learning

Decomp Academy is an interactive online platform that teaches users how to decompile GameCube games by writing byte-matching C code against real PowerPC assembly output from the 2001 MWCC compiler.

A tweet shares the insight that struggling in math, coding, or hard skills is typically due to missing prerequisites, not lack of talent, encouraging learners to fill gaps.

Recommends the Stanford open course CS336: Language Modeling from Scratch, which systematically explains the full training pipeline of language models from scratch, suitable for those preparing for AI interviews or wanting to deeply learn LLM.

Share two ways to use Claude Hooks: passively observe and record tool call logs, and actively intervene to implement a learning mode state machine. Emphasize that Hooks as hard constraints are more reliable than prompts.

A list of 10 backend development projects to build for learning, including notification systems, video processing queues, and search autocomplete.

Recommends 10 free AI learning resources, including 3Blue1Brown, Karpathy's course, Andrew Ng's newsletter, podcasts, Kaggle, and Hugging Face, claiming you can learn more in 30 days than a paid bootcamp.

Recommend an interactive visualization website called Seeing Theory to help users intuitively understand core concepts of probability and statistics, covering basic probability, distributions, inference, regression, etc., suitable for beginners and those reviewing.

Santiago Valdarrama shares a framework for building AI agents that improve over time through three learning areas: model refinement, harness optimization, and context accumulation, emphasizing the importance of learning from user corrections.

Nathan Lambert announces his goal to create a comprehensive hub for learning RLHF post-training, including a book, lectures, code, and community resources.

Recommends reading the top most cited papers on Papers with Code, one or two per week, to deeply understand influential AI research.

A compiled roadmap showing how engineers at Google, Microsoft, Meta, Amazon, and Netflix build real systems.

A GitHub repository curating project-based programming tutorials across multiple languages, helping developers build applications from scratch.

dair_ai announces a new /learn agent skill that creates a personalized learning plan and a learning hub that adapts to the learner's needs and progress.

Sidegent is a product that teaches users how to build AI agents through hands-on practice.

A clear step-by-step guide to understanding Retrieval-Augmented Generation (RAG), covering explanations, visuals, and various architectures like Naïve RAG, Advanced RAG, Graph RAG, Multimodal RAG, and Agentic RAG.

Explains the Feynman learning method and provides four prompts to use with Claude to overcome the forgetting curve by forcing retrieval and teaching, based on cognitive science research.

A reflection on the joy and power of deeply understanding code and systems, cautioning against over-reliance on LLMs and shortcuts that erode true mastery.

A recommendation of Nancy Lynch's book 'Distributed Algorithms' as a valuable resource for distributed systems professionals.

The article explores how AI is used in education and whether AI editing tools can replace human editors, seeking insights on their real benefits.

Harrison Chase shares insights on the Agent Development Cycle, emphasizing that shipping the first version is only the beginning and that a repeatable lifecycle is key to building reliable agents.