Nicheloom visits: 58,111 all-time · 7,637 in the last 7 days

The opportunity tracker for new startups.

Today: 1,953 launches · Niche of the day: specialized calculation tools · See what launched today →

OpenDecision

a 400M zero-shot model makes local decisions, plays Doom

Get picks like this daily. Today's launch numbers, the top launches, AI and tech news, and a daily opportunity pick, straight to your inbox.

Want to reach these readers? Sponsor an issue.

Jev's launch inspired me to work on this project. I had used fine tuned bert models, zero-shot models to achieve jev like functionality in healthcare insurance fraud audits in the last 2-3 years with good results.Jev's launch showed that there is still market and demand for these type of systems. So I wanted to build a FOSS version which would push the research and tinkering (R&T) in this direction.Do not use this where decisions can be costly. There's an accuracy/intelligence ceiling with these models no matter what the api seller tells you.Use this where you can tell the affected party - "oh yeah, sometimes it can mess up" and you both can have a laugh on the expense of machines.On Jev and Typesafe - I think Jev is great, especially the speed, the documentation and the product design itself.Happy to answer any questions.Thanks, D

HN · launched Sep 21, 2026 · 6 upvotes (top 67%) · 0 views

Niche: reinforcement learning frameworks · 72 launches, 3 in the last 7 days · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project

Visit website ↗ Source listing ↗

Could you build this?

Partial The Python API wrappers and schema primitives are easy to code, but training, calibrating, and distilling a specialized 400M zero-shot NLI model for low-latency decision making requires ML engineering expertise.

What it would actually take: The core engine requires fine-tuning a small transformer cross-encoder (such as DeBERTa-v3) on structured natural language inference, question-answering, and state-evaluation datasets using PyTorch. The inference pipeline requires probability calibration (temperature scaling) to ensure consistent thresholding for choice/relation primitives, along with ONNX/TensorRT quantization to hit sub-100ms inference times on local consumer CPUs.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one : 424 launches clear the similarity bar, closest 8 shown.

Attention rank: #347 of 425 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 321 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.