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206 件
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スライド 5 枚9:58EN
Matthew Berman· 2024年8月2日Abstract AI: A Proposed Routing Layer for Language ModelsMatthew Berman · Forward FutureMatthew Berman outlines a startup idea for choosing models and workflows by cost, speed and quality.Model routing & gatewaysAI cost & performanceエージェント工学+2
1:07:05EN
Sequoia Capital· 2024年7月16日ポッドキャストAlphaGo Combined Learning and Search. Language Models Only Did the First Half.Reflection AI's Misha Laskin tells Sequoia why prompted coding agents stall in the teens, and why the missing piece is still a ground-truth reward.エージェントエージェント工学Reinforcement Learning+3
1:16:22EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月9日ポッドキャストHe Spent 20 Hours Crafting a Prompt. DSPy Beat Him Anywayホスト Nathan LabenzSander Schulhoff walks through The Prompt Report — a 78-page survey of what actually works when you prompt large language models.Prompting & in-context learningPrompt optimizationエージェント工学+3
59:11EN
Sequoia Capital· 2024年6月25日ポッドキャストDon't Train a Model. Factory's Founders Say Go Dig for GoldMatan Grinberg and Eno Reyes tell Sequoia's Training Data why they sell a fleet of job-specific droids on top of other people's models — and how that stack just took SWE-bench from 14% to 19%.コーディングエージェントエージェント工学評価(Evals)+3
1:37:07EN
Cognitive Revolution "How AI Changes Everything"· 2024年6月22日ポッドキャストA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.ホスト Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.モデルと学習AI in healthcareMultimodal AI+3
49:50EN
Sequoia Capital· 2024年6月18日ポッドキャストUnconstrained agents captivated Twitter. Custom ones are what ship.LangChain founder Harrison Chase tells Sequoia’s Training Data what an agent actually is, why AutoGPT stalled, and which cognitive architectures reach production.エージェントエージェント工学Agent orchestration+3
スライド1:00EN
Matthew Berman· 2024年5月13日Andrew Ng on Four Agentic AI Design PatternsMatthew Berman, Andrew Ng · Forward FutureA short excerpt identifies reflection, tools, planning and multi-agent collaboration as approaches to building AI agents.AI agentsエージェント工学Multi-agent systems
29:28EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年5月2日ポッドキャスト · 2 人Devin Will Not Replace Engineers. It Will Multiply Them, Says Cognition’s Scott Wuホスト Sarah Guo, Elad GilThe IOI champion behind Devin explains why an AI software engineer that you can watch over the shoulder creates more human engineers, not fewer.コーディングエージェントエージェント工学Developer productivity+3
スライド 4 枚16:22EN
Matthew Berman· 2024年5月2日Harrison Chase on Planning, Interface Design and Memory for AI AgentsMatthew Berman, Harrison Chase · Forward FutureThe LangChain CEO outlines open questions for agent builders, with commentary from Matthew Berman on oversight, workflows and memory.AI agentsAgent orchestrationAgent memory+2
スライド 9 枚19:09EN
Matthew Berman· 2024年4月28日OSWorld: Evaluating Computer-Use Agents in Real ApplicationsMatthew Berman · Forward FutureMatthew Berman explains the 2024 benchmark’s observation–action loop, task-specific checks and early results, with the limitations of its experimental setup.Computer use評価(Evals)Benchmarks+1
スライド 7 枚13:59EN
Matthew Berman· 2024年4月5日SWE-agent Tutorial: Navigating Repositories and Attempting GitHub FixesMatthew Berman, Carlos · Forward FutureMatthew Berman sets up a research coding agent, hits an installation problem and a cost limit, then shows an author’s successful SymPy benchmark example.コーディングエージェントエージェント工学開発ワークフローとテスト+2
スライド 4 枚14:58EN
Matthew Berman· 2024年3月30日OpenDevin’s Early Coding Agent: A 2024 Installation and DemoMatthew Berman · Forward FutureMatthew Berman installs an early open-source coding-agent project and tries a calculator and a simple web page, while encountering setup and browser issues.コーディングエージェントエージェント工学Software engineering+1
27:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月28日ポッドキャスト · 2 人From a side project to 700 integrations: Harrison Chase on what still blocks agentsホスト Sarah Guo, Elad GilThe LangChain CEO walks through a two-year churn of patterns, and why memory, planning, and few-shot data still matter more than swapping models.エージェントエージェント工学Agent memory+3
42:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月7日ポッドキャスト · 2 人Meta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion Questionホスト Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Gemini, Mistral, NVIDIA’s upgrade cycle, Klarna’s support bot, and why the real AI spend still sits with the hyperscalers.企業と戦略AI Strategyエンタープライズ AI+3
スライド 4 枚55:29EN
Matthew Berman· 2024年2月24日Building a CrewAI Newsletter Team: Scraping, Summarizing and DebuggingMatthew Berman, Joe · Forward FutureMatthew Berman builds a two-agent newsletter prototype, works through input and scraping errors, and shows what still needs to be fixed.Multi-agent systemsエージェント工学Workflow automation+2
4:56EN
Cognitive Revolution "How AI Changes Everything"· 2024年2月1日ポッドキャストIf the Mind Is a Tool, AI Looks Less Like a Threatホスト Nathan LabenzYohei Nakajima and Nathan Labenz pause a TED AI talk to connect agent-building habits with East–West ideas of identity.エージェントエージェント工学Knowledge graphs+3
1:45:21EN
Cognitive Revolution "How AI Changes Everything"· 2024年1月30日ポッドキャスト600,000 Attack Prompts, and Nearly 40% Still Pwned GPT-4ホスト Nathan LabenzSander Schulhoff walks Nathan Labenz through the prompting habits that still work, the role-play myth that does not, and the global HackAPrompt contest that mapped how language models get hijacked.セキュリティと安全性Prompt injectionAI security+3
スライド 4 枚15:11EN
Matthew Berman· 2024年1月25日Building a Transcript-to-Article Team in AutoGen StudioMatthew Berman · Forward FutureMatthew Berman configures a retrieval skill, two agents and a group workflow, then troubleshoots missing credentials and output.エージェントエージェント工学
1:43:07EN
Cognitive Revolution "How AI Changes Everything"· 2024年1月20日ポッドキャストAn Agent Posted Five Tweets — and 5,000 People Didn't Noticeホスト Nathan LabenzMultiOn founder Div Garg walks Cognitive Revolution through what web agents can actually do in early 2024, what they cost, and why guardrails have to sit outside the model.エージェントエージェント工学コンテキストエンジニアリング+3
46:36EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年1月18日ポッドキャスト · 2 人After a decade of code search, Sourcegraph’s CTO says the coding-AI fight is outside the modelホスト Sarah Guo, Elad GilBeyang Liu on Cody, why keyword search still does most of RAG, and why CS fundamentals get more valuable as AI compresses the middleware.コーディングエージェント検索拡張生成(RAG)コンテキストエンジニアリング+3
1:50:57EN
Cognitive Revolution "How AI Changes Everything"· 2024年1月12日ポッドキャストTwenty-Eight Days After GPT-4, an AI Designed and Ran a Chemistry Experimentホスト Nathan LabenzGabe Gomes and his Carnegie Mellon students built Coscientist, a language-model agent that plans, codes, and executes real experiments on a remote cloud lab.エージェントAI for scienceエージェント工学+3
1:06:39EN
Unsupervised Learning: With Jacob Effron· 2024年1月9日ポッドキャストOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need Yearsホスト Jacob EffronLogan Kilpatrick on picking GPT-3.5 versus GPT-4, why plugins stalled, and why the public internet is not ready for autonomous agents.企業と戦略AI StrategyPrompting & in-context learning+3
1:22:59EN
Cognitive Revolution "How AI Changes Everything"· 2023年12月2日ポッドキャストIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%ホスト Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.エージェントエージェント工学Reasoning & test-time compute+3
32:48EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年11月16日ポッドキャスト · 2 人Fourteen People, $200 Million of Compute, and the Reliability Bottleneck for Agentsホスト Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.エージェントコーディングエージェントエージェント工学+3
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