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슬라이드 3장10:28EN
Matthew Berman· 2025년 8월 5일OpenAI’s gpt-oss Models: Open Weights, Benchmarks and SafetyMatthew Berman · Forward FutureMatthew Berman reviews the 120b and 20b models, local deployment claims, selected evaluations and the scope of OpenAI’s safety testing.모델과 학습AI research평가(Evals)+1
슬라이드3:13EN
Matthew Berman· 2025년 8월 5일Claude Opus 4.1: Anthropic’s August 2025 Benchmark UpdateMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported coding, reasoning and tool-use results, including one benchmark that declined.모델과 학습평가(Evals)코딩 에이전트
슬라이드 9장15:30EN
Matthew Berman· 2025년 7월 31일Qwen3 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 models코딩 에이전트평가(Evals)+2
30:11EN
Sequoia Capital· 2025년 7월 30일팟캐스트From 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.모델과 학습Reasoning & test-time computeBenchmarks+3
1:02:54EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 7월 17일팟캐스트 · 2명Enterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other Half진행자 Sarah Guo, Elad GilFormer Gemini reward-model lead Misha Laskin on Asimov, why comprehension beats generation, and why there is no such thing as generalization—only pulling the test distribution into train.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
38:10EN
Cognitive Revolution "How AI Changes Everything"· 2025년 7월 16일팟캐스트Plus Five for Format, Plus Ten if It Compiles진행자 Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.모델과 학습파인튜닝Post-training+3
41:13EN
Sequoia Capital· 2025년 7월 11일팟캐스트From 49 Multiplications to 48: DeepMind’s AI Rewrites a Fifty-Year Math RecordPushmeet Kohli explains how AlphaEvolve couples Gemini with evaluators to invent algorithms, prove old conjectures, and change which scientific problems are even worth attempting.에이전트AI for scienceMulti-agent systems+3
42:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 6월 26일팟캐스트 · 2명AlphaEvolve found algorithms humans missed for 50 years — then sped up Gemini 23%진행자 Sarah Guo, Elad GilGoogle DeepMind’s Pushmeet Kohli and Matej Balog tell No Priors how an evolutionary coding agent searches algorithm space the way AlphaGo searched Go.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
54:53EN
Unsupervised Learning: With Jacob Effron· 2025년 6월 17일팟캐스트A Free Voting Board Burned $2 Million. Then It Raised $100 Million.진행자 Jacob EffronDatabricks co-founder Ion Stoica explains why LMArena exists, why China is winning open-source models, and why reliability—not another scaling law—is still the thing holding AI back.평가와 관측 가능성평가(Evals)Benchmarks+3
슬라이드 5장11:24EN
Matthew Berman· 2025년 6월 10일Runner H and Surfer-H: A Browser Agent and Its Holo1 ModelsMatthew Berman · Forward FutureMatthew Berman demonstrates H Company’s browser agent and reviews its research on visual navigation. The paper’s benchmark configuration and the demo’s missing links show why results need context.Computer useAI agentsOpen-weight models+1
슬라이드 9장22:05EN
Matthew Berman· 2025년 6월 9일Apple’s “Illusion of Thinking”: Puzzle Limits and a Code-Assisted TestMatthew Berman, Ilya Sutskever · Forward FutureMatthew Berman reviews Apple’s reasoning-model experiments and tests a different way to solve Tower of Hanoi using generated code.Reasoning & test-time computeBenchmarks평가(Evals)+1
44:21EN
Unsupervised Learning: With Jacob Effron· 2025년 6월 4일팟캐스트Mercor's CEO: Knowledge Work's Next Job Is Writing Evals진행자 Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.평가와 관측 가능성평가(Evals)Data quality & curation+3
슬라이드 4장18:13EN
Matthew Berman· 2025년 6월 2일Darwin Gödel Machine: How a Coding Agent Tests Changes to Its Own SoftwareMatthew Berman · Forward FutureMatthew Berman explains Sakana AI’s evolutionary search over agent code, its benchmark results and the limits of claims about recursive self-improvement.에이전트 엔지니어링코딩 에이전트평가(Evals)+2
38:25EN
Sequoia Capital· 2025년 5월 27일팟캐스트A thousand airplanes will never train an AI engineerFormer Airbus CTO Paul Eremenko explains why physical-world engineering still has no coding-AI moment, and how P-1 AI's agent Archie is supposed to get there anyway.에이전트에이전트 엔지니어링Synthetic data+3
1:01:51EN
Cognitive Revolution "How AI Changes Everything"· 2025년 5월 22일팟캐스트Claude 3.7 Circumvented Replit's Guardrails, Then Tried to Trick the User진행자 Nathan LabenzReplit CEO Amjad Masad on vibe coding, agent architecture, computer use, and why Claude 3.7 is too autonomous to serve as a polite assistant.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
57:31EN
Unsupervised Learning: With Jacob Effron· 2025년 5월 22일팟캐스트The New Ruler for Models Is How Long They Can Work진행자 Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.코딩 에이전트에이전트 엔지니어링평가(Evals)+3
2:02:51EN
Cognitive Revolution "How AI Changes Everything"· 2025년 5월 8일팟캐스트At a 60-Person Safety Offsite, Only Four Had Seen Nonprofit OpenAI진행자 Nathan LabenzFormer OpenAI scientist Steven Adler on the Anthropic split, GPT-4, safety evaluations, and why he filed a brief to keep the company under nonprofit control.보안과 안전안전과 거버넌스평가(Evals)+3
47:00EN
Unsupervised Learning: With Jacob Effron· 2025년 5월 8일팟캐스트An Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decade진행자 Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.모델과 학습평가(Evals)Post-training+3
2:29:00EN
Cognitive Revolution "How AI Changes Everything"· 2025년 5월 4일팟캐스트Assume the Model Is Already Scheming — Then Put It to Work진행자 Nathan LabenzRedwood Research CEO Buck Shlegeris tells Rob Wiblin how to extract useful labor from AIs you cannot certify as aligned, using audits, honeypots, resampling, and factored cognition.보안과 안전안전과 거버넌스AI security+3
51:19EN
Unsupervised Learning: With Jacob Effron· 2025년 4월 29일팟캐스트Sal Khan's $15 Tutor Hit 1.4 Million Users. Making Kids Actually Use It Is the Product진행자 Jacob EffronThe Khan Academy founder tells Redpoint why an AI tutor still cannot teach most students alone, and why districts are paying $15 a year after an $86 billion tutoring hangover.제품과 UXAI in EducationProduct Strategy+3
슬라이드 4장19:06EN
Matthew Berman· 2025년 4월 25일Sleep-Time Compute: Preparing AI Context Before a QuestionMatthew Berman · Forward FutureMatthew Berman reviews a 2025 paper on moving some reasoning ahead of a user query, and explains where its experiments found benefits and limits.Reasoning & test-time computeAgent memoryAI cost & performance+2
30:46EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 4월 24일팟캐스트 · 2명Deep Research Turned Web Browsing into a Trainable Problem진행자 Sarah Guo, Elad GilOpenAI researcher Isa Fulford explains why a side project on read-only synthesis became an agent, when reinforcement fine-tuning is worth it, and why models will hack the search engine you gave them.에이전트에이전트 엔지니어링Reinforcement Learning+3
슬라이드 10장26:35EN
Matthew Berman· 2025년 4월 23일Testing Gemini 2.5 Flash on Coding and Video UnderstandingMatthew Berman · Forward FutureMatthew Berman compares Google’s faster model with Gemini 2.5 Pro through interactive simulations, games and questions about a long video.모델과 학습코딩 에이전트Multimodal AI+2
58:11EN
Sequoia Capital· 2025년 4월 15일팟캐스트Patrick Hsu Wants an App Store for Biology, Not Just Better DrugsThe Arc Institute co-founder argues that Evo 2, trained on genomes rather than proteins, should underpin tools for every step of the scientific method.모델과 학습AI for sciencePretraining & midtraining+3
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