Nicheloom

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duomind

When Jev meets LLM -- Pair a small local LLM (System 2) with Jev model (System 1) to make small model more faster and accurate — OpenAI-compatible, private, and almost free to run in Kilo code and Cline.

Details

External ID
1392305809
Source
GITHUB
Company
—
Product
duomind
Website domain
github.io
Launched
Sept. 28, 2026
Cohort
—
Upvotes
16
Upvotes percentile
0.5194722008711248
Tags
claudecode, cline, coding-assistant, duomind, haggingface, jev, jev-api, jev-model, kilo-code, local-llm, ollama, openai-compatible, system-1-system-2
Fetched at
Oct. 1, 2026, 1:02 a.m.
Updated at
Oct. 1, 2026, 1:02 a.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
hybrid system-1 and system-2 inference runner for local llms
Manually corrected
False

Could you build this?

Partial Creating the OpenAI-compatible proxy server is simple, but designing and tuning the dual-system routing/Jev classification model requires specialized machine learning knowledge.

What it would actually take: The architecture requires an ultra-low-latency classification engine (the 'Jev' System 1 model, likely a lightweight fastText or quantized linear classifier) running in C++/Rust or Python, chained to a local llama.cpp/Ollama runner for System 2 generation. The hard part is building the speculative decoding, caching, or prompt-routing logic that genuinely beats standard local inference without degrading output quality, demanding applied ML systems expertise.

Competitors

Other products that read as similar to this one — 154 launches clear the similarity bar, closest 8 shown.

Attention rank: #102 of 155 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 322 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.