calebwritescode
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Wednesday 30 July 2025 19:57:54 GMT
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Running Won't Save You 🇳🇿 :
I just wish the AI companies would focus on the UX an UI experience, making it easier to store and manage chats. Projects were a good first step but still lack basic functionality. Managing context rot and improving the AI's ability to reference other chats and files would skyrocket the usability and effectiveness of even today's models
2025-07-30 21:34:35
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Djassem :
i bet all my money on anthropic
2025-07-30 23:21:25
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Daniel.3579 :
LLM are great, but still error-prone in advance code like CVCUDA and Metal Shader
2025-07-31 05:30:41
1
user699058 :
Do u mean 500 million? U said 5 million but wrote 500 million
2025-07-31 00:32:57
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3x+1 :
@3x+1:Abstract🤔 This paper introduces a novel, secure, and scalable multi-expert intent extraction architecture designed for high throughput and precision in complex conversational AI systems. Leveraging a 52^9-layer Hilbert space for semantic routing, the system dynamically dispatches input text to specialized, LLM-backed expert modules distributed across a Hilbert grid. We detail the integration of AWS Key Management Service (KMS) for robust HMAC-SHA3-256 message integrity and general data encryption (e.g., spillway logs), ensuring cryptographic trust across distributed nodes. A shared aiohttp.ClientSession for LLM API interactions yields a 4.5x performance improvement in latency-sensitive tasks. Comprehensive testing validates resilience to transient failures via retry mechanisms and scalability to millions of active layers at high request rates. This work advances secure, high-performance intent extraction for next-generation AI communication systems leveraging a novel Hilbert-distributed architecture. 1. Introduction The increasing complexity of conversational AI demands innovative architectures for efficient, secure, and low-latency intent extraction [10, 11]. Traditional systems often struggle with diverse and nuanced user intents, particularly in specialized domains [5]. Additionally, the need for robust security mechanisms to ensure data integrity and confidentiality across distributed components is critical [5]. This paper proposes a 52^9-layer Hilbert-distributed multi-expert intent extraction architecture, where specialized LLM-backed expert modules are mapped onto a Hilbert space [1]. Queries are routed to relevant experts based on semantic proximity, enabling precise intent extraction. Key contributions include: * A KMS-integrated HMAC-SHA3-256 signing mechanism for cryptographic verification of inter-module communications [4]. * A shared aiohttp.ClientSession for asynchronous LLM API calls, reducing network overhead [3]. * Resilient design with retry logic for external APIs [7] and a KMS-encrypted spillway for anomalous data logging. The architecture is engineered for high-dimensional intent spaces and high request volumes, ideal for advanced hum
2025-08-01 07:04:02
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![Souleymane Sidibé, alias « Prince Solo » ou le « Prince du Wassolo », était un virtuose de la musique malienne, célèbre pour sa maîtrise exceptionnelle du kamélé n'goni et sa voix authentique. Tragiquement, l'artiste est décédé dans un accident de la route le 22 mars 2025. Sa disparition a suscité une vive vague d'émotion au Mali, saluée par de grandes figures de la musique comme Oumou Sangaré. [1, 2] Son style et son héritage musical [1] Originaire de la région culturelle du Wassoulou (ou Wassolo) au sud du Mali, il a grandement contribué à moderniser et populariser le genre traditionnel régional. [1, 2] Le Kamélé n'goni : Il en était l'un des plus illustres ambassadeurs, faisant vibrer cet instrument traditionnel avec une énergie unique.Thématiques : Ses morceaux abordaient le quotidien, les valeurs de la société malienne, les épreuves de la vie et la paix. [1]#Afrique #burkinatiktok🇧🇫🇧🇫🇧🇫❤️ #guinéenne_fière #mali #cotedivoire🇨🇮225](/video/cover/7647308461180570898.webp)
