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Driving Confidence: How Machine Learning and Hybrid Models Are Reducing Range Anxiety in Electric Vehicles

Driving Confidence: How Machine Learning and Hybrid Models Are Reducing Range Anxiety in Electric Vehicles

A comprehensive 2025 systematic review shows that neural‑networks and hybrid approaches now dominate EV range prediction, slicing error margins to under two kilometres in real‑world tests. By marrying data‑driven models with physics‑based simulations, researchers are finally tackling the long‑standing range‑anxiety problem that has stalled mass adoption.
Audarma: LLM‑Powered Dynamic Translation for Next.js with Smart Caching

Audarma: LLM‑Powered Dynamic Translation for Next.js with Smart Caching

Audarma tackles the challenge of translating dynamic, user‑generated content at scale by treating entire views as single translation units, caching results, and integrating seamlessly with existing i18n libraries. Its dual‑mode operation and adapter pattern promise cost savings, faster UX, and a roadmap toward server‑side support and streaming.