X post · by Fayaz Ahmed
Fayaz Ahmed’s image classifier: OCR reads each image, Jev sorts it.
Fayaz Ahmed
@fayazara
Made myself a little image classifier with OCR + Jev It was able to categorise ~900 images in 40 seconds Pretty cool
Sep 18, 2026 · 332 likesOpen on X
Fayaz Ahmed built himself an image classifier. OCR turns each image into text, and Jev picks its category. It sorted about 900 images in 40 seconds.
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Riley Brown
@rileybrown
Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.
vogel
@ryanvogel
this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away
Hassan
@nutlope
Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on @togethercompute - $0.003 (1/3 of a cent) from Jev on @typesafeai. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.
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