Rectfillcurve – generate rectangle-filling curves

Hacker News

How do you visit every coordinate in an NxM grid once? The easiest is to process line-by-line, from the first to last column, but if you want better caching you might try alternating the column direction for each row. The Morton/Z-order and Hilbert order give even better cache coherency for some tasks, although the classic versions only work on squares with power-of-two length sides. Luckily for me, people have developed generalized versions of those algorithms which can handle arbitrary-sized rectangles. I've taken those and packaged all of the those curves into "rectfillcurve", with an iterator API for generating those curves, and a bonus "mlcg curve" with a pseudo-random visit order that should have poor cache behavior. Implemented in stand-alone C, and also available as a Python module.

Visit product View on Hacker NewsJul 17, 2023

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

77%77% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.

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

42%42% 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

32%32% 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

16%16% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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