Defeating AI by making knowledge accessible to Humans

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PeakSlab is a libre pwa offline-first dictionary platform from scratch in under 128kb. The core wasm file of the app is written in C and is 38kb compiled which includes the ZSTD decoder and the custom dictionary format file loader and searcher. I started writing this because I live in Cambodia and all the dictionary apps meet at least two of the following criteria: Slow, Online-only, Incomplete. So I started making .slob files for aard dictionary to help with learning the Khmer language. But aard only searches inside the headwords and there was no iOS port for my coworkers. I looked at other programs (like stardict), but they didn't seem to have any releases in ages and were super buggy. So a couple years ago I made a SQLite3 based web app so that it'd be easy to share with other people. But I couldn't help feeling like maybe SQLite wasn't the right solution. (SQLite databases aren't compressed, and the runtime for SQLite is 1.3mb and I still had to hack it to get it to work for my dictionary, kinda slow and if I was to make more indexes the file size would balloon drastically. Also I hate editing database files) So a few months ago I wrote my own binary dictionary format and it's fast and tiny and has some really nice features. For the dictionaries I just write a tsv file and use my generator program to turn it into a .peak.zst file. I put together examples for a bunch of different languages, but Khmer is the most complete one. See the list at https://peakslab.org

Visit product View on Hacker NewsApr 27, 2026

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92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, tiny, code · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, month · Missing: mobile apps, personal, entrepreneurs

43%43% 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

18%18% 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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