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WebhookHub – Instantly inspect and replay webhooks

Hacker News

WebhookHub – Instantly inspect and replay webhooks

Hi HN, I built WebhookHub because I kept running into the same pain whenever testing integrations: webhooks are hard to debug. They arrive at odd times, you need to expose a local server, or set up ngrok, and there’s no easy way to replay them. WebhookHub gives you an instant endpoint - no config or code needed. You can: Create a webhook URL with one click Inspect headers and payloads in a clean UI Replay requests to your local or staging server Share endpoints (public links) with your team I know there are other tools in this space, but my focus is speed and simplicity - a tool you can use in 10 seconds without installing anything. I’d love for you to try it and tell me what you think. Here’s the link again: https://webhook-hub.up.railway.app/

Visit productView on Hacker NewsAug 25, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: grok, code · Missing: mac, agents, macos
54%54% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% 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: io · Missing: https docs, excited, just released
42%42% 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: way · Missing: mobile apps, ios, personal
39%39% 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
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
19%19% 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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