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2 contributions to Burstiness and Perplexity
SEO prompt based on MUVERA
Built an SEO prompt based on MUVERA (Claude Sonnet 4) pls check, rate or criticize. https://pastebin.com/U86NyH1n
𝐌𝐔𝐕𝐄𝐑𝐀: 𝐓𝐡𝐞 𝐒𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐓𝐡𝐚𝐭 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠
𝐇𝐨𝐰 𝐆𝐨𝐨𝐠𝐥𝐞 𝐉𝐮𝐬𝐭 𝐌𝐚𝐝𝐞 𝐌𝐮𝐥𝐭𝐢-𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡 𝐋𝐢𝐠𝐡𝐭𝐧𝐢𝐧𝐠 𝐅𝐚𝐬𝐭 (𝐀𝐧𝐝 𝐖𝐡𝐲 𝐄𝐯𝐞𝐫𝐲 𝐒𝐄𝐎 𝐒𝐡𝐨𝐮𝐥𝐝 𝐂𝐚𝐫𝐞) (My thoughts on how this will cleave semantic search going forward) MUVERA (Multi-Vector Retrieval via Fixed Dimensional Encodings) represents a paradigm-shifting breakthrough that solves the fundamental scalability challenges of multi-vector embeddings while preserving their superior semantic understanding capabilities. This Google Research innovation transforms complex multi-vector similarity calculations into simple dot product operations, enabling sophisticated semantic search at web scale without prohibitive computational costs[1][2][3]. Key Technical Breakthrough: Transforming Multi-Vector to Single-Vector MIPS MUVERA's core innovation lies in Fixed Dimensional Encodings (FDEs) - a mathematically elegant approach that converts variable-length multi-vector embeddings into single, fixed-size vectors whose inner product approximates the original multi-vector similarity[1][2][3]. This transformation enables the use of highly optimized Maximum Inner Product Search (MIPS) algorithms, leveraging decades of algorithmic optimization for efficient retrieval[4][5]. The algorithm operates through a sophisticated four-step process: LSH-based partitioning using SimHash, representative sub-vector creation through aggregation, multiple repetitions for robustness, and concatenation into fixed-dimensional encodings[1][2]. This data-oblivious approach provides theoretical guarantees for approximation quality while maintaining consistency across diverse datasets and applications. Performance Achievements and Real-World Implementation MUVERA delivers remarkable performance improvements across multiple dimensions. On the BEIR benchmark suite, it achieves an average of 10% higher recall compared to previous state-of-the-art systems while simultaneously reducing query latency by 90%[1][6][3]. Memory footprint reductions of approximately 70% make multi-vector approaches viable for organizations previously constrained by infrastructure costs[7][8].
0 likes • Jul 5
How should a single article whithin a topical map be optimized then? A single user intent and different chunks of related concepts and entities? just built it: https://www.skool.com/burstiness-and-perplexity/seo-prompt-based-on-muvera
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Trent Hoggar
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3points to level up
@trent-hoggar-3433
SEO, lead generation, copywriting, and video marketing.

Active 13h ago
Joined Feb 11, 2025
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