Why Jev Is Changing How We Build With AI with Diogo Almeida - #779
播客收听
问这期播客
会先在本集摘要、章节、转录和笔记里找答案。
TL;DR · AI 摘要
Jev模型通过强化学习和校准决策，提升AI在软件中的可靠性和实用性，挑战传统LLM方法。
核心要点
- Jev采用强化学习从校准决策（RLCD）提升模型可靠性
- 传统文本生成模型不适用于现实自动化决策场景
- AI应作为软件原语而非单一模型主导系统
结构提纲
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思维导图
用一张图看清主题之间的关系。
查看大纲文本（无障碍 / 无 JS 友好）
- Jev模型与AI构建
- 核心机制
- RLCD校准决策
- 区别于LLM方法
- 技术影响
- 重塑软件架构
- 提升可靠性
金句 / Highlights
值得收藏与分享的关键句。
生成文本的模型在现实自动化决策中表现不佳
RLCD通过校准决策提升AI可靠性
AI系统应更工程化而非依赖单一模型
章节
- 要点
Jev采用强化学习从校准决策（RLCD）提升模型可靠性
Jev采用强化学习从校准决策（RLCD）提升模型可靠性
- 要点
传统文本生成模型不适用于现实自动化决策场景
传统文本生成模型不适用于现实自动化决策场景
- 要点
AI应作为软件原语而非单一模型主导系统
AI应作为软件原语而非单一模型主导系统
转录
这期还没有可搜索转录。后续抓到带时间戳的内容后会自动补到这里。
节目笔记
Why Jev Is Changing How We Build With AI | TWIML - The Voice of Machine Learning & AI
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Why Jev Is Changing How We Build With AI with Diogo Almeida
EPISODE 779
|
OCTOBER 6, 2026
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About this Episode
In this episode, Diogo Almeida, co-founder and CEO of TypeSafe, joins us to discuss Jev, TypeSafe’s recently released model for bringing fast, reliable intelligence directly into software. We explore the idea of “machine-native intelligence” and why Diogo believes models optimized for generating text are poorly suited to many of the decisions required for real-world automation. He explains how Jev differs from traditional classifiers and LLM-based approaches, the role of reinforcement learning from calibrated decisions (RLCD), and why calibration and reliability are central to making AI useful as a software primitive. We also discuss the relationship between models and code, why Diogo believes AI systems should become more engineered rather than relying on a single model to do everything, and how Jev-like models could reshape agents, tool use, and the architecture of AI-powered software.
About the Guest
#### Diogo Almeida
TypeSafe AI
Connect with Diogo
Resources
- TypeSafe AI
- Introducing System One Models & Jev
- Jev API Documentation
- The Bitterest Lesson
- Lies, Damned Lies, and Benchmarks
- (KV) Cache Rules Everything Around Me
- The Bitter Lesson — Richard Sutton
- Deep Reinforcement Learning from Human Preferences
- Learning to Summarize from Human Feedback
- Training Language Models to Follow Instructions with Human Feedback — InstructGPT
- GPT-4 Technical Report
- Language Models (Mostly) Know What They Know
- Claude Code
- OpenClaw
- Qwen
- n8n
- Millennium Prize Problems
- OpenAI Decisions API — DevDay 2026 Recap
- [public] thoughts on a typesafe coding agent
- Deep Learning: Modular in Theory, Inflexible in Practice with Diogo Almeida - #8
- From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778
- Waymo’s Foundation Model for Autonomous Driving with Dragomir Anguelov - #725
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