Yonatan Back, a product manager with no programming background, created StrikeRadar in a single day.
By Shula Rosen
An Israeli-built dashboard is attempting to estimate, in real time, the probability of a US military strike on Iran by analyzing live data such as flight cancellations, aircraft movements, news reports, and weather patterns, Ynet reports.
The platform, called StrikeRadar, was created by Israeli product manager Yonatan Back, who said he built the system in a single day using the Claude language model despite having no programming background.
The model designed the structure, generated the code, and guided the site’s launch.
StrikeRadar aggregates multiple data feeds through APIs, combining raw indicators with processed inputs. Among the signals it tracks are unusual movements of American refueling aircraft in the Persian Gulf, disruptions to civilian aviation inside Iran, and developing news reports. These factors are weighted and translated into a constantly updated probability score displayed on a public dashboard.
Back said the system also relies on “reference data,” comparing current patterns to those that preceded earlier US operations against Iran in June 2025. When similar indicators appear — such as increased aerial refueling activity — the algorithm assigns them greater significance in the calculation.
The project enters a field that already includes prediction markets and institutional analytics firms. Platforms such as Polymarket derive probabilities from user wagers, while GeoQuant provides risk scoring for professional investors through closed statistical models. StrikeRadar differs in that it aims to remain data-driven without financial incentives or expert interpretation shaping the result.
The tool reflects broader public interest in faster, more transparent strategic analysis. In previous decades, conflict forecasting depended largely on historical modeling and specialist assessment. Advances in AI now allow pattern recognition across continuous streams of live data.
Back said the system is intended as a support mechanism rather than a decision-making authority. While algorithms can produce percentage estimates, he noted that real-world outcomes can still be shaped by sudden political decisions or a single social media post.
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