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toks

exact tokenizers ids, as fast as the hardware allows

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This is 1 of 225 launches in machine learning developer tools — see how it stacks up on momentum and crowding →

984 other launches read as similar to this one →

Details

External ID
1405607848
Source
GITHUB
Company
—
Product
toks
Website domain
actual.inc
Launched
Oct. 5, 2026
Cohort
—
Upvotes
93
Upvotes percentile
0.82
Tags
assembly, avx2, bpe, c, huggingface, neon, sentencepiece, simd, tokenizer, wordpiece

Enrichment

Niche
machine learning developer tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
fast hardware-accelerated tokenizer for ai models
Manually corrected
False

Could you build this?

No Achieving hardware-saturating tokenization speeds requires expert-level systems programming, low-level SIMD optimization, and cache-conscious memory layout engineering.

What it would actually take: This involves writing C++ or Rust using AVX-512/NEON SIMD intrinsics, custom trie/hash map data structures, and lock-free thread pooling to implement BPE and WordPiece algorithms matching HuggingFace tokenizers bit-for-bit. The barrier is extreme low-level profiling, cache-line alignment, and micro-architectural optimizations that surpass standard compilers.

Competitors

Other products that read as similar to this one — 984 launches clear the similarity bar, closest 8 shown.

Attention rank: #180 of 985 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 339 days after the earliest competitor.

Other launches for this product