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Tinker with Meta's "tokenizer-free" patcher

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Tinker with Meta's "tokenizer-free" patcher

If the future is that we tend towards replacing current tokenisation, I wanted to build intuitions around one of the core contribution of Meta's Byte Latent Transformer: entropy-based patching. What are it's strengths and weaknesses? No better way of doing that than via tinkering with visualisations in a HF space so thought I'd share! A few things that emerge as a result that you can try yourself: 1. robustness - high entropy means more compute will get dedicated to those bytes which include cases like low resource languages, spelling tasks etc 2. compute efficiency 2a. low entropy means less compute spent for those bytes 2b. in-context learning applies to tokenisation! It induces low entropy regions later on in the sequence and has to waste less compute! I'm writing a blog post on an expanded version of this, updates via https://lucalp.dev or https://x.com/lucalp__

Visit productView on Hacker NewsMay 21, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: context, visual, tasks · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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