In this iteration, we’ll build a local version of Cursor — a terminal-based AI coding agent that runs entirely offline using open-source models. Note: The term “local” is often misused. Here, it means everything — from model inference to file access — runs entirely on your
@whats_ai
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Local coding assistants protect privacy without external servers
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Most coding assistants send your code to external servers, even for simple tasks like reading files or running shell commands. That’s a problem if you care about privacy, work in secure environments, or just want full control.
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Open Local Cursor: Offline Terminal AI Coding Agent
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Let's build an open Local Cursor — a terminal-based AI coding agent that runs entirely offline using open-source models.
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Making Prompts Robust and Ready for Re-Use
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New video! Making Prompts Robust and Ready for Re-Use In this video straight out from our recent "AI for Business" course, we show step by step how to turn “good-enough” prompts into rock-solid, reusable tools. You’ll learn a simple workflow—experiment, A/B test, and
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Building on LLMs: RAG, Fine-tuning, Personalization Essentials
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How to Really Build on Top of LLMs (full training session) Personalization, databases, retrieval (RAG, CAG), frameworks, fine-tuning… We will discuss… Some theory for the essentials
LLM limitations
Context window
Knowledge issues
Embeddings + encoders
Long context
RAG
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RAG vs CAG: Technical Breakdown and Code Comparison
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New video! In this one, we are finally settling it between RAG and CAG… with code! A few weeks ago, I partnered with my friend Miguel Otero Pedrido to make a hands-on technical breakdown to choosing the right approach. Here it is… (link to the article and code in the video
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AI-for-LLMs Bootcamp Delivered at Europol Innovation Lab Paris
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Just wrapped an incredible week in Paris delivering an AI-for-LLMs bootcamp to the Innovation Lab at Europol. Can you find a better place for a training than the center of Paris?! And likewise, we couldn’t have asked for a better team to train. All passionate learners and
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AnswerThis AI Tool Discount Code FRAN10 Available
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Try out that cool AnswerThis tool (use code FRAN10 for 10% off!):
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Master’s Research on Convolutional Networks Interpretability Journey
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Three years ago, I was wrapping up my Master’s thesis: two years of research on convolutional networks’ interpretability. Back then, I was deep in both writing and research, crafting thesis drafts while also scripting YouTube videos to explain research papers. I lived in
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MCP Explained: Universal API for Prompts Data Tools
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New video! MCP Explained: Universal API for Prompts, Data, and Tools Check it out: