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296 件
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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
スライド 5 枚24:54EN
Matthew Berman· 2023年12月22日Matthew Berman's 2023 Prediction for AI and the Future of ProgrammingMatthew Berman, Matt Welsh, McKay Wrigley · Forward FutureBerman argues that natural-language interfaces and AI agents could transform software development, while acknowledging the limits of coding tools and his uncertain timeline.Software engineeringコーディングエージェントHuman-AI Collaboration+2
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
1:15:39EN
Cognitive Revolution "How AI Changes Everything"· 2023年11月29日ポッドキャストAfter 20 Years of Frontend, v0 Wrote Better Code Than Guillermo Rauch Didホスト Nathan LabenzThe Vercel CEO tells Nathan Labenz why every product needs ghost text, why Software 2.0 will ossify the old stack, and why betting against open source is still the expensive mistake.コーディングエージェントVibe Coding開発ワークフローとテスト+3
スライド 4 枚7:48EN
Matthew Berman· 2023年11月26日Testing Dolphin 2.2 Yi 34B on Coding and Reasoning TasksMatthew Berman · Forward FutureMatthew Berman runs a quantized Yi fine-tune through informal coding, writing and logic prompts in November 2023, with uneven results.Open-weight modelsFoundation ModelsBenchmarks+2
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
スライド 3 枚5:31EN
Matthew Berman· 2023年10月30日Connecting MemGPT to AutoGen AgentsMatthew Berman · Forward FutureIn a 2023 tutorial, Matthew Berman configures a MemGPT-backed coding agent alongside an AutoGen product manager and runs a short group chat.Agent memoryMulti-agent systemsコーディングエージェント
スライド 4 枚7:02EN
Matthew Berman· 2023年10月18日Connecting AutoGen to a RunPod-Hosted Model with Text Generation WebUIMatthew Berman · Forward FutureIn a 2023 walkthrough, Matthew Berman connects AutoGen to Dolphin 2.1 Mistral 7B through an OpenAI-compatible API and tests a small coding task.エージェント工学Open-weight modelsMulti-agent systems+2
スライド 5 枚15:03EN
Matthew Berman· 2023年10月10日A 2023 AutoGen Tutorial: Local Agent Setup and Code ExecutionMatthew Berman · Forward FutureMatthew Berman builds a local assistant and user-proxy workflow, then tests how the agents generate and run Python. AutoGen’s packages and APIs have changed since this tutorial.Multi-agent systemsエージェント工学コーディングエージェント+2
1:32:41EN
Cognitive Revolution "How AI Changes Everything"· 2023年10月5日ポッドキャストThere are maybe 5,000 people who can train frontier models. The rest need AI engineersホスト Nathan LabenzSwyx of Latent Space tells Nathan Labenz how a new job is forming, why OpenAI will Sherlock its developers, and what still does not count as an agent.企業と戦略エージェント工学コーディングエージェント+3
スライド 3 枚7:32EN
Matthew Berman· 2023年10月4日MageGPT Generates a Wasp To-Do App: A 2023 DemonstrationMatthew Berman · Forward FutureMatthew Berman uses a Wasp-sponsored app generator to build, run and deploy a basic full-stack to-do app.コーディングエージェントSoftware engineering開発ワークフローとテスト
スライド 6 枚18:23EN
Matthew Berman· 2023年10月2日Early ChatGPT Vision Examples: From Diagrams to Code and Visual MistakesMatthew Berman · Forward FutureMatthew Berman surveys 2023 image-input demonstrations, including education, design and programming, and examines examples that were not verified or got details wrong.Computer VisionMultimodal AIEducation+2
2:17:30EN
Cognitive Revolution "How AI Changes Everything"· 2023年9月29日ポッドキャストGemini Became the Daily Driver. A GPT-5 Worthy of the Name Would Make Agents Work.ホスト Nathan LabenzZvi Mowshowitz returns to Cognitive Revolution to unpack Gemini 1.5, Sora, superalignment, sleeper agents, and the live-player map of frontier labs.企業と戦略AI StrategyProduct Strategy+3
29:38EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年9月21日ポッドキャスト · 2 人A Beginner Just Shipped a $250K Business. Professionals Got 30 Percent.ホスト 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.コーディングエージェントDeveloper productivityOpen-weight models+3
スライド 4 枚9:59EN
Matthew Berman· 2023年9月7日Open 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.コーディングエージェントLocal inference開発ワークフローとテスト+2
スライド 3 枚6:11EN
Matthew Berman· 2023年8月31日Running 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 inferenceコーディングエージェントOpen-weight models+1
スライド 5 枚10:20EN
Matthew Berman· 2023年8月30日Phind 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 modelsコーディングエージェント評価(Evals)+2
1:39:53EN
Cognitive Revolution "How AI Changes Everything"· 2023年8月8日ポッドキャストUnder 100 People, 24 Million Users: Replit's Bet on Artificial Developer Intelligenceホスト 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.コーディングエージェントDeveloper productivityAI startups & small teams+3
1:45:46EN
Cognitive Revolution "How AI Changes Everything"· 2023年8月4日ポッドキャストReplit's $10 AI Programmer Already Pays for Itself. The Ambition Is Bots Hiring Botsホスト Nathan LabenzTyler Angert, Replit's first product designer, explains Ghostwriter, a virtual developer, and why the company calls itself the perfect substrate for AGI.コーディングエージェントDeveloper productivityProduct Strategy+3
スライド 3 枚12:20EN
Matthew Berman· 2023年7月10日Testing 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 models評価(Evals)モデルと学習+1
スライド 4 枚6:26EN
Matthew Berman· 2023年6月14日Installing 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.コーディングエージェント開発ワークフローとテストSoftware engineering
スライド 5 枚7:37EN
Matthew Berman· 2023年5月25日Smol 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 engineeringコーディングエージェントDeveloper productivity+1
59:38EN
Jane Street· 2023年5月18日Richard 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 ecosystemsコーディングエージェント+1
41:40EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年3月30日ポッドキャスト · 2 人Every 10 Milliseconds Cost Copilot 1% of Completionsホスト 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.コーディングエージェントProduct engineeringAI interface design+3
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