Deep research and expert guides on maintaining your visual health.
Recent work (for example, Andrej Karpathy’s “autoresearch” project () ()) suggests that AI agents can autonomously run hundreds of small experiments...
Our visual field test is inspired by the perimetry methods eye care professionals use. Check for blind spots and track changes over time.
Test Your VisionAutoresearch is an approach that uses automation and intelligent software to run parts of the scientific process with minimal manual intervention. It brings together tools like machine learning, automated lab equipment, data pipelines, and structured workflows to generate hypotheses, run experiments or simulations, analyze results, and suggest next steps in an iterative loop. The aim is not to replace human scientists but to take over repetitive, time-consuming, or highly technical tasks so people can focus on interpretation, design, and judgment. An autoresearch system can collect and clean data, evaluate many experimental options in parallel, and document decisions so work can be reproduced. Because it works at scale, it can explore far more possibilities than traditional, manual methods and often speeds up discovery cycles considerably. This matters because autoresearch can lower barriers to complex investigations, reduce costs, and make advanced research practices accessible to smaller teams or community groups. It can improve reproducibility by standardizing procedures and keeping detailed records of every automated step. At the same time, it raises important concerns around bias in algorithms, the need for careful validation of automated results, and the risk of leaning too heavily on machine-generated suggestions. Human oversight, transparent methods, and ethical safeguards are essential to get reliable, trustworthy outcomes. When used responsibly, this approach can accelerate problem-solving and broaden who can contribute to research.
