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I collect 136 remote design jobs at GitHub, Chili Piper, Mondo, others

Hacker News

I collect 136 remote design jobs at GitHub, Chili Piper, Mondo, others

Hey HN! During the period from January 13 to 19, I collected 136 jobs from popular job boards like Glassdoor, Flex Jobs, We Work Remotely, and others, as well as from Facebook, Twitter, and Reddit. It takes about 3 hours a day to visit 78 sources that I prepared in advance. Here they are! UX/UI Design Jobs – https://meerkad.com/remote-ux-ui-design-jobs Product Design Jobs – https://meerkad.com/remote-product-design-jobs Graphic Design Jobs – https://meerkad.com/remote-graphic-design-jobs All Design Jobs - https://meerkad.com/remote-design-jobs ### Pros Let's be honest, standard job boards - sucks! Look at the main differences between this list and those job boards that you could see: - On average, five job openings for designers per week are published on each job board. I collect them manually and publish hundreds of vacancies during the same time in one place; - No need to view all the job boards, I already did it for you; - No need to register on all job boards; - Job boards refer to each other. I collect direct links to the CV submission page.

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Actual performance

4points
2comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
60%60% 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 · Missing: mobile apps, ios, personal
41%41% 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
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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