Henry's Blog

So I recently wanted to see whether an AI agent could self-improve a harness to solve terminal bench tasks.

Follow-up (July 2026): Harness Training: a deterministic framework and results.

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Motivation and Goals Abstraction layers of Machine Learning Libraries Comparing this project to PyTorch for the same functionality Efficiency vs Learning Technical Design Tensor Class Tensor-level Operations Function class Computational Graph Tensor.backw…

Video games were very attractive to me when I was kid.

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Policy Gradient Problem Setting Agent Environment Reward Technical Details Policy Network Design Training Key Considerations Key Challenges Dying ReLU neurons High-variance rewards and incomplete state information Results Next Steps This is the first post…

Previous Knowledge Required Goals Showcase Training Set Test Set Input Data Neural Network Architecture (All 3 Versions) Version 1: Hand-coded neural network Version 2: Keras model Version 3: TensorFlow computation graph Previous Knowledge Required Unders…

The A3C method in Reinforcement Learning (RL) combines both a critic’s value function (how good a state is) and an actor’s policy (a set of action probability for a given state).

I’ve been bugged by the native state management system in React that I finally had to take a stab at Redux.

This post is adapted from my answer on Reddit.

It’s been a while since my last post.

When choosing a career path, the most important thing is to do the research and plan ahead.

How I ever wonder, how does so many people have so many different answers to a simple “1+1” problem.

It has been approximately 24 days since classes have started and I have gotten used to the work and life style in business school, the case method and socializing all the way.