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Ackal – a distributed gRPC health checking service

Hacker News

Ackal – a distributed gRPC health checking service

So I wrote a thing. It’s called Ackal (https://www.ack.al) and it’s a tool for deploying distributed gRPC health checks and monitoring the results using Prometheus-compatible tools. I’d really like to find 5-10 beta testers who are willing to test Ackal and provide feedback so that I can improve the system before launch. Any help will be really appreciated: reviewing the web content for errors and omissions; testing the system end-to-end; expectations for such a system etc. I'm interested to better understand how you are health checking your gRPC services. Testers need not have gRPC services deployed. However, if you want to test your own gRPC services, these must implement the gRPC Health Checking Protocol (https://github.com/grpc/grpc/blob/master/doc/health-checking.md) in order to be health-checkable by Ackal. Testers should be able to run OCI containers locally to access Ackal’s CLI (ackalctl) and a secure proxy (ackalctl-http-proxy) to access service (Prometheus) metrics. I’ve been using Linux for development but would like to have testers on Windows and macOS too. Testers will need a Google, Microsoft or GitHub account to authenticate. I’m interested to know whether folks would expect to authenticate using Facebook, Twitter or Apple credentials too. If you’re willing to help, please email info@ack.al. Please let me know whether you’re testing as a personal interest or on behalf of your employer. If, on behalf of your employer, please let me know who that is. I will register an account for you and provide you with the details that you’ll need to proceed. Until you are registered to use Ackal, you will be unable to use it. Ackal uses multiple Google Cloud services (Firebase Auth, Firestore, Cloud Run, Kubernetes Engine, kubebuilder etc.) and Stripe for subscriptions. If there’s interest, I’ll document the experience of developing the system, particularly the split between coding the app and developing everything necessary to support it (Stripe, website, branding, docs etc.).

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor
83%83% 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 · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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.
TrustMRRLess likely to generate early MRR · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
16%16% 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.

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