Jev is HERE. How to use it
In this episode, I talk with Ryan Vogel about Jev, a new type of AI built for classification. Ryan shows how Jev takes an input plus an output schema and returns a probability for each choice in about 200 milliseconds. He demos Jev sorting 1,700 emails for 18 cents total, then covers lead scoring, support routing, video clipping, and browser control. I push him on the startup angle: find a business with an expensive queue of incoming information and put Jev at the front of it. You leave with a clear mental model, real use cases, and a simple way to try it today. Links Mentioned: Jev/Typeface AI: https://typesafe.ai AI Gateway: https://vercel.com/ai-gateway Timestamps 00:00 – Intro 02:27 – What Jev Is and Why It Matters 04:32 – Email Triage Demo 07:19 – Jev as an AI Decision Maker 15:46 – How to Use Jev in a Business 20:48 – Startup Idea: Local Services Matching and Instant Quotes 22:51 – Use Case 1: Bitcoin Signal Test and Limits 24:03 – Use Case 2: Auto-Clipping Long Videos 25:27 – Use Case 3: Browser Control: Flight Pick in 7.1 Seconds 26:18 – How to Get Access 27:25 – Closing Thoughts Key Points • Jev is a classifier: an input and an output schema go in, and a probability for each choice comes out. • Ryan's demo scores 1,700 emails for 18 cents total. • Each Jev query takes about 200 milliseconds, whatever the input and output structure. • Use Jev at any point where a business makes fast, repeatable decisions on incoming data. • Keep Jev in an advisory role, and save frontier models for high-intelligence tasks like trading. • Instant access runs through the Vercel Gateway, and a waitlist covers direct access. Numbered Section Summaries 1. What Jev Actually Is Ryan explains Jev as a classifier. You define an input and an output schema, and Jev returns a probability for each option, for example 80% orange, 10% red, and 10% blue for the color of an iPhone. 2. The 18-Cent Email Demo Ryan runs Jev on 1,700 of his own emails and scores each one for category, priority, spam score, and reply likelihood. The run uses 4.2 million input tokens and 500,000 output tokens and costs 18 cents total. 3. A Decision Model, Distinct From a Chat Model Jev returns only structured output, meaning the numbers and categories from your schema, with zero visible reasoning. Developers can drop that type-safe output straight into code. Ryan also tests Jev as a letter-by-letter text generator to show how a decision model differs from a chat model. 4. Cheap Enough to Experiment A $5 intro credit lasts Ryan's team two days of heavy use, and he estimates $10 could last about three months. His girlfriend's graphic design agency uses Jev to score contact-form leads on a scale from 0 to 1, so high-value leads get a fast reply. 5. The AI Traffic Cop I frame Jev as a traffic cop: information comes in, and Jev decides what it is, how important it is, and what happens next. A high-confidence lead goes to a human, a lower score goes to automation or an LLM, and the lowest scores get ignored. Ryan adds support routing, where 200-millisecond answers replace slow streaming responses. 6. Put Jev at the Front of the Queue I ask how to find a business with an expensive queue of incoming information and put Jev at the front of it. Ryan describes a local services platform that matches a request like "I need my driveway power washed" to the best nearby business and turns "instant quote" forms into truly instant quotes. 7. Where Jev Struggles Ryan's Bitcoin test, which asks Jev to buy, hold, or sell every minute, performs poorly. OpenAI's latest frontier model does a little better because it cross-references news. Ryan's advice: use Jev for routing-style decisions and keep it away from your portfolio. 8. Clips, Browser Control, and Getting Started Ryan's video clipper scores 17 moments in about three seconds, and he builds it in about 10 minutes. A Browser Use demo shows Jev picking a flight from Zurich to London in 7.1 seconds. To start, use the Vercel Gateway and ask your AI agent which of your daily workflows could use a decision maker like Jev. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND RYAN ON SOCIAL X: https://x.com/ryanvogel Youtube: https://www.youtube.com/@vogeldev/videos
Read Video · Транскрипция и инсайты
У этого выпуска есть полная расшифровка + AI-анализ
Бесплатный аккаунт · без карты · 150 кредитов при регистрации, достаточно для этого эпизода
- 📄 Полная транскрипция с временными метками
- ✨ AI-резюме, ключевые слова и ментальная карта
- 💡 Ключевые тезисы и цитаты
- Говорящий 1
- Говорящий 2
- Говорящий 1
- Говорящий 2
Таймлайн эпизода
Introduction to Jev, a new classifier AI created by the researcher behind ChatGPT, and why it matters.
- Jev was created by Dooo Almeida, the same researcher whose work built ChatGPT, and it represents a fundamentally new way of doing AI.
- Unlike LLMs that stream text slowly, Jev is a classifier that outputs probabilities across a predefined schema, making it fast, cheap, and type-safe for developers.
- Jev is invite-only at publishing time, but access is available immediately through the Vercel gateway.
Ryan demos Jev by classifying 1,700 real emails into categories, priority, spam score, and reply likelihood.
- Jev takes a full email object as input and returns four outputs: category (shopping, work, marketing, finance, security), priority (low to urgent), spam score as a percentage, and reply percentage.
- Processing 1,700 emails with 4.2 million input tokens and 500,000 output tokens cost only 18 cents total, demonstrating the extreme price advantage over LLMs.
- Because Jev returns probabilities rather than binary answers, an email can be scored as 80% orange, 10% red, 10% blue, giving nuanced confidence rather than a definitive label.
Greg and Ryan build a mental model of Jev as a decision model rather than a conversational LLM.
- Jev does not generate text or reason out loud like reasoning models; it simply evaluates an input against a schema and returns a probability distribution over the defined choices.
- The schema is essentially a type-safe output structure, so developers can use Jev's numeric and categorical outputs directly in code without additional data processing.
- Ryan compares Jev to a decision model like himself typing each letter of 'hello' — each keystroke is a decision, and Jev makes similar split-second probabilistic decisions.
Ключевые понятия
- Jev— The new classifier AI model that is the central topic of the episode.
- classifier AI— Jev's core nature: it makes probabilistic decisions rather than generating text.
- decision model— The mental model for understanding Jev as an AI that outputs decisions, not conversations.
Знаковые цитаты
We had 4.2 million input tokens and 500,000 output tokens. The entire cost was 18 cents for each one of those emails.
🤯— The scale of cost efficiency is shocking—processing 1,700 emails for less than a quarter reveals a new economic reality for AI applications.
It's a decision model. And that's what I pointed it out. Like all of these are just decisions. It's not cuz everyone has started to assimilate AI with LLMs which is like that next token prediction where it's a conversational agent. This isn't that at all.
💡— This directly challenges the common assumption that all AI must be conversational, reframing AI as a decision-making tool.
Действенные выводы
🤖AI and Technology
Jev is a new type of AI—a classifier that makes probabilistic decisions rather than generating text, offering speed and cost advantages over LLMs.
This week, sign up for Jev access via Vercel gateway and run a simple classification task on your own data (e.g., categorize 100 emails) to experience the speed and cost.
Jev's type-safe schema output allows direct integration into code, eliminating data processing steps.
Explore the Jev API documentation and write a small script that uses Jev to classify a sample dataset, then integrate the output into a simple application.
🚀Business and Startups
Jev unlocks startup ideas by automating decision-making in workflows with expensive queues of incoming information.
Identify one decision point in your business or daily workflow (e.g., lead qualification, support triage) and prototype a Jev-based solution this week.
The low cost and speed of Jev enable instant quotes and real-time matching services that were previously impractical.
If you run a service business, add a Jev-powered instant quote form to your website to capture leads faster than competitors.
Транскрипция и инсайты создаются автоматически и могут содержать ошибки. Точность зависит от качества звука и чёткости речи дикторов — если что-то выглядит неверно, исходная запись всегда остаётся главным источником.
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