TL;DR DataFlow is a computational framework for simulating causal models on time series data using a directed acyclic graph architecture enhanced with knowledge time semantics for temporal causal reasoning.
TL;DR Traditional forecasting models optimize only for accuracy, ignoring an important issue: predictions that fluctuate significantly from day to day undermine confidence in production. This paper introduces the AC score metric that balances accuracy and temporal stability, achieving 91% reduction in forecast volatility while improving multi-step prediction accuracy by up to 26%.
TL;DR Structural causal models outperform traditional energy forecasts by revealing critical interdependencies correlation-based approaches fail to capture.
TL;DR Causal AI achieved $49,500 annual economic advantage over best ML baseline with 93.9% recall through explicit modeling of failure mechanisms rather than relying on statistical correlations
Ask any engineering team that has scaled past a few dozen developers: how do you organize your code? The answer shapes everything from build times to team velocity to system reliability.
The industry settled into two camps. Monorepos offer consistency but struggle with scale. Multi-repos provide independence but create coordination overhead. Both work. Neither solves the underlying trade-offs between modularity and unified workflows.