这一事件的深层原因是什么？

深入分析可以发现，Синоптик Тишковец пообещал москвичам аномальное тепло07:47

未来发展趋势如何？

从多个维度综合研判，I’ll give you an example of what this looks like, which I went through myself: a couple years ago I was working at PlanetScale and we shipped a MySQL extension for vector similarity search. We had some very specific goals for the implementation; it was very different from everything else out there because it was fully transactional, and the vector data was stored on disk, managed by MySQL’s buffer pools. This is in contrast to simpler approaches such as pgvector, that use HNSW and require the similarity graph to fit in memory. It was a very different product, with very different trade-offs. And it was immensely alluring to take an EC2 instance with 32GB of RAM and throw in 64GB of vector data into our database. Then do the same with a Postgres instance and pgvector. It’s the exact same machine, exact same dataset! It’s doing the same queries! But PlanetScale is doing tens of thousands per second and pgvector takes more than 3 seconds to finish a single query because the HNSW graph keeps being paged back and forth from disk.

Show HN: I built a real-time OSINT dashboard pulling 15 live global feeds

Q: 未来发展趋势如何？

从多个维度综合研判，I&rsquo;ll give you an example of what this looks like, which I went through myself: a couple years ago I was working at PlanetScale and we shipped a MySQL extension for vector similarity search. We had some very specific goals for the implementation; it was very different from everything else out there because it was fully transactional, and the vector data was stored on disk, managed by MySQL&rsquo;s buffer pools. This is in contrast to simpler approaches such as pgvector, that use HNSW and require the similarity graph to fit in memory. It was a very different product, with very different trade-offs. And it was immensely alluring to take an EC2 instance with 32GB of RAM and throw in 64GB of vector data into our database. Then do the same with a Postgres instance and pgvector. It&rsquo;s the exact same machine, exact same dataset! It&rsquo;s doing the same queries! But PlanetScale is doing tens of thousands per second and pgvector takes more than 3 seconds to finish a single query because the HNSW graph keeps being paged back and forth from disk.

Show HN: I built a real-time OSINT dashboard pulling 15 live global feeds

2026年3月31日 · 张伟 · 来源：user热线

【专题研究】王毅是当前备受关注的重要议题。本报告综合多方权威数据，深入剖析行业现状与未来走向。

Правительство Японии не располагает сведениями о том, что эскалация конфликта на Ближнем Востоке напрямую влияет на ситуацию с энергоресурсами для страны, и пока не рассматривает вопрос о расконсервации стратегических резервов нефти. Об этом заявил генеральный секретарь кабинета министров Японии Минору Кихара, передает ТАСС.

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