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23 件
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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 4 枚9:15EN
MBMatthew Berman· 2026年8月17日Six Open-Source Projects for Local AI, Agents, and 3D DesignMatthew Berman · Forward FutureMatthew Berman tours tools for running and training models, giving agents new skills, coordinating work, browsing, and generating 3D meshes.Open-source ecosystemsAgent skillsLocal inference+2
1:04:14EN
Unsupervised Learning: With Jacob Effron· 2026年7月31日ポッドキャストThe Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your Houseホスト Jacob EffronxAI co-founder Igor Babuschkin left to start River AI, arguing coding agents have crossed a threshold, closed labs are getting squeezed, and the next prize is personal, local intelligence.エージェントAI startups & small teamsCompany Building+3
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 2026年7月4日ポッドキャスト19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chipホスト Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.モデルと学習Local inferenceScaling Laws+3
スライド 4 枚18:09EN
MBMatthew 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
1:32:38EN
Cognitive Revolution "How AI Changes Everything"· 2025年12月6日ポッドキャストPolish Is 1% of Frontier Training Data, So Poland Built Its Own Modelホスト Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.企業と戦略Open-weight modelsData quality & curation+3
スライド 3 枚5:27EN
MBMatthew 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
0:28EN
MBMatthew Berman· 2024年11月6日Comparing Three Local Coding ModelsMatthew Berman · Forward FutureMatthew Berman introduces a sponsored comparison of DeepSeek Coder V2, Yi-Coder and Qwen Coder running on a workstation.Local inferenceコーディングエージェントOpen-weight models
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月30日ポッドキャストMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.ホスト Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.モデルと学習Local inference評価(Evals)+3
スライド 9 枚19:03EN
MBMatthew Berman· 2024年10月30日AI News in October 2024: Apple Intelligence, Coding Assistants, Search, and AgentsMatthew Berman · Forward FutureMatthew Berman reviews the first Apple Intelligence release, new coding tools, AI search disputes, local models, and reports of browser agents.AI agentsAI Adoptionコーディングエージェント+2
スライド 2 枚9:15EN
MBMatthew Berman· 2024年9月25日Llama 3.2 Adds Vision and Smaller On-Device ModelsMatthew Berman · Forward FutureIn a Meta-partnered 2024 video, Matthew Berman reviews the model family, its tooling and a brief hosted 1B demonstration.Open-weight modelsMultimodal AILocal inference+1
1:16:11EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月11日ポッドキャストScooping Food Into Boxes Is 70 Percent of Plant Laborホスト Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.RoboticsData quality & curationLocal inference+3
34:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年6月27日ポッドキャスト · 2 人Five Independent Teams Found the Same 10-to-1 Mix That Beats Transformersホスト Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.モデルと学習Transformers & attention音声 AI+3
スライド 5 枚13:18EN
MBMatthew 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 4 枚9:53EN
MBMatthew Berman· 2024年5月28日Testing Microsoft Phi-3 Medium Locally: Mixed Coding and Reasoning ResultsMatthew Berman · Forward FutureMatthew Berman runs a quantized Phi-3 Medium on a MacBook and explores coding, arithmetic and reasoning prompts.Open-weight modelsLocal inferenceBenchmarks+1
スライド 4 枚25:00EN
MBMatthew Berman· 2024年5月23日Microsoft Build 2024: Copilot+ PCs, Phi-3 and AI DemosMatthew Berman, Satya Nadella, Sam Altman, Seth Juarez · Forward FutureMatthew Berman reviews selected Microsoft keynote announcements, product demonstrations and a conversation with Sam Altman.AI platform engineeringLocal inferenceMultimodal AI+2
29:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年5月9日ポッドキャスト · 2 人The Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power Plantホスト Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.インフラとデプロイAI cost & performanceコンテキストエンジニアリング+3
38:33EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月21日ポッドキャスト · 2 人Speed Is the Thing You Can Buy: Baseten’s Bet on Inferenceホスト Sarah Guo, Elad GilTuhin Srivastava tells Sarah Guo and Elad Gil why a six-person chatbot company running a billion tokens a day should not be building its own serving stack.インフラとデプロイInferenceAI cost & performance+3
スライド 2 枚5:53EN
MBMatthew 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 7 枚4:36EN
MBMatthew Berman· 2023年6月20日Testing WizardCoder 15B as a Local Coding AssistantMatthew Berman · Forward FutureMatthew Berman runs the 2023 WizardCoder model locally, testing small Python exercises and a longer Snake game.モデルと学習Local inferenceSoftware engineering+2
スライド 2 枚10:40EN
MBMatthew Berman· 2023年6月7日Testing Nous Hermes 13B Locally: Mixed Reasoning and Unsafe ResponsesMatthew Berman · Forward FutureMatthew Berman compares a 2023 quantized model on hosted and local hardware, with uneven results across coding, logic and safety.Open-weight modelsLocal inferenceBenchmarks+1
1:13:52EN
MBMatthew Berman· 2023年4月15日Andriy Mulyar on GPT4All, Data Quality and Open Local ModelsMatthew Berman, Andriy Mulyar · Forward FutureThe Nomic AI co-founder explains how GPT4All emerged, why training data matters, and the tradeoffs between local and centralized AI.Local inferenceOpen-weight modelsData quality & curation+2
1:18:48EN
Lex FridmanポッドキャストData Movement, Not Math: How to Make Deep Learning Fit on Robots and Phonesホスト Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.インフラとデプロイInferenceAI cost & performance+3