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スライド 2 枚32:20EN
MBMatthew Berman· 2026年10月23日Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1
スライド 5 枚52:29EN
MBMatthew Berman· 2026年7月24日Claude Opus 5: Benchmarks, Cost per Task, and Early ImpressionsMatthew Berman · Forward FutureMatthew Berman reacts to the model launch, examines reported benchmark and pricing data, and explains why the cost of completing a task matters more than token price alone.AnthropicBenchmarksAI cost & performance+2
スライド 6 枚2:34:17EN
MBMatthew Berman· 2026年5月28日Matthew Berman Tests Claude Opus 4.8 and Dynamic Workflows LiveMatthew Berman · Forward FutureA launch-day walkthrough compares Anthropic’s reported benchmarks with informal coding experiments and a multi-agent website redesign.コーディングエージェントAI cost & performance評価(Evals)+2
スライド 5 枚1:42:54EN
MBMatthew Berman· 2026年5月12日AI and Cybersecurity: Matthew Berman’s Warnings, Predictions and Open QuestionsMatthew Berman · Forward FutureA livestream reviews reported AI-assisted threats and debates whether stronger defensive models can keep pace with attackers.AI securityセキュリティと安全性AI Safety+2
スライド 9 枚40:00EN
MBMatthew Berman· 2026年2月24日How Matthew Berman Built an OpenClaw Workflow for His TeamMatthew Berman · Forward FutureA tour of sponsorship triage, CRM, meeting notes, security, scheduling, memory and backup after a month of heavy use.Personal agentsWorkflow automationエージェント工学+2
スライド 7 枚32:22EN
MBMatthew Berman· 2025年10月16日Qwen Image Edit Plus Compared With Nano Banana, Seedream and GPT Image 1Matthew Berman · Forward FutureMatthew Berman compares four image-editing models across compositing, precise edits, style transfer, object removal and weather effects.モデルと学習評価(Evals)
スライド 2 枚30:29EN
MBMatthew Berman· 2025年1月3日How Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.Reinforcement Learningモデルと学習AI research+1