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59 件
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スライド 2 枚0:36EN
Matthew Berman· 2026年10月25日Kimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.評価(Evals)コーディングエージェントOpen-weight models
0:50EN
Matthew Berman· 2026年9月2日Matthew Berman on Using Idle Computers for AI InferenceMatthew Berman · Forward FutureBerman points to Darkbloom as a way to put spare computing capacity to work on AI models, while raising the question of how far distributed inference can go.Distributed training & inferenceインフラとデプロイInference+2
スライド 7 枚18:57EN
Matthew Berman· 2026年8月29日GLM-5.3-Flash: Ox Alpha Benchmarks, Costs and Coding TestsMatthew Berman · Forward FutureMatthew Berman reviews Z.ai’s open-weight model, compares benchmark costs and tries coding and presentation tasks against a competing model.Open-weight modelsBenchmarksAI cost & performance+2
スライド 4 枚13:58EN
Matthew Berman· 2026年8月7日Google’s AI Leadership Changes and the Innovator’s DilemmaMatthew Berman · Forward FutureMatthew Berman considers Google’s research history, two leadership changes and an open-model strategy in August 2026.AI StrategyLeadershipOpen-weight models+2
スライド 5 枚15:37EN
Matthew Berman· 2026年8月4日Matthew Berman on Qwen3.8-Max, AI Costs and Open-Weight CompetitionMatthew Berman · Forward FutureA 2026 review of reported benchmark results and research demonstrations leads to questions about deployment economics and geopolitical dependence.Open-weight modelsBenchmarksAI cost & performance+2
スライド 5 枚52:29EN
Matthew 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 3 枚16:09EN
Matthew Berman· 2026年7月23日How to Compare AI Task Costs and Split Coding Work Across ModelsMatthew Berman · Forward FutureMatthew Berman compares token prices with benchmark task costs and proposes a planning, execution and review workflow using different models.AI cost & performanceModel routing & gateways開発ワークフローとテスト+2
1:03EN
Matthew Berman· 2026年7月15日Reported Chinese AI Export Discussions and a Strategic DebateMatthew Berman · Forward FutureMatthew Berman considers reported restrictions on overseas access to Chinese AI models and questions whether limiting their distribution would serve China’s interests.AI RegulationGeopoliticsOpen-weight models
0:42EN
Matthew Berman· 2026年6月30日Matthew Berman's Concern About Access to Frontier AIMatthew Berman · Forward FutureIn a short commentary, Berman argues that restricted model access could disadvantage startups and slow US innovation.AI RegulationOpen-weight modelsAI Strategy
スライド 5 枚9:47EN
Matthew Berman· 2026年4月3日Gemma 4: Smaller Open-Weight Models for Local AIMatthew Berman · Forward FutureMatthew Berman reviews Google’s model sizes, multimodal features and reported benchmarks, with an eye toward local agents.Open-weight modelsLocal inferenceFoundation Models+2
スライド1:09EN
Matthew Berman· 2026年3月18日The Benefits and Tradeoffs of Running AI Models LocallyMatthew Berman · Forward FutureMatthew Berman weighs the privacy, control and costs of local models against technical complexity and performance tradeoffs.Local inferenceOpen-weight modelsAI cost & performance
スライド 3 枚20:35EN
Matthew Berman· 2026年3月16日A Beginner’s Guide to AI Assistants, Media Models and Coding AgentsMatthew Berman · Forward FutureMatthew Berman surveys hosted assistants, downloadable models, image and video tools, world models, coding agents and audio generation in a March 2026 overview.Generative mediaAI agentsOpen-weight models+2
スライド 4 枚18:09EN
Matthew Berman· 2025年12月15日Training a GPT-OSS 2048 Strategy With Verifiable RewardsMatthew Berman · Forward FutureMatthew Berman walks through a local Unsloth notebook, reward checks and one 2048 training run on an RTX 5090.Reinforcement LearningPost-trainingOpen-weight models+2
スライド 4 枚15:01EN
Matthew Berman· 2025年12月13日AI News in December 2025: Disney, DeepSeek, Runway and MetaMatthew Berman · Forward FutureMatthew Berman reviews an OpenAI licensing deal, reports about AI chips and Meta’s model strategy, video generation, and Rivian’s autonomy plans.AI StrategyOpenAIGenerative media+2
スライド 5 枚9:41EN
Matthew Berman· 2025年12月9日AI News in December 2025: Devstral 2, Agent Standards and Data-Center PowerMatthew Berman · Forward FutureMatthew Berman discusses model-release predictions, open agent infrastructure and ideas for powering terrestrial and space-based computing.コーディングエージェントMCPOpen-weight models+2
スライド 5 枚7:49EN
Matthew Berman· 2025年12月2日DeepSeek V3.2: Reasoning, Sparse Attention and Agent TrainingMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s December 2025 model release, its benchmark results and the techniques used to improve long-context efficiency and tool use.Foundation ModelsOpen-weight modelsReasoning & test-time compute+2
スライド 9 枚15:30EN
Matthew Berman· 2025年7月31日Qwen3 Tested: Coding Demos, Long Context and Response BehaviorMatthew Berman · Forward FutureMatthew Berman tests generated simulations, political and safety prompts, hand tracking and word puzzles using a hosted Qwen3 model.Open-weight modelsコーディングエージェント評価(Evals)+2
スライド 6 枚11:28EN
Matthew Berman· 2025年7月30日GLM-4.5: Coding Demos, Model Sizes and Z.ai’s BenchmarksMatthew Berman · Forward FutureMatthew Berman tests interactive applications built with GLM-4.5 and examines Z.ai’s reported comparisons with leading models in July 2025.Open-weight modelsコーディングエージェントBenchmarks+2
スライド 7 枚14:04EN
Matthew Berman· 2025年4月29日Qwen3: Open Weights, Adjustable Reasoning and Tool UseMatthew Berman · Forward FutureMatthew Berman reviews the April 2025 Qwen3 family, its benchmark claims, training pipeline and local demonstration.Open-weight modelsモデルと学習Reasoning & test-time compute+2
スライド 5 枚7:41EN
Matthew Berman· 2025年1月30日CES 2025 and Running DeepSeek R1 Distills on an RTX 5090Matthew Berman · Forward FutureMatthew Berman tours AI demonstrations at CES and runs two quantized DeepSeek R1 distilled models locally on an NVIDIA GPU.Local inferenceOpen-weight modelsSemiconductors+2
スライド 3 枚5:27EN
Matthew Berman· 2025年1月29日Ways to Try DeepSeek R1: Hosted and Local Distilled ModelsMatthew Berman · Forward FutureMatthew Berman demonstrates DeepSeek chat, Groq’s hosted distill and a local model in LM Studio.Local inferenceOpen-weight modelsQuantization
スライド 4 枚20:34EN
Matthew Berman· 2025年1月28日DeepSeek R1 and the Debate Over AI Costs and ComputeMatthew Berman · Forward FutureMatthew Berman examines 2025 reactions to DeepSeek, from training efficiency and market losses to open weights, privacy and the possibility of a demand rebound.モデルと学習AI cost & performanceOpen-weight models+2
スライド0:55EN
Matthew Berman· 2025年1月23日A Quick RTX 5090 Local AI TestMatthew Berman · Forward FutureMatthew Berman runs an 8B DeepSeek R1 distillation locally and reports the speed of one generation.Local inferenceSemiconductorsOpen-weight models
スライド 5 枚8:52EN
Matthew Berman· 2025年1月21日Industry Reactions to DeepSeek R1’s 2025 ReleaseMatthew Berman · Forward FutureMatthew Berman reviews early reactions to DeepSeek R1, from open-weight reasoning models and local hardware to benchmark results and API pricing.Open-weight modelsReasoning & test-time computeBenchmarks+2
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