Tag: LLM

A few months ago I came across something that really caught my attention: the possibility of having my own “ChatGPT” running at home, without sending data anywhere, using only a Raspberry Pi 5. Sounds too good to be true, right?

Well, it turns out that with Ollama and a Pi 5 it’s perfectly possible to set up a local AI server that works surprisingly well. Let me tell you my experience and how you can do it too.

Lately, there’s been talk of AI agents everywhere. Every company has their roadmap full of “agents that will revolutionize this and that,” but when you scratch a little, you realize few have actually managed to build something useful that works in production.

Recently I read a very interesting article by LangChain about how to build agents in a practical way, and it seems to me a very sensible approach I wanted to share with you. I’ve adapted it with my own reflections after having banged my head more than once trying to implement “intelligent” systems that weren’t really that intelligent.

A few months ago, when Anthropic launched their MCP (Model Context Protocol), I knew we’d see interesting integrations between LLMs and databases. What I didn’t expect was to see something as polished and functional as ClickHouse’s AgentHouse so soon.

I’m planning to test this demo soon, but just reading about it, the idea of being able to ask a database questions like “What are the most popular GitHub repositories this month?” and getting not just an answer, but automatic visualizations, seems fascinating.

In my years developing software, I’ve learned that the best tools are those that eliminate unnecessary friction. And LM Studio has just taken a huge step in that direction: it’s now completely free for enterprise use.

This may sound like “just another AI news item,” but for those of us who have been experimenting with local models for a while, this is an important paradigm shift.

The problem that existed before

Since its launch in May 2023, LM Studio was always free for personal use. But if you wanted to use it in your company, you had to contact them to obtain a commercial license. This created exactly the type of friction that kills team experimentation.

A few years ago, many AI researchers (even the most reputable) predicted that prompt engineering would be a temporary skill that would quickly disappear. They were completely wrong. Not only has it not disappeared, but it has evolved into something much more sophisticated: Context Engineering.

And no, it’s not just another buzzword. It’s a natural evolution that reflects the real complexity of working with LLMs in production applications.

From prompt engineering to context engineering

The problem with the term “prompt engineering” is that many people confuse it with blind prompting - simply writing a question in ChatGPT and expecting a result. That’s not engineering, that’s using a tool.

The hype vs reality: reflections from a developer with 30 years of experience

This morning I came across a talk that made me reflect quite a bit about all this fuss surrounding AI and software development. The speaker, with a healthy dose of skepticism, does a “reality check” on all the grandiose claims we’re hearing everywhere.

The complete talk that inspired these reflections. It’s worth watching in full.

Creating long, well-founded articles has traditionally been a complex task requiring advanced research and writing skills. Recently, researchers from Stanford presented STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking), a revolutionary system that automates the Wikipedia-style article writing process from scratch, and the results are truly impressive.

In this detailed analysis, we’ll explore how STORM is transforming the way we think about AI-assisted writing and why this approach could forever change the way we create informative content.