Machine Intelligence - 2026
A chronological archive of Machine Intelligence reading list posts.
2024
April 2024
🔗 Generative AI is still a solution in search of a problem
🔗 Financial Market Applications of LLMs
🔗 Accelerating AI Image Generation with MIT's Novel Framework
🔗 Adobe Is Buying Videos for $3 Per Minute to Build AI Model
🔗 Here’s Proof You Can Train an AI Model Without Slurping Copyrighted Content
🔗 Cheat Sheet: 5 prompt frameworks to level up your prompts
🔗 Embeddings are a good starting point for the AI curious app developer
🔗 How to unleash the power of AI, with Ethan Mollick
🔗 Hello OLMo: A truly open LLM
🔗 MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
🔗 3Blue1Brown: Neural Networks
🔗 Comprehensive Guide to AI Tools and Resources
🔗 Enhancing GPT's Response Accuracy Through Embeddings-Based Search
🔗 How (Specifically) AI Will 100x Human Creativity and Output
🔗 An Agentic Design for AI Consciousness
🔗 VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
🔗 The Question No LLM Can Answer
🔗 Stanford AI Index Report 2024: An In-depth Analysis of AI's Current State
🔗 LLM inference speed of light
🔗 Dify
🔗 Thoughts on the Future of Software Development
🔗 Making Deep Learning Go Brrrr From First Principles
🔗 GR00T: NVIDIA's moonshot to solve embedded AI
🔗 CometLLM: Logging and Visualizing LLM Prompts
🔗 Natural language instructions induce compositional generalization in networks of neurons
🔗 Full Steam Ahead: The 2024 MAD (Machine Learning, AI & Data) Landscape
🔗 What Is an AI Anyway? | Mustafa Suleyman | TED
🔗 LLM in a flash: Efficient Large Language Model Inference with Limited Memory
🔗 The Pipe
🔗 RAFT: A new way to teach LLMs to be better at RAG
🔗 The Death of the Big 4: AI-Enabled Services Are Opening a Whole New Market
🔗 Anthropic's Prompt Engineering Interactive Tutorial
🔗 CoreNet: A Library for Training Deep Neural Networks
🔗 Outset.ai: Revolutionizing User Surveys with GPT-4
🔗 This prompting technique is insanely useful
🔗 The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey
🔗 What Computers Cannot Do: The Consequences of Turing-Completeness
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