Walkthroughs, demos, and deep dives. Our notes first, the original next.
The full collection
197 videos · showing 49–72
This Portuguese-language tutorial explains Jev's probabilistic decision model architecture. The presenter showcases Playground modes including yes/no, score, and choice with custom criteria, then plans an architectural integration into an existing recommendation application using an LLM skill.
Duration: 34 minutes 21 seconds.Henri8 explores TypeSafe's Jev decision model via Vercel AI Gateway. He explains how typed questions replace conversational text for agent routing, triage, and guardrails, detailing two-stage architectures where LLMs interpret, Jev decides, and code executes based on confidence thresholds.
Duration: 20 minutes 29 seconds.This video introduces TypeSafe AI's Jev decision model using a duplicate charge support scenario. It details fixed-weight inference across three primitives—Choice, Noul, and Score—explains probability calibration via RLCD, and demonstrates why deterministic application logic must handle downstream execution.
Duration: 5 minutes 27 seconds.Jack Roberts builds three no-code web applications demonstrating Jev's capabilities in text classification, real-time rubric scoring, and semantic UI component ranking without traditional vector databases.
Duration: 14 minutes 3 seconds.ZazenCodes demonstrates how to query TypeSafe's Jev model using the Python SDK. The tutorial covers Jev's core decision primitives—Noul, Choice, and Score—along with parallel multi-question evaluation and an automated support ticket triage loop.
Duration: 20 minutes 58 seconds.This video introduces TypeSafe AI's decision model Jev, explaining its probability-focused architecture, vendor-claimed low latency and pricing, and community experiments like fast moderation and trading.
Duration: 6 minutes 17 seconds.Julian Ivanov demonstrates Jev through interactive emoji and Wikipedia browser-automation apps, contrasting its zero-shot classification and probabilities against generative LLMs. He reviews agent routing, SEO auditing, and ad-filtering use cases, while highlighting hard limits around arithmetic, adversarial prompt susceptibility, context windows, and privacy trade-offs against open local alternatives like Laya.
Duration: 24 minutes 53 seconds.Kaya Rezende examines practical use cases for TypeSafe AI's Jev model, spanning browser-based flight booking, automated ad classification via OCR, and community experiments like trading bots. The video highlights how low-latency structured decisions can serve as lightweight intermediaries for LLM routing, guardrails, lead qualification, and large-scale catalog analysis.
Duration: 5 minutes 39 seconds.This video breaks down Jev, a non-chat classification model by TypeSafe. It explains why software decisions do not require text generation, how Jev returns probabilities and confidence scores over fixed options, the three supported question types, and how confidence thresholds route uncertain cases to humans.
Duration: 2 minutes 46 seconds.This video breaks down TypeSafe's Jev model, explaining how it replaces token-by-token text generation with direct, structured choices across three question formats. It also explores using Jev's confidence scores to route uncertain decisions to human reviewers or larger LLMs rather than replacing general-purpose generative models entirely.
Duration: 3 minutes 4 seconds.Midudev explores Jev by TypeSafe AI, demonstrating how it produces typed decisions instead of text. Through live examples like chat moderation and shell guardrails, he explains its parallel evaluation API, non-deterministic nature, and potential as a UI or model router.
Duration: 26 minutes 24 seconds.This overview explains TypeSafe AI's Jev model, exploring its architecture as a fast System 1 model that outputs predefined structured decisions rather than text. It covers agent routing, latency tradeoffs, and TypeSafe's reported benchmarks.
Duration: 7 minutes 30 seconds.Explains TypeSafe AI's Jev model, showing how it processes input text in a single pass to return typed probabilities across three question primitives. It critiques vendor speed and hallucination marketing claims, detailing confidence calibration and a practical 100-row evaluation methodology.
Duration: 16 minutes 5 seconds.Rafael Voss explains TypeSafe AI's Jev model as a fast, low-cost System 1 decision engine. He contrasts it with generative System 2 LLMs and outlines three tiers of practical usage: LLM/skill routing, discrete data and email classification, and agentic computer use.
Duration: 14 minutes 47 seconds.Learn how to integrate TypeSafe AI's Jev model into Python scripts using typesafe-sdk and OpenRouter, classifying text inputs with the Noul primitive.
Duration: 17 minutes 2 seconds.Schovia explains Jev's classification design, playground state-versus-questions setup, SDK usage, parallel evaluation, confidence scoring, and browser agent benchmark results.
Duration: 12 minutes 56 seconds.This video walks through TypeSafe's Jev console, showing how to test Noul, score, and choice schemas in the playground before building an email triage demo.
Duration: 17 minutes 5 seconds.This video breaks down recent Jev updates, comparing TypeSafe's cloud decision model with alternatives like DiffusionGemma and local Laya, while detailing practical production patterns including RAG routing, semantic search, and agent gating.
Duration: 21 minutes 44 seconds.StatLearn Tech explains how to represent state and independent typed questions, then works through Choice, Score, and Noul criteria. The reviewed sections focus on selecting the right question shape and making binary criteria explicit.
Duration: 46 minutes 48 seconds.This video examines where TypeSafe Jev fits in software architectures, comparing it to BERT, analyzing multi-step browser execution, demonstrating 3-question customer support triaging, and using confidence thresholds for human escalation.
Duration: 12 minutes 24 seconds.This overview explains TypeSafe AI's Jev as a 'system one' decision model for agent routing and classification. It covers Jev's output primitives, parallel evaluation architecture, REST API differences from standard chat endpoints, and crucial accuracy versus latency trade-offs compared to traditional LLMs.
Duration: 7 minutes 5 seconds.This video introduces TypeSafe AI's Jev model, explaining its fast System 1 decision architecture and three distinct query modes. It also details pricing, API access, and reported real-world demonstrations.
Duration: 10 minutes 7 seconds.AI Jason explains Jev's architecture as a discrete decision-making model that outputs probability distributions over defined options. He illustrates how to construct business guardrails using confidence thresholds and demonstrates three real-world pipelines: fraud detection, buyer intent lead qualification, and social media post classification.
Duration: 14 minutes 29 seconds.Abhijeet Kalamkar introduces TypeSafe AI's Jev decision model in Hindi. He explains Jev's core assessment categories—Noul, choice, and score—explores official playground templates, and tests a custom decision dashboard with live customer support, sponsorship triage, and comment classification examples.
Duration: 18 minutes 42 seconds.
