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29:38EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 9월 21일팟캐스트 · 2명A Beginner Just Shipped a $250K Business. Professionals Got 30 Percent.진행자 Sarah Guo, Elad GilReplit CEO Amjad Masad on Ghostwriter, why a 3-billion-parameter model was enough, and the agents he wants earning bounties while you sleep.코딩 에이전트Developer productivityOpen-weight models+3
슬라이드 4장9:59EN
Matthew Berman· 2023년 9월 7일Open Interpreter: A 2023 Local Code-Execution WalkthroughMatthew Berman · Forward FutureMatthew Berman installs Open Interpreter, analyzes local files with GPT-4 and tries a local Code Llama setup, with mixed results.코딩 에이전트Local inference개발 워크플로와 테스트+2
슬라이드 4장4:35EN
Matthew Berman· 2023년 9월 5일Running WizardCoder on a Cloud GPU: A 2023 RunPod WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates downloading a quantized 13B coding model into a hosted text-generation interface, then explains how to stop the pod and avoid ongoing charges.모델과 학습인프라와 배포코딩 에이전트
슬라이드 3장14:59EN
Matthew Berman· 2023년 9월 2일AI News in September 2023: Tesla, Code Llama, Drone Racing and CopyrightMatthew Berman · Forward FutureMatthew Berman surveys a week of AI announcements, from Tesla's driving demonstration and ChatGPT Enterprise to model releases, generative media and a copyright inquiry.AI Strategy코딩 에이전트Autonomous Driving+2
슬라이드 3장6:11EN
Matthew Berman· 2023년 8월 31일Running WizardCoder Locally with Text Generation WebUIMatthew Berman · Forward FutureMatthew Berman walks through a 2023 setup for a quantized Code Llama-based coding model, from the Python environment to a simple test prompt.Local inference코딩 에이전트Open-weight models+1
슬라이드 5장10:20EN
Matthew Berman· 2023년 8월 30일Phind Code Llama 34B vs GPT-4: A 2023 Python Coding TestMatthew Berman · Forward FutureMatthew Berman compares a quantized Phind fine-tune with GPT-4 on games, short functions and refactoring, with mixed results.Open-weight models코딩 에이전트평가(Evals)+2
1:39:53EN
Cognitive Revolution "How AI Changes Everything"· 2023년 8월 8일팟캐스트Under 100 People, 24 Million Users: Replit's Bet on Artificial Developer Intelligence진행자 Nathan LabenzMichele Catasta, Replit's new VP of AI, lays out a generate-execute-learn loop he will not call AGI, a 2x-to-1000x productivity ladder, and a keystroke-level data advantage.코딩 에이전트Developer productivityAI startups & small teams+3
1:45:46EN
Cognitive Revolution "How AI Changes Everything"· 2023년 8월 4일팟캐스트Replit's $10 AI Programmer Already Pays for Itself. The Ambition Is Bots Hiring Bots진행자 Nathan LabenzTyler Angert, Replit's first product designer, explains Ghostwriter, a virtual developer, and why the company calls itself the perfect substrate for AGI.코딩 에이전트Developer productivityProduct Strategy+3
슬라이드 4장11:07EN
Matthew Berman· 2023년 7월 24일Installing Llama 2 13B Chat and Testing Its ResponsesMatthew Berman · Forward FutureMatthew Berman demonstrates a 2023 local setup and tests a 13-billion-parameter chat model on coding, writing, arithmetic and reasoning. His small set of prompts illustrates both useful outputs and mistakes.Local inferenceOpen-weight models평가(Evals)+2
슬라이드 3장12:20EN
Matthew Berman· 2023년 7월 10일Testing UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.Open-weight models평가(Evals)모델과 학습+1
슬라이드 3장7:05EN
Matthew Berman· 2023년 7월 2일Aider’s Early Coding Assistant: Snake and an Existing Rails ProjectMatthew Berman · Forward FutureMatthew Berman demonstrates the terminal tool’s 2023 workflow, from generating and iterating on a game to proposing a dependency change in a repository.코딩 에이전트개발 워크플로와 테스트Software engineering+1
슬라이드 4장6:26EN
Matthew Berman· 2023년 6월 14일Installing and Trying GPT-Engineer: A 2023 Snake Game WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates the early project-generation workflow, from a Conda environment and prompt to clarification questions and generated Python files.코딩 에이전트개발 워크플로와 테스트Software engineering
슬라이드 5장7:37EN
Matthew Berman· 2023년 5월 25일Smol Developer: Generating a Small Python Project From a PromptMatthew Berman · Forward FutureMatthew Berman walks through a 2023 project generator, from a simple Ruby example to a multi-file ChatGPT API program.Software engineering코딩 에이전트Developer productivity+1
59:38EN
Jane Street· 2023년 5월 18일Richard Eisenberg on Dependent Types, OCaml, and AI-Assisted ProgrammingYaron Minsky, Richard Eisenberg · Jane StreetHow type systems can express correctness and performance, and why precise, readable specifications still matter when AI writes code.Software engineeringOpen-source ecosystems코딩 에이전트+1
41:40EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 3월 30일팟캐스트 · 2명Every 10 Milliseconds Cost Copilot 1% of Completions진행자 Sarah Guo, Elad GilGitHub Copilot's chief architect, Alex Graveley, walks through how a leftover code model with a sub-10% test pass rate became a product developers would not quit — and what he is building next at Minion AI.코딩 에이전트Product engineeringAI interface design+3
58:08EN
20VC with Harry Stebbings· 2023년 3월 11일팟캐스트 · 2명Amjad Masad on Replit, Company Culture and AI-Assisted Software게스트 Amjad Masad · 진행자 Harry StebbingsThe Replit founder discusses hiring, developer productivity and the opportunities and risks he saw in AI in 2023.코딩 에이전트Developer productivityEntrepreneurship+2
32:48EN
Cognitive Revolution "How AI Changes Everything"· 2023년 2월 23일팟캐스트A Hundred Million to Train, a Penny to Infer진행자 Nathan LabenzIn a February 2023 roundtable, two founders argue on mic over whether AGI ends humanity — and who actually captures the value if it does not.기업과 전략AGI(범용 인공지능)Existential Risk+3
2:51:33EN
Lex Fridman· 2021년 8월 29일팟캐스트 · 2명The Self Is a GPT Prompt: Wojciech Zaremba on Consciousness, Codex, and Fermi's Silence게스트 Wojciech Zaremba · 진행자 Lex FridmanOpenAI co-founder Wojciech Zaremba tells Lex Fridman he thinks we might be alone in the universe — and that GPT-3, Codex, and a one-handed robot already hint at how intelligence is built.모델과 학습코딩 에이전트머신러닝 기초+3
1:25:13EN
Lenny's Podcast팟캐스트Codex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammate진행자 Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.코딩 에이전트Code review & verificationDeveloper productivity+3
1:39:51EN
Lenny's Podcast팟캐스트After the Inflection Point: Simon Willison on Agentic Engineering, Dark Factories and the Coming AI Challenger Disaster진행자 Lenny RachitskyDjango co-creator Simon Willison explains what changed in November 2025, how he works with coding agents, and why prompt injection remains the field's biggest unsolved risk.코딩 에이전트에이전트 엔지니어링Prompt injection+3
1:33:50EN
Lenny's Podcast팟캐스트Evals Are the New PRDs: Inside Anthropic's Research Product Team진행자 Lenny RachitskyDianne Penn, Anthropic's head of product for research and Labs, explains how the company found its identity, how her team turns user feedback into evals, and why human judgment still matters.제품과 UXAI Product ManagementProduct Strategy+3
1:11:06EN
Unsupervised Learning: With Jacob Effron팟캐스트Replit's Amjad Masad on Building Your Own Model, Latency, and the Next Billion Software Creators진행자 Jacob EffronThe Replit founder explains why AI should be native to the product, why he trained a small in-house model, and why he thinks software engineering is about to split in two.제품과 UX코딩 에이전트Vibe Coding+3
1:53:14EN
Cognitive Revolution "How AI Changes Everything"팟캐스트Worth Thousands, Priced at $20: Zvi Mowshowitz on OpenAI's Flurry, Gemini Bets, Deepfakes and a Compute Cap진행자 Nathan LabenzZvi Mowshowitz and Nathan Labenz survey OpenAI's launch spree, AI pricing and bundling, data pollution, deepfakes, the reversal curse, calls to cap training compute, and Amazon's Anthropic deal.기업과 전략AI StrategyProduct Strategy+3
1:04:25EN
Unsupervised Learning: With Jacob Effron팟캐스트Inside GitHub's Copilot Bet: Agents, Open Source, and Why the Developer Job Endures진행자 Jacob EffronGitHub CEO Thomas explains how Copilot went from a research bet to 1.3 million developers, and what agents, fine-tuning and a multi-model world mean for the next five years of software.코딩 에이전트Developer productivityAI Product Management+3
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