The article describes a controlled experiment in which four AI assistants—Gemini, DeepSeek, ChatGPT, and Claude—were tested on a forecasting task that included four hidden traps: leakage, reporting delays, promotion effects, and structural breaks. The study examines how each assistant handled these challenges and compares their performance. The post was published on Towards Data Science.
By Spyros Georgopoulos
One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code?
The post AI Made Me 5x Faster. It Also Made Me 5x Wors...
By Gursimar Singh
A tiny GPT model was trained to memorize every fact presented to it, yet it failed to answer any question that required combining two of those facts. The author tested a one-line fix suggested in a recent paper, which only partially resolved the issue. The post highlights the limitations of small language models in integrating multiple pieces of information.
By Utkarsh Mangal
The article discusses a research paper suggesting that reward labels have minimal impact on how AI agents learn. The author tested this claim on three different models, finding that one model behaved exactly as the incorrect reward labels instructed. The piece highlights how a single model can be misled by flawed feedback.
By Abdullahi Dattijo
Simon Willison introduces GPT‑6 Astra, a new model that offers improved attention to detail, better prompt comprehension, and the ability to generate more sophisticated outputs. The model excels at creating 3D renderings, producing detailed scenes such as gardens, shipyards, animals, cityscapes, and even Dyson spheres. Willison highlights its whimsical creativity, noting examples like a pelican wearing a red neckerchief riding a bicycle.
Without these, Claude will be confidently wrong. The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .
By Haden Pelletier