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jev-style

Small, calibrated decision models on your own machine: systemone-compatible local server, 6 agent skills, Claude Code guard, MCP tools. Weights on Hugging Face.

Details

External ID
1387781216
Source
GITHUB
Company
—
Product
jev-style
Website domain
jevstyle.com
Launched
Sept. 25, 2026
Cohort
—
Upvotes
9
Upvotes percentile
0.21822956699974377
Tags
agent-skills, calibration, claude-code, decision-model, gguf, guardrails, jev, llm-routing, local-llm, mcp, mlx, qwen3
Fetched at
Sept. 29, 2026, 1:02 a.m.
Updated at
Sept. 29, 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
local decision models and agent tooling server
Manually corrected
False

Could you build this?

Partial While the local HTTP/MCP server and agent tooling glue code are straightforward to vibe-code, training, fine-tuning, and calibrating custom small decision models published to Hugging Face requires specialized ML training pipelines and curated datasets.

What it would actually take: The system combines a local inference runtime (like llama.cpp or PyTorch/ONNX server) wrapping MCP endpoints with trained model weights. The primary bottleneck is curating domain-specific decision datasets, fine-tuning SLMs (1B-3B parameters), and performing probability calibration (e.g., temperature scaling/Platt scaling) to output reliable confidence scores. It requires an ML engineer with experience in LLM distillation and evaluation benchmarking.

Competitors

Other products that read as similar to this one — 312 launches clear the similarity bar, closest 8 shown.

Attention rank: #287 of 313 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 197 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.