A critique of Reformist RL's inefficiency and a proposal for more effective alternatives in reinforcement learning.
A simplified, non-technical definition of reinforcement learning as an iterative optimization process based on external feedback.
A technical lecture on applying policy gradient methods to derive optimization algorithms, focusing on the unbiased gradient estimator and its applications.
Discusses handling class imbalance in predictive modeling, using medical and zebra analogies to explain adjusting for prior probabilities and error costs.
Summary of a talk on using R for geospatial predictive mapping, covering methods like Kriging and Random Forests, and tools for evaluating prediction reliability.
Explores the fundamental differences between animal intelligence and AI/LLM intelligence, focusing on their distinct evolutionary and optimization pressures.
Analysis of the rising prominence of Chinese AI labs like DeepSeek and Kimi in the global AI landscape and their rapid technological advancements.
Explores the concept of 'human collapse' from a tech podcast, arguing for seeking 'entropy' and new inputs to stay creative and unpredictable, with mentions of AI tools.
Explores the concept of AI Agents, defining them and examining their role in the AI ecosystem, with references to LangChain and Anthropic.
A list of over 50 Python project ideas for beginners and advanced learners, covering algorithms, networking, and machine learning.
Explains the difference between AI and Machine Learning, with AI as the goal of intelligent systems and ML as a key approach to achieve it.
A blog post exploring the differences between AI and ML, clarifying terminology and common misconceptions in the field.
Explains how rerankers improve search and AI results by reordering retrieved documents for better precision and relevance.
A curated list of 9 top engineering blogs from major tech companies, detailing how they build and scale real-world AI systems.
A defense of systematic AI evaluation (evals) in development, arguing they are essential for measuring application quality and improving models.
A curated list of key LLM research papers from Jan-June 2025, organized by topic including reasoning models, RL methods, and efficient training.
A curated list of key LLM research papers from the first half of 2025, organized by topic such as reasoning models and reinforcement learning.
A tutorial on building a transformer-based language model in R from scratch, covering tokenization, self-attention, and text generation.
Explains the differences between Machine Learning and Generative AI, with examples and industry applications.
A summary of a practical session on analyzing and improving LLM applications by identifying failure modes through data clustering and iterative testing.
