Your daily dose of complex AI concepts made simple, practical, and accessible for everyone.
machine-learning
Decision Trees: A Complete Guide with Hand-Worked Examples
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blogpost
Knowledge Distillation: How Small Models Learn from Big Ones
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blogpost
LLM-as-Judge: How to Evaluate AI Models Automatically at Scale
Read post →Ask which post fits your problem, or anything about Peri's work on LLMs, RAG, and agents. It'll link you straight to the article.
blogpost
LLMs no longer require a data center. Phi-3, Gemma, and Apple Intelligence run directly on device, with no data leaving your phone....
blogpost
AI coding assistants have moved well beyond tab-completion. Cursor edits across files, GitHub Copilot reviews pull requests, and Devin claims to handle...
blogpost
Prompt engineering is giving way to something deeper: context engineering. How you structure what goes into the context window, what you include,...
ai-engineering
Vision Language Models bridge the gap between pixels and language. This post covers how CLIP encodes images, how visual tokens are projected...
ai-engineering
Mixture of Experts scales model capacity without scaling compute. Instead of activating all parameters for every token, MoE routes each token to...
blogpost
Diffusion models generate images by learning to reverse a noise process. No math required. Here is the intuition behind Stable Diffusion, DALL-E,...
blogpost
Stateless LLMs forget everything when the context window closes. Building agents that actually remember requires understanding four distinct memory types and when...
blogpost
Reasoning models like o1, o3, and DeepSeek R1 don't just predict the next token. They think through problems step by step before...
ai-engineering
Full fine-tuning a 7B parameter model requires 112 GB of VRAM and costs thousands of dollars. LoRA reduces trainable parameters by 99%...
ai-engineering
Standard vector RAG fails on multi-hop questions that require connecting information across multiple documents. Graph RAG solves this by building a knowledge...