本文へスキップ

最新のポッドキャスト 知識。

Podwise が処理した新着エピソード。検索、閲覧し、アプリで続きを読めます。

要約したばかり

再生する前に、各エピソードの内容を読めます。

Stanford Online

Stanford CS329A Self-Improving AI Agents | Part 2 | Test-Time Compute Scaling

AI 要約 Inference scaling enhances Large Language Model performance by increasing test-time compute rather than modifying model parameters. Repeated sampling, where models generate multiple candidates for a single input, allows smaller models to surpass larger, proprietary systems when…

  • LLM (Large Language Model)
  • Inference Scaling
  • Infinite Monkey Theorem

English要約 · 文字起こし

Stanford Online

Stanford CS329A Self-Improving AI Agents | Part 1 | Course Overview

AI 要約 Large language models are transitioning from static chatbots into autonomous agents capable of end-to-end task completion through planning, multi-step reasoning, and self-correction. While initial performance gains relied on scaling compute, data, and parameters, recent…

  • LLM (Large Language Model)
  • Scaling Laws
  • Chain of Thought

English要約 · 文字起こし

C哥聊科技

【C哥日课】未来的某种形状:私人定制产品

AI 要約 AI 时代正在推翻追求 “最大公约数” 和 “低门槛” 的古典产品逻辑,将老需求与个人审美、习惯及趣味相结合,裂变出指数级的非标新需求。过去因技术限制而隐形的个性化需求,如今正通过按需生成走向现实,即使是服务于个位众数的极小规模需求也具备商业价值。由于个体无法完全手搓满足所有需求,未来的数字化产品将从 “算法推荐” 进化为 “私人定制”,形成供给与需求同步爆炸的多对多手工艺服务模式。这种变革意味着用户不再是被动搜索或接受推送,而是以极低成本获得量身定制的旅行攻略或数字化产品,彻底重塑互联网服务的交付范式。

  • 最大公约数需求
  • 标准化服务
  • 按需生成

00:02:18Chinese要約 · 文字起こし

The Geek Narrator

The LLVM of Databases - DataFusion Internals ft. Andrew Lamb

AI 要約 DataFusion has matured into a foundational component for diverse, specialized data systems, enabling developers to build high-performance, bespoke query engines. The engine optimizes performance through sophisticated logical and physical planning, including predicate pushdown,…

  • DataFusion
  • Parquet
  • SQL (Structured Query Language)

English要約 · 文字起こし

Decoder with Nilay Patel

The OpenAI board member who fired Sam Altman says we still can't trust AI CEOs

AI 要約 AI safety and regulation require moving beyond the current reliance on individual trustworthiness toward robust institutional accountability and technical oversight. Helen Toner, Executive Director of the Center for Security and Emerging Technology, argues that the industry’s…

  • AGI
  • Alignment
  • Recursive Self-Improvement

1 h 11 minEnglish要約 · 文字起こし

George Gammon

You Won't Believe The Fed's New Plan (Interest Rate Reset)

AI 要約 The Federal Reserve’s potential 50 basis point interest rate hike reflects a strategy driven by institutional self-preservation rather than economic stabilization. Recent volatility in the two-year Treasury yield, triggered by Christopher Waller’s hawkish remarks, highlights how…

  • Federal Reserve
  • Basis Point
  • CPI (Consumer Price Index)

English要約 · 文字起こし

さらに新しいエピソード

  1. 要約12:56
  2. MCP Goes Stateless

    Agentic Conversations (formally mlops.community)

    要約00:52:27
  3. AI Hype vs. Real Value

    Agentic Conversations (formally mlops.community)

    要約00:42:37
  4. What Happens When Every Developer Has 20 AI Agents?

    Agentic Conversations (formally mlops.community)

    要約00:34:37
  5. AI Agents Should Be Treated Like Hackers

    Agentic Conversations (formally mlops.community)

    要約00:31:35
  6. The Current State of Agentic Retrieval - Qdrant Roundtable

    Agentic Conversations (formally mlops.community)

    要約00:58:54
  7. Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + Chronon

    Agentic Conversations (formally mlops.community)

    要約00:51:27
  8. Architecting Modern AI Systems: Platforms, Agents, and Integration

    Agentic Conversations (formally mlops.community)

    要約00:56:59
  9. Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce

    Agentic Conversations (formally mlops.community)

    要約01:18:38
  10. How We Cut LLM Latency 70% With TensorRT in Production

    Agentic Conversations (formally mlops.community)

    要約01:05:20
  11. Operationalizing AI Agents: From Experimentation to Production // Databricks Roundtable

    Agentic Conversations (formally mlops.community)

    要約01:01:13
  12. The Future of Information Retrieval: From Dense Vectors to Cognitive Search

    Agentic Conversations (formally mlops.community)

    要約01:02:53
  13. Rethinking Notebooks Powered by AI

    Agentic Conversations (formally mlops.community)

    要約00:26:13
  14. Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale

    Agentic Conversations (formally mlops.community)

    要約00:57:24
  15. The Hidden Bottlenecks Slowing Down AI Agents

    Agentic Conversations (formally mlops.community)

    要約00:47:59
  16. AI Agent Development Tradeoffs You NEED to Know

    Agentic Conversations (formally mlops.community)

    要約00:57:06
  17. AI Data Engineers - Data Engineering After AI // Vikram Chennai // #309

    Agentic Conversations (formally mlops.community)

    要約00:49:40
  18. Streaming Ecosystem Complexities and Cost Management // Rohit Agrawal // #302

    Agentic Conversations (formally mlops.community)

    要約00:48:51
  19. Efficient GPU infrastructure at LinkedIn // Animesh Singh // MLOps Podcast #299

    Agentic Conversations (formally mlops.community)

    要約00:59:13
  20. GenAI Traffic: Why API Infrastructure Must Evolve... Again // Erica Hughberg // #296

    Agentic Conversations (formally mlops.community)

    要約01:06:24
  21. 要約22 min
  22. Your Toddler Isn’t Listening. Here’s Why.

    Respectful Parenting: Janet Lansbury Unruffled

    要約29 min
  23. 要約00:38:00
  24. The New Problems AI Is Creating (And How People Are Solving Them)

    The AI Daily Brief: Artificial Intelligence News and Analysis

    要約00:29:13
  25. 要約35 min
Podwise

すべての新着に 要約を。

Podwise で番組をフォローし、聴く前に各新着エピソードを読みましょう。