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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

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18,108 görüntüleme • 1 ay önce •via X (Twitter)

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58 Yorum

dale profil fotoğrafı

dale1 ay önce

@damienhe @polygres @fdotinc @grok wdyt :D

dale profil fotoğrafı

dale1 ay önce

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

stavan profil fotoğrafı

stavan1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc lol

Shengkun Ye profil fotoğrafı

Shengkun Ye1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Shakil profil fotoğrafı

Shakil1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Manny Hernandez profil fotoğrafı

Manny Hernandez1 ay önce

@daltonmeon @damienhe @polygres @fdotinc so cool!

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Thanks manny!

Massi profil fotoğrafı

Massi1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Ty!

Andrey profil fotoğrafı

Andrey1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Hehehee

gz profil fotoğrafı

gz1 ay önce

@daltonmeon @damienhe @polygres @fdotinc 🚀

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Bambambam profil fotoğrafı

Bambambam1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Jason Jin profil fotoğrafı

Jason Jin1 ay önce

@daltonmeon @damienhe @polygres @fdotinc goatttt

dale profil fotoğrafı

dale1 ay önce

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

Nour Zahzah profil fotoğrafı

Nour Zahzah1 ay önce

@daltonmeon @damienhe @polygres @fdotinc nice

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Thank you!

DylanBryan profil fotoğrafı

DylanBryan1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Much needed

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc LFG!

Wass profil fotoğrafı

Wass1 ay önce

@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.

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc yeah

Hemant profil fotoğrafı

Hemant1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc It is!

Sheral Tech profil fotoğrafı

Sheral Tech1 ay önce

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

Kingsley Uyi Idehen profil fotoğrafı

Kingsley Uyi Idehen1 ay önce

@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?

Rain Miao profil fotoğrafı

Rain Miao1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Rain Miao profil fotoğrafı

Rain Miao1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Veljko Kovac profil fotoğrafı

Veljko Kovac1 ay önce

@daltonmeon @damienhe @polygres @fdotinc cool work

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Thank you!

MAFIA BOD profil fotoğrafı

MAFIA BOD1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Legend ⭐

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Nima Mozhgani 🫐 profil fotoğrafı

Nima Mozhgani 🫐1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Adityaa profil fotoğrafı

Adityaa1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Goated

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc g bro

Annalea L profil fotoğrafı

Annalea L1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Annalea L profil fotoğrafı

Annalea L1 ay önce

@daltonmeon @damienhe @polygres @fdotinc YES!

Aakash Vidiyala profil fotoğrafı

Aakash Vidiyala1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Taimoor Hassan 🇺🇸🇵🇰 profil fotoğrafı

Taimoor Hassan 🇺🇸🇵🇰1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Taimoor Hassan 🇺🇸🇵🇰 profil fotoğrafı

Taimoor Hassan 🇺🇸🇵🇰1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Likewise brotha 🫡

Ander Vallejo profil fotoğrafı

Ander Vallejo1 ay önce

@daltonmeon @damienhe @polygres @fdotinc LFG

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Leo Oliemans | Refinery profil fotoğrafı

Leo Oliemans | Refinery1 ay önce

@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?

Jay @ LegalOS profil fotoğrafı

Jay @ LegalOS1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

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

Jay @ LegalOS profil fotoğrafı

Jay @ LegalOS1 ay önce

@daltonmeon @damienhe @polygres @fdotinc bro pls

Michel aka Agent B profil fotoğrafı

Michel aka Agent B1 ay önce

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

dale profil fotoğrafı

dale1 ay önce

@daltonmeon @damienhe @polygres @fdotinc thank you!

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dale

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dale

15,067 görüntüleme • 4 ay önce

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dale

30,770 görüntüleme • 1 ay önce

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Redacted

13,335 görüntüleme • 3 ay önce

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 görüntüleme • 10 ay önce

Qullamaggie on Minervini’s 18,000 Chart Database “Wait, Minervini has an 18,000-page chart database on paper? Guys, that’s what you have to do. I think 18,000 may be a bit overkill, and especially on paper. But if you have like one note or ever note or something like that, there’s no reason why you should… you know you… everyone should build a chart database, a pattern database right, at least a couple of thousand charts and setups. And then you trade those setups over and over, again right. Like, I have three setups and variations that I trade over and over again. I don’t do any rocket science at all.”

Qullamaggie on Minervini’s 18,000 Chart Database “Wait, Minervini has an 18,000-page chart database on paper? Guys, that’s what you have to do. I think 18,000 may be a bit overkill, and especially on paper. But if you have like one note or ever note or something like that, there’s no reason why you should… you know you… everyone should build a chart database, a pattern database right, at least a couple of thousand charts and setups. And then you trade those setups over and over, again right. Like, I have three setups and variations that I trade over and over again. I don’t do any rocket science at all.”

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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:

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

144,343 görüntüleme • 2 yıl önce

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dale

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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 görüntüleme • 27 gün önce

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 görüntüleme • 1 yıl önce

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 görüntüleme • 2 ay önce

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 görüntüleme • 6 ay önce

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 görüntüleme • 2 yıl önce

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:

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Evernote

29,921 görüntüleme • 2 yıl önce

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 görüntüleme • 7 ay önce