I Made a Ultra-Low Latency IP Geolocation API in Go

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Hey! I'm a solo developer having fun learning Go. I have a side hustle and wanted to implement currency localization. I could use existing IP databases, but hosting on serverless is slow. So I built my own. I was surprised how fun it can be to use Go, and it's fast. Thankfully, Go's speed makes up for my dodgy code. Anyway, I'd love to get some feedback on my website/API. After a few weeks of testing and losing money on ads, I'm confident it's very reliable. This isn't a promo. It's basicly free (30k lookups/month), so there's no need to pay. Any advice or tips on how to grow and what you would do differently?

Visit product View on Hacker NewsNov 28, 2024

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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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

69%69% 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: code · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

22%22% 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.

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