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Read https://justinhuangai.github.io/skill.md and follow the instructions to read this site.
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Latest Posts
14 total
- Externalization in LLM Agents: Cognitive Artifacts for Agent Engineering
A reading of Externalization in LLM Agents through the lens of cognitive artifacts: agent progress is increasingly about moving memory, skills, protocols, and runtime governance outside the model.
- AutoCodeBench: When LLMs Generate Code Benchmarks
Why the Elixir column stands out in AutoCodeBench, and how it opens up a discussion of difficulty equivalence in automatically generated multilingual code benchmarks
- Attention Residuals: Making Residual Connections Attention-Like
A reading of Kimi Team's Attention Residuals technical report: why residual connections should become attention-like too, and how Full AttnRes / Block AttnRes turn that idea into a trainable, deployable system
- Training Compute-Optimal Large Language Models: What Chinchilla Changed
The Chinchilla paper — why most large models were undertrained, and how to spend your compute budget wisely, with real Python code examples
- Scaling Laws for Neural Language Models: The Mathematics of Scale
The mathematics of scale — why bigger models are predictably better, with real Python code examples
- OpenClaw Architecture: Inside a Self-Hosted AI Assistant 🦞
Dissecting the engineering skeleton of a self-hosted AI assistant, based on v2026.3.8 source code
- Language Models are Few-Shot Learners: GPT-3 and In-Context Learning
Larger models, better at eliciting abilities from context, with real Python code examples
- OpenClaw Ecosystem: From Open-Source Project to AI Assistant Platform 🦞
A sharper look at OpenClaw: stars are not an ecosystem; structures that reduce supply and usage friction are.
- BERT: The Pre-Training Blueprint for Language Understanding
Establishing the pre-training paradigm, with real Python code examples
- Sequence to Sequence Learning: The Encoder-Decoder Blueprint
Establishing the encoder-decoder paradigm, with real Python code examples
- Clawdbot: An Early Sample of Personal AI Sovereignty
Clawdbot shows that the hard question for self-hosted AI agents is not how smart the model is, but who owns control, context, and execution rights.
- Neural Machine Translation by Jointly Learning to Align and Translate: Attention Before Transformers
The origin of attention mechanism, with real Python code examples
- Attention Is All You Need: The Transformer Blueprint
A study note on the Transformer paper, with real Python code examples
- Welcome to Justin Huang Blog
A personal technical blog for reading AI papers, agent systems, software infrastructure, and the causal chains behind them.
Read this site
Read https://justinhuangai.github.io/skill.md and follow the instructions to read this site.
- 1. Read skill.md first.
- 2. Follow the instructions to read this site.
Latest Posts
14 total
- Externalization in LLM Agents: Cognitive Artifacts for Agent Engineering .md
- AutoCodeBench: When LLMs Generate Code Benchmarks .md
- Attention Residuals: Making Residual Connections Attention-Like .md
- Training Compute-Optimal Large Language Models: What Chinchilla Changed .md
- Scaling Laws for Neural Language Models: The Mathematics of Scale .md
- OpenClaw Architecture: Inside a Self-Hosted AI Assistant 🦞 .md
- Language Models are Few-Shot Learners: GPT-3 and In-Context Learning .md
- OpenClaw Ecosystem: From Open-Source Project to AI Assistant Platform 🦞 .md
- BERT: The Pre-Training Blueprint for Language Understanding .md
- Sequence to Sequence Learning: The Encoder-Decoder Blueprint .md
- Clawdbot: An Early Sample of Personal AI Sovereignty .md
- Neural Machine Translation by Jointly Learning to Align and Translate: Attention Before Transformers .md
- Attention Is All You Need: The Transformer Blueprint .md
- Welcome to Justin Huang Blog .md