In this follow-up experiment, we tested how generative AI models handle handwritten text when provided as image input rather than PDF. Unlike in the earlier test, all models were able to process the images and return readable outputs. Performance Comparison 🏆 Champion as of October 2: Copilot, Gemini 2.5 Pro,
Optical character recognition (OCR) of handwritten text remains a demanding task, particularly once the focus shifts beyond English. In this experiment, we assessed a range of generative AI models on three handwritten text samples—one each in English, French, and Hungarian—to examine cross-linguistic performance. While accuracy was consistently high
We tested the new Sonnet 4.5 model on a demanding academic task: transforming a research paper into a structured 15-slide outline for a conference presentation. The prompt required not only conceptual rigour and narrative coherence but also attention to visual communication, with clear guidance on where charts, tables, and
In this post we present a comparative evaluation of several generative AI models, focusing on their ability to carry out a straightforward sentiment analysis task. The models were asked to classify customer reviews as either positive or negative, following a simple binary labelling instruction. While the task itself was unambiguous,
When using generative AI for structured data collection, the language of the prompt can make a real difference. In our test with Hungarian population statistics, both Claude Opus 4.1 and Sonnet 4 produced accurate outputs when prompted in Hungarian – but with an English prompt, Sonnet 4 generated rounded figures
In this post we examine how Claude Opus 4.1 can be applied to data collection tasks through a prompt-based approach. The model accessed and structured monthly U.S. unemployment rate data from the Federal Reserve Economic Data (FRED) platform for the period January 2000 to December 2024. The results
In this post we put Notebook LM’s new literature review feature to the test. The model was given six pre-selected sources on a single topic and asked to produce a structured review. The results were not bad at all: the output drew appropriately on the provided references and offered
In this post we present a comparative test of several generative AI models, focusing on their ability to retrieve official sports statistics. The task involved collecting NBA game data through a single prompt, with the expectation that the models would accurately locate, extract, and structure the requested information. While the
In this post we explore how Manus AI can be used to collect official housing price index data from the U.S. Census Bureau. The task focused on the Single-Family Houses Sold series, requesting annual figures to be gathered and organised into a clean, structured dataset suitable for further research.
In this short post, we demonstrate how GPT-4o with Deep Research mode can be used to retrieve and structure real-world macroeconomic data directly from authoritative sources. Using a single natural language instruction, the model successfully extracted GDP per capita (current US$) data for Hungary, Poland, Slovakia, and Czechia from the
Prezi’s new AI tool promises to transform documents into ready-made presentations. Our tests show that while the tool produces visually appealing slides, the generated content often drifts away from the uploaded source material. Using sustainability reports from Audi and the Hungarian National Bank, as well as an academic article,
In our earlier blog post, Harnessing GenAI for Searching Literature: Current Limitations and Practical Considerations, we examined the reliability of generative AI models for scholarly literature searching. To assess whether the newest releases represent any improvement, we tested them on the same narrowly defined academic topic. The results indicate modest
