Tempmail3 – Instant disposable email service, zero signup, open-source

Hacker News

I built *Tempmail3*—a lightweight, disposable email service that works instantly and requires *no signup or verification*. *Why I built this* I often needed quick email addresses for testing or sign-ups—but existing services were slow, cluttered, or required entering my real email. So I built Tempmail3 to be as fast and frictionless as possible. *What it does* - Generate a temporary email with one click. - View incoming emails in real time via the browser. - Discard or regenerate the address anytime. - (Optional) This is an open-source project: [GitHub link]. *Technical details & challenges* - Back-end is in Go, using in-memory store for emails to guarantee speed. - Email retrieval via WebSocket for live updates. - Rate-limiting to prevent abuse, while keeping user experience seamless. - Lightweight CSS/UI for ultra-fast load times. *Differences from other services* - No ads, trackers, or cluttered UI. - Minimalistic, lightning-fast UX. - Open-source and privacy-focused—no logs retained more than 1 hour. - Hosted on low-tier infrastructure—demonstrates that even with limited resources, it stays responsive. I’d love feedback on: - UX or UI improvements—what would make me use this everyday? - Abuse prevention—any suggestions for better rate-limiting or abuse mitigation? - Feature ideas—e.g. custom domains, API, etc. Happy to walk through any part of the design or code if anyone is curious!

Visit product View on Hacker NewsAug 20, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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

74%74% 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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% 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.

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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