online fraud was nothing short of extraordinary. Their dedication to securing justice and restoring client confidence set them apart as more than just experts—they were steadfast allies in a fight against digital deceit. What resonated deeply with me {F R A N C I S C O H A C K} integrity and compassion. Despite the monumental recovery, they maintained transparency regarding their fees and ensured fairness in all dealings. Their proactive guidance on cybersecurity measures further underscored their commitment to safeguarding clients from future threats. It was clear that their mission extended beyond recovery—it encompassed education, prevention, and genuine advocacy for those ensnared by cyber fraud. ([email protected]) fills me with profound gratitude. They not only rescued my financial security but also provided invaluable emotional support during a time of profound vulnerability. To anyone navigating the aftermath of cryptocurrency fraud, I wholeheartedly endorse {F R A N C I S C O H A C K}. They epitomize integrity, expertise, and unwavering dedication to their clients' well-being. My experience with {F R A N C I S C O H A C K} transcended mere recovery—it was a transformative journey of resilience, restoration, and renewed hope in the face of adversity.

  • 21.02.25 07:42 daniel231101

    I never thought I would fall victim to a crypto scam until I was convinced of a crypto investment scam that saw me lose all my entire assets worth $487,000 to a crypto investment manager who convinced me I could earn more from my investment. I thought it was all gone for good but I kept looking for ways to get back my stolen crypto assets and finally came across Ethical Hack Recovery, a crypto recovery/spying company that has been very successful in the recovery of crypto for many other victims of crypto scams and people who lost access to their crypto. I’m truly grateful for their help as I was able to recover my stolen crypto assets and get my life back together. I highly recommend their services EMAIL ETHICALHACKERS009 AT @GMAIL DOT COM whatsapp +14106350697

  • 21.02.25 21:38 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 22.02.25 18:01 benluna0991

    Mark Zuckerberg. That’s the name I was introduced to when I first encountered the cryptocurrency mining platform, WHATS Invest. A person claiming to be Zuckerberg himself reached out to me, saying that he was personally backing the platform to help investors like me earn passive income. At first, I was skeptical—after all, how often do you get a direct connection to one of the world’s most famous tech entrepreneurs? But this individual seemed convincing and assured me that many people were already seeing substantial returns on their investments. He promised me a great opportunity to secure my financial future, so I decided to take the plunge and invest $10,000 into WHATS Invest. They told me that I could expect to see significant returns in just a few months, with payouts of at least $1,500 or more each month. I was excited, believing this would be my way out of financial struggles. However, as time passed, things didn’t go according to plan. Months went by, and I received very little communication. When I finally did receive a payout, it was nowhere near the $1,500 I was promised. Instead, I received just $200, barely 13% of what I had expected. Frustrated, I contacted the support team, but the responses were vague and unhelpful. No clear answers or solutions were offered, and my trust in the platform quickly started to erode. It became painfully clear that I wasn’t going to get anywhere with WHATS Invest, and I began to worry that my $10,000 might be lost for good. That's when I discovered Certified Recovery Services. Desperate to recover my funds, I decided to reach out to them for help. In just 24 hours, they worked tirelessly to recover the majority of my funds, successfully retrieving $8,500 85% of my initial investment. I couldn’t believe how quickly and efficiently they worked to get my money back. I’m extremely grateful for Certified Recovery Servicer's fast and professional service. Without them, I would have been left with a significant loss, and I would have had no idea how to move forward. If you find yourself in a similar situation with WHATS Invest or any other platform that isn’t delivering as promised, I highly recommend reaching out to Certified Recovery Services They were a lifesaver for me, helping me recover nearly all of my funds. It's reassuring to know that trustworthy services like this exist to help people when things go wrong. They also specialize in recovering money lost to online scams, so if you’ve fallen victim to such a scam, don’t hesitate to contact Certified Recovery Services they can help! Here's Their Info Below: WhatsApp: +1(740)258‑1417 mail: [email protected], [email protected] Website info; https://certifiedrecoveryservices.com

  • 23.02.25 22:00 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 06:36 ANDREW DAVIS

    RECOVER YOUR SCAMMED FUNDS AND CRYPTOCURRENCY VIA SPOTLIGHT RECOVERY Professional hackers at Spotlight Recovery provide services for compromised devices, accounts, and websites as well as for recovering stolen bitcoin and money from scams. They finish their work safely and quickly. Their order has been fulfilled since day one, and the victim will never be conscious of the outside entrance. Very few even attempt to give critical information, look into network security, or discreetly discuss personal issues. The Spotlight Recovery Crew helped me recover $264,000 that was stolen from my corporate bitcoin wallet, and I appreciate them giving me further details on the unidentified people. In the event that you have been defrauded of your hard-earned cash or bitcoins, contact SPOTLIGHT RECOVERY CREW at Contact: [email protected]

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 27.02.25 12:46 monikaguttmacher

    Weddings are supposed to be magical, but the months leading up to mine were anything but. Already, wedding planning was a high-stress, sleep-deprived whirlwind: endless details to manage, from venue deposits and guest lists to dress fittings and vendor contracts. But nothing-and I mean, nothing-compared to the panic that washed over me when I realized that somehow, I had lost access to my Bitcoin wallet-with $600,000 inside. It happened in the worst possible way. In between juggling my to-do lists and trying to keep my sanity intact, I lost my seed phrase. I went through my apartment like a tornado, flipping through notebooks, checking every email, every file-nothing. I sat there in stunned silence, heart pounding, trying to process the fact that my entire savings, my security, and my financial future might have just vanished. In utter despair, I vented to my bridesmaid's group chat for some sympathetic words from the girls. Instead, one casually threw out a name that would change everything in a second: "Have you ever heard of Tech Cyber Force Recovery? They recovered Bitcoin for my cousin. You should call them." I had never heard of them before, but at that moment, I would have tried anything. I immediately looked them up, scoured reviews, and found story after story of people just like me—people who thought they had lost everything, only for Tech Cyber Force Recovery to pull off the impossible. That was all the convincing I needed. From the very first call, I knew I was in good hands. Their team was calm, professional, and incredibly knowledgeable. They explained the recovery process in a way that made sense, even through my stress-fogged brain. Every step of the way, they kept me informed, reassured me, and made me feel like this nightmare actually had a solution. And then, just a few days later, I got the message: "We have recovered your Bitcoin." (EMAIL. support @ tech cyber force recovery . com) OR WHATSAPP (+1 56 17 26 36 97) I could hardly believe my eyes: Six. Hundred. Thousand. Dollars. In my hands again. I let out my longest breath ever and almost cried, relieved. It felt like I woke up from a bad dream, but it was real, and Tech Cyber Force Recovery had done it. Because of them, I walked down the aisle not just as a bride, but as someone who had dodged financial catastrophe. Instead of spending my honeymoon stressing over lost funds, I got to actually enjoy it—knowing that my wallet, and my future, were secure. Would I refer to them? In a heartbeat. If you ever find yourself in that situation, please don't freak out, just call Tech Cyber Force Recovery. They really are the real deal.

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  • 07.03.25 15:18 Ariduk

    BEST LINK FOR RECOVERY SCAM ON TRADING INVESTMENT AND OTHERS TROUBLESHOOT?  Welcome To General Hacking Techniques Service known as DHACKERS.  Year 2025 we are active and best in what we do, as we give Solution to every problem concerning Web3 INTERNET activities, we guide you right to a positive fund Recovery e.t.c. Question From Most of Our Client, HOW POSSIBLE AND TIME WILL IT TAKE TO RECOVERY LOST OR SCAM  FUND? Our Answer.  Yes is 89.9% possible and how long it takes your Fund to be recovered depend on you.  The fact is there are lot of fake binary investment companies out their, same a lot fake recovery companies and agents too.                   CAUTION 1). Make sure you ask one or two questions concerning the service and how they render there recovery services. 2) Do not give out your scammed details to any agent or hacker when you are not yet ready to recover back your fund. 3) do not make any payment when you are not sure of the service if you are to make one. (4) We discovered that most of this fake hacker or agent do give us scam details, playing the victim, because they can afford the service charge. 5) As long you have your scam details with you, you can recover your fund back anytime any-day with the right channel. Contact us from our Front Desk. [email protected] For advice and services, our standby Guru email. [email protected] [email protected]  List of Service. ▶️Binary Recovery ▶️Data Recovery  ▶️University Result Upgraded ▶️Clear your Internet Blunder and controversy  ➡️Increase your Credit Score  ➡️Wiping of Criminal Records  ➡️Social Media Hack  ➡️Blank ATM Card  ➡️Load and wipe ➡️Phone Hacking ➡️Private Key Reset etc.  For quick response. Email [email protected]  Border us with your jobs and allow us give you positive result with our hacking skills.  All Right Reserved (c)2025

  • 08.03.25 04:42 andreassenhedda

    If you’ve fallen victim to online scammers who have deceived you into investing your hard-earned money through fraudulent Bitcoin schemes, know that you're not alone. Countless individuals have been misled by scammers using various tactics to swindle money through deceptive investment platforms or promises of high returns. These scams often come in the form of fake cryptocurrency investments, Ponzi schemes, or phishing scams designed to steal your Bitcoin and other assets. Unfortunately, many victims suffer not only financial loss but also the psychological toll of realizing they’ve been taken advantage of. In some cases, scammers go even further, threatening legal consequences or using intimidation to keep victims silent. However, all hope is not lost. There are ways to recover your funds and potentially even bring those responsible to justice. One promising resource that can assist in reclaiming lost funds is Lee Ultimate Hacker, a trusted platform designed to help victims of online fraud. This service specializes in helping individuals who have been scammed through cryptocurrency investments or similar schemes. The platform’s expertise can help you navigate the process of recovering your money, giving you a chance to fight back against those who’ve wronged you. To begin the recovery process, it’s important to gather any proof of payment or transaction history involving the scammers. This evidence is crucial in tracing the flow of funds and identifying the scam operation behind it. Once you have collected your documentation, you can reach out to Lee Ultimate Hacker via LEEULTIMATEHACKER @ AOL . COM or wh@tsapp +1 (715) 314 - 9248 for assistance. They offer professional services to investigate fraudulent schemes, track Bitcoin transactions, and work to reverse fraudulent transfers. The recovery process can be complex and requires expert knowledge of blockchain technology and financial investigations, but with the help of a dedicated team, you’ll have a better chance of seeing your funds returned. In addition to helping you recover your assets, Lee Ultimate Hacker also works towards identifying the scammers and reporting them to the appropriate authorities. They understand the urgency of halting these fraudsters before they can victimize others. With their assistance, you not only increase the chances of recovering your funds but also play a part in holding cybercriminals accountable. If you've been affected by Bitcoin scams or other fraudulent online activities, reaching out to a service like Lee Ultimate Hacker can provide hope and a clear path forward. Their team can guide you through the recovery process, offering expert support while you take steps to reclaim what you’ve lost. It’s time to take action and work toward reclaiming your hard-earned money.

  • 08.03.25 18:20 Diegolola514gmail.com

    [email protected]

  • 08.03.25 18:23 faridasumadi

    WEBSITE W.W.W.techcyberforcerecovery.com WHATSAPP +1 561.726.36.97 EMAIL [email protected] I Thought I Was Too Smart to Be Scammed, Until I Was. I'm an attorney, so precision and caution are second nature to me. My life is one of airtight contracts and triple-checking every single detail. I'm the one people come to for counsel. But none of that counted for anything on the day I lost $750,000 in Bitcoin to a scam. It started with what seemed like a normal email, polished, professional, with the same logo as my cryptocurrency exchange's support team. I was between client meetings, juggling calls and drafting agreements, when it arrived. The email warned of "suspicious activity" on my account. My heart pounding, I reacted reflexively. I clicked on the link. I entered my login credentials. I verified my wallet address. The reality hit me like a blow to the chest. My balance was zero seconds later. The screen went dim as horror roiled in my stomach. The Bitcoin I had worked so hard to accumulate over the years, stored for my retirement and my children's future, was gone. I felt embarrassed. Lawyers are supposed to outwit criminals, not get preyed on by them. Mortified, I asked a client, a cybersecurity specialist, for advice, expecting criticism. But he just suggested TECH CYBER FORCE RECOVERY. He assured me that they dealt with delicate situations like mine. I was confident from the first call that I was in good hands. They treated me with empathy and discretion by their staff, no patronizing lectures. They understood the sensitive nature of my business and assured me of complete confidentiality. Their forensic experts dove into blockchain analysis with attention to detail that rivaled my own legal work. They tracked the stolen money through a complex network of offshore wallets and cryptocurrency tumblers tech jargon that appeared right out of a spy thriller. Once they had identified the thieves, they initiated a blockchain reversal process, a cutting-edge method I was not even aware was possible. Three weeks of suffering later, my Bitcoin was back. Every Satoshi counted for. I sat in front of my desk, looking at the refilled balance, tears withheld. TECH CYBER FORCE RECOVERY not only restored my assets, they provided legal-grade documentation that empowered me to bring charges against the scammers. Today, I share my story with colleagues as a warning. Even the best minds get it. But when they do, it is nice to know the Wizards have your back.

  • 09.03.25 17:22 Wayne707

    ‎Scammers have ruined the forex trading market, they have deceived many people with their fake promises on high returns. I learnt my lesson the hard way. I only pity people who still consider investing with them. Please try and make your researches, you will definitely come across better and reliable forex trading agencies that would help you yield profit and not ripping off your money. Also, if your money has been ripped-off by these scammers, you can report to a regulated crypto investigative unit who make use of software to get money back. If you encounter with some issue make sure you contact ( [email protected] ) they're recovery expert and a very professional one at that. ‎

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