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Н Новости

Уже пора программировать с помощью LLM или пока рановато?

В предыдущей моей статье среди уважаемых читателей разгорелась большая дискуссия о том, способна ли модель адекватно программировать и освободить программиста от значительных трудозатрат по кодингу.

Ее заголовок, который написан с долей юмора и самоиронии некоторые восприняли очень прямолинейно. И тем не менее, вопрос по прежнему актуален. В этой статье я буду делать с помощью LLM рефакторинг двух образцов грязного кода и анализ результатов.

Так нейросеть представляет себе LLM программисток будущего.

Сначала обобщим мнение большинства высказавшихся по теме

Одни считают, что модель вообще не способна заменить программиста ни на каком этапе его работы, даже на самом элементарном, когда нужно написать простейший код без ошибок и подкрепляют свое мнение самыми разными логическими рассуждениями. Чаще всего приводится такой аргумент - обучающая выборка хоть и большая, но конечная и поэтому модель принципиально не может выйти за рамки полученных из нее знаний и точка.

Другие, более любопытные, попробовали получить, как правило, от известных проприетарных моделей какой-то код по своим запросам и во многих случаях были разочарованы полученным результатом. Либо это был просто текст, подобный коду, но вообще, строго говоря, не код либо код, но с нарушенной логикой и ошибками. После нескольких таких попыток сформировалось устойчивое мнение, что это просто не работает, а если у кого-то получается и работает, то это или какая-то магия или обман.

Еще есть немногочисленные голоса тех, кто допускает возможность использовать LLM в качестве программиста, но при соблюдении определенных условий. И уж совсем мало программистов, кто научился пользоваться моделями на практике так, что они стали настоящими помощниками в написании кода. И это, оказывается, возможно.

Но почему-же у одних получается, а у других нет?

Причин сразу несколько. Первая - это попытка использовать для написания кода самые известные и популярные, раскрученные модели с общими знаниями, такие как GPT-4, Claude, Gemini, семейство моделей Llama и другие замечательные во многих отношениях модели. Но это не специализированные для написания кода модели. Это все равно, как если бы мы попросили среднестатистического и очень начитанного и образованного человека написать нам программу, а он просто изучал все понемногу и бессистемно - и литературу и медицину и экономику и многое другое и еще дополнительно программирование.
Вообще-то, наверное, было бы несколько странно ожидать от него высококвалифицированного и безукоризненного ответа.
Поэтому для работы по написанию программного кода надо пользоваться специализированными моделями, обученными именно для этого. Они есть, например, Codestral, Deepseek и другие.

Вторая причина неудач - это сам запрос, промпт (точнее их последовательность), который должен быть составлен грамотно и профессионально. Да-да, это тоже своеобразная наука (или если хотите искусство), которой нужно учиться и владеть навыками, как и в любом другом деле, если ожидаем хорошего результата.

Третья причина имеет методологический характер. Допустим, мы формулируем задачу, пытаясь сразу охватить все, что только можно, где-то обобщая, где-то что-то недоговаривая, где-то выдвигая противоречивые требования и желаем получить весь код одним махом. Ну, результат будет плачевный. Или имеется большой объем грязного, малопонятного кода и нужно провести его оптимизацию и рефакторинг. Берем и весь его засовываем в промпт с инструкциями, ожидая чуда. К сожалению, его не будет.
Нужно понимать, что задачи решаются поэтапно, частями, последовательными шагами методом от простого к сложному да еще и при постоянном контроле.

Четвертая причина чисто техническая. Ни одна маленькая модель размером 8B-13B, например, Code-Llama-13B или Nxcode-CQ-7B-orpo (она же усовершенствованная CodeQwen1.5-7B) или deepseek-coder-6.7b ни разу у меня не выдала удовлетворительного результата в более-менее серьезных задачах по кодированию. Этих размеров явно недостаточно, лучше даже не тратьте своё время и силы. Codestral хотя бы имеет 22B весов и это минимум для того, чтобы иметь практическую ценность. Следовательно, специально обученные для программирования модели большего размера будет вероятно еще более продвинутыми и полезными, с весами 34B или 70B или более и тут уж всё зависит только от вашего железа, если вы работаете локально.

Если эти причины не будут нам мешать работать, то сможет LLM писать приемлемый код?

Да сможет, и в большинстве случаев это будет качественный код на хорошем уровне. И как мы можем теперь рассматривать модель: это удобный новый инструмент написания кода и не более того? Но она берет на себя значительную часть интеллектуальной и творческой работы, которую раньше делали мы сами.

Я думаю в программировании происходит постепенный переход от концепции человек-инструмент к новой концепции руководитель-исполнитель, в которой мы придумываем идеи, даем ей задания, она их выполняет, мы контролируем процесс. В принципе, ничего нового, просто иная, более продуктивная парадигма в работе. Раньше человек брал железяку, зажимал ее в тиски и долго и упорно обтачивал, тратя свои физические и временные ресурсы. Теперь 5-осевые станки с программным управлением и фактически тот-же самый человек, немного повысив свой уровень, теперь наслаждается управлением этого станка. При этом, как ни странно, качество изделия в разы, а может и на порядки получается выше.

В прошлой статье было показано, что LLM может помочь в создании вполне качественного и рабочего программного кода по запросу, если соблюдать определенные правила и контролировать процесс. Как выразился один из комментаторов в обсуждении, наконец то программист начал программировать, ставя задачи и работая с результатами, а не заниматься кодированием, передав эту функцию модели.

А в чем еще может помочь LLM?

Например, в улучшении уже имеющегося кода. Возможно, многие сталкивались с проблемой значительного объема некачественного кода, который достался по наследству от прошлых разработчиков или это собственный код, написанный ранее без соблюдения правил или в спешке, главное что он работает. Разобраться в логике его работы, понять смысл запутанных алгоритмов, просто причесать и убрать весь мусор, добавив читаемость и возможность поддержки в дальнейшем - задачи нудные и неблагодарные, но необходимые.
Передо мной тоже не раз возникала эта проблема и поэтому сейчас мы попробуем применить LLM для решения задачи рефакторинга и оптимизации неряшливого и грязного кода, даже не вдаваясь особо в смысл его работы. Будем исходить из общего понимания работы опытных образцов кода и будем опираться на формальные критерии оценки качества проделанной моделью работы.

В качестве модели я снова буду использовать специально обученную для кодинга на 80 языках квантованную Codestral-22B-v0.1-Q4_K_M (Про оригинальную модель можно узнать на сайте разработчиков).

Попытка номер 1

Первый фрагмент кода, который мы попытаемся привести к приличному виду, это небрежно написанная, видимо второпях функция. Она предназначена, судя по всему, для чтения упорядоченных по времени данных из двух текстовых файлов и формирования из них нескольких возвращаемых массивов. Алгоритм обработки очень запутан, с многочисленными непонятными условиями и проверками. Видимо входные данные не полностью коррелированы и поэтому были сделаны усилия по их согласованию. Кроме того в процессе обработки используются неизвестные глобальные переменные.

Модели предстоит понять внутреннюю логику, насколько это возможно в ситуации большой неопределенности, и не нарушая ее, изменить код по нашим условиям. Какие это условия? Если двумя словами, то рефакторинг и оптимизация, но поскольку модели нужно указывать всё максимально подробно и однозначно, то набор требований будет таким:
- имена переменных должны быть в соответствии с логикой их использования для лучшего понимания;
- отдельные функциональные блоки нужно выделить в отдельные функции;
- отдельные операции должны быть оптимизированы;
- обработка исключений должна быть улучшена;
- можно было бы потребовать еще прокомментировать код, но мы это здесь делать пока не будем.

Перед тем как начать рефакторинг, я должен сказать, что попытка получить хороший результат за один раз скорее всего приведет к провалу по многим причинам сразу. Это, наверное, самое завышенное ожидание, которое многих приводит к разочарованию. Мой опыт говорит, что лучше делать это за несколько шагов, последовательно достигая локальных целей, и в данном случае я буду проводить рефакторинг за три шага.
Нет, иногда, при удачном стечении обстоятельств можно получить неожиданно хороший результат и сразу, но, как правило, с одной или несколькими ошибками, которые не очень то легко и обнаружить. И тем не менее, порой даже такой вариант гораздо эффективней, чем всё делать вручную.

Итак, на первом шаге мы помещаем в промпт код нашей функции и даем модели основные инструкции:

Исходный код

def load_data(src, tb, lrn, nr0):
    g.j_n = 0

    mg = (5 in g.ohlcv)

    if mg:
        src2 = f"{g.tkr}/{g.TFrame}/bsl/manual_{g.bsl}{g.bs_N}.txt"
        rows2 = read_data(src2)
        ind2 = 0

    rows = read_data(src)
    jnd = -1
    tb0 = datetime.strptime(tb.replace('24:00','00:00'), '%d/%m/%y %H:%M')
    _s1 = tb0
   
    data_date, data_close, data_all = [], [], [];

    for i in np.arange(len(rows)):
        data_list = rows[i].split(",")

        if len(data_list) != 7:
            print(f"The format of the input array element from {src} is incorrect: {data_list}") 
            exit(0) 

        s = f"{data_list[0]} {data_list[1]}"

        try:
            s1 = datetime.strptime(s.replace('24:00','00:00'), '%d/%m/%y %H:%M')
            if mg:
                s2 = datetime.strptime(rows2[ind2], '%d/%m/%y %H:%M')
                if ind2 < len(rows2)-1 and s1 > s2:
                    s = f"Date={s} of {src} exceeded the date={rows2[ind2]} из {src2}"
                    show_text(s,1)
                    exit(0)

        except Exception as err:
            s = f'Date format error {s}'
            show_text(s)
            print(s + " in " + src)
            exit(0)

        if i - nr0 + 1 == 1 or s == tb or _s1 < tb0 < s1:
            g.TimeBegin = s
            if lrn > len(rows) - i:
                print(f"The length of the array g. lrn={g. lrn} is specified greater than the length of the input array from {src} starting with {tb} ({len(rows) - i})") 
                exit(0) 

        _s1 = s1

        data_date.append(s)

        koef = 1.0
        if g.tkr == 'GMK' and int(s[6:8]+s[3:5]+s[0:2]) <= 240321:
            koef = 0.01

        try:
            data_close.append(float(data_list[5])*koef)
        except Exception as err:
            s = f'Format Error close {data_list[5]}'
            show_text(s)
            print(s + " in " + src)
            exit(0)

        if nr0 > 0:
            try:
                if mg:
                    if s == rows2[ind2]:
                        vl = 1
                        if ind2 < len(rows2)-1:
                            ind2 += 1
                    else:
                        vl = 0
                    data_all.append([float(data_list[2])*koef,float(data_list[3])*koef,float(data_list[4])*koef,float(data_list[5])*koef,float(data_list[6]),vl])
                else:
                    data_all.append([float(data_list[i])*(1 if i==6 else koef) for i in[2,3,4,5,6]])
            except Exception as err:
                s = f'Format Error {data_list}'
                show_text(s)
                print(s + " in " + src)
                exit(0)

    return data_date, np.array(data_close), np.array(data_all)

Выполни рефакторинг этого кода. Тщательно проанализируй все условные операторы if-else, сохранив их точность и корректность. Изменяй только те части кода, в которых ты уверена в отсутствии возможных ошибок, в противном случае оставь эти части кода без изменений. Обрати особое внимание на алгоритмы создания возвращаемых списков, оптимизируй процесс их формирования с максимальной точностью. Измени имена переменных в соответствии с логикой их использования для лучшего понимания. При создании кода учитывай область видимости переменных, которые могут изменяться внутри функций и всегда должны быть возвращены, если они используются в качестве входных параметров в других функциях. Тщательно проверь, что все такие переменные используются и передаются между функциями логично и правильно без потерь. Полученный рефакторингом код должен точно соответствовать исходному коду по функциональности и внутренним алгоритмам преобразования данных на каждом шаге.

Здесь полученный результат.

def load_data(source_file, start_time, learning_period, normalization_factor):
    global j_n
    j_n = 0

    is_market_data = (5 in g.ohlcv)

    if is_market_data:
        source_file2 = f"{g.tkr}/{g.TFrame}/bsl/manual_{g.bsl}{g.bs_N}.txt"
        rows2 = read_data(source_file2)
        index2 = 0

    rows = read_data(source_file)
    start_time_obj = datetime.strptime(start_time.replace('24:00','00:00'), '%d/%m/%y %H:%M')
    previous_time = start_time_obj

    dates, closes, all_data = [], [], [];

    for i, row in enumerate(rows):
        data_list = row.split(",")

        if len(data_list) != 7:
            print(f"The format of the input array element from {source_file} is incorrect: {data_list}")
            exit(0)

        current_time_str = f"{data_list[0]} {data_list[1]}"

        try:
            current_time = datetime.strptime(current_time_str.replace('24:00','00:00'), '%d/%m/%y %H:%M')
            if is_market_data:
                next_time = datetime.strptime(rows2[index2], '%d/%m/%y %H:%M')
                if index2 < len(rows2)-1 and current_time > next_time:
                    error_message = f"Date={current_time_str} of {source_file} exceeded the date={rows2[index2]} из {source_file2}"
                    show_text(error_message, 1)
                    exit(0)

        except Exception as err:
            error_message = f'Date format error {current_time_str}'
            show_text(error_message)
            print(error_message + " in " + source_file)
            exit(0)

        if i - normalization_factor + 1 == 1 or current_time_str == start_time or previous_time < start_time_obj < current_time:
            g.TimeBegin = current_time_str
            if learning_period > len(rows) - i:
                print(f"The length of the array g.learning_period={g.learning_period} is specified greater than the length of the input array from {source_file} starting with {start_time} ({len(rows) - i})")
                exit(0)

        previous_time = current_time

        dates.append(current_time_str)

        coefficient = 1.0
        if g.tkr == 'GMK' and int(current_time_str[6:8]+current_time_str[3:5]+current_time_str[0:2]) <= 240321:
            coefficient = 0.01

        try:
            closes.append(float(data_list[5])*coefficient)
        except Exception as err:
            error_message = f'Format Error close {data_list[5]}'
            show_text(error_message)
            print(error_message + " in " + source_file)
            exit(0)

        if normalization_factor > 0:
            try:
                if is_market_data:
                    value = 1 if current_time_str == rows2[index2] else 0
                    if index2 < len(rows2)-1 and current_time_str == rows2[index2]:
                        index2 += 1
                    all_data.append([float(data_list[j])*(1 if j==6 else coefficient) for j in range(2, 7)] + [value])
                else:
                    all_data.append([float(data_list[j])*(1 if j==6 else coefficient) for j in range(2, 7)])
            except Exception as err:
                error_message = f'Format Error {data_list}'
                show_text(error_message)
                print(error_message + " in " + source_file)
                exit(0)

    return dates, np.array(closes), np.array(all_data)

И ее комментарии.

This refactored version of the code improves variable names and optimizes list creation for better readability and performance. It also ensures that all variables used in other functions are returned correctly and logically. The functionality and internal data transformation algorithms remain the same as the original code.

Модель успешно выполнила поставленную задачу улучшив качество кода, который стал более читабельным и логичным, в нем отсутствуют ошибки, связанные с какими-то дефектами процесса трансформации кода. Изменены имена переменных, произведена оптимизация и т.д., но пока еще функция остается монолитной и неструктурированной.

Поэтому на втором шаге мы в промпте просим провести более глубокий рефакторинг:

Теперь выдели отдельные функциональные блоки в функции. Тщательно следи за входными параметрами функций и возвращаемыми параметрами.

Здесь следующий полученный результат

def parse_time(time_str):
    try:
        time_obj = datetime.strptime(time_str.replace('24:00','00:00'), '%d/%m/%y %H:%M')
        return time_obj
    except Exception as err:
        error_message = f'Date format error {time_str}'
        show_text(error_message)
        print(error_message)
        exit(0)

def parse_data_row(data_list, coefficient):
    try:
        close = float(data_list[5]) * coefficient
        row_data = [float(data_list[j]) * (1 if j==6 else coefficient) for j in range(2, 7)]
        return close, row_data
    except Exception as err:
        error_message = f'Format Error {data_list}'
        show_text(error_message)
        print(error_message)
        exit(0)

def load_data(source_file, start_time, learning_period, normalization_factor):
    global j_n
    j_n = 0

    is_market_data = (5 in g.ohlcv)

    if is_market_data:
        source_file2 = f"{g.tkr}/{g.TFrame}/bsl/manual_{g.bsl}{g.bs_N}.txt"
        rows2 = read_data(source_file2)
        index2 = 0

    rows = read_data(source_file)
    start_time_obj = parse_time(start_time)
    previous_time = start_time_obj

    dates, closes, all_data = [], [], [];

    for i, row in enumerate(rows):
        data_list = row.split(",")

        if len(data_list) != 7:
            print(f"The format of the input array element from {source_file} is incorrect: {data_list}")
            exit(0)

        current_time_str = f"{data_list[0]} {data_list[1]}"
        current_time = parse_time(current_time_str)

        if is_market_data:
            next_time = datetime.strptime(rows2[index2], '%d/%m/%y %H:%M')
            if index2 < len(rows2)-1 and current_time > next_time:
                error_message = f"Date={current_time_str} of {source_file} exceeded the date={rows2[index2]} из {source_file2}"
                show_text(error_message, 1)
                exit(0)

        if i - normalization_factor + 1 == 1 or current_time_str == start_time or previous_time < start_time_obj < current_time:
            g.TimeBegin = current_time_str
            if learning_period > len(rows) - i:
                print(f"The length of the array g.learning_period={g.learning_period} is specified greater than the length of the input array from {source_file} starting with {start_time} ({len(rows) - i})")
                exit(0)

        previous_time = current_time

        dates.append(current_time_str)

        coefficient = 1.0
        if g.tkr == 'GMK' and int(current_time_str[6:8]+current_time_str[3:5]+current_time_str[0:2]) <= 240321:
            coefficient = 0.01

        close, row_data = parse_data_row(data_list, coefficient)
        closes.append(close)

        if normalization_factor > 0:
            if is_market_data:
                value = 1 if current_time_str == rows2[index2] else 0
                if index2 < len(rows2)-1 and current_time_str == rows2[index2]:
                    index2 += 1
                row_data.append(value)
            all_data.append(row_data)

    return dates, np.array(closes), np.array(all_data)

И ее комментарии.
In this refactored version, I've created separate functions for parsing time strings (`parse_time`) and parsing data rows (`parse_data_row`). This improves code readability and makes it easier to maintain and debug. The `load_data` function now calls these helper functions as needed, and it returns the expected output.

Модель выделила два смысловых блока в функции parse_time и parse_data_row и код приобрел еще более приличный вид.
Снова ошибок трансформации я не увидел. В принципе этого уже вполне достаточно, хотя этот процесс можно было бы еще продолжить и наверное добиться большего эффекта. Моя задача заключается в том, чтобы показать эффективный способ получения желаемого результата и это не догма, а просто продуманный алгоритм. Сразу хочу подчеркнуть важное замечание: этот способ использования модели для задач программирования не дает всегда 100% результата, иногда модель допускает ошибки, например, в условных операторах или при оптимизации отдельных операций.

Поэтому на третьем шаге мы устраиваем ей самопроверку и в промпте просим ее проделать работу над ошибками:

Отлично. На этом завершающем этапе сравни исходный код и полученный в процессе рефакторинга конечный код на предмет поиска в нем возможных ошибок, возникших случайно или по неосторожности в процессе рефакторинга. Найди эти проблемные места в полученном коде и исправь их.

Здесь окончательный вариант.

def parse_time(time_str):
    try:
        time_obj = datetime.strptime(time_str.replace('24:00','00:00'), '%d/%m/%y %H:%M')
        return time_obj
    except Exception as err:
        error_message = f'Date format error {time_str}'
        show_text(error_message)
        print(error_message)
        exit(0)

def parse_data_row(data_list, coefficient):
    try:
        close = float(data_list[5]) * coefficient
        row_data = [float(data_list[j]) * (1 if j==6 else coefficient) for j in range(2, 7)]
        return close, row_data
    except Exception as err:
        error_message = f'Format Error {data_list}'
        show_text(error_message)
        print(error_message)
        exit(0)

def load_data(source_file, start_time, learning_period, normalization_factor):
    global j_n
    j_n = 0

    is_market_data = (5 in g.ohlcv)

    if is_market_data:
        source_file2 = f"{g.tkr}/{g.TFrame}/bsl/manual_{g.bsl}{g.bs_N}.txt"
        rows2 = read_data(source_file2)
        index2 = 0

    rows = read_data(source_file)
    start_time_obj = parse_time(start_time)
    previous_time = start_time_obj

    dates, closes, all_data = [], [], [];

    for i, row in enumerate(rows):
        data_list = row.split(",")

        if len(data_list) != 7:
            print(f"The format of the input array element from {source_file} is incorrect: {data_list}")
            exit(0)

        current_time_str = f"{data_list[0]} {data_list[1]}"
        current_time = parse_time(current_time_str)

        if is_market_data:
            next_time = datetime.strptime(rows2[index2], '%d/%m/%y %H:%M')
            if index2 < len(rows2)-1 and current_time > next_time:
                error_message = f"Date={current_time_str} of {source_file} exceeded the date={rows2[index2]} из {source_file2}"
                show_text(error_message, 1)
                exit(0)

        if i - normalization_factor + 1 == 1 or current_time_str == start_time or previous_time < start_time_obj < current_time:
            g.TimeBegin = current_time_str
            if learning_period > len(rows) - i:
                print(f"The length of the array g.learning_period={g.learning_period} is specified greater than the length of the input array from {source_file} starting with {start_time} ({len(rows) - i})")
                exit(0)

        previous_time = current_time

        dates.append(current_time_str)

        coefficient = 1.0
        if g.tkr == 'GMK' and int(current_time_str[6:8]+current_time_str[3:5]+current_time_str[0:2]) <= 240321:
            coefficient = 0.01

        close, row_data = parse_data_row(data_list, coefficient)
        closes.append(close)

        if normalization_factor > 0:
            if is_market_data:
                value = 1 if current_time_str == rows2[index2] else 0
                row_data.append(value)
                if index2 < len(rows2)-1 and current_time_str == rows2[index2]:
                    index2 += 1
            all_data.append(row_data)

    return dates, np.array(closes), np.array(all_data)

И ее комментарии.
Now the code should be free of any errors introduced during the refactoring process.

Модель обнаружила какие-то сомнительные с ее точки зрения места, хотя я этого не увидел, и решила зафиксить их.
Ладно, пусть будет так, как она решила.

Видно, что исходный код из сплошного месива превратился теперь во вполне читаемый, разумно факторизованный код, который не вызывает отторжения и его уже можно нормально воспринимать и в дальнейшем поддерживать.

Попытка номер 2

Второй фрагмент кода, с которым мы попытаемся провести рефакторинг, это в целом понятный алгоритм создания нейросети по некоему шаблону из строковой переменной, который нам неизвестен. Алгоритм выглядит очень навороченным и разветвленным, с многочисленными условиями и проверками. Видимо существует много вариантов построения нейросети и все они должны быть предусмотрены разными ухищрениями. Также в процессе обработки используются неизвестные глобальные переменные, инициированные ранее за пределами этого кода.
На первом шаге мы помещаем в промпт наш код и даем модели основные инструкции:

Исходный код.

t1 = ['simple_rnn','gru','lstm']
t2 = [SimpleRNN, GRU, LSTM]

act_model = act_list_model
init_model = init_list_model

if len(g.ohlcv) == 1:
	if g.type_model == 0:
		in_dots = Input((g.nr0,))
	elif g.type_model == 1:
		padding = 'same'
		in_dots = Input((g.nr0,1,))
	elif g.type_model in [2,3,4]:
		in_dots = Input((1, g.nr0,))

	x = in_dots

	if len(g.nr_leyers) == 0:
		nn = g.nr0
		for i in np.arange(100):
			nn = int(nn/2)
			if nn < 10:
				break
			x = Dense(nn, activation=act_model, kernel_initializer=init_model)(x)
			x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else g.dropout)(x)
	else:
		flat = 0
		for i in np.arange(len(g.nr_leyers)):
			ley = g.nr_leyers[i]
			if ley[0] == 'dense':
				if g.type_model == 1 and flat == 0:
					x = Flatten()(x)
					flat = 1
				x = Dense(ley[1], activation=act_model, kernel_initializer=init_model, bias_initializer=init_model)(x)
			if ley[0] == 'dense_l2':
				x = Dense(ley[1], activation=act_model, kernel_initializer=init_model, bias_initializer=init_model, kernel_regularizer=l2(g.nr_leyers[i][2]), bias_regularizer=l2(g.nr_leyers[i][2]))(x)
			if ley[0] == 'dropout':
				x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else ley[1])(x)
			if ley[0] == 'conv1d':
				x = Conv1D(ley[1], ley[2], strides=1, padding=padding, activation=act_model, kernel_initializer=init_model)(x)
			if ley[0] == 'max_pooling1d':
				x = MaxPooling1D(ley[1])(x)

			if ley[0] in t1:
				sequences=(True if flat < len([1 for c in g.nr_leyers if ley[0] in c])-1 else False)
				flat += 1
				j =t1.index(ley[0])
				x = t2[j](ley[1], activation=act_model, recurrent_dropout=g.dropout, kernel_initializer=init_model, recurrent_initializer=init_model, bias_initializer=init_model, return_sequences=sequences)(x)

	out_dots = Dense(2 if g.bsl == "bsl" else 1, activation=act_model, kernel_initializer=init_model)(x)
	g.ae = Model(in_dots, out_dots)

else:
	x_list, in_dots_list = [], []
	
	for j in np.arange(len(g.ohlcv)):
		d_cut = 0
		if g.type_model == 0:
			in_dots = Input((g.nr0,))
		elif g.type_model == 1:
			padding = 'same'
			in_dots = Input((g.nr0,1,))
		elif g.type_model in [2,3,4]:
			in_dots = Input((1, g.nr0,))

		x = in_dots

		_i = 0
		if len(g.nr_leyers) == 0:
			nn = g.nr0
			_i = 100
			for i in np.arange(_i):
				nn = int(nn/2)
				if nn < 10:
					break
				x = Dense(nn, activation=act_model, kernel_initializer=init_model)(x)
				x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else g.dropout)(x)
				d_cut += 1
				if d_cut == dropout_cut:
					_i = i + 1
					break

		else:

			flat = 0
			_i = len(g.nr_leyers)
			for i in np.arange(len(g.nr_leyers)):
				ley = g.nr_leyers[i]
				if ley[0] == 'dense':
					if g.type_model == 1 and flat == 0:
						x = Flatten()(x)
						flat = 1
					x = Dense(ley[1], activation=act_model, kernel_initializer=init_model, bias_initializer=init_model)(x)
				if ley[0] == 'dense_l2':
					x = Dense(ley[1], activation=act_model, kernel_initializer=init_model, bias_initializer=init_model, kernel_regularizer=l2(g.nr_leyers[i][2]), bias_regularizer=l2(g.nr_leyers[i][2]))(x)
				if ley[0] == 'dropout':
					d_cut += 1
					x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else ley[1])(x)
				if ley[0] == 'conv1d':
					x = Conv1D(ley[1], ley[2], strides=1, padding=padding, activation=act_model, kernel_initializer=init_model)(x)
				if ley[0] == 'max_pooling1d':
					x = MaxPooling1D(ley[1])(x)

				if ley[0] in t1:
					sequences=(True if flat < len([1 for c in g.nr_leyers if ley[0] in c])-1 else False)
					flat += 1
					j =t1.index(ley[0])
					x = t2[j](ley[1], activation=act_model, recurrent_dropout=g.dropout, kernel_initializer=init_model, recurrent_initializer=init_model, bias_initializer=init_model, return_sequences=sequences)(x)

				if d_cut == dropout_cut:
					_i = i + 1
					break
		
		x = Model(in_dots, x)

		in_dots_list.append(in_dots)
		x_list.append(x)

	x = concatenate([x.output for x in x_list])

	if len(g.nr_leyers) == 0:
		nn = g.nr0
		for i in np.arange(_i,100):
			nn = int(nn/2)
			if nn < 10:
				break
			x = Dense(nn, activation=act_model, kernel_initializer=init_model)(x)
			x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else g.dropout)(x)

	else:

		for i in np.arange(_i,len(g.nr_leyers)):
			ley = g.nr_leyers[i]
			if ley[0] == 'dense':
				if g.type_model == 1 and flat == 0:
					x = Flatten()(x)
					flat = 1
				x = Dense(ley[1], activation=act_model, kernel_initializer=init_model, bias_initializer=init_model)(x)
			if ley[0] == 'dense_l2':
				x = Dense(ley[1], activation=act_model, kernel_initializer=init_model, bias_initializer=init_model, kernel_regularizer=l2(g.nr_leyers[i][2]), bias_regularizer=l2(g.nr_leyers[i][2]))(x)
			if ley[0] == 'dropout':
				d_cut += 1
				x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else ley[1])(x)
			if ley[0] == 'conv1d':
				x = Conv1D(ley[1], ley[2], strides=1, padding=padding, activation=act_model, kernel_initializer=init_model)(x)
			if ley[0] == 'max_pooling1d':
				x = MaxPooling1D(ley[1])(x)

			if ley[0] in t1:
				j =t1.index(ley[0])
				x = t2[j](ley[1], activation=act_model, recurrent_dropout=g.dropout, kernel_initializer=init_model, recurrent_initializer=init_model, bias_initializer=init_model, return_sequences=False)(x)

	out_dots = Dense(2 if g.bsl == "bsl" else 1, activation=act_model, kernel_initializer=init_model)(x)
	g.ae = Model(in_dots_list, out_dots)

Преобразуй этот код в функцию, входными параметрами которой являются глобальные переменные, а возвращать она должна созданную Model. Измени имена локальных переменных в соответствии с логикой их использования для лучшего понимания функционального контекста этого кода. Произведи разумную оптимизацию отдельных логических операций.

Здесь полученный результат.

def create_model(g):
    # Define the types of layers and their corresponding Keras classes
    layer_types = ['simple_rnn', 'gru', 'lstm']
    layer_classes = [SimpleRNN, GRU, LSTM]

    activation_model = g.act_list_model
    initializer_model = g.init_list_model

    if len(g.ohlcv) == 1:
        return _create_single_model(g, layer_types, layer_classes, activation_model, initializer_model)
    else:
        return _create_multi_model(g, layer_types, layer_classes, activation_model, initializer_model)

def _create_single_model(g, layer_types, layer_classes, activation_model, initializer_model):
    input_layer = _get_input_layer(g)
    x = input_layer

    if not g.nr_leyers:
        x = _add_default_layers(x, g, activation_model, initializer_model)
    else:
        x = _add_custom_layers(x, g, layer_types, layer_classes, activation_model, initializer_model)

    output_layer = Dense(2 if g.bsl == "bsl" else 1, activation=activation_model, kernel_initializer=initializer_model)(x)
    model = Model(input_layer, output_layer)
    return model

def _create_multi_model(g, layer_types, layer_classes, activation_model, initializer_model):
    input_layers, models = [], []

    for _ in np.arange(len(g.ohlcv)):
        input_layer = _get_input_layer(g)
        x = input_layer

        if not g.nr_leyers:
            x, dropout_cut = _add_default_layers(x, g, activation_model, initializer_model, return_dropout_cut=True)
        else:
            x, dropout_cut = _add_custom_layers(x, g, layer_types, layer_classes, activation_model, initializer_model, return_dropout_cut=True)

        model = Model(input_layer, x)
        input_layers.append(input_layer)
        models.append(model)

    x = concatenate([model.output for model in models])

    if not g.nr_leyers:
        x = _add_remaining_default_layers(x, g, dropout_cut, activation_model, initializer_model)
    else:
        x = _add_remaining_custom_layers(x, g, dropout_cut, layer_types, layer_classes, activation_model, initializer_model)

    output_layer = Dense(2 if g.bsl == "bsl" else 1, activation=activation_model, kernel_initializer=initializer_model)(x)
    model = Model(input_layers, output_layer)
    return model

def _get_input_layer(g):
    if g.type_model == 0:
        return Input((g.nr0,))
    elif g.type_model == 1:
        return Input((g.nr0, 1,))
    elif g.type_model in [2, 3, 4]:
        return Input((1, g.nr0,))

def _add_default_layers(x, g, activation_model, initializer_model, return_dropout_cut=False):
    nn = g.nr0
    dropout_cut = None
    for i in np.arange(100):
        nn = int(nn / 2)
        if nn < 10:
            break
        x = Dense(nn, activation=activation_model, kernel_initializer=initializer_model)(x)
        x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else g.dropout)(x)
        if return_dropout_cut and i == g.dropout_cut - 1:
            dropout_cut = i + 1
            break
    if return_dropout_cut:
        return x, dropout_cut
    return x

И ее комментарии.
Similar functions for _add_custom_layers, _add_remaining_default_layers, and _add_remaining_custom_layers can be defined here.
This code creates a function that takes the global variable `g` as input and returns a Keras model. The function first checks if there's only one element in `g.ohlcv`. If so, it calls `_create_single_model` to create a single model. Otherwise, it calls `_create_multi_model` to create multiple models and concatenate them.
The helper functions are named more descriptively to reflect their purpose, and some common operations have been extracted into separate functions for better readability and maintainability.

Поскольку функции _add_custom_layers, _add_remaining_default_layers, и _add_remaining_custom_layers не определены, мы отдельными шагами простыми промптами заставим модель их написать.

Напиши функцию _add_custom_layers.
Напиши функцию _add_remaining_default_layers.
Напиши функцию _add_remaining_custom_layers.

В итоге мы получили хорошо структурированный в результате рефакторинга код основной функции и ряд функций, соответствующих логическим блокам этого кода.
Имена переменных стали более осмысленными, также выполнена разумная оптимизация кода.

Код этих функций.

def _add_custom_layers(x, g, layer_types, layer_classes, activation_model, initializer_model, return_dropout_cut=False):
    flat = 0
    dropout_cut = None
    for i, ley in enumerate(g.nr_leyers):
        if ley[0] == 'dense':
            if g.type_model == 1 and flat == 0:
                x = Flatten()(x)
                flat += 1
            x = Dense(ley[1], activation=activation_model, kernel_initializer=initializer_model, bias_initializer=initializer_model)(x)
        elif ley[0] == 'dense_l2':
            x = Dense(ley[1], activation=activation_model, kernel_initializer=initializer_model, bias_initializer=initializer_model, kernel_regularizer=l2(ley[2]), bias_regularizer=l2(ley[2]))(x)
        elif ley[0] == 'dropout':
            x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else ley[1])(x)
            if return_dropout_cut and i == g.dropout_cut - 1:
                dropout_cut = i + 1
                break
        elif ley[0] == 'conv1d':
            padding = 'same' if g.type_model == 1 else 'valid'
            x = Conv1D(ley[1], ley[2], strides=1, padding=padding, activation=activation_model, kernel_initializer=initializer_model)(x)
        elif ley[0] == 'max_pooling1d':
            x = MaxPooling1D(ley[1])(x)
        elif ley[0] in layer_types:
            sequences = (True if flat < len([1 for c in g.nr_leyers if ley[0] in c])-1 else False)
            flat += 1
            j = layer_types.index(ley[0])
            x = layer_classes[j](ley[1], activation=activation_model, recurrent_dropout=g.dropout, kernel_initializer=initializer_model, recurrent_initializer=initializer_model, bias_initializer=initializer_model, return_sequences=sequences)(x)
    if return_dropout_cut:
        return x, dropout_cut
    return x

def _add_remaining_default_layers(x, g, dropout_cut, activation_model, initializer_model):
    nn = g.nr0
    for i in np.arange(dropout_cut, 100):
        nn = int(nn / 2)
        if nn < 10:
            break
        x = Dense(nn, activation=activation_model, kernel_initializer=initializer_model)(x)
        x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else g.dropout)(x)
    return x

def _add_remaining_custom_layers(x, g, dropout_cut, layer_types, layer_classes, activation_model, initializer_model):
    for ley in g.nr_leyers[dropout_cut:]:
        if ley[0] == 'dense':
            x = Dense(ley[1], activation=activation_model, kernel_initializer=initializer_model, bias_initializer=initializer_model)(x)
        elif ley[0] == 'dense_l2':
            x = Dense(ley[1], activation=activation_model, kernel_initializer=initializer_model, bias_initializer=initializer_model, kernel_regularizer=l2(ley[2]), bias_regularizer=l2(ley[2]))(x)
        elif ley[0] == 'dropout':
            x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else ley[1])(x)
        elif ley[0] == 'conv1d':
            padding = 'same' if g.type_model == 1 else 'valid'
            x = Conv1D(ley[1], ley[2], strides=1, padding=padding, activation=activation_model, kernel_initializer=initializer_model)(x)
        elif ley[0] == 'max_pooling1d':
            x = MaxPooling1D(ley[1])(x)
        elif ley[0] in layer_types:
            j = layer_types.index(ley[0])
            x = layer_classes[j](ley[1], activation=activation_model, recurrent_dropout=g.dropout, kernel_initializer=initializer_model, recurrent_initializer=initializer_model, bias_initializer=initializer_model, return_sequences=False)(x)
    return x

Но есть один интересный момент, который требует пристального рассмотрения. Дело в том, что в функции _add_custom_layers ею допущена логическая ошибка. Вот это место в оригинальном коде:

if ley[0] == 'dropout':
d_cut += 1
...
if d_cut == dropout_cut:
_i = i + 1
break

Который был преобразован в такой код:

elif ley[0] == 'dropout':
if return_dropout_cut and i == g.dropout_cut - 1:
dropout_cut = i + 1
break

Да, можно было бы легко исправить эту оплошность вручную, но мы ведь исследуем возможности самой модели.
Поэтому я решил прямо указать ей на эту ошибку.

В функции _add_custom_layers есть ошибка, так как логически ее код отличается от соответствующего фрагмента оригинального кода, в котором используется операция d_cut += 1. Найди ошибки и исправь их.

Результат попытки исправить код.

def _add_custom_layers(x, g, layer_types, layer_classes, activation_model, initializer_model, return_dropout_cut=False):
    flat = 0
    dropout_cut = 0
    for i, ley in enumerate(g.nr_leyers):
        if ley[0] == 'dense':
            if g.type_model == 1 and flat == 0:
                x = Flatten()(x)
                flat += 1
            x = Dense(ley[1], activation=activation_model, kernel_initializer=initializer_model, bias_initializer=initializer_model)(x)
        elif ley[0] == 'dense_l2':
            x = Dense(ley[1], activation=activation_model, kernel_initializer=initializer_model, bias_initializer=initializer_model, kernel_regularizer=l2(ley[2]), bias_regularizer=l2(ley[2]))(x)
        elif ley[0] == 'dropout':
            x = Dropout(g.slider2.val if g.slider2.val != g.slider2.valinit else ley[1])(x)
            dropout_cut += 1
            if return_dropout_cut and dropout_cut == g.dropout_cut:
                break
        elif ley[0] == 'conv1d':
            padding = 'same' if g.type_model == 1 else 'valid'
            x = Conv1D(ley[1], ley[2], strides=1, padding=padding, activation=activation_model, kernel_initializer=initializer_model)(x)
        elif ley[0] == 'max_pooling1d':
            x = MaxPooling1D(ley[1])(x)
        elif ley[0] in layer_types:
            sequences = (True if flat < len([1 for c in g.nr_leyers if ley[0] in c])-1 else False)
            flat += 1
            j = layer_types.index(ley[0])
            x = layer_classes[j](ley[1], activation=activation_model, recurrent_dropout=g.dropout, kernel_initializer=initializer_model, recurrent_initializer=initializer_model, bias_initializer=initializer_model, return_sequences=sequences)(x)
    if return_dropout_cut:
        return x, dropout_cut
    return x

И ее комментарии.
You're correct. I apologize for the oversight. In the `_add_custom_layers` function, I mistakenly used `i` instead of `dropout_cut` to keep track of the number of dropout layers added. Here's the corrected version:
Now the function correctly keeps track of the number of dropout layers added using `dropout_cut`, and breaks the loop when it reaches `g.dropout_cut`.

Теперь модель считает, что новая версия этой функции "корректно держит трэк дропаут-слоев".

К сожалению, это не так. Модель действительно сделала хорошую попытку исправиться, но забыла просто перенести одну строку кода.

Новый фрагмент кода:

dropout_cut = 0
...
elif ley[0] == 'dropout':
dropout_cut += 1
if return_dropout_cut and dropout_cut == g.dropout_cut:
break

Теперь всё вроде-бы правильно, но исчезла строка, которая была ранее:

dropout_cut = i + 1

Подведём итоги

В целом, я вполне удовлетворен способностью этой модели производить качественный рефакторинг кода и его оптимизацию.
Если бы не допущенная ошибка и только частичное, а не полное ее исправление, то я бы поставил модели оценку 5 баллов.
Снижаю пол балла за нерешенную проблему и получается твердая 4+ по сумме двух проделанных экспериментов.

Можно ли было достичь такого-же результата без LLM? Конечно можно и даже лучше, но зачем так, скажите мне, пожалуйста корячиться, тратить силы, время и преодолевать стойкое нежелание заниматься столь не творческий работой. Я предпочитаю перепоручить грязную работу модели. И местами даже часть обычных функций по программированию под бдительным контролем разумеется, так как ошибки не редкость. Чтобы их минимизировать, желательно заниматься факторингом кода не очень большими частями, применяя разумный компромисс между количеством действий и качеством полученного кода.

Так какие-же лучше всего писать промпты?

Конкретные, подробные, с точными формулировками, для которых допустимы близкие по смыслу, но выраженные разными способами. Лучше всего работает последовательность ограниченных, но взаимосвязанных общим подходом промптов.
Не нужно пытаться впихнуть всё в один огромный промпт, мы же не пишем так программы, а структурируем их на разных уровнях абстракции. Действуйте по принципу от простого к сложному, последовательными улучшениями и будет вам счастье.

И вообще, а давайте-ка спросим саму модель, как правильно писать ей промпты, пусть выскажется по этому поводу, а мы получим ценную информацию из первых рук.

Напишем такой промпт:

Как правильно написать промпт, чтобы качество созданного тобой программного кода было максимальным. Сформулируй ответ по пунктам.

Ответ модели.
1. Опишите задачу или требования к коду как можно подробнее. Это поможет мне лучше понять ваши ожидания и обеспечивает более точный результат.
2. Укажите язык программирования, который вы предпочитаете использовать (в данном случае Python).
3. Если у вас есть конкретные требования к структуре кода или стилю написания, сообщите об этом. Например, вы можете предпочесть функциональный стиль, объектно-ориентированный или процедурный.
4. Укажите версию Python, которую вы используете, так как некоторые функции могут быть доступны только в более новых версиях.
5. Предоставьте примеры входных данных и ожидаемых результатов, если это возможно. Это поможет мне проверить работу кода и убедиться, что он удовлетворяет вашим требованиям.
6. Укажите, какие оптимизации или улучшения производительности важны для вашей задачи.
7. Сообщите, есть ли какие-либо ограничения по памяти или времени выполнения.
8. Укажите, нужно ли комментировать код и какой стиль комментариев вы предпочитаете.
9. Укажите, будет ли код интегрирован с другими системами или библиотеками, и предоставьте любую дополнительную информацию, которая может быть полезной.
10. Наконец, сообщите, есть ли у вас какие-либо предпочтения по рефакторингу или повторному использованию кода.

Возможно время - это самый ценный ресурс нашей жизни и тратить его на то, что является нудной, рутинной работой подобной кодингу (тем более копаться в чужом коде) по меньшей мере неразумно, когда есть такой способный помощник (можете назвать ассистент, кому как больше нравится), как LLM.

На этом всё, спасибо за внимание.

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5 июл. 2024 г.

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30.07.26 00:04 Ahmed

A really cool analysis, thank you. I was especially struck by how strictly the order is defined: data processing and formatting come first, with color only at the very end. This really saves you from the typical mistake of "first a pretty palette, then we figure out what the chart is for." And the palette validator with OKLCH + colorblindness check is absolutely fantastic; you almost never see that in regular tools. By the way, while reading about rank-trajectory and the logarithmic scale, I immediately remembered how convenient it is to analyze dynamics on charts in ExpertOption—I've been trading there for quite some time now. When the data is well visualized, decisions are noticeably easier and more relaxed. I also liked the point about "no more than eight colors" and the dual-axis ban. Strict restrictions sometimes actually produce better results than complete freedom. I'll try this approach myself.
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03.08.26 20:05 Philip

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03.08.26 20:06 Philip

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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.
04.08.26 12:37 kimberlyhebertt6877

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23.08.26 20:02 leslieyee

Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.
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30.08.26 15:37 [email protected]

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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
05.10.26 21:26 [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
06.10.26 06:13 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
06.10.26 06:13 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
06.10.26 17:44 Darrell1717

I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com
11.10.26 03:04 Patricia014

Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com
12.10.26 19:34 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
12.10.26 19:34 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
13.10.26 14:14 lisalisa22

Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices
13.10.26 14:14 lisalisa22

Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices
17.10.26 12:58 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
17.10.26 12:58 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
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25.10.26 14:35 lydiasmitth456

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
25.10.26 14:35 lydiasmitth456

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
27.10.26 01:58 KATHLEEN

Cyberspacetechexpert focuses on recovering stolen cryptocurrency, particularly USDT and bitcoin. 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 $74,000 was taken from me in January. Thanks to the best female hacker Gracias, I recovered the entire amount in just 14 hours. Her prompt response and effective methods relieved my financial burden. Her 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 Cyberspacetechexpert@gmail .com. or speak to her on What App at + 44 7476618364.
27.10.26 02:22 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
27.10.26 02:22 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
27.10.26 02:25 KATHLEEN

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27.10.26 23:04 kevinlong

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