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i — An experimental tensor DSL/compiler with explicit scheduling

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HN · launched Jun 2, 2026 · 5 upvotes (top 87%) · 0 views

Niche: programming language compilers · 77 launches, 2 in the last 7 days ▼ -60% · Explore this niche →

Category and tags
  • Horizontal
  • Dev tools
  • Developer
  • Not AI
  • Hobby / open-source project

Visit website ↗ Source listing ↗

More like this: all 77 launches in programming language compilers →

Could you build this?

No Building a domain-specific compiler with tensor IR representations, polyhedral loop transformations, and explicit GPU/hardware scheduling is a hardcore compiler engineering problem.

What it would actually take: The project requires an AST/intermediate representation (IR) supporting tensor operations, an explicit scheduling language (similar to Halide or TVM), loop tiling/vectorization algorithms, and backends lowering to LLVM or CUDA/SPIR-V. Implementing this demands deep expertise in compiler architecture, polyhedral compilation theory, GPU microarchitecture, and memory hierarchy optimization.

Discussion

1 comment analyzed.

Competitors mentioned: Triton, TVM, Halide

Concerns raised: Not fast yet, Proof-of-concept stage, Small ecosystem/Python front-end

Competitors

Other products that read as similar to this one : 3447 launches clear the similarity bar, closest 8 shown.

Attention rank: #2921 of 3448 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 214 days after the earliest competitor.

  • Iron · hn · 2026-05-27 · 8 upvotes · similarity 0.63
  • GitHub · hn · 2026-01-16 · 6 upvotes · similarity 0.62
  • KohakUnlock · github · 2026-10-25 · 366 upvotes · similarity 0.62
  • NeuralScript · hn · 2026-05-04 · 5 upvotes · similarity 0.61
  • latch · github · 2026-10-25 · 14 upvotes · similarity 0.60
  • Phobos · hn · 2026-07-07 · 11 upvotes · similarity 0.60
  • matrix-multiplication-all-fields · github · 2026-10-07 · 27 upvotes · similarity 0.60
  • solidcore · github · 2026-10-12 · 73 upvotes · similarity 0.59

Related data

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