Taming the AI Hardware Cambrian Explosion — Abdul Dakkak, Modular

Lays out a vendor argument and benchmarks for portable inference across GPU vendors, relevant if you worry about hardware lock-in and serving cost. Treat the speedups as self-reported.

• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

• videoSonarQube + OpenAI: Agentic Development — Killian Carlsen-Phelan, Sonar
• videoThe Autonomous Computer: Infrastructure for Computer Use Agents — Ang Li, Simular
• videoHow Secrets Leak Through AI Agents (and How to Stop It) — Venice AI
• videoFrom Zero to Leaderboard: Agent Evaluation — Wolfram Ravenwolf, Weights & Biases

• videoThe Shape of Compute (Chris Lattner of Modular)Latent Space
• videoCan LLMs Write Fast Multi-GPU Kernels? — Simran Arora, Together AIAI Engineer
• videoSame Model, Different Speed: Why Your Inference Provider Matters — FriendliAIAI Engineer

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