This project, `autoresearch-opencode`, is an autonomous experiment loop designed for use with OpenCode. It's a port of `pi-autoresearch`, but implemented as a pure skill, eliminating the need for an MCP server and relying solely on instructions the agent follows using its built-in tools. The skill allows users to automate optimization tasks, as demonstrated by the example of optimizing the BogoSort algorithm which achieved a 7,802x speedup by leveraging Python's `bisect` module for sorted-state detection.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
This article explores five Python decorators that can be used to optimize LLM-based applications. These decorators leverage libraries like functools, diskcache, tenacity, ratelimit, and magnetic to address common challenges such as caching, network resilience, rate limiting, and structured output binding. The article provides code examples to illustrate how each decorator can be implemented and used to improve the performance and reliability of LLM applications.
CUDA Tile is a new Python package that simplifies GPU programming by automatically tiling loops, handling data transfer, and optimizing memory access. It allows developers to write concise and readable code that leverages the full power of NVIDIA GPUs without needing to manually manage the complexities of parallel programming.
A comprehensive guide covering the most critical machine learning equations, including probability, linear algebra, optimization, and advanced concepts, with Python implementations.
Optuna is an open-source hyperparameter optimization framework designed to automate the hyperparameter search process for machine learning models. It supports various frameworks like TensorFlow, Keras, Scikit-Learn, XGBoost, and LightGBM, offering features like eager search spaces, state-of-the-art algorithms, and easy parallelization.
"An example of simultaneously optimizing two policies for two adversarial agents, looking specifically at the cat and mouse game."
The article explores developing strategies for two players with conflicting goals, using methods like game trees, reinforcement learning, and hill-climbing optimization. The focus is on determining optimal policies for each player to either catch or evade capture, considering board configurations and player turn orders. The article further details how hill climbing is applied to improve strategies incrementally, using variations in policies to evaluate and enhance performance over numerous iterations.
The article explores developing strategies for two players with conflicting goals, using methods like game trees, reinforcement learning, and hill-climbing optimization. The focus is on determining optimal policies for each player to either catch or evade capture, considering board configurations and player turn orders. The article further details how hill climbing is applied to improve strategies incrementally, using variations in policies to evaluate and enhance performance over numerous iterations.
Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering
