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16 件
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スライド 9 枚13:09EN
Matthew Berman· 2026年8月14日AI News: Ultrafast Inference, Claude Watermarks and New ModelsMatthew Berman · Forward FutureMatthew Berman reviews an ultrafast coding preview, text watermarking, Grok Bot, open-weight models and Computer History.InferenceOpen-weight modelsコーディングエージェント+2
スライド 2 枚0:24EN
Matthew Berman· 2026年1月26日Matthew Berman Checks His Clawdbot Token CostsMatthew Berman · Forward FutureA short usage check reveals unexpectedly high model spending over two days.AI cost & performancePersonal agentsInference
スライド 2 枚1:38EN
Matthew Berman· 2025年12月29日NVIDIA’s Groq Licensing Deal and the Inference MarketMatthew Berman · Forward FutureMatthew Berman discusses why NVIDIA licensed Groq’s inference technology and hired members of its team.SemiconductorsInferenceAI cost & performance+1
スライド 4 枚8:47EN
Matthew Berman· 2025年9月16日Why Identical LLM Prompts Can Produce Different Answers at Temperature ZeroMatthew Berman · Forward FutureMatthew Berman explains Thinking Machines Lab’s work on batch-invariant inference and what its reproducibility experiment actually showed.InferenceAI researchオブザーバビリティと信頼性+1
スライド 4 枚17:51EN
Matthew Berman· 2025年8月28日AI News in August 2025: Claude for Chrome, New Models and AI InfrastructureMatthew Berman · Forward FutureMatthew Berman reviews a browser-agent preview, model releases, reported Apple discussions and research on inference and weather.AI agentsOpen-weight modelsGenerative media+2
スライド 5 枚10:01EN
Matthew Berman· 2025年2月5日DeepSeek R1 Code Optimization and a $3 Visual Counting ExperimentMatthew Berman · Forward FutureMatthew Berman examines a human-guided coding improvement and a small reinforcement-learning experiment, then considers their implications for AI research.Reinforcement LearningGPU programming & kernelsAI research+2
スライド 2 枚30:29EN
Matthew 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
スライド0:54EN
Matthew Berman· 2024年9月16日OpenAI’s o1 Preview: A First Look at Longer ReasoningMatthew Berman · Forward FutureMatthew Berman introduces the o1-preview and o1-mini models and tries a Python game prompt.モデルと学習Inference
スライド 6 枚11:20EN
Matthew Berman· 2024年7月1日Matthew Berman Adapts Mixture of Agents to Groq InferenceMatthew Berman · Forward FutureA 2024 tutorial changes an open-source multi-model ensemble to use Groq, then tests its interactive output and fixes a streaming error.Multi-agent systemsAgent orchestrationInference+1
スライド 5 枚13:18EN
Matthew Berman· 2024年6月20日Testing Local AI on a Dual RTX 6000 WorkstationMatthew Berman · Forward FutureMatthew Berman tests language models, concurrent inference, and image generation on a Dell Precision tower supplied by Dell and NVIDIA.Local inferenceInferenceモデルと学習+2
スライド 6 枚9:02EN
Matthew Berman· 2024年4月1日Matthew Berman Tries LLM Colosseum, a Street Fighter Model BenchmarkMatthew Berman · Forward FutureA 2024 tutorial shows language models choosing fighting-game moves, explains the game-state prompt and walks through local setup.評価(Evals)Multi-agent systemsExperimentation+1
スライド 4 枚14:11EN
Matthew Berman· 2024年3月23日LM Studio in 2024: Local Models, Parallel Inference and a Local APIMatthew Berman · Forward FutureMatthew Berman demonstrates model downloads, hardware settings, JSON output and serving multiple local models.Open-weight modelsInference
スライド 3 枚51:11EN
Matthew Berman· 2024年3月22日Inside Groq’s LPU: Compilers, Chip Networks and Fast InferenceMatthew Berman, Andrew Ling, Igor Arsovski · Forward FutureGroq engineers Andrew Ling and Igor Arsovski explain their hardware and software design in a sponsored 2024 interview.インフラとデプロイInferenceAI cost & performance
スライド 6 枚12:49EN
Matthew Berman· 2023年9月14日Falcon 180B: A 2023 Review of TII’s Open-Weight ModelMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and writing prompts while examining the model’s training scale and license.Open-weight modelsBenchmarksモデルと学習+1
スライド 2 枚5:53EN
Matthew Berman· 2023年7月18日Petals: Distributing Large Language Model Inference Across DevicesMatthew Berman · Forward FutureIn 2023, Matthew Berman explains Petals as a way to use large models through a network of participating computers rather than loading every layer on one device.Distributed training & inferenceLocal inferenceInference+2
スライド 3 枚8:17EN
Matthew Berman· 2023年6月22日Using OpenLLM to Serve Open Models Through an APIMatthew Berman · Forward FutureMatthew Berman demonstrates a local model server, its interactive API and a Python client in a 2023 walkthrough.Open-weight modelsInferenceインフラとデプロイ+1