JProx – Japan residential proxy API for scraping Japanese sites

Hacker News

I built JProx to solve a specific problem: scraping Japanese sites (Mercari, Rakuten, SUUMO) that aggressively block foreign IPs and datacenter proxies. Features: - Japanese residential IPs (Tokyo) - Simple REST API with Claude MCP support - 1,000 free requests/month - $7/mo for 5,000 requests Built with: FastAPI, Next.js, PostgreSQL I'm a solo developer in Japan. Would love feedback on the pricing and API design.

Visit product View on Hacker NewsJan 31, 2026

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

74%74% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000 · Missing: https docs, excited, just released

45%45% 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 · Strong signals: month · Missing: mobile apps, ios, personal

36%36% 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

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

27%27% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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