Introduce repos.finddirty.py, a Python utility that scans a specified directory for Git repositories and reports those with uncommitted changes. The script uses pathlib for filesystem traversal, subprocess to execute git status commands, and sys for argument handling. It defines is_git_repo() to check for .git directories and is_dirty() to parse git status output for "tree clean" indicators, returning None for non-repo paths. Licensed under MIT.
Implement tokenizer.c containing is_valid_char helper and main function for reading text files, extracting alphanumeric words with dash/underscore support, and tracking word positions, line numbers, and paragraph counts across the input stream.
Updated the pricing documentation to reflect the new 2026 per-token costs for OpenAI's premium tier models, including GPT-5 Ultra and o3-max, with input/output rates of $150/$600 per million tokens respectively.
Introduce a new markdown file detailing usage-based fees for input and output tokens of OpenAI's highest-tier models, with specific per-token rates for GPT-4o and GPT-4o-2024-11-20, and a step-by-step methodology to convert abstract token pricing into human-understandable time-based costs for enterprise applications.
Added a new `stream_response` function that yields tokens incrementally by setting `stream=True` on the chat completion call and iterating over response chunks. The example now includes a `main` block that calls this function with a sample prompt, demonstrating real-time output for interactive use cases. Also fixed grammar and punctuation in docstrings, corrected the pip install package name from `openapi` to `openai`, and improved clarity of setup instructions.
Introduce a new 219-line example script that implements an Agent class for building AI agents using the OpenAI API. The script includes setup instructions, fake API credentials for educational purposes, async support via asyncio, and a full chat history tracking mechanism through the messages property.
Introduce a new file containing LLM-generated analysis of user rants, where each entry evaluates whether a given rant is spam and provides a contextual interpretation of the user's intent or emotional state.
Introduces a new env.py utility that loads a base64-decoded password from the SECRET environment variable when imported, and provides an interactive password-to-base64 conversion when run as a script.
Extend the Python IMAP usage documentation with a complete code example demonstrating SSL-based connection to an IMAP server, authentication, and listing of available mail folders. The example includes proper error handling and connection cleanup.
The documentation now includes a comprehensive guide covering data preparation, model architecture selection, hyperparameter tuning, and evaluation metrics for the pattern recognition training pipeline.
