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Santiago

@svpino

Jun 6

Bye-bye, virtual assistants! Here is the most useful agent of 2025.

An agent with access to your Gmail, Calendar, and Drive, and the ability to do things for you is pretty mind-blowing.

I asked it to read my emails and reply to every cold outreach message.

My mind is blown!

AI Secretary and the folks @genspark_ai will start printing money!

You can try this out here:

Check their announcement video and you'll see some of the crazy things it can do for you. genspark.ai

The first obvious way I've been using AI Secretary:

100x better email search.

For example, I just asked it to "show me the last 3 emails asking for an invoice for the Machine Learning School cohort."

I also asked it to label every "email containing feedback about the cohort."

Read 6 tweets

Santiago

@svpino

Jun 5

You can now have a literal army of coding interns working for you while you sleep!

Remote Agent is now generally available. This is how we all get to experience what AI is really about.

Here is what you need to know:

Remote Agent is a coding agent based on @augmentcode. They were gracious enough to partner with me on this post.

Remote Agent:

• Runs in the cloud
• Works autonomously
• Can handle small tasks from your backlog

Here is a link to try it out: fnf.dev/4jobOrw

If you have a list of things you've always wanted to solve, let an agent do them:

• Refactor code and ensure tests still run
• Find and fix bugs
• Close open tickets from your backlog
• Update documentation
• Write tests for untested code

Read 5 tweets

Santiago

@svpino

Jun 4

Knowledge graphs are infinitely better than vector search for building the memory of AI agents.

With five lines of code, you can build a knowledge graph with your data.

When you see the results, you'll never go back to vector-mediocrity-land.

Here is a quick video:

Cognee is open-source and outperforms any basic vector search approach in terms of retrieval relevance.

• Easy to use
• Reduces hallucinations (by a ton!)
• Open-source

Here is a link to the repository: github.com/topoteretes/co…

Here is the paper explaining how Cognee works and achieves these results:

arxiv.org/abs/2505.24478

Read 4 tweets

Santiago

@svpino

May 26

Cursor, WindSurf, and Copilot suck with Jupyter notebooks. They are great when you are writing regular code, but notebooks are a different monster.

Vincent is an extension fine-tuned to work with notebooks.

10x better than the other tools!

Here is a quick video:

You can try Vincent for free. Here is a link to the extension:

It works with any of the VSCode forks, including Cursor and Windsurf. The free plan will give you enough to test it out.marketplace.visualstudio.com/items?itemName…

The extension will feel familiar to you:

• You can use it with any of the major models (GPT-X, Gemini, Claude)
• It has an option to Chat and Edit with the model
• It has an Agent mode to make changes to the notebook autonomously

But the killer feature is the Report View.

Read 4 tweets

Santiago

@svpino

May 19

I added a Knowledge Graph to Cursor using MCP.

You gotta see this working!

Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them.

How this works:

1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents.

2. Graphiti connects to Neo4j running locally.

Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project.

Huge!

Here is the video I recorded.

To get this working on your computer, follow the instructions on this link:

github.com/getzep/graphit…

Something super cool about using Graphiti's MCP server:

You can use one model to develop the requirements and a completely different model to implement the code. This is a huge plus because you could use the stronger model at each stage.

Also, Graphiti supports custom entities, which you can use when running the MCP server.

You can use these custom entities to structure and recall domain-specific information, which will tenfold the accuracy of your results.

Here is an example of what these look like:

github.com/getzep/graphit…

By the way, knowledge graphs for agents are a big thing.

A few ridiculous and eye-opening benchmarks comparing an AI Agent using knowledge graphs with state-of-the-art methods:

• 94.8% accuracy versus 93.4% in the Deep Memory Retrieval (DMR) benchmark.

• 71.2% accuracy versus 60.2% on conversations simulating real-world enterprise use cases.

• 2.58s of latency versus 28.9s.

• 38.4% improvement in temporal reasoning.

You'll find these benchmarks in this paper: fnf.dev/3CLQjBK

Read 4 tweets

Santiago

@svpino

Apr 30

Improve your LLM-based applications by 200%:

Build an LLM-as-a-Judge evaluator and integrate it with your system.

This sounds harder than it is.

Here is how to do it and the things you need to keep in mind:

1/11

(LLM-as-a-judge is one of the topics I teach in my cohort. The next iteration starts next week. You can join at .)

LLM-as-a-Judge is a technique that uses an LLM to evaluate the quality of the outputs from your application.

2/11ml.school

There are three specific scenarios you can test with a judge:

1. Choose the best of 2 answers (pairwise comparison)

2. Assess specific qualities of an answer (reference-free)

3. Evaluate the answer based on additional context (reference-based)

3/11

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