Jigsaw: How The Avalanches assembled 'Since I Left You' from 3500 old records
The story of Since I Left You begins in Melbourne’s late-90s record rooms, where The Avalanches were a loose collective of teenage DJs turning second-hand vinyl into raw material for experimentation.
Robbie Chater, Tony Di Blasi and Darren Seltmann were shaped by hip-hop sampling and club culture, where turntables worked as instruments rather than playback devices. This was also the era when vinyl digging became a method of discovery, where discarded records were treated as unfinished musical language rather than nostalgia.
The Avalanches drew influence from Public Enemy’s dense sample layering and The Dust Brothers’ production style, but expanded this into long-form collage rather than isolated references. Since I Left You, released in 2000 arrived as their debut album, built from an estimated 3,500 vinyl samples processed through Akai hardware samplers and early sequencing systems.
Robbie Chater said, “We were trying to build something that felt like a journey, not a collection of songs,” describing the album’s central idea. Key fragments include “The Canyon of My Dreams” by The Mystic Moods Orchestra and “Everyday” by The Main Attraction, both reshaped into texture rather than recognisable samples.
The album arrived as sampling laws were tightening in late 1990s, after legal cases in the US made clearance expensive and restrictive for producers. That shift made large-scale sample-based projects increasingly difficult, placing Since I Left You in a rare position at the moment of its release. The album later won ARIA Awards for Breakthrough Artist and Best Dance Release, and was shortlisted for the Mercury Prize in the UK.
Its legacy lies in how it reframed sampling as reconstruction rather than borrowing, where fragments lose identity inside a continuous sound world. Debates around sampling often focus on legality or originality, but Since I Left You pushes the idea further. Every listen reveals new connections between vinyl fragments and production culture, making discovery part of the experience across generations.
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Marketing Dataset —
Давайте создадим пример уровня Medium (средний).
В маркетинге молочной продукции одна из ключевых задач — это управление ассортиментом (коммерческое соседство) и оценка эффективности промо-акций.
В этом примере мы обучим модель не просто анализировать тренд, а оценивать эффект «каннибализации» (когда запуск промо на один товар забирает продажи у другого товара той же группы) и рассчитывать чистый финансовый эффект.
Новый пример для датасета (Уровень: Medium)
json
{
“datasets”: [
{
“instruction”: “Оцени эффективность двухнедельной промо-акции на ‘Йогурт клубничный 2.5%’ в торговой точке. Рассчитай изменение выручки, оцени эффект каннибализации внутри категории ‘Йогурты ложковые’ и сделай вывод о целесообразности повторения акции.”,
“input”: “Данные по продажам категории ‘Йогурты ложковые’ за 2 недели до промо (базовый период) и 2 недели во время промо:\n1. ‘Йогурт клубничный 2.5%’ (промо-товар):\n- База: 1 200 шт. по цене 65 руб.\n- Промо: 2 800 шт. по скидке за 49 руб.\n2. ‘Йогурт черничный 2.5%’ (регулярная цена, товар-аналог):\n- База: 1 000 шт. по цене 65 руб.\n- Промо-период: 650 шт. по цене 65 руб.\nСебестоимость (закупка) клубничного йогурта составляет 40 руб., черничного — 40 руб.”,
“output”: “1. Финансовые показатели промо-товара:\n- Выручка в базовый период: 78 000 руб. (1 200 шт. * 65 руб.).\n- Выручка в промо-период: 137 200 руб. (2 800 шт. * 49 руб.). Сдельная выручка выросла на 75.9%.\n- Валовая прибыль упала с 25 руб. до 9 руб. с одной штуки. Маржинальность снизилась с 38.5% до 18.4%.\n\n**2. Анализ каннибализации:\n- Продажи черничного йогурта (товара-аналога) упали на 350 шт. (с 1 000 до 650 шт.), то есть на 35%.\n Снижение выручки по черничному йогурту составило 22 750 руб. (350 шт. * 65 руб.).\n- Это доказывает жесткое переключение покупательского спроса внутри бренда из-за глубокой скидки.\n\n3. Общий экономический эффект:**\n- Суммарная выручка двух позиций до промо: 143 000 руб., во время промо: 179 450 руб. Чистый прирост выручки: +36 450 руб. (25.5%).\n- Суммарная маржинальная прибыль до промо: 55 000 руб. (30 000 клубника + 25 000 черника). Во время промо: 41 450 руб. (25 200 клубника + 16 250 черника).\n- Чистый эффект по прибыли: -13 550 руб. Акция принесла убыток на уровне маржи.\n\n**Вывод и рекомендации:**\nПромо-акция признана неэффективной (инвестиции в скидку не окупились приростом объема). Повторять акцию в текущем виде не рекомендуется. Для будущих промо необходимо: 1) уменьшить глубину скидки (установить цену не ниже 55 руб.), 2) запускать кросс-промо (например, ‘1+1’ или комбо-наборы), чтобы привлекать новую аудиторию, а не переключать существующих лояльных клиентов.”,
“category”: “Промо-аналитика”,
“subcategory”: “2.3 Эффективность маркетинговых акций”,
“difficulty”: “medium”
}
]
}
Почему этот пример полезен для компактной модели:
Развивает логику: Модель учится смотреть на категорию целиком, а не радоваться росту продаж одного товара в изоляции.
Строгая математика: Все цифры (выручка, маржа, потери от каннибализации) сопоставлены без логических дыр.
При обучении модель зафиксирует правильные паттерны расчетов.
Valery Shmelev (Deutsche: Valery Shmeleff)
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AI Prompts —
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http://webdoc.shmeleff.com/page10.htm
https://oflameron.com/
AI Fine Tuning —
Recommendations for using this JSON for training:
Feature Engineering: The uptime_seconds field is key. The model should learn to associate low numeric values (e.g., <300) with the “anomaly_detected”: true flag.
Negative Examples: The normal_ecmp_stable_01 example is critical. Without it, the model might start treating any multiple paths as anomalies, instead of comparing their ages.
Training Format: You can convert this JSON to the format {"instruction": “Analyze the output of show ip route for anomalies”, “input”: “”, “output”: “”} for standard fine-tuning (e.g., Alpaca or ChatML formats).
Protocol Context: The reasoning clearly states why a low uptime is bad for BGP/OSPF, but might be fine for a static route just added by the administrator. This helps the model make context-sensitive inferences.
http://vbproject.shmeleff.com/page00003.htm
http://vbproject.shmeleff.com/page00005.htm
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