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Simstack, SSH escape room for developers

Hacker News

Simstack, SSH escape room for developers

I built a realistic prod system under heavy pressure where you SSH in and solve real problems. You get your own server (real box) and have to fix various things up under fire. a friend told me it reminded them of DOOM for SREs, another called it a "flight simulator" because you can do anything (and reboot if you crash). someone else said it was like "an escape room for engineers", in that you have to find your own way around an unfamiliar box and solve puzzles. idk, maybe it's fun. try it! how it works: you get two Hetzner servers: a traffic generator (real traffic from NYC taxi data highly compressed to 12k rps) firing tons of requests at a user server, which has some (realistically) broken stuff trying to get that data to a chart. your job is to get it working and get the whole system latency down. You can use whatever tools or techniques you like to do so. so far I've seen people messing around in databases, adding caching, rewriting services in go. There's no one right solution! why I'm building it: I was a school teacher 15 years ago and have been training developers for the last 10. most dev training (tutorials, toy projects &c) feels nothing like the "real job". I wanted to see if we can make training more realistic, challenging, fun.

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6points
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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
83%83% 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 HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
18%18% 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.

Incorrect prediction on native model

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