KronLSTM
Flash-linear-attention compatible KronLSTM model and kernels by NXAI.
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GITHUB · launched Oct 19, 2026 · 47 upvotes (top 29%)
- Horizontal
- Model & infra
- Developer
- AI-native
- Hobby / open-source project
Similar launches
See all 2198 similar launches →Could you build this?
No This is a novel neural architecture (KronLSTM) with custom kernels for efficient training and inference. Building it requires ML research expertise and GPU kernel engineering, which vibe coding cannot substitute for.
What it would actually take: Reproducing it means implementing the Kronecker-structured LSTM math and writing fused, hardware-efficient kernels (typically Triton or CUDA) compatible with the flash-linear-attention framework. Validation needs training runs at meaningful scale to match reported benchmarks. This takes ML researchers and kernel engineers with GPU infrastructure.
Competitors
Other products that read as similar to this one : 2198 launches clear the similarity bar, closest 8 shown.
Attention rank: #653 of 2199 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 354 days after the earliest competitor.
- DeepSelect · github · 2026-09-09 · 327 upvotes · similarity 0.65
- FlashMask · github · 2026-10-14 · 35 upvotes · similarity 0.59
- glance-speedlab · github · 2026-09-23 · 48 upvotes · similarity 0.56
- Open-source AMDGCN kernels for optimizing LLM inference · hn · 2026-08-25 · 5 upvotes · similarity 0.56
- DASA · github · 2026-10-13 · 29 upvotes · similarity 0.55
- LLM Attention Visualization · hn · 2026-09-08 · 173 upvotes · similarity 0.55
- flat · github · 2026-10-12 · 19 upvotes · similarity 0.54
- LoRA gradients on Apple's Neural Engine at 2.8W · hn · 2026-03-06 · 6 upvotes · similarity 0.54
Other launches for this product
- No other launches for this product.
More details
- External ID
- 1425757357
- Company
- n/a
- Product
- KronLSTM
- Website domain
- github.com
- Cohort
- n/a
- Upvotes percentile
- 0.71
- All tags
- n/a
- AI summary
- kronlstm model and kernels for flash-linear-attention
- Manually corrected
- No