How many seasons does Melbourne (VIC) have?

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I live in Melbourne (Vic, Aus), and I've always felt like the standard 4 seasons have never applied well. A chat with my partner inspired me to actually try and work out if there was a better way This is not as tied to local flora and fauna as the pre-colonial calendars, and isn't supposed to be a replacement for them or anything else. Just an exploration of the data, with an interactive visualisation for exploring different breakdown and trends There is a page explaining the whole methodology, and I would love to get feedback on both the approach and the results https://seasons.lotlsoft.com/ https://seasons.lotlsoft.com/methodology

Visit product View on Hacker NewsMar 31, 2026

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

65%65% 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

54%54% 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.

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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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