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By now, everyone knows that single-vector embedding models are hugely limiting for modern workflows. But they contain than you think: you can extract sparse Latent Terms from them. And it turns out that BM25 is all you need to turn this vocabulary into a strong retriever.

Mixedbread

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40,023 views • 1 month ago •via X (Twitter)

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Everyone knows this Ren and Stimpy animation for the "Don't reference modern shit, you idiot" scene, but I think this joke that comes before it is pretty underrated. I quote this to myself a lot lol.

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They think it’s a joke that you’re paying for their lifestyle, they think you owe it to them. THIS is the attitude you see all over the internet from people who are on food stamps. It sure looks like they’re just as able bodied as anyone. If you can dance you can work!

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This finishing concept is great when you’re not in a blow by situation. Skip into them so that they are forced to absorb your contact. When you land and you want to transition your way to your back foot so that you can feed without being out of control

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Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

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If you encore a Prankster Pokemon into a the previous move and that move was an attacking move but this turn they went for a Status move, then it will get the Prankster priority. But now that also means that it won’t hit dark-type Pokemon that turn.

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