Episode Details

Researchers at Imperial College London have developed a deep learning AI model that analyzes standard 10-second ECGs in under two seconds to identify hidden structural heart conditions—such as heart failure and valve disease—that remain undetectable to human clinicians. Meanwhile, Anthropic released Claude Fable 5.1, an optimized model version reducing agentic execution costs by up to 45% through a 75% drop in cache read prices while setting new benchmarks on Terminal-Bench and Humanity’s Last Exam.

We’ll talk about:

• A new model trained on millions of international ECGs that extracts subtle waveform anomalies in under 2 seconds to diagnose hidden heart disease with 83%–93% accuracy.
• Anthropic upgrading its Fable lineup with 75% cheaper prompt cache reads, reduced false-positive refusals for technical tasks, and extended 1M context windows.
• OpenAI sharing performance metrics for its upcoming Astra model family, including a perfect score on ExploitBench and zero-day discovery in testing.
• YC-backed startup AfterQuery hitting a $3.2 billion valuation and $100 million ARR run rate within five months of its Series A round.

Keywords: Imperial College London cardiac AI, Claude Fable 5.1, OpenAI Astra.

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