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4 diapositivas48:20EN
Theo - t3․gg· 10 nov 2026Fable 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.Agentes de programaciónBenchmarksModelos y entrenamiento
43:35EN
Silicon Valley Girl· 10 nov 2026Isomorphic Labs’ Max Jaderberg on narrowing drug designs with AIInvitado: Max Jaderberg · Presenta: Marina MogilkoThe researcher explains virtual screening, real laboratory checks and why agent performance must be measured against useful outcomes.Drug DiscoveryAI for scienceAI agents+2
4 diapositivas38:50EN
AI Engineer· 10 nov 2026LinkedIn’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.Ingeniería de agentesEvaluaciones y observabilidadAgent orchestration+1
12:48EN
AI Engineer· 10 nov 2026Brian 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.Agentes de programaciónFlujos de desarrollo y pruebasSoftware engineering+1
59:06EN
a16z· 10 nov 2026Dharmesh 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.AI startups & small teamsProduct StrategyIA empresarial+2
Diapositivas12:20EN
AI Engineer· 10 nov 2026Ayush 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.Agentes de programaciónFlujos de desarrollo y pruebasIngeniería de agentes+1
1:46:17EN
The Knowledge Project Podcast· 10 nov 2026Pódcast · 2 vocesJon McNeill on Tesla’s speed: delete steps before automatingInvitado: Jon McNeill · Presenta: Shane ParrishThe former Tesla president explains the sales bottleneck, product feedback and five-step simplification method behind rapid growth.LeadershipDecision MakingProduct Management+2
23:03EN
Masters of Scale· 10 nov 2026Pódcast · 3 vocesTubi and e.l.f. grew by serving audiences incumbents overlookedInvitado: Anjali Sud, Kory Marchisotto · Presenta: Bob SafianAnjali Sud and Kory Marchisotto explain free streaming, affordable beauty and direct feedback from fans.Product StrategyGrowth StrategyDigital Media+1
43:05EN
The Builders· 10 nov 2026Alex Karp says AI could widen inequality—and rejects a wealth taxInvitado: Alex Karp · Presenta: Walter IsaacsonThe Palantir CEO links data sovereignty, surveillance limits and a proposed tax-and-efficiency trade-off.AI RegulationPrivacy & data governanceEconomics+1
5 diapositivas12:57EN
AI Engineer· 10 nov 2026Harvey’s agents scaled until their platform and merge queue brokeJoey Wang · HarveyAfter an alert spawned 900 agents and one engineer launched 950 tasks, Harvey added deduplication and fair scheduling.Ingeniería de agentesAgent orchestrationDeveloper productivity+1
31:18EN
Hard Fork· 10 nov 2026Pódcast · 2 vocesNvidia’s board investigates reports involving Jensen HuangInvitado: Stephen Witt · Presenta: Max ReadJournalist Stephen Witt discusses leadership risk, export controls and unanswered questions around Megaspeed.SemiconductorsGeopoliticsAI Regulation+1
3 diapositivas51:48EN
AI Engineer· 10 nov 2026Compose small decisions before reaching for a generative modelRaymond Weitekamp · OpenProseRaymond Weitekamp demonstrates recursive decision models and sketches a voice-first computer interface.AI agentsIngeniería de agentesAgent orchestration
Diapositivas23:00EN
AI Engineer· 10 nov 2026A passing PR check needs evidence tied to the submitted codeStephen Chin, Cassandra Chin · Neo4jStephen and Cassandra Chin show how Tracetoon connects agent traces, code changes and checks before writing a reviewable pull request.Agentes de programaciónFlujos de desarrollo y pruebasSoftware engineering+1
4 diapositivas22:49EN
AI Engineer· 10 nov 2026When many coding agents share one codebase, delivery must handle concurrencyPatrick Debois · TesslPatrick Debois explains how shared planning, merge strategies and risk-based review can help teams coordinate agents.Agentes de programaciónSoftware engineeringFábricas de software
6 diapositivas23:57EN
Google Cloud Tech· 10 nov 2026Six front-end ideas that make AI-built apps easier to debugDevin Chasanoff · Google CloudDevin Chasanoff uses an interactive game tracker to show where data lives, how values are derived and how components communicate.Software engineeringAgentes de programaciónFlujos de desarrollo y pruebas+1
4 diapositivas38:07EN
GOTO Conferences· 10 nov 2026A smoother reorganization starts by letting teams design itJoakim Sundén · Better Product WorkJoakim Sundén pairs leadership’s strategy with employees’ knowledge of daily work, then lets people select teams.ManagementLeadershipProduct Management
3 diapositivas1:25:52EN
Latent Space· 10 nov 2026Pódcast · 2 vocesZeno Rocha on building email tools for people and agentsInvitado: Zeno Rocha · Presenta: Shawn WangThe Resend co-founder explains deliverability, agent-facing interfaces and why he writes product information for machines to read.Product StrategyAI agentsProduct-Led Growth
2 diapositivas15:41EN
AI Engineer· 10 nov 2026Meta’s on-call AI teammate earns autonomy one action at a timeChinmay Gandhi, Swaapnika Motapothula, Vlad Tsvang · MetaThe team connects incident context and shared memory while keeping critical decisions and code review with people.AI agentsInfraestructura y despliegueIngeniería de agentes+1
2 diapositivas14:30EN
AI Engineer· 10 nov 2026Why reading a skill file cannot reveal everything an agent will doRoey Ben Chaim · ZenityZenity’s Roey Ben Chaim describes sandbox testing that follows dependencies and observes process, file and network activity.AI securityIngeniería de agentesSeguridad+1
4 diapositivas1:17:53EN
Stanford Online· 10 nov 2026How language-model design shapes context engineering for agentsMichael Ryan · Stanford UniversityStanford’s Michael Ryan connects decoding, attention and inference to the practical choices agent builders make.AI agentsIngeniería de contextoInference
Diapositivas20:06EN
AI Engineer· 10 nov 2026Prasanna Sankar: As agents write more code, coordination becomes the bottleneckPrasanna Sankar · VorfluxThe Vorflux founder describes cloud environments, review and merge queues, and agents that help initiate routine work.Agentes de programaciónAgent orchestrationFlujos de desarrollo y pruebas+1
2 diapositivas1:16:16EN
Stanford Online· 10 nov 2026Cynthia Bailey: Prompt clearly, verify evidence and keep your own judgmentCynthia Bailey · Stanford UniversityThe Stanford lecturer explains practical prompting, context management and ways to prevent skills from quietly fading.Prompting & in-context learningHuman-AI CollaborationEducation
1:25:20EN
The Pragmatic Engineer· 10 nov 2026Pódcast · 2 vocesSean Allen: Make software correctness part of the designInvitado: Sean AllenThe Antithesis engineer argues for earlier guarantees, more thoughtful test workloads and planning for production failures.Flujos de desarrollo y pruebasSoftware engineeringSeguridad+1
2 diapositivas28:00EN
AI Engineer· 10 nov 2026Robinhood’s AI reviewer merges 60% of data PRs without human reviewChetas Joshi · RobinhoodScripts verify live facts, an agent traces code risks, and people retain sensitive decisions.Ingeniería de agentesCode review & verificationValidación con humanos en el bucle+1
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