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Изворен канал @pythonotes · Post #32 · 7 фев.

Скорее всего уже слышали, что складывать строки через + это плохая практика. Падение производительности, и всё такое. Без лишних слов, давайте измерять: from timeit import timeit def t1(): # складываем 10 строк через + из переменной t = 'text' for _ in range(1000): s = t + t + t + t + t + t + t + t + t def t2(): # склеиваем список строк через метод join arr = ['text'] * 10 for _ in range(1000): s = ''.join(arr) def t3(): # складываем через + но не из переменной а непосредственно инлайн объекты for _ in range(1000): s = 'text' + 'text' + 'text' + ... # всего 10 раз Теперь каждую строку склейки запустим по 10М раз >>> timeit(t1, number=10000) 0.21951690399964718 >>> timeit(t2, number=10000) 1.4978306379998685 >>> timeit(t3, number=10000) 0.2213820789993406 Хм, а нам говорили что через "+" это плохо и медленно ))) 😁 Тут стоит учитывать, что речь идёт о склейке множества длинных строк. Давайте изменим условия: def t4(): t = 'text'*100 for _ in range(1000): s = t + t + t + t + t + t + t + t + t def t5(): arr = ['text'*100] * 10 for _ in range(1000): s = ''.join(arr) def t6(): for _ in range(1000): s = 'text'*100 + 'text'*100 + ... # всего 10 раз >>> timeit(t4, number=10000) 12.795130728000004 >>> timeit(t5, number=10000) 2.642637542999182 >>> timeit(t6, number=10000) 0.2184546610005782 Вот, уже другой разговор, сразу видна разница, в среднем в 6 раз. Но погодите, почему последний тест t6() по скорости такой же как и t3()? Ведь строки теперь в 100 раз длиннее! Это вопросы оптимизации кода, какие простые изменения ускоряют или замедляют выполнение программы. Мы столкнулись с примером обхода обращения к переменной. Например, именно так работает директива #define в С++, во время компиляции подставляя значение переменной вместо ссылки на неё. В Python это тоже работает, но часто ли вы сможете встретить такой способ работы со строками? К сожалению, способ почти только теоретический. В целом, тесты показали то, что мы хотели. Делаем выводы самостоятельно. Полный листинг 🌍 #tricks

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AI & Law

@ai_and_law · Post #740 · 12.01.2026 г., 08:04

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🇺🇸🎼Universal Music Group and NVIDIA Partner on AI Music Discovery Universal Music Group (UMG) announced a collaboration with NVIDIA to develop AI tools for music discovery, creation, and engagement, using NVIDIA’s AI infrastructure and UMG’s music catalog. The partnership focuses on joint R&D aimed at advancing human music creation, ensuring rightsholder compensation, and improving attribution and protection of music-based content. As part of the project, the companies will extend NVIDIA’s Music Flamingo model to process full-length tracks, capturing elements such as harmony, structure, timbre, lyrics, and cultural context. The stated goal is to move beyond existing search and personalization models toward more interactive and contextual music discovery, while also encouraging artist adoption of AI-based creation tools. UMG and NVIDIA emphasize that the collaboration is framed around “responsible AI,” with safeguards intended to protect artists’ works, respect copyright, and ensure proper attribution. The partnership reflects UMG’s broader strategy of engaging with AI developers to shape how generative and discovery technologies are deployed in the music industry. #AICopyright#MusicIndustry#ResponsibleAI#AIRegulation

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AI & Law

@ai_and_law · Post #730 · 24.12.2025 г., 08:04

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🇺🇸🎼Cross-Border AI Music on Trial Independent musicians in Illinois have filed the first U.S. federal lawsuit targeting foreign-owned AI music generators, alleging copyright infringement and unfair practices by Mureka, an AI platform operated by Kunlun Tech Co., Ltd and Skywork AI Pte. Ltd. In Attack the Sound et al. v. Kunlun et al., plaintiffs claim that Mureka was trained on copied and stored sound recordings and musical works without permission, and that users can upload songs as “reference tracks” to imitate music or lyrics without consent or compensation. The complaint seeks injunctive relief and damages, alleging violations of U.S. copyright law, the DMCA, and the Illinois Biometric Information Privacy Act, including claims tied to voiceprints. Plaintiffs argue that Mureka—marketed as an “ultimate AI song generator” and used by more than 10 million users—directly competes with creators as a cheaper substitute for human creativity, disproportionately harming independent artists lacking label bargaining power. Filed by counsel from Loevy + Loevy, the case follows similar actions against U.S.-based AI music firms and is positioned as a landmark test of whether large-scale AI music systems owned abroad can operate in the U.S. market while respecting domestic IP and biometric protections. #AI#Copyright#MusicIndustry#IP

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AI & Law

@ai_and_law · Post #394 · 11.09.2024 г., 07:04

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Criminal Indictment Exposes $10 Million AI Music Streaming Fraud In a groundbreaking case, a North Carolina musician, Michael Smith, has been indicted for orchestrating a massive streaming fraud scheme that allegedly exploited AI-generated tracks to rake in over $10 million in royalties. This marks the first criminal case involving artificially inflated music streaming, highlighting the emerging risks as AI tools become more embedded in the music industry. Smith is accused of partnering with an AI music company to create a vast library of tracks, which he then fraudulently boosted using a network of bot accounts across major platforms like Spotify, Apple Music, and YouTube Music. The complex scheme, which began in 2017 and continued through 2024, involved deceiving distributors, financial institutions, and even the Mechanical Licensing Collective (MLC), which eventually caught on and halted royalty payments. This case underscores the growing challenge of maintaining integrity in the digital music ecosystem as AI continues to evolve. As the DOJ takes action, the music industry must ramp up efforts to detect and prevent such fraudulent activities to protect legitimate creators and maintain trust in digital platforms. #AI#MusicIndustry#StreamingFraud#DigitalLaw#Copyright

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