Apple licenses Google's 1.2T-parameter Gemini AI for Siri in a $1B/year deal, a strategic interim step before its own model in 2026.
A retrospective on ChatGPT's third anniversary, covering its surprising launch, initial internal skepticism, and unprecedented growth to 800 million users.
Wikipedia's new guideline advises against using LLMs to generate new articles from scratch, highlighting limitations of AI in content creation.
Moonshot AI's Kimi K2 Thinking is a 1 trillion parameter open-weight model optimized for multi-step reasoning and long-running tool calls.
Explores the common practice of developers assigning personas to Large Language Models (LLMs) to better understand their quirks and behaviors.
The article argues that AI's non-deterministic nature clashes with traditional computer interfaces, creating a fundamental human-AI interaction problem.
A hands-on guide for JavaScript developers to learn Generative AI and LLMs through interactive lessons, projects, and a companion app.
An introduction to reasoning in Large Language Models, covering concepts like chain-of-thought and methods to improve LLM reasoning abilities.
Explores four main approaches to building and enhancing reasoning capabilities in Large Language Models (LLMs) for complex tasks.
A researcher reflects on 2024 highlights in AI, covering societal impacts, software tools like Scikit-learn, and technical research on tabular data and language models.
Explains how multimodal LLMs work, compares recent models like Llama 3.2, and outlines two main architectural approaches for building them.
Explores whether large language models like ChatGPT truly reason or merely recite memorized text from their training data, examining their logical capabilities.
Explores methods for using and finetuning pretrained large language models, including feature-based approaches and parameter updates.
Argues that the term 'Open Source' is misleading for LLMs and proposes the new term 'PALE LLMs' (Publicly Available, Locally Executable).
Explores the balanced use of AI coding tools like GitHub Copilot, discussing benefits, risks of hallucinations, and best practices for developers.
Exploring mixed-precision techniques to speed up large language model training and inference by up to 3x without losing accuracy.
A guide to parameter-efficient finetuning methods for large language models, covering techniques like prefix tuning and LLaMA-Adapters.
Guide to finetuning large language models on a single GPU using gradient accumulation to overcome memory limitations.
A curated reading list of key academic papers for understanding the development and architecture of large language models and transformers.
Analyzes the limitations of AI chatbots like ChatGPT in providing accurate technical answers and discusses the need for curated data and human experts.
