TurboBench, the Compression Lie Detector, 100 Codecs, Daily Update

Hacker News

TurboBench: The Ultimate Data Compression Benchmark 100+ Codecs. The Powerhouse in a Single Executable — The Compression Lie Detector Tired of misleading benchmarks with I/O overhead, cache effects, and CPU throttling? TurboBench delivers high-precision, zero-overhead results you can actually trust. Why TurboBench Stands Out The only benchmark that includes LzTurbo alongside 100+ popular, latest, and fastest compressors (LZ77, Rolz, BWT, Context Mixing, Entropy Coders, and more). - 100% in-memory testing — no disk I/O to skew your results. Supports multiple files, recursive directories, and smart concatenation of small files into multiblock tests. Built to avoid common pitfalls like cache scenarios and throttling found in other tools. - Unlock peak performance — TurboBench reduces throttling by intelligently controlling the CPU frequency - Automatic sorting, updating, and merging of result files. A nightly github release is created automatically whenever submodules change. - Creates a separate result file (file@SET.tbb) for each compressor set - TurboBench rank aggregation crowns the top compression libraries with — see who's king!

Visit product View on Hacker NewsSep 16, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, para · Missing: reddit linkedin, podcasting, latex

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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