YoutuBlog(HackerTube): YouTube 발표와 팟캐스트를 글로 읽기
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25:17ENPeter Yang splits Claude Code work between Opus and FableHe uses Opus for routine execution and reserves Fable for harder decisions, code audits and family-care coordination.
19:44ENWhat a managed agent harness has to do to keep workingCharlie, Steve · OpenAIOpenAI engineers explain remote sandboxes, long-running state and a human-approved flight rebooking demo.
1:24:54ENZach Reitano: US healthcare must answer to patients게스트 Zach Reitano · 진행자 Patrick O'ShaughnessyRo's founder argues for transparent prices and patient choice, and describes the company's 2022 reset.
1:13:14ENRonnie Chatterji: AI may change work before it changes job counts게스트 Ronnie Chatterji · 진행자 Reid Hoffman, Aria FingerOpenAI’s chief economist expects productivity gains, but says attributing them to AI and managing uneven effects will take work.
27:14ENJohn Kerry: Cheaper clean energy needs faster deploymentAaron Rutkoff, John Kerry · BloombergKerry argues clean energy can compete on price, while warning that permitting delays and climate damage add costs.
59:16ENTerence Tao: AI proofs make mathematical understanding more urgent게스트 Terence Tao · 진행자 Tyler CowenTao explains why proof abundance may change research, education and the value of human explanation.
26:47ENDan Shipper: AI makes generating ideas cheap; people still choose what matters진행자 Natalia QuinteroThe Every co-founder uses one customer survey to explain why different perspectives can produce different work from the same model.
27:09ENA six-week coding agent run and what it took to superviseOpenAI engineers describe a CI maintenance agent, long-running context and the cost of keeping it working.
20:48ENSites adds personalized connectors and agent-friendly web appsKatia, John · OpenAIOpenAI demonstrates scheduled updates, per-user plugins and WebMCP in two Sites applications.
29:37ENTheo finds a security use for Gemini 4 Carbon despite coding frustrationsTheoHis tests show fewer false code-analysis claims than Argon, but latency and reliability still push him toward other models.
21:35ENEric Ries: Why successful companies lose the purpose that made them work게스트 Eric Ries · 진행자 Krista TippettRies argues that pressure from capital can erode a company’s mission, and that trust deserves a place in business decisions.
56:06ENMike Volpi: AI infrastructure can endure a market correctionRaghu Raghuram, Guido Appenzeller, Mike Volpi · Andreessen HorowitzThe former Cisco executive sees useful assets beyond the hype, but questions who will own them and how systems will evolve.
1:42:10ENHow quickly could AI improve itself? Four forecasters disagreeYaron Minsky, Jaime Sevilla, Ege Erdil, Ryan Greenblatt, Daniel Kokotajlo · Jane StreetOne forecaster puts AI research automation at 50% by 2028; another sees a slower path as more likely, with a real chance of rapid progress.
18:18ENFable 5.2 leads a week of model releases, but cost changes the choiceMatthew Berman · Forward FutureMatthew Berman compares Fable and Gemini Carbon, reviews smaller models and discusses Nvidia’s investigation.
12:38ENHow Duolingo uses agents to add tests without skipping human reviewKush Agrawal · DuolingoA scheduled pipeline raised iOS coverage by generating tests, repairing CI and routing pull requests to engineers for approval.
1:03:19ENManage AI sources: retrieve selectively and withhold context when usefulCynthia Bailey · Stanford UniversityStanford lecturer Cynthia Bailey explains when to retrieve documents, leave context out and guard against instructions hidden in sources.
2:55:43ENPaddy Pimblett on fighting, weight cuts and the lightweight division게스트 Paddy Pimblett · 진행자 Joe RoganPimblett explains his Saint Denis finish, his difficult recovery from ankle surgery, and why he refuses to fight teammates.
48:20ENFable 5.2 is powerful, but Theo Browne still prefers Opus for everyday codingTheo Browne · T3After a day of tests, Browne compares benchmarks, usage limits and the mistakes that still require a human reviewer.
43:35ENIsomorphic Labs’ Max Jaderberg on narrowing drug designs with AI게스트 Max Jaderberg · 진행자 Marina MogilkoThe researcher explains virtual screening, real laboratory checks and why agent performance must be measured against useful outcomes.
38:50ENLinkedIn’s self-improving agent first had to fix its judgesZiwei Guo, Saloni Gandhi · LinkedInA prompt-optimization loop improved a career agent, but unreliable evaluators required human calibration first.
12:48ENBrian Douglas: Turn agent corrections into reusable review knowledgeBrian Douglas · Paper ComputeThe Paper Compute developer captures expert pushback in coding sessions and tests ways to reuse it during review.
59:06ENDharmesh Shah: Find startup opportunities beyond the AI labs’ pathJoe Schmidt, Dharmesh Shah · Andreessen HorowitzHubSpot’s co-founder explains how to test narrow ideas, build around customer work and use AI without relying on a temporary model gap.
12:20ENAyush Bhardwaj: Agent workarounds are feedback for your CLIAyush Bhardwaj · Allos AIAn audit of coding sessions shows where output, errors and stale instructions force agents to improvise.
1:46:17ENJon McNeill on Tesla’s speed: delete steps before automating게스트 Jon McNeill · 진행자 Shane ParrishThe former Tesla president explains the sales bottleneck, product feedback and five-step simplification method behind rapid growth.
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