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Independent radar of Jev builds, experiments and use cases — scored by Jev, not by likes. Not affiliated with TypeSafe AI.

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1449 published builds · 6019 buildersBuilt with Amp · © 2026 Jev Radar

VERIFYAgents & automation

AI Dyson Sphere Construction

parth 🔥@hTrapVader

Project photo 1

Sep 22

81 views on X · does not affect score

An AI is attempting to construct a Dyson sphere, currently facing challenges with an oil conveyor and verifying factory tasks using JEV.

TakeAstra plans; @typesafeai's JEV picks checked factory tasks.

An AI is trying to build a Dyson sphere. Currently losing an argument with an oil conveyor. Astra plans; @typesafeai's JEV picks checked factory tasks. Three completed and verified so far. Come spot the next bottleneck: twitch.tv/parthintelligence x.com/hTrapVader/status/210221502369125627

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Project photo 1

Jev Decision Model

Jev is a typed decision model that returns a probability for a given state (question, answer, evidence) instead of generating prose or structured data, making it faster and cheaper than traditional AI judges.

Jose Luis Latorre | #MVP | #K6 | @DotNetZurich@joslat

40 views on XJev score 79

TypeSafe Computer Use

This project enables a Mac to achieve plain-English goals by combining OCR and the Accessibility tree to drive actions, avoiding large model loops for screenshots.

Yash Chaudhary@YashChaudhary

4 views on XJev score 78

Jev Decision Layer for Voice Pipelines

Jev acts as a decision layer in voice pipelines, utilizing typed primitives like Noul (yes/no with probability), Choice (labeled decision), and Score (scalar on a defined rubric) to process conversational turns before and during LLM responses.

Himanshu@Fabulous_7781

72 views on XJev score 76

Jev Skill Review

Jev is a decision model used to review a corpus of skills, providing ratings across rubric dimensions and identifying skills that may need distillation.

Kunal Jain@Kunal_Jain9

90 views on XJev score 76

Quality37

NoveltyiHow surprising the Jev use is versus an obvious typed-decision call. Generic mentions score low; a new primitive scores high.56
UsefuliHow reusable the technique is for another builder. Toys score low; a production-quality idea scores high.20
SubstanceiHow much concrete evidence supports the claim: code, numbers, demo, architecture. Hype scores low.31

Quality is Jev’s judgment of the idea. Likes, reposts and views do not affect it. Relevance is a separate gate.