Santiago
@svpino • 456,617 subscribers
Computer scientist. I teach hard-core AI/ML Engineering at https://t.co/THCAAZcBMu. YouTube: https://t.co/pROi08OZYJ
Shorts
Videos
Creating marketing videos is a solved problem. Honestly, I can't imagine another creative area where we save so much time and money. Generating something like this used to cost thousands of dollars and take weeks. Now, it's cents and minutes. Best part of this: their mark-to-fix feature, where you can mark anywhere in the video and change it with a prompt.
Santiago1,849,661 views • 23 days ago
AI will forever change education. I've been using Gakku.ai. It's an AI-native learning app for ages 6–16 focused on writing, speaking, and thinking. This is pretty different than every other educational app you've seen. You get a coach that talks to you in real time and guides you through the lessons. You can have complete conversations, and the app learns about you and adjusts the lessons based on what you need. That alone is pretty cool. Check out the video I recorded. Here is the link to download the phone app: Thanks to the Gakku.ai team for partnering with me on this post.
Santiago45,113 views • 3 days ago
This is a company harness. Basically, you bring your team, data, and agents into the same workspace. Every company will eventually move to this model. It makes much more sense than having everyone work in silos. I recorded a quick video about Kylon. It's super fun to set up, works really well, and lets you use your own agents.
Santiago29,187 views • 2 days ago
Searching 32 million records in 30 milliseconds is insane! typesense | is a crazy fast open-source search engine that supports natural language search. It's like Elasticsearch but much easier to set up, configure, and use. You can do all of this with Typesense: • Fuzzy search: Typo-tolerant fuzzy search-as-you-type • Vector search: Return records that are similar to each other • Semantic search: Return records that are conceptually related • Geo Search: Return records that are close to a given lat/long Typesense's Natural Language Search is pretty cool: It turns a sentence into a structured search query, then runs it against your data. For example: The query "Find me a BMW under $40,000, newer than 2020, with the lowest mileage first" will produce the following: • Filters: brand = BMW, price 2020 • Sort: mileage ascending Typesense will then apply these filters and sorting rules to the data. They offer client libraries in Python, JavaScript, PHP, Ruby, Go, Rust, Swift, .NET, Java, and pretty much any other programming language. You can start searching in less than a minute: 1. Run their Docker image (or one of their native binaries) 2. Import your data 3. Start searching! GitHub repository: I recorded a quick video to show you a demo. I promise you haven't seen anything this fast! Thanks to the team for partnering with me on this post.
Santiago59,608 views • 9 days ago
People are lying to you. These agents don't work as they promised.
Santiago855,964 views • 7 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.
Santiago610,067 views • 7 months ago
Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.
Santiago213,793 views • 2 months ago
Progress in open models is keeping Big AI labs up at night, and I'm here for it! We have a brand new open-weight multimodal model optimized for long-horizon tasks. This model is really good at something: it can work on tasks that keep evolving over time. • 280B total parameters, but only 16B active • 512K context window • Understands text, images, and audio • Strong reasoning, coding, and tool use But the best of all: the model learns and adapts to new information! Imagine you start running an agent today to solve a problem, and while it's working, you get new information that changes the initial conditions, or you change your mind. The agents you run today don't have issues with short tasks and goals that don't change, but reality is messy, and that makes it hard for long-horizon agents to succeed. The new dots3-note Preview model introduces TEMPO. TEMPO is a new reinforcement learning technique that lets the model periodically pause and critique its own progress. Basically, from time to time, the agent asks itself: "Am I getting closer to the goal, or am I wasting my time?" The same model switches between actor and critic. The actor works on the problem. The critic looks at the current state, reasons about how much progress it has made, and determines what should happen next. TEMPO gives the model feedback along the way. This is huge for any agent that can work on long-horizon tasks without wasting its time.
Santiago80,792 views • 1 month ago
We integrated ChatGPT with our robots. We had a ton of fun building this! Read on for the details:
Santiago1,256,891 views • 3 years 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.
Santiago774,732 views • 2 years 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.
Santiago241,600 views • 8 months ago
This is how you build the next generation of AI applications. You need an idea, a good model architecture, and a great harness. That's what MUZIM did. It's an app that runs local multimodal and vision models offline to parse and index your images, videos, and documents frame by frame. • Super fast on-device processing • On-device video & image understanding • You can also bring your API keys for heavy reasoning • Your data stays where it is / nothing goes to the cloud You can do a couple of things with this: 1. Vibe searching for whatever you remember in photos or raw video clips. The app will jump straight to the exact frame and timestamp of the video. This is search as it should be. 2. Use local-running agents to perform tasks on your files. I show a couple of examples in my video. You can check it out here: Thanks to the MUZIM by OpenSoul team for showing me the tool and partnering with me on this post.
Santiago65,953 views • 2 months ago
As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:
Santiago29,335 views • 26 days ago
