Jev 与 ChatGPT Sites 实战：8 个用例与内部工具
做法与步骤
- Jev 输出决策而非文本，将非结构化输入转为分数、分类、概率或函数调用；开发者应识别应用需做的决策，而非当作聊天机器人。
- Jev 的速度和成本解锁新应用：John 23 次开发运行仅花 73 美分，Claire 处理 5GB JSON 花 40 美分。
- 正确心智模型是 if/else 语句而非提示工程：在传统程序需要条件、switch 或分支决策处使用 Jev，由开发者定义路径，Jev 用自然语言选择。
- 路由是最实用模式之一：单一自然语言输入可识别正确工具、推断意图并触发函数，如待办事项和全能搜索栏演示。
- 分层分类常优于依赖单一完美决策：多次分类串联，因每次调用成本低，增加一遍可提升准确率而不昂贵。
- 国际象棋基准显示优势：分析整局游戏不到一秒，比低推理 LLM 快 10 倍、便宜 4 倍且不牺牲准确率；动作有限时决策模型可胜出。
对实践的启示
对 AI 应用开发者：可将 Jev 用于路由、分类等决策场景，用分层分类提升准确率，并考虑暴力分析策略；ChatGPT Sites 适合快速构建个性化内部工具甚至临时软件。对用 AI 干活的人：AI 可准备沟通但发送权保留给人，快速模型能保持创意 momentum。
作者承认的反方与局限
Jev 并非万能：创意推理、开放式分析、头脑风暴和图像解读仍属于 LLM；最大收益来自有意结合两种模型。Kath 也明确不让 AI 未经批准以她名义发送消息。
原文摘引
Jev outputs decisions instead of text, and that constraint is its defining feature.
The right mental model for Jev is an if/else statement, not prompt engineering.
Software can now be temporary and still worth building.
▸ 展开原文全文（40 段，抓取自 Lenny's Newsletter）
Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist
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John Lindquist is an educator and developer who created egghead.io and now runs mega.dev, a hands-on program for developers building with AI agents. In this episode, he walks through eight Jev demos, including a real-time voice assistant, data deduplication, app routing, chess analysis, multi-agent coordination, and a live presentation coach. He also explains why Jev is better understood as a fast, inexpensive decision engine than a chatbot, how layering multiple classifications improves accuracy, and when a traditional LLM is still the better tool.
Biggest takeaways:
- Jev outputs decisions instead of text, and that constraint is its defining feature. It turns unstructured input into structured results such as scores, classifications, probabilities, and function calls. Once developers stop treating it like a chatbot and identify the decisions their applications need to make, many previously impractical use cases become obvious.
- Jev’s speed and cost unlock a different class of applications. Across eight demos, John shows what becomes possible when decisions cost almost nothing and return nearly instantly. He spent just 73 cents across 23 development runs, while Claire processed five gigabytes of JSON for 40 cents.
- The right mental model for Jev is an if/else statement, not prompt engineering. John recommends using it wherever a traditional program would contain a condition, switch statement, or branching decision. Developers define the available paths, and Jev uses natural language to select the right one.
- Routing is one of Jev’s most immediately useful patterns. A single natural-language input can identify the right tool, infer the intended action, and trigger the appropriate function. John’s to-do and omnibar demos show the kind of fast, flexible command layer that voice assistants have long promised but rarely delivered.
- Layered classification often works better than relying on one perfect decision. John frequently runs one classification, feeds its result into another, and combines the steps later as real usage data accumulates. Because each call costs so little, adding another pass can meaningfully improve accuracy without making the workflow expensive.
- The chess benchmark makes Jev’s advantage concrete. It analyzed a full game in under a second, running ten times faster and costing four times less than the low-reasoning LLM without sacrificing accuracy. When the possible actions are finite, a decision model can outperform a generative model on the metrics that matter.
- Jev makes “efficient inefficiency” a viable strategy. Evaluating every chess move, mapping every route through Wikipedia, or checking every agent’s path for collisions may sound wasteful. But when each decision is fast and inexpensive, brute-force analysis can be simpler and more effective than carefully narrowing the search space.
- Knowing when not to use Jev matters just as much. Jev works best when the action space is defined and natural language must be routed into it. Creative reasoning, open-ended analysis, brainstorming, and image interpretation still belong to LLMs. The biggest gains come from intentionally combining both types of models.
Blog and detailed workflow walkthroughs from this episode:
John Lindquist’s 6 Jev Workflows for Apps, Data, and AI Agents: https://www.chatprd.ai/how-i-ai/john-lindquist-jev-workflows
↳ How to Build a Real-Time Voice-Controlled To-Do List App with Jev: https://www.chatprd.ai/how-i-ai/workflows/jev-voice-controlled-todo-app
↳ How to Deduplicate and Clean Large Datasets Using AI Decision Models: https://www.chatprd.ai/how-i-ai/workflows/jev-data-deduplication
↳ How to Build a Smart Command Bar and App Router with Jev: https://www.chatprd.ai/how-i-ai/workflows/jev-smart-command-bar-app-router
How OpenAI uses ChatGPT Sites (live at DevDay!) | Kath Korevec (Product Lead)
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Kath Korevec is a member of the Product staff at OpenAI who has been building and using ChatGPT Sites internally for more than a year. In this episode, she demonstrates how OpenAI uses Sites to turn live Slack, Notion, and calendar data into personalized internal tools, including an incident command center that adapts to each visitor. She also shows how she automated music discovery with Reddit and Spotify and built a community dungeon crawler through shared skills, and explains why fast models are changing how people create personal software.
Biggest takeaways:
- Plugin Insights lets one site automatically personalize itself for every visitor. Kath connected an incident command site to Slack and Notion, allowing it to infer someone’s team from their channel access and display only the relevant incidents and runbooks. Connector authentication handles the personalization without requiring custom logic.
- Developers do not have to write the connector logic themselves. Codex can infer the appropriate plugins when a prompt mentions collaboration, Notion, calendars, or other connected tools. Kath often uses the phrase “use Plugin Insights to build this site,” but says Codex frequently reaches for the right connectors without being explicitly told.
- Sites is infrastructure, not simply a hosting layer. It includes D1 storage, an R2 bucket, MCP plugin hosting, and support for co-editors. Kath’s team uses it for production work, including the slides used during the OpenAI DevDay keynote, while many others still treat it as a prototyping tool.
- Software can now be temporary and still worth building. Kath might create a personalized site for one business trip, use it for a week, and discard it afterward. When development overhead falls far enough, even a tool with a seven-day lifespan can deliver meaningful value.
- Kath’s automated music discovery workflow shows what personal software can already do. Her site uses computer control to find popular Reddit playlists every Monday, creates a fresh Spotify playlist, and plays it automatically. During the demo, it surfaced a song she had never heard and genuinely liked.
- Skills let a community extend an application without accessing its codebase. Kath published a dungeon-building skill that defines the room dimensions, controls, and structure. Anyone can install it, create a themed room with Astra, and add the result to her shared dungeon crawler.
- Faster models can make builders more creative. Kath and Claire both found that slow generation breaks the creative thread, while immediate results encourage continued experimentation. During one Astra ultrafast session, Claire’s kids called out requests like “jellyfish belly” and “add a chicken,” and she kept building without losing momentum.
- Kath draws a clear boundary around AI acting in her name. She uses Codex for research, thinking, and drafting, but will not let it email or message people without her approval. Her rule is simple: AI can help prepare the communication, but the send button remains hers.
Blog and detailed workflow walkthroughs from this episode:
Kath Korevec’s ChatGPT Sites Workflows: Dashboards, Music, and Games: https://www.chatprd.ai/how-i-ai/kath-korevec-chatgpt-sites-workflows
↳ How to Build a Real-Time Incident Command Dashboard with ChatGPT Sites: https://www.chatprd.ai/how-i-ai/workflows/chatgpt-sites-incident-dashboard
↳ How to Create an Automated Weekly Music Discovery Playlist with AI: https://www.chatprd.ai/how-i-ai/workflows/chatgpt-sites-weekly-music-playlist
↳ How to Create a Collaborative 3D Game with ChatGPT Sites and Custom Skills: https://www.chatprd.ai/how-i-ai/workflows/chatgpt-sites-collaborative-3d-game
OpenAI Dev Day 2026: The releases that actually matter
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In this solo episode, Claire