💓 Ticker: How Not to Die of Heart Disease
Hecht blends storytelling with actionable medical insight, emphasizing how technology and informed patients can transform preventive care from privilege into a public good—a compelling roadmap for anyone focused on long-term health.
In a deeply personal essay, Jared Hecht shares how early diagnostic testing revealed warning signs of heart disease and reshaped his approach to prevention. He argues that traditional healthcare often overlooks critical biomarkers like ApoB and advocates for proactive, data-driven monitoring. The piece offers a concise guide to accessible testing, lifestyle improvements, and medication options for reducing cardiovascular risk.
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🤖 AI Benchmarks Are a Bad Joke — and Model Makers Are Laughing
By exposing flaws in how AI progress is measured, this study challenges the credibility of current evaluation norms and could accelerate the shift toward more transparent, reproducible standards—crucial for separating real innovation from marketing hype.
A study from the Oxford Internet Institute and partner universities reveals that only 16% of 445 benchmarks used to evaluate large language models meet scientific standards. Many lack consistent definitions for metrics like reasoning or safety, while 27% rely on convenience samples that compromise credibility. Researchers propose eight reforms to enhance validity and transparency—critical, as benchmark scores underpin most AI performance claims, including those for OpenAI’s GPT-5.
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📉 $1T in Tech Stocks Sold Off as Market Grows Skeptical of AI
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⚙️ Cerebras Code Adds GLM 4.6 Support — Reaching 1000 Tokens per Second
This update showcases Cerebras’ steady momentum in AI-assisted programming, matching top proprietary models and underscoring the accelerating maturity of open-source coding ecosystems.
Cerebras introduces support for its GLM-4.6 model, positioning it as one of the most advanced open coding models worldwide. The model leads the Berkeley Function Calling Leaderboard and rivals Sonnet 4.5 in web-development tasks, setting new benchmarks for speed and performance in AI-driven code generation.
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🧠 Small Language Models Are the Future of Agentic AI
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