Launch HN: Silurian (YC S24) – Simulate the Earth

Model performance and comparisons

• Startup claims its Generative Forecasting Transformer (GFT) outperforms Microsoft’s Aurora, which in turn outperforms GraphCast, based on internal and published metrics.
• A Google-affiliated commenter notes that another model (NeuralGCM) is actually top of the WeatherBench leaderboard and includes explicit physics, indicating the landscape is competitive and evolving.
• Users request public benchmark results (e.g., WeatherBench scores) and clearer quantitative comparisons.

Physics-based vs data-driven forecasting

• Several commenters frame this as another instance of “The Bitter Lesson”: scaling general-purpose methods beating hand-crafted physics.
• Others are skeptical: physics-based ensemble models have well-defined skill metrics and handle non-stationarity (e.g., climate change) more transparently.
• Clarification: modern ML weather models are trained on 4D “movies” of reanalysis fields and learn to predict the next frame; errors still grow chaotically over time, as with physics models.

Scope, use cases, and business viability

• Claimed plan: start with weather, then integrate into domains dependent on it—energy, agriculture, logistics, defense, insurance.
• Commenters see clear commercial value in better forecasts for power trading, grid line ratings (regulation-driven), flood and wildfire risk, surf and sports forecasting, and hurricane tracking.
• Others question defensibility given heavy government and big-tech investment and lack of proprietary data.

Other phenomena: earthquakes, flooding, grid

• Multiple requests to “do earthquakes next”; replies note limited data, strong chaos, and likely short useful lead times (minutes–hours).
• Flooding and wildfire called out as especially high-value but hard: need much finer spatial resolution and high-quality terrain / land-use data.
• Grid-level simulation seen as politically and data-access constrained; focusing on adequacy and renewables correlation is advised.

Visualization and product clarity

• Significant side-thread about the site looking like a clone of nullschool.
• Clarified: it uses an open-source version of that visualization, with attribution; the startup’s contribution is the forecast data/model.
• Some argue the UI should better highlight what’s new to non-experts.

Climate and long-term simulation

• Plan to extend from weather to climate with distributional, not pointwise, forecasting.
• A related Google effort suggests training on short-term weather can yield realistic multi-year climate behavior, supporting this direction.