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NatChecker – free online NAT type detector (no login, one click)

Hacker News

NatChecker – free online NAT type detector (no login, one click)

Hi everyone, I built NatChecker – a free online tool that helps users easily check their network’s NAT type with one click, no login or setup required. Many gamers and developers who use peer-to-peer (P2P) networking need to know their NAT type — for example, when hosting multiplayer games or running PCDN (peer-to-peer content delivery). A good NAT type (like Full Cone / NAT1 / NAT2) makes it easier to connect or host, while stricter types (Symmetric / NAT3) can block connections. The tool runs entirely in your browser using WebRTC and public STUN servers, without collecting any data or requiring an account. My goal was to make NAT detection simple, private, and instant. Feedback and suggestions are very welcome — especially if you have ideas to make the detection more accurate or faster!

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Actual performance

4points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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, users · 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 · Strong signals: users · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos
24%24% 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
15%15% 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.

Incorrect prediction on native model

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