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DomainDash – uptime/SSL/domain checks for client sites

Hacker News

DomainDash – uptime/SSL/domain checks for client sites

I built DomainDash because I needed a service which checks uptime, SSL and domain expiration for the client sites that I run. Also, in my time as a contractor, I've worked for multiple companies who have had serious outages caused by an expired SSL cert that someone has forgotten about (one of them was a FTSE 50 and their ecom went down for like a day). I know there are a lot of existing products out there which do this already, but I wanted to have a go at it myself for a few reasons: - I wanted to learn about multi-region AWS and Lambdas - I thought I could put my own spin on it - the focus is plain English explanations, not pages of data - Like a lot of developers, I have a graveyard of side project domain names which don't do anything. So I really wanted to see if I could follow something through (I registered this domain in like 2022 when that FTSE 50 company had their outage and the idea properly came to fruition in my mind). The stack is quite interesting - it's a Laravel based web app which acts as the control plane. The checks are tiny Rust based Lambdas which are deployed to multiple AWS regions, giving it a global viewpoint. The Laravel app dispatches the Lambda via scheduled, queued jobs and the Lambdas report back via signed webhooks (which just receive the check data and pop it onto a queue for async processing). No cross-region DB connection issues or connection limits to deal with that way. It uses TimescaleDB and hypertables for the check results - each domain with 1min uptime and the other checks writes a few thousand rows per day, so it needs to handle this volume properly. It's early stage right now - 3 customers and it's run about 1.8m checks so far.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: tiny, plain · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, 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
51%51% 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
49%49% 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
27%27% 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
22%22% 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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