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Infra.new, an AI Agent for Terraform

Hacker News

Infra.new, an AI Agent for Terraform

Hey HN, we built infra.new, an AI agent for Terraform that configures your infrastructure for cost and quality. General-purpose AI tools like ChatGPT and Cursor are most helpful for coding when the cost of running the code and verifying the output is low. For example, if you’re making a landing page, it’s cheap and (relatively) low-risk to let an LLM edit the files and check if the outputs look good in the browser. Cloud configuration is different because mistakes can be costly and more difficult to recover from. With infra.new we’re attempting to make this experience better by catching issues before you deploy. Key Features: - Fresh Documentation: The agent pulls the latest documentation for Terraform, GitHub Actions, and other tools before every change, which reduces hallucinations and stale configuration options. - Real-Time Cost Calculator: You can see a breakdown of your cloud costs update as your configuration changes, so you know how much it will cost before you deploy. - Automated Error Detection & Fixes: A language server scans your configuration for issues and applies fixes automatically, reducing manual debugging. - Two-Way GitHub Sync: Import your existing configuration, ask the agent to look for optimizations, then export the changes back to GitHub. Team Features: - Private Modules: Connect your private Terraform Registries hosted on GitHub, SpaceLift or Terraform Cloud - Private Templates: Set up ready-to-use templates that can be customized with a few prompts. - Self-documenting: Export changelogs, system diagrams, and cost reports with a click. You can try it without signing up at infra.new I’d love to hear what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, new · Missing: mac, agents, macos
94%94% 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: mistakes · Missing: supports, reddit linkedin, podcasting
83%83% 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: exist, existing, io · Missing: https docs, excited, just released
46%46% 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 · Strong signals: host · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
31%31% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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