Moonshot AI · Community reports
Jev + Kimi K3: email screening and a coding workflow
A measured email handoff sends uncertain cases to Kimi K3. A separate developer uses Kimi to build a Jev-powered comment filter.
We checked the cited posts and documentation. Measurements are attributed to their authors and have not been independently rerun by this site.
Sending uncertain emails to Kimi K3
Hassan tests 100 emails, split evenly between legitimate and fraudulent examples. Jev performs the first classification pass in 1.42 seconds. The workflow sends 31 cases below a 95% confidence threshold to Kimi K3 for further analysis.
The reported full pipeline takes 16 seconds and gets 96 of 100 emails correct. The stated cost is about $0.07, with $0.003 attributed to Jev and $0.068 to Kimi. The 1.42-second number describes only the first stage; using it as the combined workflow’s duration would omit the Kimi work.
Sources:[19] Hassan
A demo threshold is not a fraud policy
The post reports a live demonstration, not a production evaluation across changing fraud patterns. Its 96 correct decisions still leave four errors, and an even split of fraudulent and legitimate emails does not establish performance in an inbox with a different mix.
TypeSafe recommends selecting confidence thresholds for the domain and the consequences of an action. The 95% boundary in Hassan’s demo is therefore a description of his setup, not a generally safe cutoff for financial or account decisions.
Sources:[19] Hassan[2] TypeSafe AI
Kimi can help build the application without running its classifier
yonsakhan describes pair-programming with Kimi K3 to build a userscript for X comments. The post says Jev handles semantic spam checks, including obfuscated text, while the script collapses matches, lets users recover mistakes and caches results locally.
This is a different meaning of using Jev with Kimi. In Hassan’s report, Kimi receives uncertain emails at runtime. In the comment-filter report, Kimi helped develop the software. The post does not say that every classification calls both models.
Sources:[20] yonsakhan[19] Hassan
Questions about Jev + Kimi
Did the Jev + Kimi pipeline finish in 1.42 seconds?
No. Hassan reports 1.42 seconds for Jev’s first pass and 16 seconds for the complete pipeline including Kimi K3.
Sources:[19] Hassan
Does the Kimi-assisted comment filter call Kimi at runtime?
The cited post says Kimi K3 helped develop the userscript and Jev performs the semantic checks. It does not establish a runtime Kimi call.
Sources:[20] yonsakhan
Sources and reading notes
The summaries below are paraphrases. Each link opens the original source, including its surrounding context.
Hassan (@nutlope)
Published:
Source checked:
[19] Jev first-pass email screening with Kimi K3 fallback
Hassan tests 50 legitimate and 50 fraudulent emails. Jev classifies them in 1.42 seconds; 31 cases below a 95% confidence threshold go to Kimi K3. The full pipeline takes 16 seconds, gets 96 of 100 correct and costs about $0.07.
Author-reported demonstration, not independently rerun here. The stated cost split is $0.003 for Jev and $0.068 for Kimi. The 1.42-second figure excludes the Kimi stage.
TypeSafe AI
Source checked:
[2] Confidence, probabilities and fallback decisions
Choice and Score include confidence derived from their probability distributions. Noul does not have a separate confidence field. TypeSafe recommends choosing thresholds for the domain and consequences of an action.
Implementation reference. A confidence value is not a guarantee that an individual decision is correct.
yonsakhan (@yonsakhan)
Published:
Source checked:
[20] Using Kimi K3 to develop a Jev-powered comment filter
The author says they developed an X comment-filtering userscript with Kimi K3 and use Jev for semantic spam decisions, including obfuscated text. They describe reversible collapsing and a local cache.
A development report. Kimi helped write the software; the post does not establish that Kimi participates in its runtime classification loop.