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YANchor

YANchor-4B: Effective Long-Horizon Reasoning in O(N) Time with O(1) Memory. Inference and evaluation code.

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This is 1 of 143 launches in large language models — see how it stacks up on momentum and crowding →

2575 other launches read as similar to this one →

Details

External ID
1409138084
Source
GITHUB
Company
—
Product
YANchor
Website domain
github.com
Launched
Oct. 7, 2026
Cohort
—
Upvotes
155
Upvotes percentile
0.90
Tags
—

Enrichment

Niche
large language models
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
inference and evaluation code for reasoning models
Manually corrected
False

Could you build this?

No YANchor-4B is a novel machine learning research architecture for long-horizon reasoning operating in O(N) time and O(1) memory, requiring deep ML research and training expertise.

What it would actually take: Developing this requires novel mathematical formulation of recurrent or state-space memory mechanisms, custom CUDA kernels for hardware efficiency, and pre-training or fine-tuning a 4-billion parameter model on massive compute clusters. The hard part is the underlying algorithmic innovation that circumvents traditional KV cache scaling limits while maintaining reasoning fidelity. This demands PhD-level AI research scientists and high-performance GPU compute.

Competitors

Other products that read as similar to this one — 2575 launches clear the similarity bar, closest 8 shown.

Attention rank: #229 of 2576 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 342 days after the earliest competitor.

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