SYDA seamlessly generates realistic synthetic test data—including structured, unstructured, PDF, and HTML—using AI and large language models. It preserves referential integrity, maintains privacy compliance, and accelerates development workflows. SYDA enables both highly regulated industries such as healthcare and banking, as well as non-regulated environments like software testing, research, and analytics, to safely simulate diverse data scenarios without exposing sensitive information.
First public release of aialib — a reproducible pipeline and annotated threat library for AI-generated code vulnerabilities. Ships a deterministic generator–detector–annotator pipeline (Bandit, Semgrep, AI-aware risk detector) that turns one YAML prompt manifest into a CSV / SQLite / SARIF dataset. Dataset: 504 annotated findings drawn from 2,144 generated Python samples across four LLM providers (OpenAI gpt-4o-mini, gpt-4.1; Anthropic claude-sonnet-4-20250514; MBZUAI-IFM K2-Think-v2). Each entry carries CWE, OWASP, AI-cause, propagation vector, human–AI factor, and MITRE ATLAS technique labels, plus token and AST fingerprints for deduplicati
Keras GPT Copilot is the first Python package designed to integrate an LLM copilot within the model development workflow, offering iterative feedback options for enhancing the performance of your Keras deep learning models. Utilizing the power of OpenAI's GPT models, Keras GPT Copilot can use any of the compatible models (GPT4 is recommended). However, the prompt-only mode allows for compatibility with other large language models.
