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TF-Whatsup – Highlight Relevant Terraform Release Notes

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TF-Whatsup – Highlight Relevant Terraform Release Notes

I started this tool because Terraform releases often and has great notes, but it's sometimes hard for me to see what's relevant during any particular update. This pulls all the notes from providers that it can find, and highlights notes for resources you use. It's early days, but I'd love feedback here or on the GitHub repo, especially if you have ideas for improvement that aren't already on the backlog. Side note on the tech: I wrote this as a .NET global tool and it's also published for Linux and Mac, including single-file binaries that don't require .NET itself to be installed. I slapped it together pretty quickly and it works for my purposes, but obviously I've got some cleanup to do to get it to my typical standards. PRs welcome!

Visit productView on Hacker NewsJan 13, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, single, notes · Missing: agents, macos, agent
78%78% 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: started, including · Missing: supports, reddit linkedin, podcasting
72%72% 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: ide, io, including · Missing: https docs, excited, just released
45%45% 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 · Missing: mobile apps, ios, personal
37%37% 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
33%33% 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
15%15% 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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