Every meeting from first 8 years of scaling up [video]

Hacker News

I had so much fun with this. Shows the change from early days of building Sanity.io through to growth (and the pretty intense 2020-2021 period). Calendar data processed in Python (filtering >3h events and Reclaim entries), then animated with Geometry Nodes in Blender. The 2K animation was rendered in Cycles on runpod.io (12 RTX 4090s for 48hrs). Colors indicate meeting types - blue for 1:1s, block size/saturation scaling with attendee count. Soundtrack driven by MIDI from exported from Python to Ableton Live.

Visit product View on Hacker NewsJan 16, 2025

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Actual performance

4points

2comments

Did not reach leaderboard

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

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

Incorrect prediction on native model

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