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Jev AI Full Course: Build Faster, Cheaper AI Agents (10 Use Cases + Voice Browser & Token Savings)
This episode is a step-by-step course on Jev AI (J-E-V), a “System One” decision model from TypeSafe AI that doesn’t write, but rapidly picks from allowed answers and returns confidence scores, with claims of being 20–200x faster and 40–400x cheaper than typical models. It explains Jev’s three question types (choice, score, null), batching many decisions in one request, and why confidence enables safer automation and escalation. The script covers how to access Jev (free until Sept 25, then via OpenRouter API), examples like paper categorization, email sorting, lead scoring with mismatch flagging, internal linking across 586 pages, browser agents finding flights, model routing and tool-use safety checks via LangChain, context reduction from ~1M to 86K tokens, and demos including a real-time voice-driven “mirror” app and an open-source Jev Voice Browser using Chrome speech tools, Jev decisions, and Playwright.
00:00 Course Overview
02:00 What Jev Is
04:35 Decision Types
06:27 Early Build Examples
08:59 Internal Linking Case
10:58 Two Key Features
12:51 Context Compaction Debate
14:10 Limits and Pricing
16:14 Why It Matters
19:28 Real Time App Demo
22:14 Build the App Steps
28:15 Voice Browser Project
29:59 How Voice Browser Works
32:50 Speed Benchmarks
33:24 Instant Decision Gap
33:53 Search by Voice Demos
34:43 Pointing Prevents Hallucinations
35:16 Boardroom Offer Pitch
36:03 How Voice Browser Clicks
36:27 Snapshot Labels Confidence
37:18 Install Jev Voice Browser
37:46 Wrap Up and Next Video
38:30 Ten Use Cases Overview
39:22 System One Model Explained
41:07 Use Case 1 Inbox Sorting
41:57 Use Case 2 Keyword Intent
43:12 Use Case 3 Lead Scoring
44:10 Use Case 4 Auto Linking
45:50 Boardroom Community Plug
46:40 Use Case 5 Publish Traffic Light
47:45 Use Case 6 Model Router
48:59 Use Case 7 Context Meter
50:10 Use Case 8 Competitor Monitor
50:46 Use Case 9 Talk to Browser
51:41 Use Case 10 Task Board
52:50 What Changed Summary
53:48 Final Boardroom CTA
54:43 Ten Builds Closing
54:59 Stop Wasting Tokens
55:46 Tokens Explained
56:07 Why Chats Get Expensive
56:58 Big Models Waste Tokens
57:25 What Jev Is
58:43 Outfit Race Benchmark
59:50 Voice Browser and Email Speed
01:00:55 Forum Training Plug
01:01:19 Three Step Implementation
01:03:19 Costs and Confidence Limits
01:04:06 Final Thoughts and Outro
Want to make money and save time with AI? Join here: https://www.skool.com/ai-profit-lab-7462/about
Get a FREE AI Automation Strategy Session: https://juliangoldieaiautomation.com/
Get a FREE AI SEO Strategy Session: https://go.juliangoldie.com/strategy-session?utm=julian
Jev AI Full Course: Build Faster, Cheaper AI Agents (10 Use Cases + Voice Browser & Token Savings)
This episode is a step-by-step course on Jev AI (J-E-V), a “System One” decision model from TypeSafe AI that doesn’t write, but rapidly picks from allowed answers and returns confidence scores, with claims of being 20–200x faster and 40–400x cheaper than typical models. It explains Jev’s three question types (choice, score, null), batching many decisions in one request, and why confidence enables safer automation and escalation. The script covers how to access Jev (free until Sept 25, then via OpenRouter API), examples like paper categorization, email sorting, lead scoring with mismatch flagging, internal linking across 586 pages, browser agents finding flights, model routing and tool-use safety checks via LangChain, context reduction from ~1M to 86K tokens, and demos including a real-time voice-driven “mirror” app and an open-source Jev Voice Browser using Chrome speech tools, Jev decisions, and Playwright.
00:00 Course Overview
02:00 What Jev Is
04:35 Decision Types
06:27 Early Build Examples
08:59 Internal Linking Case
10:58 Two Key Features
12:51 Context Compaction Debate
14:10 Limits and Pricing
16:14 Why It Matters
19:28 Real Time App Demo
22:14 Build the App Steps
28:15 Voice Browser Project
29:59 How Voice Browser Works
32:50 Speed Benchmarks
33:24 Instant Decision Gap
33:53 Search by Voice Demos
34:43 Pointing Prevents Hallucinations
35:16 Boardroom Offer Pitch
36:03 How Voice Browser Clicks
36:27 Snapshot Labels Confidence
37:18 Install Jev Voice Browser
37:46 Wrap Up and Next Video
38:30 Ten Use Cases Overview
39:22 System One Model Explained
41:07 Use Case 1 Inbox Sorting
41:57 Use Case 2 Keyword Intent
43:12 Use Case 3 Lead Scoring
44:10 Use Case 4 Auto Linking
45:50 Boardroom Community Plug
46:40 Use Case 5 Publish Traffic Light
47:45 Use Case 6 Model Router
48:59 Use Case 7 Context Meter
50:10 Use Case 8 Competitor Monitor
50:46 Use Case 9 Talk to Browser
51:41 Use Case 10 Task Board
52:50 What Changed Summary
53:48 Final Boardroom CTA
54:43 Ten Builds Closing
54:59 Stop Wasting Tokens
55:46 Tokens Explained
56:07 Why Chats Get Expensive
56:58 Big Models Waste Tokens
57:25 What Jev Is
58:43 Outfit Race Benchmark
59:50 Voice Browser and Email Speed
01:00:55 Forum Training Plug
01:01:19 Three Step Implementation
01:03:19 Costs and Confidence Limits
01:04:06 Final Thoughts and Outro
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