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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.

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GITHUB · launched Sep 25, 2026 · 9 upvotes (top 80%) · 0 views

Niche: local ai model runners and clients · 110 launches, 6 in the last 7 days ▼ -57% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project
  • agent-skills
  • calibration
  • claude-code
  • decision-model
  • gguf
  • guardrails
  • +6 more

Visit website ↗ Source listing ↗

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 : 465 launches clear the similarity bar, closest 8 shown.

Attention rank: #416 of 466 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 329 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.