Terrabook – Replit for Infrastructure

Hacker News

Hi HN, one of the founders here. We were thinking hard on what would lower the entry barrier / smoothen the learning curve for infrastructure. It's got to be easy to build so something visual probably; and it's got to be "real" so perhaps some kind of infra-as-code underneath. We experimented a lot with the low-code angle (the most recent OpsFlow which we got tons of feedback on from HN - thank you!!) And then it hit us. With python, there's a huge difference in ease of use between a Jupyter notebook and locally run python code, even though the code is the same. So we don't even need to hide the code, as long as it is easy to write. Replit demonstrates the same concept even more vividly; but we thought we'd borrow the blocks idea from Jupyter / Google Colab. This is how Terrabook came to be - just blocks of Terraform really. The key thing is that they are pre-filled, and you can write whatever you want. a. Mix and match pre built terraform. b. Modify pre built blocks to create your own custom terraform c. Deploy to AWS in one click. We also added templates for frontend, backend & databases - so you just click a button and get a working piece. HN has been a constant source of constructive feedback for us, and we would be extremely grateful for insight on how you feel TerraBook is. Feel free to drop your harshest feedback in the comments below!

Visit product View on Hacker NewsJul 22, 2022

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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: google, visual, code · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

67%67% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% 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

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