Runs ~0.2s per decision on my DGX Spark.

  10/10 programming language detection
  9/10 human language detection
  10/12 unit magnitude comparison

It (DiffusionGemma with the Jev mode) can also solve an ASCII maze.

> They were building in stealth for 2 years, I was building in stealth for 2 hours…

> Happy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe.

> A Jev-like model takes a piece of text and a list of N text options. It returns one probability for each option. It does this in one pass instead of writing an answer word by word.

I’ve read TypeSafe’s announcement, watched the home assistant demo, and still had no idea what it was. If instead those three sentences were in the announcement…

1. All of my hooks can move off of deterministic regexes, which are limited and cannot tell you more than if a certain string is present or not. 2. My PostGres second brain on Neon currently relies totally on LLM inference to classify signal vs. noise. Jev probably slots in perfectly here. 3. I see interesting use cases for detecting AI slop. Currently we're relying on a model's inference based on some exemplars to emulate and bad examples to avoid. Sometimes it works, sometimes it doesn't. 4. My mediagen workflow where my LLM helps me write the optimal prompt depending on the image/video model called, calling a diffusion model on fal.ai, generating images, and iterating if necessary, all rely on a preflight cost check that is currently extremely unreliable. We're talking orders of magnitude differences in expected costs vs. actual costs. A preflight score from Jev before a paid generation could be valuable. 5. Model-routing, obviously. 6. For me personally, I have around sixty skills (I know, I have to prune) whose descriptions compete for the same triggers, and my check_skill_collisions.py script can only compare description text. A 'Choice' primitive over skill names is another potential use case.