As AI image generation tools become increasingly sophisticated, the ability to distinguish between AI-generated and authentic photographs has emerged as a critical challenge. Platforms like Winston.AI and AI or Not claim to address this need by detecting synthetic imagery with high accuracy. But how well do these detection systems
As audio becomes an increasingly common input for AI assistants, it becomes natural to ask whether these platforms can also transcribe pre-recorded audio accurately. In this benchmark, we tested five leading generative AI models on identical English and Hungarian audio files to assess their transcription accuracy, language support, and practical
As large language models become increasingly sophisticated tools for academic writing support and web search, a critical question emerges: can the models be used also to reliably identify plagiarism? To test this capability, we embedded verbatim, unattributed passages from published academic articles into newly generated texts and asked multiple GenAI
Every time you prompt a large language model, you're witnessing the result of thousands of sequential sampling decisions—each token drawn from a probability distribution over the model's vocabulary. In standard chat interfaces, these distributions are sampled with fixed settings that users cannot adjust. But API
As AI models increasingly demonstrate coding capabilities, understanding AI-written codes' reliability and ease of use becomes essential for researchers and practitioners. In this benchmark study, we evaluated four recent models—GPT 5.2 Thinking, Opus 4.5, Gemini 3 Pro, and Grok 4.1 Expert—on two algorithm problems
Can AI detection tools reliably distinguish between human and machine-generated text? In our previous testing, we found that platforms like Originality.ai and ZeroGPT and GenAI models, frequently misclassified both AI and human text. Now, PangramLabs has gained attention with claims of near-perfect accuracy, verified by third-party organizations. In this
Log probabilities (logprobs) provide a window into how confident a language model is about its predictions. In this technical implementation, we demonstrate how to access and interpret logprobs via the OpenAI API, using a series of increasingly difficult multiplication tasks. Our experiment reveals that declining confidence scores can effectively signal
Claude Projects recently introduced a Retrieval-Augmented Generation (RAG) feature that extends its capabilities beyond the standard context window. Unlike traditional approaches that require custom vector databases and API integration, Claude's implementation offers a streamlined, no-code solution for handling large knowledge bases directly within the Projects interface. In this
Tool use (also called function calling) is one of the most powerful capabilities available in modern language models. It allows models to extend beyond text generation by interacting with external systems, databases, or custom logic—making AI agents capable of real-world tasks like checking weather, querying APIs, or running calculations.
To assess whether leading AI models can reliably distinguish between human-written and AI-generated text, we tested GPT-5.2, Claude Sonnet 4.5, and Gemini 3 on a set of cover letters. The models were asked to identify AI-generated content and then to rewrite letters to appear more human-written. The results
