A Pokémon CLI/TUI Tool Written in Go

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Hello, all! I've been learning Go for around a year (off and on) and the best way for me to learn is to create a project. So, I've been building a Pokémon CLI/TUI tool where you can view data about different Pokémon details such as a Pokémon's base stats, typing, abilities, learnable moves; details about a specific ability, typing, or move; and a search feature to help you find a Pokémon, ability or move. I'm sure some of this code is not great but it works for now and with every minor version, I take steps to look through the code and see where optimizations can be made. There may be crappy code pieces that I just can't see due to lack of experience. I decided against using a CLI library like Cobra/Kong because I wanted to stick to the std lib as much as possible to not bloat the application. Using the flag package as been just fine. Although, I have been debating on using the spf13/pflag library to enforce POSIX/GNU style flags. Thanks for letting me share!

Visit product View on Hacker NewsJun 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · 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.

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

8%8% 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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