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    <description>Latest posts from the turbopuffer blog</description>
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    <lastBuildDate>Fri, 13 Mar 2026 17:42:24 GMT</lastBuildDate>
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      <title>Rust zero-cost abstractions vs. SIMD</title>
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      <description>A customer query was taking over 4× longer than it should have. The profiler pointed at Rust code we&#039;d assumed was free. We followed the trail all the way down to assembly to find the true cost.</description>
      <pubDate>Wed, 18 Feb 2026 00:00:00 GMT</pubDate>
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      <title>How to build a distributed queue in a single JSON file on object storage</title>
      <link>https://turbopuffer.com/blog/object-storage-queue</link>
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      <description>How to build a single global queue for distributed systems on object storage: Start with a single file on object storage, then add write batching, a stateless broker, and high-availability.</description>
      <pubDate>Thu, 12 Feb 2026 00:00:00 GMT</pubDate>
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      <title>ANN v3: 200ms p99 query latency over 100 billion vectors</title>
      <link>https://turbopuffer.com/blog/ann-v3</link>
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      <description>Our latest ANN release supports scales of 100+ billion vectors in a single search index, with 200ms p99 query latency at 1k QPS and 92% recall.</description>
      <pubDate>Wed, 21 Jan 2026 00:00:00 GMT</pubDate>
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      <title>Designing inverted indexes in a KV-store on object storage</title>
      <link>https://turbopuffer.com/blog/fts-v2-postings</link>
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      <description>How we redesigned our inverted index structure using fixed-sized posting blocks to achieve 10x smaller indexes and dramatically better throughput.</description>
      <pubDate>Wed, 14 Jan 2026 00:00:00 GMT</pubDate>
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      <title>Why BM25 queries with more terms can be faster (and other scaling surprises)</title>
      <link>https://turbopuffer.com/blog/bm25-latency-musings</link>
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      <description>I analyzed how BM25 query latencies scale with document count and top_k. Longer queries scale less efficiently, and essential terms impact performance in some surprising ways.</description>
      <pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate>
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      <title>Vectorized MAXSCORE over WAND, especially for long LLM-generated queries</title>
      <link>https://turbopuffer.com/blog/fts-v2-maxscore</link>
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      <description>turbopuffer has improved text search performance up to 20x by adopting the same text search algorithm as Apache Lucene, a vectorized variant of block-max MAXSCORE</description>
      <pubDate>Tue, 09 Dec 2025 00:00:00 GMT</pubDate>
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      <title>FTS v2: up to 20x faster full-text search</title>
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      <description>turbopuffer&#039;s full-text search engine is getting a major upgrade for 20x better full-text search performance</description>
      <pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate>
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      <title>Native filtering for high-recall vector search</title>
      <link>https://turbopuffer.com/blog/native-filtering</link>
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      <description>Describing how turbopuffer ensures high recall (accuracy) on queries that use attribute filters.</description>
      <pubDate>Tue, 21 Jan 2025 00:00:00 GMT</pubDate>
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      <title>Continuous recall measurement</title>
      <link>https://turbopuffer.com/blog/continuous-recall</link>
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      <description>Describing how turbopuffer measures the recall (accuracy) of its vector indexes in production continuously. This ensures that turbopuffer&#039;s search results are accurate and reliable, despite using approximate nearest-neighbour algorithms to speed up queries.</description>
      <pubDate>Wed, 04 Sep 2024 00:00:00 GMT</pubDate>
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      <title>turbopuffer: fast search on object storage</title>
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      <description>Inaugural blog post about the development of turbopuffer, a search engine that uses object storage and SSD caching for cost-effective, low latency search. This post describes into the motivation behind its creation, its unique architecture, and how it significantly reduces costs for large-scale vector searches. Discover how turbopuffer is transforming search infrastructure for companies like Cursor and Suno, offering a scalable and reliable solution.</description>
      <pubDate>Mon, 08 Jul 2024 00:00:00 GMT</pubDate>
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