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You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it... show more

Santiago

452,233 subscribers

209,434 Aufrufe • vor 2 Jahren •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

10 Kommentare

Profilbild von Raul Junco

Raul Juncovor 2 Jahren

A common business requirement is solved in a smart way.

Profilbild von Kris Lukanov

Kris Lukanovvor 2 Jahren

That's super cool 😎

Profilbild von John

Johnvor 2 Jahren

Looks very interesting, but I would like to know more about the actual connection to your DB. ie: does MindsDB have access to your actual login details for that db?

Profilbild von Juan Andrés Arriaga

Juan Andrés Arriagavor 2 Jahren

Kind of worried about security with this implementation. This is a great start but may not be useful with sensitive data

Profilbild von Rajasekar Nonburaj (𝑅𝒥)

Rajasekar Nonburaj (𝑅𝒥)vor 2 Jahren

Super cool and remind me of sparksql.

Profilbild von Munsif

Munsifvor 2 Jahren

Looks great

Profilbild von Yippee Ki Yay

Yippee Ki Yayvor 2 Jahren

what about SQLite

Profilbild von Marcus Gill Greenwood

Marcus Gill Greenwoodvor 2 Jahren

please tell me it caches! 😬

Profilbild von Luke Skyward

Luke Skywardvor 2 Jahren

Smart!

Profilbild von Yasar Arafath

Yasar Arafathvor 2 Jahren

@jaleeledathol

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Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

39,331 Aufrufe • vor 4 Monaten

This is a very clever idea to use an LLM with your SQL data. SQL + AI has been tried before, but one of the best parts of this solution is getting the same exact OpenAI's completion API. In other words: You are now talking to an LLM that knows everything about your database and you don't even notice it! I recorded a 2-min video to show you how it works. Thanks to the mindshub team for the collaboration, listening to my feedback, and helping me understand what they built. Go to to try this yourself.

This is a very clever idea to use an LLM with your SQL data. SQL + AI has been tried before, but one of the best parts of this solution is getting the same exact OpenAI's completion API. In other words: You are now talking to an LLM that knows everything about your database and you don't even notice it! I recorded a 2-min video to show you how it works. Thanks to the mindshub team for the collaboration, listening to my feedback, and helping me understand what they built. Go to to try this yourself.

Santiago

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109,517 Aufrufe • vor 1 Jahr

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Santiago

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Santiago

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Santiago

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Santiago

72,306 Aufrufe • vor 1 Jahr

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Santiago

78,133 Aufrufe • vor 1 Jahr

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Santiago

44,783 Aufrufe • vor 1 Jahr

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Santiago

70,786 Aufrufe • vor 8 Monaten

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Santiago

160,154 Aufrufe • vor 1 Jahr

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45,976 Aufrufe • vor 3 Monaten

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UI x AI by Christabel

22,535 Aufrufe • vor 13 Tagen

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Spenser Skates

32,306 Aufrufe • vor 8 Monaten

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The Food You Eat Is Fake, Here’s Some Proof “Fake Fruits In Walmart GMO Is Killing Us” 🥭 ☣️🚫 “This is a mango and it looks fake, it feels fake like I'm beyond pissed and this is like look at this. This is GMO. I'm gonna give it a big no. Like I’m beyond pissed… like look at this this is GMO” “Exactly and this is why I say none of you are vegetarians none of you are vegans none of you are eating real food I'm gonna show it to you here now look this is an organic pair but on the pair you see the label right you see these labels they put on it says trust well can we really trust it now if you look at the number there if you can see it on the live can you see it all right if you look at the number there is designated by a 9, 8 or 4 if it's a 4 it's pesticides right so it's conventionally raised which means it's on a regular farm there's no real regulation they can do whatever they need to do to grow that fruit” This is a long video with a lot of great information, I can’t transcribe it all due to X’s text limits but it’s worth a watch

Wall Street Apes

404,582 Aufrufe • vor 2 Jahren

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You can now fine-tune Llama 3 without writing a single line of code! We are moving at breakneck speed. I recorded a video to show you how to fine-tune any open-source model in a few minutes. I'm using a GPT capable of taking a problem and turning it into a fine-tuned model that will solve it. You don't have to write any code. You only need to explain to a GPT what problem you want to solve and tell it you want to use Llama 3. For example, "fine-tune Llama 3" or "deploy zephyr." It feels magic. The system will recommend a dataset and fine-tune the model for you. I'm using Monster API, a platform that specializes in making fine-tuning and deploying open-source models easy and fast. Their stack is well-optimized to maximize fine-tuning efficiency using techniques like Q-Lora and vLLM. They are behind the GPT. Here is what you need to do: 1. Create an account at 2. Load the GPT with the link below This is as simple as it gets. When you are done, you can click a button to deploy the model and start using it. I have 10,000 free credits for anyone using the code "SANTIAGO" in the dashboard. You can use these credits to access, fine-tune, and deploy these open-source models. You can also keep up with their latest updates, and get free credits and special offers on their Discord server:

Santiago

324,578 Aufrufe • vor 2 Jahren

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

You can now try Llama 3.1 405B for free (link below)! This is the largest open-source model out there, and for the first time, an open model is competitive with closed models. This time around, Meta did something new: Llama 3.1 has a license that allows developers to use it to enhance other models. For the first time, you can distill Llama 3.1 405B's capabilities into a smaller, more practical model for your use case. First, here is the link where you can play with Llama 3.1 for free: The model is hosted in Tune Studio, an end-to-end platform for developing applications using Large Language Models. They are sponsoring this post. Take a look at the attached video. It will show you how you can fine-tune a simple model using Llama 3.1 without leaving the platform: 1. You can create an empty dataset 2. Use the playground to generate and record interactions with Llama 3.1 3. Modify the dataset directly using the playground 4. Export the data and fine-tune a smaller model Fast and easy! As long as you have a web browser, you can start experimenting with fine-tuning and Llama 3.1. That's all it takes!

Santiago

55,609 Aufrufe • vor 1 Jahr