The commit introduces a custom greeting message tailored specifically for the user's favorite jew, enhancing the personalization of the user interface by directly addressing the individual with a warm and affectionate tone.
feat: implement json and rss export functionality
feat: add command line interface for running the extractor
maintenance: update dependencies and add type hints
docs: add documentation for the extractor class and methods
Adds a new search module that queries the DuckDuckGo Instant Answer API, parses JSON responses into structured result objects, and handles network errors and empty results gracefully. Includes unit tests for the parser and integration test for the full search flow.
Add core data structures and algorithms for modeling city-scale systems at the molecular level, including particle-based building blocks, self-assembly protocols, and energy distribution networks operating at sub-micrometer precision.
Implement a new Gitea API client providing methods for repository management (create, list, delete) and issue tracking (create, update, close). The client uses OAuth2 token authentication and supports paginated responses for listing endpoints.
- Integrate portscan monitoring into the security module with new detection rules
- Update ad-blocking filters to include recently identified advertising domains
- Refactor existing ad-blocking logic to improve performance and reduce false positives
Implements a dedicated port scanning utility targeting camera IP ranges, enabling detection of open ports and active services on connected surveillance devices. The module includes TCP connect scanning with configurable timeout and port range parameters, outputting results in structured format for further processing.
- Implement OpenAPI service class with methods for API specification generation
- Add request validation and response formatting utilities
- Include support for multiple API version endpoints
The commit introduces a new rendering function that draws borders around each tile on the game board, enhancing visual clarity and structure. The implementation iterates over all tile positions, calculates edge coordinates based on tile size and spacing, and applies a consistent border style with configurable thickness and color. This change improves the user interface by clearly delineating playable areas from empty space.
Implement a new async RPC client module for snek_rpc, including connection pooling, request serialization, and error handling for remote procedure calls.
The profiles table previously lacked an index on the user_id column, causing full table scans during user lookups. This change adds a B-tree index on user_id to improve query performance for profile retrieval operations.
Implement a new monitoring module that periodically checks Docker volume sizes and reports disk usage statistics. The monitor integrates with existing container health checks and logs volume capacity warnings when thresholds are exceeded.
- Add core WordleBot class with methods for generating guesses and processing color-coded feedback
- Implement letter frequency analysis and candidate word filtering based on previous guesses
- Include support for hard mode constraints and optimal guess selection strategy
The new index on the user_id column in the profiles table improves query performance for user-specific lookups, reducing full table scans during authentication and profile retrieval operations.
Introduces a new mouse automation module providing functions for absolute and relative cursor movement, button click simulation (left, right, middle), and scroll wheel control. Includes coordinate validation and platform-agnostic input event handling.
Implement a new shortcuts trainer component that displays key combinations and provides real-time visual feedback on user input. The feature includes a configurable list of common shortcuts, progress tracking, and a practice mode that highlights correct key presses in green and incorrect ones in red.
This new document provides a structured framework for analyzing and interpreting dream content, including symbolic mapping, narrative patterns, and psychological archetypes. It serves as a reference for both automated and manual dream analysis workflows.
Implement a new tokenizer.py file that provides core tokenization functionality including whitespace splitting, punctuation handling, and vocabulary construction from input text.
This commit introduces a new document outlining the costs associated with OpenAI's highest-tier, most expensive AI models, including per-token pricing for GPT-4 Turbo and specialized enterprise offerings. The file details usage-based fees for both input and output tokens, highlighting the premium charged for advanced reasoning and larger context windows.
- Implemented async streaming response handling for real-time token output
- Added configurable temperature, max_tokens, and top_p parameters via environment variables
- Refactored agent initialization to support both streaming and non-streaming modes
This new module provides a Python implementation for encoding and decoding HTTP chunked transfer encoding, including support for chunk extensions and trailer headers.
Updated the OpenAI agent example to demonstrate streaming responses by modifying the chat completion call to include `stream=True` and iterating over response chunks. Added a new `stream_response` function that yields tokens incrementally, showcasing real-time output for interactive use cases. The example now includes a `main` block that calls this function with a sample prompt, improving clarity for developers integrating streaming into their agents.
This commit introduces a new example script that showcases how to build and run an AI agent using the OpenAI API, including setup instructions and basic interaction patterns.
- Include overview of project purpose and key features
- Add step-by-step installation and configuration guide
- Provide usage examples and API documentation references
Introduces a new env.py module that centralizes environment variable handling. The module loads configuration from a .env file using python-dotenv and validates required variables like DATABASE_URL and SECRET_KEY, raising clear errors for missing critical settings.
The documentation now includes a comprehensive guide covering data preparation, model architecture selection, hyperparameter tuning, and evaluation metrics for the pattern recognition training pipeline.
retoor