Practical typography résumé template recreated in LaTeX

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Most of the resume templates are so large and convoluted that it becomes a nightmare when you try to modify the formatting even slightly. I understand that it's required to cover many edge cases but it feels bloated to use a 800 lines long custom class for a one page résumé. I tried to create a template that uses the basic LaTeX commands, keeping it easy to follow and modify. In the Milky rover template I tried recreate the beautiful sample shown in Practical typography. Considering it's not using the same fonts, how close is it to the real thing?

Visit product View on Hacker NewsMar 24, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, latex · Missing: supports, reddit linkedin, podcasting

93%93% 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 NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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