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Advent of Distributed Systems

Hacker News

Advent of Distributed Systems

Hey! I built a playground called Advent of Distributed Systems ( https://aods.cryingpotato.com/ ) where you can work through the Fly.io distributed systems challenges ( https://fly.io/dist-sys/1/ ) directly in your browser. Running challenges like this directly in the browser has often been the best way for me to get the activation energy to start them since it bypasses all the annoying dev environment setup that has to happen as a precursor to working on it. The coding environment was built with another project I'm working on called Cannon ( https://cannon.cryingpotato.com/ ) that aims to let you embed codeblocks of any language in your browser. Right now the Go environment runs on a Modal backend using their sandbox, but I'm hoping to use the excellent work done on Hackpad ( https://github.com/hack-pad/hackpad/tree/main ) to run the whole thing in your browser, with no network calls necessary, soon. Let me know what you think - week 3 is coming out soon!

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
77%77% 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: cursor, using, coding · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: soon, calls · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% 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
17%17% 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
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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