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Как мы учили Yandex Code Assistant помогать разработчикам с написанием кода и делать их счастливыми

Привет, Хабр! Меня зовут Виктор Плошихин, я руковожу ML‑лабораторией в Yandex Infrastructure, команде, которая создаёт платформу для разработчиков Яндекса. Мой коллега Константин Моксин @kamoksin работает разработчиком‑аналитиком в этой же лаборатории — и мы сами пишем очень много кода. Этой осенью мы запустили Yandex Code Assistant — помощник для работы с кодом — и открыли к нему бесплатный доступ в режиме тестирования на платформе Yandex Cloud. И нам было важно не просто научить нейросеть писать код, но и сделать так, чтобы разработчики были довольны работой этого помощника.

В статье расскажем, для чего мы сделали кодового ассистента, как начинали с нуля, и как замеряли его качество метрикой «Счастье разработчиков».

За время тестирования решения внутри Яндекса было более 40 А/B-тестов разных гипотез о «Счастье разработчика»

Какие задачи решают разработчики, и как тут помогают LLM

Любой продукт, основанный на коде, зависит от качества кода и скорости его написания. Многие процессы разработки направлены на то, чтобы обеспечить эти качество и скорость:

найм и развитие хороших специалистов;
код‑ревью такими же прекрасными специалистами;
использование навороченных IDE с индексацией кода;
покрытие кода тестами;
создание документации по проекту.

Всё это относительно простые способы. Да, в этих процессах замешаны люди, все склонны допускать ошибки, тем не менее, оно работает. Но недавно появились модные фантастические LLM и продукты на их основе, которые обещали существенно ускорить и упростить процесс написания кода и обучения разработчиков.

Что умеют современные AI-ассистенты

По сути все подобные продукты предлагают два инструмента. Наиболее используемый — это inline‑подсказки. Разработчик пишет код, ему серым шрифтом показываются какие‑то подсказки. Можно нажать Tab и принять подсказку, или нажать Esc и отвергнуть её. Это неплохо снижает количество рутинных действий, которые разработчик давно освоил.

Второй продукт — это чат, где можно спросить нейронку о более сложных вещах. Если наш разработчик сомневается, есть ли ошибка в большом фрагменте кода, можно спросить в чате, что здесь не так, фактически запросить ревью. Или можно попросить написать юнит‑тест.

Мы проанализировали собственный опыт, пообщались с коллегами‑разработчиками, выяснили, что inline‑подсказки правда востребованы, и в нашем продукте решили сосредоточиться именно на них. Сразу возник вопрос, как оценить полезность этих подсказок.

Как обычно оценивается польза от внедрения таких продуктов

Мы обратились к доступным исследованиям. Вот, как оценивают использование таких инструментов в Accenture. Компания провела опросы среди своих разработчиков, насколько им полезен и удобен GitHub Copilot — один из наиболее известных инструментов.

Заметим, что здесь вообще нигде не говорится о деньгах, речь именно о счастье разработчика.

Сами создатели популярных кодовых ассистентов любят приводить эту статистику, говорить о «доказанной эффективности», «видимом улучшении». Но на самом деле все IT‑сервисы разные, и создающие их разработчики тоже разные, и пишут они на разных языках программирования. Кто‑то пишет код просто в Notepad, а кому‑то нужны навороченные IDE. Поэтому в случае любых подобных опросов важно понимать, кто именно в них участвовал.

Retention — простая метрика полезности продукта

Итак, наш проект начался в конце прошлого года со внедрения inline‑подсказок в работу наших внутренних команд в Яндексе. Как понять его успешность?

Начнём с верхнего, продуктового уровня. С одной стороны, когда мы провели кастдев и пообщались с кучей разработчиков, то услышали довольно разные ответы на вопрос, что им нужно от inline‑подсказок кодового ассистента. Такие ответы довольно трудно структурировать. Но с другой стороны, у нас есть очень понятная бизнес‑метрика — retention. В Яндексе нет принуждения к установке кодового ассистента. Нравится — используешь, не нравится — сносишь плагин. Такое голосование ногами: если на четвёртой неделе использования активность сохраняется, то пользователи считают продукт нужным для себя.

Так как мы сами пишем много кода, то хотели бы видеть законченные, не ломающие синтаксис подсказки. Желательно не очень длинные, чтобы не отвлекаться на их чтение, так как в процессе написания кода мы примерно представляем, что именно хотим написать, и важно написать это быстро.

Поэтому мы решили: давайте наш кодовый ассистент будет смотреть на текст, как на код. Построим из кода abstract syntax tree (AST) и на его основе будем предсказывать стейтменты — определённые конструкции с точки зрения языка программирования. С этой точки зрения все подсказки легко можно условно разделить на плохие и хорошие:

Первый пример — это хорошая подсказка, здесь показывается целиком завершенный стейтмент. А второй пример — не очень хорошая подсказка, так как стейтмент обрублен.

Есть ещё одно ограничение — скорость подсказок, нужно чтобы они выдавались за 500 мс, не больше.

Итак, мы определили target, в который будем учить модель — предсказание стейтментов. Как теперь проверить, что это действительно повлияет на retention?

У нашей бизнес‑метрики есть свойство: её нельзя посчитать быстро, надо ждать месяц. А двигаться вперёд надо быстро. Обычно для таких долгих метрик придумывают прокси‑метрику, которую можно посчитать в онлайне в рамках А/B‑эксперимента, и которая бы коррелировала с бизнесовой метрикой.

UnitTest — много, быстро, дёшево

Но на старте проекта вся наша аудитория составляла чуть больше десятка early adopters, на которых retention считать нет большого смысла. Они и так пользуются продуктом даже с его текущим качеством.

Поэтому мы всерьёз озаботились вопросом офлайн‑приёмки качества моделей. Важно, чтобы метрика:

интуитивно отражала верность движения: чем больше значение метрики — тем лучше продукт;
охватывала разнообразный код;
считалась быстро;
была дешёвой.

Очень желательно, чтобы оценка проводилась без участия асессоров, автоматически. В противном случае это долго, дорого и самим асессорами зачастую трудно разобраться в чужом коде.

Кодовые LLM обычно оценивают по HumanEval и расстоянию Левенштейна. Мы их тоже смотрим, но этого немного не про наш кейс инлайн‑подсказок. HumanEval не подходит для нашего случая, потому что оценивает качество моделей в целом, а не конкретные подсказки в конкретном IT‑сервисе. Расстояние Левенштейна между кодом подсказки и оригинальным кодом тоже не очень подходит: одну и ту же логику можно закодить разными способами — в этом случае код будет корректный и рабочий, но расстояние большим.

Нам было важно, чтобы подсказка не сломала уже написанный код и бизнес‑логику. Поэтому для себя мы придумали метрику UnitTest, которую можно посчитать по коду из нашего репозитория. Идея проста:

Берём код юнит‑теста.
Находим пользовательский код, который проверяется этим юнит‑тестом.
Ставим воображаемый курсор разработчика в каком‑нибудь месте этого кода.
Маскируем ряд стейтментов справа от курсора и передаём в модель код выше курсора и код после этих стейтментов.
Просим модель предсказать замаскированные стейтменты.
Итоговый код с предсказанием модели пытаемся прогнать в юнит‑тесте в п.1.
Считаем долю успешно пройденных юнит‑тестов.

Чем это лучше расстояния Левенштейна:

мы предсказываем не просто синтаксически корректный код, а ещё и код, который выполняет бизнес‑логику программы;
мы можем прогонять эту метрику на больших объёмах тестов;
метрика вообще не требует человеческого вмешательства.

Правильные target и датасет — залог успеха

Поскольку у нас была маленькая продуктовая команда, то мы не могли позволить себе такую роскошь, как претрейн базовой модели. Нужно было файнтюнить уже существующие модели. А значит, нужно подготовить пул для обучения.

В качестве кода для файнтюнига использовался наш репозиторий. Для этого мы его немного подготовили:

Мы очищаем этот код от дублей, кода роботов, делаем какой‑то сэмпл.
Строим по коду AST.
Выделяем стейтменты и дальше делаем target.

Как мы формируем target? Так как в метрике UnitTest мы маскируем стейтменты, то мы хотим предсказывать стейтменты и потом оптимизировать метрику. Давайте в пуле для обучения мы тоже будем нарезать target в виде стейтментов.

Казалось бы, нам нужно сделать простую вещь: есть готовый код, и нужно по нему эмулировать какой‑то момент, когда его редактировал разработчик. Например, интуитивно, сделать split в произвольном месте и представить: тут стоит курсор, нужно продолжить следующий стейтемент. Как выяснилось по метрикам, такой вариант не очень хорошо работает.

Сработал другой подход. Для этого мы строим синтаксическое дерево с помощью библиотеки treesitter и выбираем стейтменты определённого типа. Берём не все подряд, а только экспериментально определённые стейтменты. Например, мы не рассматривали комментарии.

Далее уже с полученными стейтментами мы эмулируем действие пользователя. Тут два варианта:

Курсор стоит в начале стейтмента.
Курсор стоит в рандомном месте стейтмента.

И есть ещё один момент: иногда молчать лучше, чем говорить. Другими словами, нам надо уметь предсказывать пустой стейтмент, то есть не отвечать ничего.

Итак, с пулом для обучения более‑менее понятно. Текущая модель дообучена на популярных в Яндексе языках:

Python,
TypeScript,
С++,
YAML,
JSON,
Kotlin,
Java,
Go,
Swift,
Scala,
YQL/SQL.

Нам было важно проводить много экспериментов, которые бы обеспечили наше движение вперёд. Так, например, оказалось важно, чтобы модель умела в fill in the middle — когда LLM знает, что надо предсказывать текст посередине. В этом случае у нас есть текст выше курсора (префикс) и ниже курсора (суффикс).

Почему так: живой человек очень часто пишет код не сверху вниз, а «прыгая» от одного куска к другому. Какой‑то кусок написал в начале, потом прыгнул в середину и что‑то подправил там и так далее.

В итоге модели мы дообучили. Дальше нужно сделать runtime‑стек, где бы они применялись и пользователь видел ваши прекрасные подсказки.

Runtime — бизнес-логика на бэкенде

Мы построили довольно простой и быстрый runtime‑стек. Но есть особенности.

Что интересно в первую очередь — у нас есть два бэкенда:

CPU‑бэкенд — шустрый и быстрый, где происходит вся бизнес‑логика. Здесь мы принимаем решение: имеет ли смысл вообще ходить за подсказкой, а если имеет, то дообогащаем контекст персональным кодом пользователя. Затем отправляем обогащённый контекст в GPU‑бэкенд.
На GPU‑бэкенде происходит инференс LLM. Результирующие ответы ранжируются первым бэкендом: если они проходят порог, то показываются пользователю.

Плагины в верхней части схемы — это просто прокси, которые берут код разработчика и отправляют его. Мы изначально придерживаемся парадигмы сосредоточения всей бизнес‑логики в бэкенде. Дело в том, что выкатка фиксов ошибок в плагинах — это на порядки более длительная история, чем выкатка фикса на бэкенде. Например, далеко не все пользователи в JetBrains включают автоапдейт, и посаженные в плагине баги могут жить очень долго.

В целом runtime‑контур получился довольно быстрый: в 99-м перцентиле мы укладываемся в ограничения в 420 мс.

Здравствуй, онлайн-приёмка!

Есть ещё одна важная деталь: после реализации runtime‑стека, у нас появилась возможность проверять качество моделей в онлайне. Как только в потоке появляются живые люди, которые пользуются продуктом, неплохо переходить с офлайн‑приёмки на онлайн — это всегда ближе к истинному состоянию продукта. Для этого мы проводим привычное в Яндексе А/B‑тестирование.

В таком тестировании важно выбрать иерархию метрик и понять, по какой метрике вы будете принимать ваш продукт. На старте мы выбрали главной Аccept Rate — отношение принятых подсказок к увиденным. Это интуитивная метрика. Есть и обратная ей — Discard Rate, который не должен расти быстрее, чем Аccept Rate.

В A/B‑экспериментах у вас есть экспериментальные выборки, в каждой из которых своя модель. Казалось бы — давайте разделим пользователей по этим выборкам и посчитаем Аccept Rate? Но не всё так просто.

На старте многих подобных экспериментов, как правило, очень мало пользователей: не хочется сильно раскручиваться, когда над качеством предстоит ещё много работы. Сначала хочется проверить всё на малых группах, но появляются вопросы, как сделать эксперименты статистически значимыми.

Разделение по пользователям для формирования выборок нам не подходит: выборки получатся слишком маленькие, или придётся долго ждать для накопления массы тестов. Поэтому мы пошли другим путём.

Один пользователь за сессию генерит много запросов в наш бэкенд. По сути, почти все нажатия на клавиши, если между ними более 400 мс — это такие запросы. Они позволяют набрать необходимое количество данных для тестирования, если разделять не по пользователям, а по запросам.

Мы показываем одному пользователю ответы от разных моделей в течении сессии и смотрим эффект. Сам эксперимент идёт, как правило, неделю, чтобы исключить сезонность в процессе написания кода.

Когда мы запустили онлайн‑эксперимент, то увидели, что с ростом Аccept Rate у нас растёт и Retention пользователей, о котором мы говорили в начале.

Здесь показано, как эксперимент развивался в первые четыре месяца этого года. Аудиторию мы старались растить постепенно, чтобы не выжечь её. Это заметно по последней строке, где MAU — это месячная аудитория нашего кодового ассистента относительно всех разработчиков Яндекса.

Также мы постепенно раскручивались в рамках сессии написания кода у разработчика: поначалу показывались лишь на 8–10% запросов.

Мы работали над качеством, растили Аccept Rate и расширяли поток запросов, на которые появляются подсказки. Но видно, что где‑то в марте‑апреле мы упёрлись в потолок. У нас вырос Аccept Rate, но случился отток аудитории.

Accept Rate — правда то, что нужно пользователю?

Стало понятно, что Accept Rate — не совсем та метрика, которая отражает желания пользователей. Например, она не учитывает длину принятой подсказки. И в этом аспекте можно накрутить метрику: показать много коротких подсказок, бо́льшую часть из которых примут, вместо одной длинной, которую не примут. Настала пора поменять метрику.

В Яндексе мы стремимся повышать уровень счастья пользователей, поэтому и тут стали думать над такой метрикой. У нас получились такие вводные размышления:

Чем длиннее принятая подсказка — тем лучше. В идеале весь код надо написать за одно нажатие на Tab, так мы поможем лучше всего.
Принятие подсказки — это ненулевые трудозатраты. Поэтому само нажатие на Tab должно штрафовать модель. Должно быть невыгодно показывать короткие подсказки, которые можно было бы написать вместо нажимания на Tab.
Показ подсказки отвлекает. Нужно её прочитать, сделать какой‑то вывод. Так что все показы тоже штрафуются, всегда и независимо от действий пользователя.
Discard — это явный негативный фидбек. Discard должен штрафоваться сильнее, так как он раздражает и требует дополнительного нажатия на Esc.

Формула счастья v1.0

В итоге удалось вывести такую формулу счастья разработчика:

$\sum(AcceptedLength - 3*NumAccepts)-0,05*\sum ShowSuggestLength-14* \sum NumDiscards$

Вот как мы её подбирали:

Берём выборки из уже прошедших онлайн‑экспериментов.
Определяем, какая выборка победила, а какая — проигравшая.
Магические коэффициенты подбираем с помощью линейной оптимизации параметров функции расчёта счастья на этих выборках.
При этом оптимизируем не дельту счастья, а логарифм дельты, чтобы разделять победителя и проигравшего на небольших дельтах. Так эксперименты будут быстрее набирать статистическую значимость.

В итоге был подобран вариант штрафов вблизи оптимума по чувствительности (дельта примерно −2%), но с максимальным штрафом за Discard. При этом все пары «победитель–проигравший» угадываются верно и статистически значимо с большим запасом.

Счастье коррелирует с retention

После перехода на новую метрику мы начали её оптимизировать. Для понимания значений метрики счастья будем смотреть на привычные Аccept Rate и Retention, а за Baseline возьмём состояние на апрель. Вот что получилось дальше:

В июне мы вырастили счастье, но при этом уронили Аccept Rate. При этом кардинально выросла бизнесовая метрика retention!

В июле и августе мы решили подрастить Аccept Rate, не роняя счастье. Пожертвовав небольшой долей потока, мы набрали Аccept Rate в 20%. Это очень хороший результат.

Сравнение с конкурентами

После всех экспериментов нам захотелось прогнать эти метрики на других аналогичных продуктах. На этом нормированном графике видно, какой мы проделали путь, чтобы сравниться по метрике счастья пользователей с конкурентом А — одним из самых популярных в мире кодовым ассистентом.

Также провели сравнение с другим кодовым ассистентом, набирающим популярность в России. Эксперимент показал у него −100% к счастью и −50% к Accept Rate относительно нас.

Влияние на бизнес

Помните, как в начале мы говорили про метрики эффективности для бизнеса?

Мы пока не научились считать, сколько кода от Code Assistant попало в репозиторий, но посчитали сколько кода принимают разработчики в течение рабочего дня. Сегодня 15% от всего кода за день разработчик пишет с помощью ассистента, а в мегабайтах это 800 Мб в день. Решение протестировали тысячи разработчиков Яндекса, 60% из которых стали постоянными пользователями сервиса.

Как добывается счастье

Итак, мы создали конвейер проверки гипотез и обучения моделей. С ноября прошлого года было около 3 000 запусков этого конвейера и более 40 внедрений в продакшн.

Но что еще нам дала работа над ассистентом? Конечно же опыт:

При создании нового всегда стоит думать, как это будет помогать вашим пользователям.
Важно определить продуктовую метрику.
Интуитивная и быстро рассчитываемая офлайн‑метрика — залог быстрого движения вперёд.
Если есть возможность — финальное решение о выкатке лучше принимать по онлайн‑метрикам.
Всегда стоит сомневаться в выборе приёмочных метрик.
Не стоит бояться менять метрики и сравнивать свой продукт с конкурентами.

Больше подробностей рассказываем на нашей ежегодной Practical ML Conf — увидимся там 14 сентября.

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12 сент. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

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06.09.24 01:44 Celinagarcia

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16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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