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The next AI database isn’t another database. it’s a layer over the one you already have. Today we're launching a virtual ai search layer you can deploy over ANY Postgres database. no migrations. no vector DB. no Neo4j. no sync. no bs. One database. Every way to search it.... show more

dale

4,021 subscribers

18,108 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 58

Фото профиля dale

dale1 месяц назад

@damienhe @polygres @fdotinc @grok wdyt :D

Фото профиля dale

dale1 месяц назад

Biggest update yet our team was cooking this last week 🫡 Back to work

Фото профиля stavan

stavan1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc 👉- - - - | | | | - - - - 👈

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc lol

Фото профиля Shengkun Ye

Shengkun Ye1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc this is huge!!

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Thank you bro!

Фото профиля Shakil

Shakil1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Waiting to try it

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Try it rn!

Фото профиля Manny Hernandez

Manny Hernandez1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc so cool!

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Thanks manny!

Фото профиля Massi

Massi1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc so cool!!

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Ty!

Фото профиля Andrey

Andrey1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc this is fkn legendary

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Hehehee

Фото профиля gz

gz1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc 🚀

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Фото профиля Bambambam

Bambambam1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Happy for you bro

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Фото профиля Jason Jin

Jason Jin1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc goatttt

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Hehe see you later!

Фото профиля Nour Zahzah

Nour Zahzah1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc nice

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Фото профиля DylanBryan

DylanBryan1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Much needed

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc LFG!

Фото профиля Wass

Wass1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc The zero-sync point matters. The moment agents depend on a copied index, the copy becomes another operational liability. Keeping Postgres as the source of truth makes this much more interesting.

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc yeah

Фото профиля Hemant

Hemant1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc its a revolution!!

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc It is!

Фото профиля Sheral Tech

Sheral Tech1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Clean solution one database every search type zero migration headaches

Фото профиля Kingsley Uyi Idehen

Kingsley Uyi Idehen1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Simple question, circa 2026 in the age of AI: Why is it DBMS specific when open standards like ODBC and JDBC exist?

Фото профиля Rain Miao

Rain Miao1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc the hair style looks sick

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc 😭 it’s just my hair grown out after 6 weeks

Фото профиля Rain Miao

Rain Miao1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc I envy your hairline

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc I have a big ass forehead lowk

Фото профиля Veljko Kovac

Veljko Kovac1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc cool work

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Фото профиля MAFIA BOD

MAFIA BOD1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Legend ⭐

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Фото профиля Nima Mozhgani 🫐

Nima Mozhgani 🫐1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Oh heck ya this looks great!

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc try it out!

Фото профиля Adityaa

Adityaa1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Goated

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc g bro

Фото профиля Annalea L

Annalea L1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Polygres is changing how juwels memory functions.

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc see you later!

Фото профиля Annalea L

Annalea L1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc YES!

Фото профиля Aakash Vidiyala

Aakash Vidiyala1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Omw to try this goated product

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc deploy it today!

Фото профиля Taimoor Hassan 🇺🇸🇵🇰

Taimoor Hassan 🇺🇸🇵🇰1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc 🔥🔥🔥🔥

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc hehehee it was great to meet you taimoor!

Фото профиля Taimoor Hassan 🇺🇸🇵🇰

Taimoor Hassan 🇺🇸🇵🇰1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Likewise brotha 🫡

Фото профиля Ander Vallejo

Ander Vallejo1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc LFG

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Фото профиля Leo Oliemans | Refinery

Leo Oliemans | Refinery1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Search over Postgres is getting easier. The operational wrinkle is what happens when the underlying row changes between retrieval and action. Do you expose freshness or conflict signals to the workflow, or leave that to each consumer?

Фото профиля Jay @ LegalOS

Jay @ LegalOS1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc i wouldnt fade this is if i were you!

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc theres a reddish light behind me my hair is naturally super dark brown

Фото профиля Jay @ LegalOS

Jay @ LegalOS1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc bro pls

Фото профиля Michel aka Agent B

Michel aka Agent B1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc Inteersting Dale ! Bravo to the team 👏👏

Фото профиля dale

dale1 месяц назад

@daltonmeon @damienhe @polygres @fdotinc thank you!

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Traditional (SQL) databases rely primarily on keyword-based searches to retrieve information. These searches match the exact words or phrases in your query to the text stored in the database. While effective for many applications, this method has limitations when it comes to understanding context or finding relevant information that doesn’t include the exact keywords. Hybrid search combines the strengths of traditional keyword-based BM25 search with the advanced capabilities of semantic search. To effectively implement a hybrid search, a vector database is essential. Vector databases go beyond just words; they understand the meaning behind the data. They transform data such as text, images, or audio into numerical representations called vectors. These vector embeddings enable the database to find similar items, even if they don't share exact keywords. When you integrate hybrid search with Retrieval-Augmented Generation (RAG) systems, you can achieve higher accuracy in retrieved context and better output in generated responses. Learn more about RAG systems in this video with Victoria Slocum:

Femke Plantinga

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dale

21,992 просмотров • 11 дней назад

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I FOUND AN APP BUILDER THAT DOESN'T CHARGE FOR AI CREDITS. every app builder i tried had the same catch: new account, new interface, and a credit meter that drains every time you ask for a change. Floot skips all of that. no separate ai, no new tool to learn it runs straight through claude, the one you already have open. describe the app in chat. it builds the real thing: working database, working backend, live link when it's done. → your claude subscription covers it, no second bill → real database, not a demo shell → see it update live as you talk to it → nothing to install, nothing to configure turned out the "app builder" i was missing was the ai i was already paying for. full build, no cuts, in the video below.

marcus

50,034 просмотров • 27 дней назад

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the TimescaleDB (by Tiger Data) team for partnering with me on this post!

Santiago

110,133 просмотров • 1 год назад

Finally, a database can generate its own embeddings now. When you add semantic search to an app, the standard step is to wire up an external embedding service, sync the vectors to a separate store, and write glue to keep everything updated as data changes. While this is a standard setup, problems show up later when your data changes, but your pipeline has already run, and search quality starts degrading in ways that are hard to pin down because nothing is explicitly broken. MongoDB Atlas handles this with auto-embedding. You point an index at a text field, specify a Voyage AI model, and Atlas generates the vectors inside the database. When a document changes, it re-embeds itself automatically, so that the search always stays grounded to the current context. I tested this against MongoDB's sample movies collection, 21,000 plots indexed with one config. A plain English query about dystopian machine-controlled futures returned semantically relevant results, despite having no keyword overlap. You can try it yourself here: One index config replaced the embedding service, the vector store, and the sync layer you used to maintain separately. The video below depicts this in action, and I worked with the MongoDB team to put this together. I have also put together a full LLM engineering roadmap covering what it actually takes to build and ship production AI systems in 2026. Read it below.

Finally, a database can generate its own embeddings now. When you add semantic search to an app, the standard step is to wire up an external embedding service, sync the vectors to a separate store, and write glue to keep everything updated as data changes. While this is a standard setup, problems show up later when your data changes, but your pipeline has already run, and search quality starts degrading in ways that are hard to pin down because nothing is explicitly broken. MongoDB Atlas handles this with auto-embedding. You point an index at a text field, specify a Voyage AI model, and Atlas generates the vectors inside the database. When a document changes, it re-embeds itself automatically, so that the search always stays grounded to the current context. I tested this against MongoDB's sample movies collection, 21,000 plots indexed with one config. A plain English query about dystopian machine-controlled futures returned semantically relevant results, despite having no keyword overlap. You can try it yourself here: One index config replaced the embedding service, the vector store, and the sync layer you used to maintain separately. The video below depicts this in action, and I worked with the MongoDB team to put this together. I have also put together a full LLM engineering roadmap covering what it actually takes to build and ship production AI systems in 2026. Read it below.

Avi Chawla

14,463 просмотров • 2 месяцев назад

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Your AI Agents deserve something More Powerful Than Vector DB Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't. HydraDB is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

Harnoor Singh

13,367 просмотров • 6 месяцев назад

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

Santiago

160,154 просмотров • 2 лет назад

Evernote Search just got a lot more powerful! No more slogging through endless notes or wracking your brain for the perfect keyword. With AI-Powered Search, all you have to do is ask. 🐘 Try it yourself! Download Evernote today:

Evernote Search just got a lot more powerful! No more slogging through endless notes or wracking your brain for the perfect keyword. With AI-Powered Search, all you have to do is ask. 🐘 Try it yourself! Download Evernote today:

Evernote

29,921 просмотров • 2 лет назад

So you forgot how you fixed that bug the other day. 😬 No worries. GitHub Copilot CLI remembers. With its local SQLite database of your session history (and a full-text search index), you can instantly pull up context from your previous work. ✅ Install Copilot CLI and save brain space. ⬇️

So you forgot how you fixed that bug the other day. 😬 No worries. GitHub Copilot CLI remembers. With its local SQLite database of your session history (and a full-text search index), you can instantly pull up context from your previous work. ✅ Install Copilot CLI and save brain space. ⬇️

GitHub

47,229 просмотров • 7 месяцев назад