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Togomak – declarative pipeline orchestrator based on HCL and Terraform

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Togomak – declarative pipeline orchestrator based on HCL and Terraform

Togomak is a command line tool that runs pipelines locally and on the cloud using HashiCorp Configuration Language with a Terraform-like architecture. I am a DevOps engineer and I wanted to build something that is CI/CD provider agnostic - something like GNU make, which I could plug into any provider of choice and not have to rewrite anything while retaining its rich features. I love Terraform and its ecosystem, so I guess I borrowed a huge chunk of their design. Togomak makes parts of your pipeline reusable, by making it a Togomak module, just like Terraform modules. It also gives you power over several HCL functions, loop expressions, templating, and customizing a pipeline according to user input. I started this project over a year ago, and over the course, I learned a lot about how Terraform works. Hopefully, some of our other Terraform friends would find this little tool useful! togomak is actually short for "to-go-and-make", and it sounds similar to "tokamak", a fusion device using magnetic fields to contain hot plasma for energy; perhaps it's containers here!

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55points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos
81%81% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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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