GitHub · by Vincent Wang-Maścianica

jevlike

A Jev-like architecture, reverse-engineered, to train your own.

Vincent Wang-Maścianica

@vinnylarouge

I reverse-engineered a jev-like architecture given its type. You can find the repo here to train your own jevlikes: github.com/vinnylarouge/j…

Sep 16, 2026 · 2.0K likesOpen on X

Vincent Wang-Maścianica reverse-engineered an architecture like Jev’s from its type signature, and published the code so you can train your own Jev-like models.

Open the source

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Hassan
@nutlope
I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on http://1kpapers.com The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesafeai. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live: http://1kpapers.com
Live siteResearch and data
1kpapers
Documents
1,018 papers
Total cost
$0.08
Median latency
256 ms
Ian Nuttall
@iannuttall
Cloudflare Workers has Jev now so I'm putting it to the test on keep.md - 7x faster search rerank compared to the current hybrid - 50x faster tagging of content vs GLM 4.7 Flash with no failures
X postResearch and data
Search and tagging on keep.md
Rerank
7x faster
Tagging
50x faster
Diogo Almeida
@CompleteSkeptic
We love how this doomo doomonstrates real-time intelligence and what can be doone with code + AI! ~10 calls/sec = ~$7/hour
X postGames and real time
Pick
Jev plays Doom
Rate
~10 queries/s
Cost
~$7/hour

jevlike

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1kpapers

Search and tagging on keep.md

Jev plays Doom