正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

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 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

58 条评论

dale 的头像

dale1 个月前

@damienhe @polygres @fdotinc @grok wdyt :D

dale 的头像

dale1 个月前

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

stavan 的头像

stavan1 个月前

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

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc lol

Shengkun Ye 的头像

Shengkun Ye1 个月前

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

dale 的头像

dale1 个月前

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

Shakil 的头像

Shakil1 个月前

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

dale 的头像

dale1 个月前

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

Manny Hernandez 的头像

Manny Hernandez1 个月前

@daltonmeon @damienhe @polygres @fdotinc so cool!

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Thanks manny!

Massi 的头像

Massi1 个月前

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

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Ty!

Andrey 的头像

Andrey1 个月前

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

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Hehehee

gz 的头像

gz1 个月前

@daltonmeon @damienhe @polygres @fdotinc 🚀

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Bambambam 的头像

Bambambam1 个月前

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

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Thank you!

Jason Jin 的头像

Jason Jin1 个月前

@daltonmeon @damienhe @polygres @fdotinc goatttt

dale 的头像

dale1 个月前

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

Nour Zahzah 的头像

Nour Zahzah1 个月前

@daltonmeon @damienhe @polygres @fdotinc nice

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Thank you!

DylanBryan 的头像

DylanBryan1 个月前

@daltonmeon @damienhe @polygres @fdotinc Much needed

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc LFG!

Wass 的头像

Wass1 个月前

@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 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc yeah

Hemant 的头像

Hemant1 个月前

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

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc It is!

Sheral Tech 的头像

Sheral Tech1 个月前

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

Kingsley Uyi Idehen 的头像

Kingsley Uyi Idehen1 个月前

@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 的头像

Rain Miao1 个月前

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

dale 的头像

dale1 个月前

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

Rain Miao 的头像

Rain Miao1 个月前

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

dale 的头像

dale1 个月前

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

Veljko Kovac 的头像

Veljko Kovac1 个月前

@daltonmeon @damienhe @polygres @fdotinc cool work

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Thank you!

MAFIA BOD 的头像

MAFIA BOD1 个月前

@daltonmeon @damienhe @polygres @fdotinc Legend ⭐

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Nima Mozhgani 🫐 的头像

Nima Mozhgani 🫐1 个月前

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

dale 的头像

dale1 个月前

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

Adityaa 的头像

Adityaa1 个月前

@daltonmeon @damienhe @polygres @fdotinc Goated

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc g bro

Annalea L 的头像

Annalea L1 个月前

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

dale 的头像

dale1 个月前

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

Annalea L 的头像

Annalea L1 个月前

@daltonmeon @damienhe @polygres @fdotinc YES!

Aakash Vidiyala 的头像

Aakash Vidiyala1 个月前

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

dale 的头像

dale1 个月前

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

Taimoor Hassan 🇺🇸🇵🇰 的头像

Taimoor Hassan 🇺🇸🇵🇰1 个月前

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

dale 的头像

dale1 个月前

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

Taimoor Hassan 🇺🇸🇵🇰 的头像

Taimoor Hassan 🇺🇸🇵🇰1 个月前

@daltonmeon @damienhe @polygres @fdotinc Likewise brotha 🫡

Ander Vallejo 的头像

Ander Vallejo1 个月前

@daltonmeon @damienhe @polygres @fdotinc LFG

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc Hehehe

Leo Oliemans | Refinery 的头像

Leo Oliemans | Refinery1 个月前

@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 的头像

Jay @ LegalOS1 个月前

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

dale 的头像

dale1 个月前

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

Jay @ LegalOS 的头像

Jay @ LegalOS1 个月前

@daltonmeon @damienhe @polygres @fdotinc bro pls

Michel aka Agent B 的头像

Michel aka Agent B1 个月前

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

dale 的头像

dale1 个月前

@daltonmeon @damienhe @polygres @fdotinc thank you!

相关视频

I made a cringy silly Cup Song parody to explain our database infrastructure because pitch vids are BORING This is either marketing innovation or a cry for help (probably no. 2) Polygres is live on Product Hunt 🚀 Turn your database into an infinite context window for AI agents: via Product Hunt 😸 Polygres Dalton Lim Damien

I made a cringy silly Cup Song parody to explain our database infrastructure because pitch vids are BORING This is either marketing innovation or a cry for help (probably no. 2) Polygres is live on Product Hunt 🚀 Turn your database into an infinite context window for AI agents: via Product Hunt 😸 Polygres Dalton Lim Damien

dale

28,970 次观看 • 2 个月前

Correa: One of your officers in Maine said to one of the individuals protesting, we're going to put your face in a database. Do you have a database of Americans? Lyons: There is no database.

Correa: One of your officers in Maine said to one of the individuals protesting, we're going to put your face in a database. Do you have a database of Americans? Lyons: There is no database.

Acyn

69,499 次观看 • 8 个月前

MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓

MOST AI STACKS ARE A COMPLETE MESS 🚨 MongoDB just dropped 3 Free AI Skill Badges covering the AI foundations that ACTUALLY matter: 1/ a unified semantic search experience 2/ a scalable vector database 3/ a persistent memory state for agents Because production AI doesn't need a dozen disconnected tools. Yet most teams still fall into the trap of building exactly that: > a specialized vector database here > a separate memory layer there > an orchestration framework on top! and suddenly... your architecture is like a bowl of spaghetti. What you should do is much simpler. These 3 free modules are hands-on lessons for building production-ready AI apps on the unified database you already know. Here's what each one covers 🧵↓

Charly Wargnier ♨️

29,504 次观看 • 3 个月前

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa Links below ↓

dale

15,067 次观看 • 4 个月前

Introducing: 🎵Dumb Ways to AI🎵 a 3-minute song-cumentary about your $40M AI startup: you have 3 databases and no idea what’s happening. Stop. Use Polygres (YC F26). It’s free right now. Use it → get credits at launch. Sign up here: This song will now follow you into your dreams. Dalton Lim Damien Founders Inc

Introducing: 🎵Dumb Ways to AI🎵 a 3-minute song-cumentary about your $40M AI startup: you have 3 databases and no idea what’s happening. Stop. Use Polygres (YC F26). It’s free right now. Use it → get credits at launch. Sign up here: This song will now follow you into your dreams. Dalton Lim Damien Founders Inc

dale

30,770 次观看 • 1 个月前

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

Victor

261,614 次观看 • 1 年前

🚨 Your search history is no longer private. Every Google query, every address, every name, every protest location can sit in a corporate database for years, waiting for the government to weaponize it. @Thomas_Drakel joins us.

🚨 Your search history is no longer private. Every Google query, every address, every name, every protest location can sit in a corporate database for years, waiting for the government to weaponize it. @Thomas_Drakel joins us.

Redacted

13,335 次观看 • 3 个月前

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 次观看 • 10 个月前

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.”

Lone

43,557 次观看 • 11 个月前

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 次观看 • 2 年前

Week 3 of YC. We just made semantic search serverless. Until now, semantic search meant embedding every document, every row and every chunk up front, and paying for it before anyone ran a single search. We flipped that. With Polygres (YC F26) you connect your data, choose what's searchable, and search it by meaning. There's no giant embedding job first. You pay for tokens only when you actually search. It runs on our brand new model that matches embedding-model recall in our (early, tbc) internal benchmarks, without neural ingestion. We believe semantic search should work like serverless compute. Acess rolling out to Polygres users (link below) or dm me.

dale

21,992 次观看 • 11 天前

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.

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 次观看 • 27 天前

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 次观看 • 1 年前

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 次观看 • 2 个月前

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 次观看 • 6 个月前

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 次观看 • 2 年前

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 次观看 • 2 年前

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 次观看 • 7 个月前