I built a tool to alert you when your automated emails fail to send

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I built wasitsent.com to alert you when automated emails fail to send. It’s built for DevOps, Marketing Ops, and RevOps teams. I am an engineer-turned-manager with over 20 years of experience building and overseeing SaaS applications. This tool solves a pain point that previously required custom-solutions. [wasitsent.com]( http://wasitsent.com/ ) monitors your automated, transactional, and scheduled emails and notifies you when they break. I built [wasitsent.com]( http://wasitsent.com/ ) because recently, two of my customers complained that their emails stopped sending and it caused a major headache for their business. I've faced this problem myself multiple time in my career. It solves a problem that I and others have faced. These are true stories: - If you were ever yelled at because the weekly sales report did not generate on Friday, this tool will save your butt. - If you were ever in that awkward meeting where you had to explain why user notifications were not sending from the web application you are building, [wasitsent.com]( http://wasitsent.com/ ) has got you covered. - If Chris went on vacation and forgot to set up the next three emails in the DRIP campaign, you will find out quickly. (Names changed to protect those that went on vacation) - If one day your dev team told you that over the past few months there were 180,000 unsent e-commerce transaction-related emails, you can make sure that does not happen again. Uptime monitoring and APM (application performance monitoring) is important. Things will break. If you can notice and react quickly, your customers won’t suffer. There are many uptime monitoring tools, but nothing exists for doing end-to-end checks on emails. Emails can break for many reasons — bad code, bad config, expired credit card in your SMTP provider, someone changed your SPF record, misconfigured SMTP server, the dev API key snuck into production, misconfigured cron, and many others. You can monitor any of them. What was missing up ‘till now was an end-to-end email sending check. Well, now you have [wasitsent.com]( http://wasitsent.com/ ) to make sure your emails are going out. It’s still at an MVP stage and I am building feverishly. It’s already useful though, so give it a whirl. All feedback welcome here, or via email at krystian.cybulski@wasitsent.com. In particular, I am curious if the messaging on the homepage explains this service well.

Visit product View on Hacker NewsOct 16, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% 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.

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

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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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