Si

Simple Embedded Audio Codec (sea-codec)

Hacker News

Simple Embedded Audio Codec (sea-codec)

SEA is a low-complexity, lossy audio codec designed for embedded devices, inspired by the awesome QOA codec. Like QOA, SEA utilizes the Least Mean Squares Filter (LMS) algorithm, but it introduces variable bitrate (VBR) support and features slightly modified quantization tables. The reference implementation is written in Rust, and a compact ~250-line decoder written in C is also available for demonstration purposes. You can test SEA in your browser here: https://daninet.github.io/sea-codec/

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% 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: code · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio, introduce · Missing: web3, chat, crypto
19%19% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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