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Sector Snapshot: Fitness Startup Funding Is Rebounding, But Investors Want AI And Data, Not Treadmills

If you’re anything like yours truly, your fitness ambitions for 2026 far exceed reality. Venture investors, luckily, seem to be more upbeat than they have been in years about the future of fitness and wellness. Startup investment in those categories totaled more than $3.6 billion in the first half of this year, putting 2026 on

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The Two Pillars of Post-training: Reinforcement Learning and Supervised Fine-Tuning

This is the second article in Sharon Zhou’s post-training series. Read part 1 here. In the first post of this series, you learned how post-training closed the fundamental gap in usability of LLMs by making them behave in a certain way. In this post, you’ll explore specific techniques you can use to change a model’s

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Why You Shouldn’t Always Trust LLMs as Judges: Understanding Bias in Automated Evaluation

In the rush to automate evaluation, from grading student code to ranking research papers, we have embraced Large Language Models as judges. They are fast. These units are cheap. They scale. However, at a workshop at DHS 2026, Bhaskarjit Sarmah made a point that stuck with me: “you can’t trust LLM as a judge. I

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Protocol-Layer Security for MCP, A2A, and Agent Gateways

TL;DR MCP, A2A, and agent gateways are becoming the connectivity layer for enterprise agents, but the security model cannot stop at “the agent has a token.” Teams need protocol-layer controls that understand who is calling, which agent is acting, which tool or peer agent is being invoked, what operation is requested, and whether the request

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