Someone asked whether I use Jev. Yes — in five places already.
Jev is a decision model, not a chat model. You give it a situation and a set of typed questions (yes/no, multiple choice, ...), and it returns calibrated probabilities instead of generated text. Sub-second responses, input-token-only pricing, and it cannot answer outside the options you define. Where you'd otherwise ask an LLM for one JSON field and throw the rest away, this is the thing to call.
Where I use it:
1. Security for my AI agent. Hermes reads untrusted pages and emails, so any of that text could carry planted instructions. The hermes-firewall plugin extracts everything the model would see (invisible Unicode, hidden HTML, OCR of images, base64) and asks Jev two questions: is there a command here unrelated to the content, and is it aimed at an AI? On a 718-item benchmark it caught 89% of attacks at 3.5% false positives, ahead of DeBERTa, a 421M local classifier and a 4B instruction model. About $0.035 per thousand scans.
https://juraj.bednar.io/en/blog-en/2026/09/28/a-prompt-injection-gate-for-my-ai-agent-what-worked-what-didnt-and-the-benchmark/2. Marketing automation. Lievik watches my content sources and rates every post against each audience (newsletter, Signal group, students) from 0 to 100 for relevance, so I know what to send whom and what they've already seen.
https://lievik.cypherpunk.today3. Nostr feed ranking. Nalgorithm scores posts from my follows against a profile of what I actually care about, instead of serving reverse-chronological order. A 602-post feed scores in 21 seconds instead of ~5 minutes, with 271 distinct scores instead of 11, and a rerun keeps the same order.
https://nalgorithm.cypherpunk.today/4. Event networking. Nostrautica connects attendees with people they should meet. On a real 39-person roster the old scorer gave 19 of them a tied first place; Jev gave nobody a tie and reproduced the order across runs.
https://nostrautica.cypherpunk.today/5. Signal group summaries. signal-summarizer turns a busy Signal group into a themed digest: it reads the text, transcribes voice notes, has a vision model describe the images and summarizes the links, then files all of it into topics. Jev handles the part that's easy to get wrong — deciding whether two themes are actually the same topic, and routing a message into the right one. On a labelled fixture it separated real merges from near-misses with AUC 1.00, ahead of plain embedding similarity, and it matched strong LLM judges on routing.
https://github.com/jooray/signal-summarizerIn all of these Jev is just one option. Each project can swap in another decision model (there are open ones you can run locally in Ollama, no cloud needed) or a plain LLM, whichever fits the job.
We now have a magic wand. I was able to implement Jev in all these projects almost immediately after the model went out, including the benchmarks. This would have taken weeks without AI for coding.
