A philosopher and a computer scientist walk into the same conclusion from opposite directions: you can't learn anything from data alone. Popper said induction was a myth. Mitchell proved it mathematically. And the punchline? Every machine learning algorithm that does generalize is secretly running deduction in disguise — the "induction" was never really there.
LangChain is a great framework for turning chatbots into functional tools. Today, we'll be having a look at LangChain's own Quick Start Tutorial and how we were able to get it to work with Ollama for a free.
Every day we hear about how AI is constantly improving in its intelligence and its capabilities, but many still wonder how AI can actually help improve their daily workloads. In today's AI tutorial, we'll be showcasing a real-world example of how we were able to build and use an AI agent to help us sift through the millions of patents that exist in the USPTO records; turning weeks of work into mere hours (including the time it took to build the AI agent).
As magical as AI systems appear to be, in order for them to actually produce anything useful they first need an ordered structure to follow. LangChain, one such structure, is a framework that can turn an AI chatbot into a useful and productive resource. In this article by Bruce Nielson, we'll be going through how it works and when to use it.
Bubble or the future of Earth's technology? AI continues to be the #1 buzzword across business channels, but many fear the potential looming collapse of the "AI bubble", and wonder if it is worth the investment risk. In today's article by Bruce Nielson, we'll be exploring both the pessimistic and the positive perspectives on this AI bubble.
In our last tutorial, we showed how to build a locally-ran Deepseek R1 chatbot using Streamlit. Today, we'll be trying a different Python package called Chainlit; a conversational AI application framework that provides an elegant, real-time chat interface out of the box.
While DeepSeek R1 is no longer at the forefront of the news, it still continues to be a strong contender among LLMs and can be especially easy to use with locally ran AI chatbots. In today's AI tutorial by Bruce Nielson, we'll be showcasing how to build a simple and transparent local chatbot using DeepSeek and Ollama.
Merry Christmas from the Mindfire Team! As an early present, here is another free article from Bruce Nielson on DSPy, a powerful tool that turns Large Language Models into structured, type-safe Python functions. Today's article will discuss how you can optimize your DSPy results using MIPROv2.
DSPy is an incredible tool that can be used to prompt LLMs with Python code. But how does it actually work? In today's quick AI tutorial, Bruce Nielson will break down how DSPy builds its unique prompts so you can better use it to improve your AI workloads.
Previously, we showcased DSPy and how it could be used to prompt LLMs like Python code. Today, we'll be showcasing another example to help you really get the hang of how DSPy can benefit your workloads.
