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Н Новости

Как я писал систему, которая упростит поиск квартиры мечты

Откуда взялась идея

Покупка квартиры — достаточно сложное и дорогое дело. Хочется выбрать лучшее из того, что есть на рынке и не прогадать по цене. С такими мыслями наша семья начинала поиски квартиры на вторичном рынке в Санкт Петербурге в начале 2023 года.

Объявления в России принято искать на многочисленных площадках с недвижимостью: Циан, Авито, Дом Клик и так далее У каждой свои механики и множество объектов недвижимости. Я применял фильтры «станция метро», «время до метро», «максимальная цена», «минимальная площадь» на каждой из них, чтобы получать список релевантных объявлений. Квартир, которые бы очень хотелось купить не нашлось и необходимо ежедневно было просматривать новые, на случай, если такие появятся. Но вскоре я понял, что это отвлекает и занимает много времени.

Кроме того, мы были готовы пожертвовать частью параметров, если цена бы была действительно хорошей. Но с учетом этого, нам пришлось бы просматривать вообще все объявления, удовлетворяющие определенному ценовому диапазону, и пытаться их как‑то сравнивать между собой.

Хотелось иметь возможность для абсолютного сравнения. Чтобы взять и выполнить сортировку по самым выгодным объектам, да еще и сразу на всех площадках.

Да, я понимал, что самые выгодные предложения — это скорее всего те, где ты в итоге можно остаться и без квартиры и без денег. Но была надежда на дополнительные фильтрации, которые отсекут совсем нереальные варианты. Например, при средней цене в 250 тысяч рублей за квадратный метр, предложения по 150 тысяч довольно подозрительны.

Формулирование задачи

Рассуждения звучали здраво, и я загорелся мыслью — написать приложение для авторанжирования объявлений недвижимости. Задача звучала амбициозно. На написание такого функционала могло уйти гораздо больше времени, чем на ручной просмотр объявлений в течение долгого времени. Зато был бы получен бесценный опыт.

Но перед тем, как что‑то писать, необходимо было определиться, что хочется в итоге получить. Была сформулирована такая цель: необходим сервис, который будет собирать объявления с разных платформ. Далее, каждому из объявлений должен был присваиваться параметр, стоит ли квартира своих денег. Параметр мог быть числом от 0 до 1. На основании этого числа уже можно было бы делать сортировку.

Оставалось придумать определение — что значит, что квартира стоит своих денег. По моим рассуждениям, выгодная квартира — это та, которая за цену квадрата предлагает больше, чем аналогичные в данной локации. Ее, в случае чего, хотя бы можно продать без сильных потерь.

Конечно, в настоящем мире все не так просто. Дома могут быть разные, рядом находиться полезные социальные объекты или вредные заводы. Но хотелось уже начать с чего‑нибудь, и не усложнять систему раньше времени. Пока остановился на том, что:

Среди рядом расположенных двух квартир с одинаковым ремонтом лучше та, что дешевле.
Среди рядом расположенных двух квартир с разным ремонтом лучше та, что предлагает меньшую стоимость за условную единицу ремонта.

Оставалось самое сложное — придумать, как оценивать ремонт. У некоторых агрегаторов были фильтры, вроде «тип ремонта: евроремонт». Но такие параметры заполнялись вручную создателем объявления и могли быть далеки от истины. Реализация этой функции на всех площадках была разная или отсутствовала вовсе. То есть готовых параметров для оценки ремонта не существовало, а значит, настало время пробовать силы в Machine Learning — получать оценку ремонта на основании фотографий. Ведь фотографии у объявления были всегда.

Обучение нейронной сети

В начале 2023 года еще не существовало LLM на любой случай жизни. И основной стратегий было обучение собственной модели с нуля или дообучение похожей под конкретную задачу. Основная проблема была в том, что я был.NET разработчиком с околонулевым опытом работы с ML. Все, что доводилось когда‑то делать, были небольшие модели для распознавания букв на основе датасета mnist. И то, такие модели обучались почти полностью пошагово по руководствам.

Требования к датасету

Для того чтобы обучить модель, нужен было сначала собрать датасет. Поскольку планировалось распознавать изображения с сайтов недвижимости, было решено скачать с них какое‑то количество фотографий для разметки. Была написана небольшая утилита, которая прошлась по нескольким страницам Циана, собрала пачку ссылок на изображения (порядка 5 тысяч) и загрузила их на локальный диск. Оставалось разметить этот датасет.

Перед тем как начать процесс разметки, я долго просматривал загруженные фотографии. Нужно было понять, что вообще можно на них увидеть. А увидеть можно было много чего — фотографии деревьев из окон, рендеры домов и помещений, фото санузлов крупным планом. Был сделан вывод — далеко не все изображения релевантны. Часть из них никак не поможет в оценке. Кроме того, некоторая их доля содержала новые квартиры с черновой отделкой. Такие объявления вообще не хотелось бы рассматривать, ведь это почти первичная недвижимость со своим ценообразованием.

Исходя из просмотренного, лучшим решением на тот момент показалось сделать три модели:

Первая определит, квартира на фото или нет. Не квартиры, или же изображения не представляющие ценности, например, фото вазы, должны были вернуть false.
Вторая отсеет те квартиры, на которых изображен голый бетон.
Третья выдаст оценку ремонту, от 0 до 1, где 1 — это ремонт мечты.

Далее, все три модели должны были вызываться из ASP NET приложения и возвращать результат предсказания, используя следующий алгоритм:

public record PredictionResult
{
    public bool IsRelevant { get; init; }
    public bool HasRenovation { get; init; }
    public float Rating { get; init; }
}

var isRelevant = await model1.IsRelevantAsync(imageBytes);
if (!isRelevant) return new PredictionResult(); // Изображение нерелевантно

var hasRenovation = await model2.HasRenovationAsync(imageBytes);
if (!hasRenovation) return new PredictionResult { IsRelevant = true }; // Изображение релевантно, но на фото черновая отделка

var rating = await model3.GetRenovationRating(imageBytes);
return new PredictionResult { IsRelevant = true, HasRenovation = true, Rating = rating }; // Изображение релевантно и получена оценка

Сбор датасета

Чтобы ускорить маркировку датасета и не допустить логических ошибок при простановке признаков, был сделан небольшой редактор на Vue JS + ASP NET.

Интерфейс редактора датасета

Редактор датасета

В интерфейс был добавлен placeholder для сообщения о том, что думает об изображении текущая модель («Do not match predicted value 0.75»). Был план собрать минимальный набор изображений, обучить модель и при разметке датасета вывести в этом поле результат предсказания. Такой подход позволил бы видеть, на каких изображениях модель больше всего ошибается, да и процесс разметки мог стать чуть веселее. Пока же этот placeholder выводил статичный текст.

Написание моделей

После сбора датасета из сотни фотографий, пришла пора заняться обучением моделей. Нужно было понять, заработает ли вообще вся эта система. Я пригляделся к запланированным к реализации моделям. Первые две из них должны были возвращать boolean ответ — что было похоже на задачу бинарной классификации. Третья должна была возвращать число от 0 до 1 — задача линейной регрессии.

Началось исследование, как реализовать эти модели. Из похожих задач удалось найти что‑то про предсказание стоимости дома на основании набора параметров. Но там использовался текст на входе, а не изображения. В то же время все модели, обрабатывающие изображения, обычно были многоклассовыми классификаторами.

Было решено скрестить найденные варианты решения задач бинарной классификации и линейной регрессии, которые работали с текстом и слои какой‑то модели, что работала с изображениями. Нужно же было с чего‑то начинать. Это был вариант, использовавший keras + tensorflow.

Модель бинарной классификации:

inputs = keras.Input(shape=(self.imagewidth, self.imagewidth, 3))
x = tf.keras.layers.Conv2D(filters=32, kernelsize=5, activation='relu')(inputs)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Conv2D(filters=48, kernelsize=5, activation='relu')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Conv2D(filters=64, kernelsize=3, activation='relu')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Conv2D(filters=80, kernelsize=3, activation='relu')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Conv2D(filters=96, kernelsize=3, activation='relu')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Dense(256, activation='relu')(x)
outputs = tf.keras.layers.Dense(2, activation=tf.keras.activations.softmax)(x)

model = keras.Model(inputs, outputs, name="binary")
model.compile(optimizer=tf.keras.optimizers.RMSprop(learningrate=1e-3),
              loss=tf.keras.losses.binarycrossentropy,
              metrics=[tf.keras.metrics.AUC()])
              
=================================================================
Total params: 210,594
Trainable params: 209,954
Non-trainable params: 640

Регрессионная модель:

inputs = keras.Input(shape=(self.imagewidth, self.imagewidth, 3))

x = tf.keras.layers.Conv2D(filters=16, kernelsize=3, activation='relu')(inputs)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Conv2D(filters=32, kernelsize=3, activation='relu')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Conv2D(filters=64, kernelsize=3, activation='relu')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Dense(512, activation='relu')(x)
outputs = tf.keras.layers.Dense(1, activation=tf.keras.activations.sigmoid)(x)

model = keras.Model(inputs, outputs, name="EfficientNet")
model.compile(optimizer=tf.keras.optimizers.Adam(learningrate=1e-3),
              loss=tf.keras.losses.binarycrossentropy,
              metrics=[tf.keras.metrics.MeanAbsoluteError()])

=================================================================
Total params: 22,176,225
Trainable params: 22,176,1
Non-trainable params: 224

Подключение моделей к редактору датасета

Обучение было успешно запущено на сете из 90 изображений (10 использовались для валидации). На выходе модели сохранялись в формате h5. Размер их получился следующим:

furniturescore.h5 - 32.8MB
isphotorelevant.h5 - 1.76MB
isrenovationexists.h5 - 1.76MB

Теперь нужно было подключить модели к редактору датасета. Для использования их в.NET была взята библиотека Keras.NET. Этот пакет позволял описывать модели, используя Keras в.NET, а также выполнять их запуск. Это не очень эффективно, но избавляло от необходимости разбираться с Python WEB приложениями и их деплоем.

Запуск модели в.NET требовал передачи объекта типа NdArray в качестве параметра (аналог numpy array). Однако библиотека позволяла получить такой объект только передав в специальную утилиту путь к файлу на диске. Существующий массив байт конвертировать в NdArray было невозможно. Из‑за этого код запуска модели выглядел так:

var tempFileName = $"{Guid.NewGuid()}.temp";
File.WriteAllBytes(tempFileName, imageBytes);

var image = ImageUtil.LoadImg(tempFileName, targetsize: (imageWidth, imageHeight, colorsCount));
File.Delete(tempFileName);

var ndArray = ImageUtil.ImageToArray(image) / maxPixelValue;

var model = BaseModel.LoadModel(path);
model.Predict(ndArray);

Код заработал. А значит вскоре в интерфейсе разметки датасета появилась возможность видеть, что на данный момент думает модель о каждом из изображений. Пока результаты были больше похоже на случайные. Но разметка датасета продолжалась.

Проблемы с качеством

Вскоре стало заметно, что очень тяжело объективно ставить оценку от 0 до 1 для каждой фотографии. Значения 0.85 и 0.8 были очень близки, а конечный результат выбирался скорее по настроению. Необходимо было сформулировать какой‑то набор критериев для выставления оценки, но мотивации что‑то выдумывать не было, ведь пришлось бы заново просматривать уже размеченный датасет. Хотелось скорее увидеть результат в работе, пусть и неидеальный. Было решено продолжить разметку на основе субъективных ощущений и оставить все проблемы с качеством на потом.

Метрики моделей постепенно улучшались, но прогрессия остановилась на ~500 изображениях. Были попытки взять готовую модель и сделать дообучение. Но это только ухудшало результаты (возможно, ввиду отсутствия опыта). Были попытки изменить количество слоев, параметров, функции активации, увеличить датасет. Каких‑то значимых изменений это не приносило.

При увеличении количества параметров, модели явно уходили в переобучение. При этом больший размер датасета, всякого рода случайные повороты и масштабирование изображений, добавление слоев Dropout ситуацию кардинально не меняли. В итоге я остановился на следующих результатах при датасете в ~2к изображений:

Квартира ли на фото, точность предсказания (val_auc) ~ 0.92
Есть ли ремонт, точность предсказания (val_auc) ~ 0.95
Определение уровня ремонта, средняя ошибка (val_mae) ~0.17

Создание приложения

Первый план

Настало время писать приложение. Закапываться в проектирование интерфейсов не хотелось и для старта было решено создать обычного Telegram бота. Пользователь сможет зайти в его настройки, выбрать параметры интересующих объявлений. А бот будет уведомлять о новых вариантах, что попали под заданные параметры.

Предполагался следующий набор сервисов:

CrawlingHost: данный сервис будет находить новые объявления и складывать их в локальную базу, получив некоторую оценку ремонта перед сохранением.
TelegramApiHost: в данный хост летят запросы от бота в Telegram, в котором можно настроить уведомления о новых объявлениях.
WorkerHost: периодически запускается, делает выборки объявлений и отправляет их пользователям.

Написание краулера

Чтобы собирать новые объекты недвижимости, предполагалась использовать следующий алгоритм:

На каждом из сайтов необходимо было получить статичную ссылку на список объявлений, отсортированных по дате размещения, начиная с самых новых. Обычно в ссылках присутствовал параметр — номер страницы. Его необходимо было бы менять при пагинации по страницам сайта недвижимости.
При запуске краулера, нужно было собрать все объявления со страницы. Если находилось объявление, которое уже сохранено в базе с той же датой обновления, считалось, что парсер добрался до ранее сохраненных объявлений. В таком случае процесс нужно было останавливать и ждать следующего запуска.
Также при сборе данных могла возникнуть проблема race condition. Это ситуация, когда во время парсинга кто-то выкладывал новое объявление. Из-за этого при переходе на следующую страницу, система могла получить объявление, только что собранное на предыдущей странице. Чтобы такие ситуации не останавливали процесс, было введено понятие "crawling session". В рамках каждой сессии сбора данных собирался массив идентификаторов встреченных объявлений. Если загруженное объявление находилось в списке обработанных в данной сессии, то оно уже не останавливало процесс, даже если точно такое же уже лежало в базе.
Также предусматривался максимальный порог "старости" объявления. Например, если какому-то из найденных объявлений было уже более месяца, сессию парсинга нужно было останавливать. Без этого при первом запуске краулер пытался бы перебрать все объявления площадки.

Само по себе написание сборщик данных не было сложным. Он был реализован как фоновая задача - BackgroundJob, который включался раз в 4 часа и собирал все, что появилось с момента последнего запуска. Сам биндинг разметки сайта недвижимости к C# объекту был сделан через библиотеку Laraue.Crawling, которая позволяла определить схему, избегая лишнего спагетти-кода:

public sealed class CianCrawlingSchema : CompiledDocumentSchema<IElementHandle, HtmlSelector, CrawlingResult>, ICianCrawlingSchema
{
    private static BindObjectExpression<IElementHandle, HtmlSelector> GetSchema(ILogger logger)
    {
        return new PuppeterSharpSchemaBuilder<CrawlingResult>()
            .HasArrayProperty(x => x.Advertisements, "article", pageBuilder =>
            {
                pageBuilder.HasProperty(
                    x => x.ShortDescription,
                    "div[data-name=Description]");
                pageBuilder.HasProperty(
                    x => x.UpdatedAt,
                    builder => builder
                        .UseSelector("div[data-name=TimeLabel] div:nth-child(2)")
                        .Map(TryParseDate));
                // биндинг остальных свойств
            })
            .Build()
            .BindingExpression;
    }
}

Далее через headless браузер с помощью библиотеки PuppeterSharp открывалась одна из страниц ресурса, и данные из верстки биндились на модель, согласно схеме.

Чтобы не бороться с защитой от ботов, было решено просто сократить запросы до такого уровня, когда защита не будет срабатывать. Опытным путем было получено значение в один запрос в минуту. Этого хватало: поток объявлений на самом деле не велик. В результате этого шага были написаны схемы для Циана и Авито, каждая из которых возвращала результат согласно контракту:

public sealed class CrawlingResult
{
    public Advertisement[] Advertisements { get; init; } = [];
}

public sealed class Advertisement
{
    public string Id { get; init; } = string.Empty;
    public string Link { get; init; } = string.Empty;
    public long TotalPrice { get; init; }
    public int RoomsCount { get; init; }
    public decimal Square { get; init; }
    public int FloorNumber { get; init; }
    public int TotalFloorsNumber { get; init; }
    public string? ShortDescription { get; init; }
    public DateTime? UpdatedAt { get; init; }
    public string[] ImageLinks { get; init; } = [];
    public FlatType FlatType { get; init; }
    public TransportStop[]? TransportStops { get; init; }
    public FlatAddress? FlatAddress { get; init; }
}

Перед сохранением результата в базу, необходимо было проставить общую оценку ремонта для квартиры. Для этого оценки отдельных фотографий должны были свестись в одну общую. Желательно было учесть и несовершенство моделей. План был следующим:

Необходимо получить оценку для всех фотографий в объявлении.
Если найдено менее 3-х релевантных изображений, ставится общая оценка 0, чтобы объявление в дальнейшем не участвовало в поиске.
Если найдены изображения с черновой отделкой - общая оценка 0.
Для остальных изображений находится медиана, чтобы избежать влияния изображений с оценкой с сильным отклонением от средней.
Здесь также появился первый концепт параметра идеальность - чем больше у квартир проблем, тем меньше ее идеальность. К проблемам относятся плохой ремонт, долгое расстояние до метро, не лучшие станции метро, и так далее. В итоге для идеальности появилась черновая формула:

var totalFine = transportStopFine + transportDistanceFine + renovationRatingFine;
var ideality = 1 - totalFine; // Для высчитывания идельности вычитаем из единицы все штрафы

База начала наполняться данными. Их оставалось вывести.

Интерфейс бота в Telegram

По задумке, пользователь мог создать себе некоторые "выборки" (Selection):

public sealed record Selection
{
    public long Id { get; set; }
    public required string Name { get; set; }
    public Guid UserId { get; set; }
    public MayBeRelativeDate? MinDate { get; set; }
    public MayBeRelativeDate? MaxDate { get; set; }
    public decimal? MinPrice { get; set; }
    public decimal? MaxPrice { get; set; }
    public int? MinRenovationRating { get; set; }
    public int? MaxRenovationRating { get; set; }
    public decimal? MinPerSquareMeterPrice { get; set; }
    public decimal? MaxPerSquareMeterPrice { get; set; }
    public decimal? MinSquare { get; set; }
    public bool ExcludeFirstFloor { get; set; }
    public bool ExcludeLastFloor { get; set; }
    public byte? MinMetroStationPriority { get; set; }
    public AdvertisementsSort SortBy { get; set; }
    public SortOrder SortOrderBy { get; set; }
    public IList<long>? MetroIds { get; set; }
    public IList<int>? RoomsCount { get; set; }
    public TimeSpan? NotificationInterval { get; set; }
    public DateTime? SentAt { get; set; }
    public int PerPage { get; set; }
}

Интерфейс настройки выборки

Настройка выборки

И в рамках созданных выборок уже просматривать объявления:

Интерфейс результата выборки

Одна из страниц выборки

Процесс вывода объявлений в телеграмме подробно описывать не буду. Там достаточно много кода, но все сводилось к тому, чтобы достать записи из БД по фильтрам из выборки и отрисовать их в интерфейсе. В любом случае, код будет доступен в репозитории.

С этого момента в боте можно было просматривать собранные с разных платформ объявления.

WorkerHost

Оставалось доработать систему, добавив автоматическую отправку новых объявлений на основе выборок. Работало это в два этапа:

Отбирались те выборки, данные по которым пришло время отправлять. Но это влиял параметр "интервал отправки", установленный пользователем.
Происходило формирование запроса к базе для выборки новых данных. Для этого отбирались объявления, удовлетворяющие условию фильтров выборки и в которых поле crawled_at было больше, чем дата последней автоотправки по этой выборке.

Пример уведомления

Уведомление о новых объявлениях

Все сервисы теперь были реализованы. Можно было запускать их и наблюдать за результатами.

Полученные результаты и покупка квартиры

Хосты приложения были развернуты на купленный VPS c 1GB RAM под Linux. Не осталось точных данных, сколько RAM и CPU тратилось на распознавание одной пачки изображений (я использовал batching по 10 изображений за раз), но цифры были вполне удовлетворительные для корректной работы системы.

Были созданы две выборки. Первая была настроена на локации, что были нам наиболее интересны. Вторая выборка распространялась на весь Санкт-Петербург, и искала квартиры с хорошим ремонтом, расположенным рядом метро и ценой за квадрат ниже рынка. Наблюдения начались.

Система работала. Уведомления приходили. Оценка ремонта, в основном отсеивала неприемлемые варианты, хотя и периодически глючила. Но действительно интересных предложений было мало. Каждые 4 часа приходило новое уведомление (или не приходило, если в выборку ничего не попадало) и необходимо было смотреть, что нашла система. Всегда находились какие-то минусы, делающие варианты не подходящими. Через пару недель этот процесс поднадоел - постоянно нужно было идти и просматривать объявления по уведомлению. Шаг нотификаций был изменен с четырех до двенадцати часов. Правда, в таком варианте был риск упустить действительно хорошие предложения.

В итоге, судьба покупки была решена в один из дней за пару часов. Жена, просматривая Циан, увидела новое объявление, подходящее нам по всем характеристикам в хорошей локации. Через час объект был просмотрен, а на следующее утро заключена предварительная сделка. Через месяц мы уже переезжали.

Это все было хорошо, но мотивация развития приложения сильно снизилась. Было понятно, что нужно как-то улучшать точность распознавания, но знаний для этого не хватало. Нужно было добиваться уменьшения фонового шума от бота - ведь не так страшно пропустить что-то важное, как получать постоянно что-то нерелевантное. Одна из новых идей была - смотреть на наличие слова "срочно" в объявлении - на таких объявлениях часто есть обоснованные скидки.

Стало неясно, для кого сейчас делается эта разработка. Силы уходили на переезд. Наступило лето - пора прогулок - и постепенно интерес к разработке угас.

Реинкарнация проекта

Откуда взялась новая идея

2024 и 2025 год шли под девизом - LLM повсюду, что провоцировало некоторое отторжение от этой тематики. Каждый второй теперь рассказывал, как ИИ консультирует его по совершенно разнообразным вопросам, а CEO компаний объявляли наперебой, что скоро мир изменится, и множество профессий вымрет.

У меня же появилось мнение, что если начать использовать LLM в своей работе, то это в перспективе плохо скажется на способности писать код. И если в будущем окажется, что LLM не получается сделать рентабельным и их отключат (или же серьезно поднимут стоимость использования), то в выигрыше останутся те, кто не разучился писать код самостоятельно.

Но вечно избегать использования LLM мне не удалось. С каждым месяцем нарастало ощущение деградации поисковых систем. Возможно, это были субъективные ощущения, но ответы на вопросы найти становилось все труднее.

Я опросил знакомых о том, кто что использует. Программисты в основном сидели в DeepSeek, остальные в каких-то сомнительных телеграм ботах с названиями вида "Free Chat GPT 4 Bot".

Мне же повезло наткнуться на проект Ollama, который позволял запускать Open Source модели на локальном компьютере. И этот концепт очень понравился, ведь в таком случае все данные оставались на локальном компьютере. Это как энциклопедия всего интернета в виде одного файла - и при этом никакой рекламы. Кроме того, если модели как-то и будут меняться со временем и деградировать, то твоя локальная останется такой же, как и была.

Были перепробованы много моделей, в итоге на постоянном использовании остались две: qwen3-coder:30b для вопросов по программированию и gemma3:12b для всего остального. Логичным следующим вопросом было - а сможет ли LLM делать оценку ремонта по фотографии, и каковы будут результаты?

Эксперименты с Ollama

Первой идеей было просто спросить у одной из моделей, что она видит на фото, передав ей url изображения с Циана. К удивлению, модель подробно расписала, что изображено на фото, описание практически полностью совпало. Был отправлен другой url - снова подробное описание квартиры, правда теперь почти ничего не сошлось.

Оказалось, что модель даже не умела загружать изображение по url, хотя ответ и возвращался всегда. На основании того, что в адресе был "cian", она просто предполагала, что на вход получила фото квартиры, и генерировала какой-то случайный результат.

Я изучил документацию и обнаружил, что с изображениями может работать только часть моделей. И передавать их нужно отдельным свойством в виде base64 строки. Кроме того, оказалось, что Ollama поддерживает функцию structured output, что позволяло задать контракт объекта, который модель должна вернуть по сделанному запросу.

В итоге был составлен такой запрос, который просил у модели вернуть данные по фото в нужном формате:

{
  "model" : "qwen2.5vl:3b",
  "prompt" : "Analyze this image and determine if it depicts a flat interior...",
  "stream" : false,
  "images" : ["base64ImageString"],
  "format" : {
    "properties" : {
      "RenovationRating" : {
        "type" : [ "number" ]
      },
      "Tags" : {
        "items" : {
          "type" : [ "string" ]
        },
        "type" : [ "array" ]
      },
      "Description" : {
        "type" : [ "string" ]
      }
    },
    "type" : [ "object" ]
  }
}

В промпте передавался длинный набор подсказок, чтобы модель понимала, что должно увеличивать оценку, а что - уменьшать. Например, использование дорогих материалов - это плюс, а торчащие коммуникации - минус. Кроме подсказок указывалось, что представляет каждое из запрошенных свойств, а так же описывались правила, когда модель должна считать изображение нерелевантным и возвращать нуль. Диапазоном оценки ремонта стало целое число 0-10, вместо плавающего 0-1, так как опытным путем выяснилось, что модели меньше галлюцинировали, работая с целыми числами.

Ответ выглядел так:

{
    "model": "qwen2.5vl:3b",
    "response": "{\"Tags\": [\"Empty room\",\"No furniture\",\"Unfinished walls\",\"No design solutions\",\"No color scheme\"],\"Description\": \"The image shows an empty room with unfinished walls and no furniture. The room appears to be in the process of being renovated, as the walls are not yet painted and there are no visible design solutions or color schemes. The lack of furniture and the unfinished state of the walls suggest that the flat requires significant renovation before it can be lived in.\"}"
}

Оценка действительно выполнялась. Вопрос был только в том, насколько хорошо и какая из моделей имеет лучшие результаты.

Выбор лучшей модели

Было решено сделать небольшой тест: выбрать 20 разнообразных изображений и получить результаты оценки от каждой из моделей. Сам тест выглядел так:

[Theory]
[InlineData("qwen2.5vl:7b")]
[InlineData("qwen2.5vl:3b")]
[InlineData("gemma3:12b")]
[InlineData("gemma3:4b")]
[InlineData("llava:7b")]
[InlineData("llama3.2-vision")]
[InlineData("qwen3:8b")]
[InlineData("qwen3-vl:8b")]
public Task ModelsTest(string model)
{
    return RunTest(model, Prompts.Prompt1);
}

Тело теста содержало последовательный запуск на модели распознавания для каждого из предопределённых изображений и сравнение полученных результатов с эталоном:

private async Task RunTest(string model, string prompt)
{
    var errors = new Dictionary<string, Result>();
    foreach (var image in testImages)
    {
        var filePath =  "C:\\images\\" + image.Key;
        var fileBytes = await File.ReadAllBytesAsync(filePath);
    
        var result = await predictor.Predict(
            model,
            prompt,
            fileBytes);
    
        errors.Add(image.Key, new Result
        {
            Tags = result.Tags,
            Diff = Math.Abs(result.RenovationRating - image.Value),
            Excepted = image.Value,
            Actual = result.RenovationRating,
            IsRelevant = result.IsRelevant,
        });
    }

    outputHelper.WriteLine($"Average error: {errors.Values.Average(x => x.Diff)}");
    outputHelper.WriteLine($"Error > 10%: {(errors.Values.Count(x => x.Diff > 1) / (double)errors.Count):P}");
    outputHelper.WriteLine($"Error > 20%: {(errors.Values.Count(x => x.Diff > 2) / (double)errors.Count):P}");
    outputHelper.WriteLine($"Relevant errors: {(errors.Values.Count(x => x.IsRelevant == (x.Excepted != 0)) / (double)errors.Count):P}");
    
    foreach (var error in errors)
    {
        outputHelper.WriteLine(
            $"{error.Key}: {error.Value.Diff} ex: {error.Value.Excepted} act: {error.Value.Actual} tags: {string.Join(',', error.Value.Tags)}");
    }
}

Вывод теста:

Average error: 1.55
Error > 10%: 30.0%
Error > 20%: 20.0%
Relevant errors: 25.0%
2612468440-4.jpg: 0 ex: 3 act: 3 tags: Old furniture,Worn carpet,Wallpaper,Simple lighting,Basic layout
2612468528-4.jpg: 0 ex: 3 act: 3 tags: Old tiles,Basic fixtures,Needs paint,Simple layout,Basic lighting
2612468865-4.jpg: 5 ex: 0 act: 5 tags: Neutral colors,Simple design,Clean lines,Functional layout,Needs minor updates
...

Интересно было понимать не только среднюю ошибку, но и то, сколько модель допускает грубых ошибок, когда, например, оценивала ремонт там, где вообще не должна была. Кроме того не рассматривались модели, которые требовали много ресурсов или работали долго. В итоге, по соотношению цена-качество победила qwen2.5vl:7b. Оценка одного изображения занимала около пяти секунд на RTX4070.

Улучшения в алгоритме распознавания

Полученные результаты были явно лучше, чем в самописной модели. Но хотелось понять, что еще можно улучшить.

По выводу теста я заметил, что достаточно много ошибок происходило в изображениях, в которых в принципе было мало информации для оценки - какое-нибудь фото пустой стены с картиной. Хотя изображение в целом было релевантным.

Здесь родилась идея о том, что было бы круто объединить все фотографии и делать оценку всего объявления сразу. Тогда у модели будет больше информации и меньше шансов ошибиться. Это должно и работать быстрее - время работы модели не сильно коррелировало с объемом входного файла. А количество распознаваний сокращалась до одного на всю квартиру.

Алгоритм был прост - все фотографии объявления объединялись в одно длинное и разделялись черными полосами. В промпте было дописано о том, что необходимо оценивать переданный коллаж изображений, разделенных черными линиями.

Это сработало - общая оценка выросла. Правда, появилась проблема: модель начинала периодически галлюцинировать и бесконечно генерировать теги. Промпты о том, что возвращать нужно не более 10 тегов, резко снижали результат оценки квартиры и не всегда спасали от галлюцинаций.

Помог в итоге магический промпт, предложенный самой же моделью - return exactly no more than 10 items. Это убрало галлюцинации, не сильно снизив точность.

В целом вся эта разработка через промпты, конечно, была такой себе. Абсолютно никакой детерминированности, любые изменения могли резко ухудшить результат. К счастью, написанные тесты позволяли хоть как-то видеть общую картину и не делать совсем уж неправильных решений.

Внедрение в проект

Новое ядро системы для распознавания изображений было готово к внедрению в приложение. Однако теперь запуск модели требовал мощную видеокарту, а сервер с GPU арендовать было дорого. Было решено, что домашний компьютер займется всей тяжелой работой. Для этого проект требовалось доработать.

Вся работа, что требовала видеокарты, была вынесена в отдельный хост - GpuWorkerHost. Парсинг был отрефакторен и теперь не пытался получить оценку о ремонте сразу, а просто складывал свои результаты в базу. Затем уже GpuWorkerHost просматривал объявления с несделанной оценкой, выполнял ее и обновлял результаты в БД.
Был добавлен ApiHost - захотелось вывести результаты красиво на нормальном фронтенде.
Появилась новая работа у WorkerHost - ему необходимо было просматривать объявления, что были оценены и проставлять им флаг - готово для API. Кроме того, в данный хост были добавлены задачи по периодическому удалению из базы тех изображений, что перестали быть доступны и по помечанию флага об удалении для тех объявлений, у которых не осталось ни одного активного изображения.

Разработка фронтенда

Мне всегда тяжело было придумывать интерфейсы - поэтому наверное и пошел в бэкенд. Поэтому запросил несколько разных версток под эту задачу у qwen3-coder:30b:

Образцы верстки, сгенерированные AI

Вайб-кодинг в деле

Далее эти прототипы были вручную объединены и доработаны напильником.

Деплой хостов

Было решено деплоить на сервер только ApiHost, чтобы не тратиться лишний раз на вычислительные ресурсы. Все остальные хосты не требовали постоянного запуска. Поэтому их можно было запустить в любое время локально, чтобы они собрали новые объявления, сделали им оценку и сложили результаты в базу.

Результат и дальнейшие планы

Изначальный план - отсортировать квартиры по самым выгодным предложениям до сих пор был не выполнен. Но был сделан шаг в этом направлении: решено искать квартиры, расположенные рядом на одной улице, и показывать, выгодная ли это сделка, сравнивая их цены и ремонт. Но такое работало только когда объявлений на улице много и было с чем сравнивать. По правильному необходимо было найти геобазу, благодаря которой можно будет делать сравнение именно по ближайшим домам, а не только тем, что расположены на одной улице.

Окончательный результат

Актуальный интерфейс

В голове крутились дальнейшие улучшения:

Сравнивать заявленные аттрибуты с текстом объявления. Часто пользователи пишут в характеристиках одно, а в описании совсем другое - шаг, чтобы привлечь внимание. Такое желательно сразу выкидывать с выдачи.
Загрузить базы домов, идущих под реновацию, и снижать итоговую оценку или показывать уведомления об опасности сделки.
Объединять дубликаты по параметрам, очищая выборки.
Искать квартиры не только для покупки, но и для аренды.
Сделать так называемую дистилляцию модели. Это когда маленькую модель обучают на результатах большой. Тогда GpuWorkerHost оказался бы на сервере и его не приходилось бы запускать на локальном компьютере.

Идей появляется много, но есть проблема — не всегда удается найти время для проекта. Сейчас его никак не получается применить, покупки квартиры не предвидится. Тем не менее мне он кажется интересным и хочется, чтобы знания, накопленные в нем, мог кто‑нибудь использовать. Я выложил исходники на Github, поэтому если вдруг появится желание подробнее ознакомиться с реализацией функционала — добро пожаловать. В репозитории отсутствует Frontend, его придется при необходимости писать самостоятельно.

Ну а если вдруг захочется просто посмотреть полученный результат, то он доступен здесь. Изначально проект делался под Санкт-Петербург, поэтому там больше всего данных. Москву и Волгоград я добавлял позднее, чтобы убедиться, что проект в теории можно отмасштабировать.

На этом все, благодарю за внимание!

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20 дек. 2025 г.

30.06.26 15:08 wendytaylor015

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18.07.26 17:12 Malthe Larsen

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18.07.26 23:51 bernalzenaida

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19.07.26 04:10 Fraddy Pual

I can't thank Fundsretriever enough for everything they did for me. My name is Vanessa Conway, and I'm here to tell you my story of how I recovered money I never thought I'd see again. A few months ago, I put a significant amount of money into what looked like a genuine online investment company. At first, it felt real—they showed me fake profits and convinced me to invest even more. But when I tried to cash out, they went quiet and started asking for extra fees. That's when it hit me—I had been scammed. I was heartbroken, frustrated, and didn't know where to turn. That money was my savings—months of hard work gone. Then I found Fundsretriever online. I reached out, hoping for a miracle. Right away, their team made me feel heard. They were responsive, knowledgeable, and walked me through everything. They didn't just take my case—they took it seriously and kept me in the loop every step of the way. I finally felt like I had real experts fighting for me. And guess what? They actually got my money back. It wasn't instant, and it took teamwork, but it happened. When I saw those funds returned, I cried with joy. I'm sharing this so that anyone else out there who's been scammed knows—don't lose hope. Do your research before investing, and stay far away from platforms that promise too much too fast. And if you've already been stung, don't wait—get professional help immediately. Thank you, Fundsretriever, from the bottom of my heart. You didn't just recover my money—you restored my faith. — Vanessa Conway 📧 [email protected] 📱 WhatsApp: +16035121448 💬 Telegram: @Fundsretriever
19.07.26 19:53 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] Call/Text Number: +1 (336) 390-6684
19.07.26 19:53 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] Call/Text Number: +1 (336) 390-6684
19.07.26 19:54 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] Call/Text Number: +1 (336) 390-6684
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.
30.07.26 17:27 wendytaylor015

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30.07.26 17:27 wendytaylor015

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31.07.26 16:21 rssllhrnsb

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03.08.26 20:05 Philip

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03.08.26 20:06 Philip

I was a victim of a crypto theft involving a Pink Drainer, which resulted in the theft of my Wrapped Bitcoin (WBTC) from my Polygon network. The experience was incredibly frustrating and distressing, as I had no idea how to recover my funds. Unfortunately, by the time I noticed the theft, the funds had already been drained to an address that I had no control over, making it seem like an irreversible situation.The attack happened when I clicked on what appeared to be a legitimate link. I didn’t realize at the time that it was a phishing attempt designed to siphon off my private keys and access my wallet. The Pink Drainer, a type of malicious script used by attackers, is specifically designed to exploit such vulnerabilities in crypto wallets. The moment I realized that my WBTC had been drained from my Polygon network. I felt completely helpless, as I didn’t have direct access to the thief's address, and there was no way to reverse the transaction on my own at that point, I started searching for ways to recover my funds, but most resources only offered generic advice that wasn’t practical in this particular case. I quickly realized that if I wanted to have any hope of getting my assets back, I would need professional assistance. After some research, I came across a reliable and trusted recovery team called Aspen Recovery Experts. Their expertise in cryptocurrency recovery, especially in cases like mine, seemed promising.I decided to reach out to Aspen Recovery Experts, and I’m incredibly grateful that I did. Their team of crypto recovery experts was able to help me trace the stolen funds and identify the path the funds took after they left my wallet. Using advanced tools and techniques, they were able to track the transactions on the blockchain, helping me understand where my WBTC had been sent. More importantly, they worked tirelessly to assist me in contacting the necessary parties and even interfaced with blockchain analysts to help facilitate the recovery process.Thanks to their efforts, I was able to successfully recover my stolen funds. The entire process took some time, but the Aspen Recovery Experts dedicated team provided regular updates and kept me informed throughout the process, which gave me a sense of hope and relief during an otherwise stressful time. If you’re ever in a similar situation, I highly recommend reaching out to a trusted recovery team like Aspen Recovery Expertshhh . Their professionalism, knowledge, and expertise were critical in helping me recover my funds and regain control of my crypto assets. Whatsapp : +1 747 231 9036 Telegram : @ prohackerspy Email : [email protected]
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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04.08.26 12:37 kimberlyhebertt6877

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06.08.26 08:11 ROMMYHENDERSON344

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06.08.26 13:56 lydiassmith567

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12.08.26 16:37 rssllhrnsb

I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Tatiana Sorina , who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina TEXT : (tatianasorina06 at G_Ma IL dot ..c 0 m)…. Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.
16.08.26 01:44 Matt Kegan

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18.08.26 04:59 marcushenderson624

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18.08.26 04:59 marcushenderson624

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19.08.26 14:50 BAYER7043

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20.08.26 11:32 michaeldavenport218

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20.08.26 11:32 michaeldavenport218

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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.
25.08.26 13:44 lydiassmith567

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25.08.26 13:44 lydiassmith567

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30.08.26 15:37 [email protected]

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30.08.26 15:37 [email protected]

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01.09.26 11:16 lisawerth897

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01.09.26 11:16 lisawerth897

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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

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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

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05.09.26 21:38 [email protected]

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09.09.26 21:31 lisawerth897

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15.09.26 03:35 elioduncan

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05.10.26 21:26 [email protected]

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06.10.26 06:13 melissathomason573

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06.10.26 06:13 melissathomason573

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06.10.26 17:44 Darrell1717

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11.10.26 03:04 Patricia014

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13.10.26 14:14 lisalisa22

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13.10.26 14:14 lisalisa22

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17.10.26 12:58 melissathomason573

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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

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25.10.26 14:35 lydiasmitth456

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27.10.26 01:58 KATHLEEN

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27.10.26 02:22 kimberlyhebertt6877

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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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