Santiago
@svpino • 452,021 subscribers
Computer scientist. I teach hard-core AI/ML Engineering at https://t.co/THCAAZcBMu. YouTube: https://t.co/pROi08OZYJ
Shorts
Videos
People are lying to you. These agents don't work as they promised.
Santiago853,987 views • 2 months ago
Some of the stories they aren't telling you: • Chevrolet's chatbot sold a car for $1 • Air Canada had to honor a refund policy that its chatbot made up • A pipeline ran 20x over cost for 6 days without anyone noticing People didn't realize because nothing broke. There were no crashes and no alerts. That's the issue with agentic applications. They always generate something that looks coherent and don't raise any suspicion unless it's too late. There's an amazing free YouTube lecture and blog post from Arsh Shah Dilbagi that will help you fix this with a practical framework. Here is what you'll learn: • How to set up end-to-end trace instrumentation • How to build alerts around a silent failure taxonomy • An eval system built from production data • Complete and concrete implementation steps Every section of the blog ends with exactly what to do next.
Santiago609,504 views • 2 months ago
Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.
Santiago86,081 views • 19 days ago
You are brain-dead if you think the CLI killed MCP. Anthropic just reported 300 million downloads for the MCP SDK (up from 100M at the beginning of the year). The complaint I keep hearing: MCP pollutes your context. That's a 1-year-old argument. Context pollution is not a protocol problem, but a client one. None of the agentic tools you use have that issue: Claude Code, Codex, Cursor, etc. All of them implement progressive disclosure and load MCP tools on demand. MCPs and CLIs complement each other: • MCP offers OAuth-based authentication, standardized transport, remote access for cloud-hosted agents, and a public registry with thousands of servers. • CLIs offer Unix-style composition (pipes, stdin/stdout), zero upfront schema cost, and massive training distribution (models have seen far more shell than any structured tool format). You don't (and shouldn't) pick a side here. You should be using both where they fit.
Santiago68,033 views • 28 days ago
This is literally the fastest way to install OpenClaw (MoltBot). This video will show you how to do it step by step. You don't need to buy a Mac Mini. We'll install it in DigitalOcean. I've installed this 12 different times already, and this is the fastest way I've found.
Santiago240,791 views • 3 months ago
Intelligence withdrawal will be brutal. Model tokens are heavily subsidized. Subsidies are disappearing, and with them, so is easy "intelligence". This is the reason for Anthropic and OpenClaw's divorce. This should be a wake-up call for everyone building on top of a single provider. Your AI setup shouldn't depend on someone else's business model.
Santiago116,041 views • 1 month ago
This is a trillion-dollar industry, and you can't solve it with an LLM: • Forecasting • Fraud detection • Churn prediction Large Language Models are fundamentally bad at solving these problems. When you feed structured data into an LLM, it doesn't see relationships, and it treats every number, date, and foreign key as a token. That's why you always get garbage back. An LLM thinks your database is a Wikipedia article. It doesn't understand its structure or its relationships. GPT-4 scores 63% on relational prediction tasks. That's the best it can do, and that's pretty much useless. You can't expect real-world business value to come from summarizing Wikipedia articles.
Santiago94,575 views • 1 month ago
Here is how you can give Claude Code access to any data that exists online. It's an easy way to make it 10x more powerful than it already is. For example: Use Claude Code to find open LinkedIn jobs in your area, tailor your resume to them, and apply for them automatically.
Santiago178,540 views • 4 months ago
We integrated ChatGPT with our robots. We had a ton of fun building this! Read on for the details:
Santiago1,256,561 views • 3 years ago
Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:
Santiago296,299 views • 8 months ago
New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!
Santiago64,906 views • 1 month ago
The first open-source implementation of the paper that will change automatic test generation is now available! In February, Meta published a paper introducing a tool to automatically increase test coverage, guaranteeing improvements over an existing code base. This is a big deal, but Meta didn't release the code. Fortunately, we now have Cover-Agent, an open-source tool you can install that implements Meta's paper to generate unit tests automatically: I recorded a quick video showing Cover-Agent in action. There are two things I want to mention: 1. Automatically generating unit tests is not new, but doing it right is difficult. If you ask ChatGPT to do it, you'll get duplicate, non-working, and meaningless tests that don't improve your code. Meta's solution only generates unique tests that run and increase code coverage. 2. People who write tests before writing the code (TDD) will find this less helpful. That's okay. Not everyone does TDD, but we all need to improve test coverage. There are many good and bad applications of AI, but this is one I'm looking forward to make part of my life.
Santiago774,388 views • 2 years ago
