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Vento – A lightweight CLI for file transfers with YAML-based automation

Hacker News

Vento – A lightweight CLI for file transfers with YAML-based automation

Hi HN, I've been working on a small but powerful CLI tool called *Vento*, inspired by tools like HULFT, but designed to be open-source, cross-platform, and developer-friendly. Vento is a lightweight Rust-based CLI that automates file transfers (SFTP/SCP for now), using simple *YAML profiles*. It supports: * Upload/download with SFTP or SCP (public key, ssh config, env auth) * Pre/post/error hooks with custom shell commands * Logging to file or stdout * File size limits for safety * Easily cron-able with external schedulers Example YAML: ```yaml - profileId: "daily-report" source: type: "local" path: "./report.csv" destination: type: "sftp" host: "example.com" authentication: method: "env_key" username: "user" envKeyRef: "SFTP_KEY_PATH" transferProtocol: protocol: "SFTP" postTransferCommand: "echo 'done!'" ``` It’s designed for devs and ops who want something dead simple to automate file delivery without complex infrastructure. GitHub: [ https://github.com/kyotalab/vento ]( https://github.com/kyotalab/vento ) v0.2.0 released now, with SCP and validation features. Happy to hear feedback, questions, or ideas! Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, open · Missing: mac, agents, macos
62%62% 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.
AppSumoStrong fit for a featured deal · Strong signals: platform, host, friendly · Missing: plus, intuitive, reviews
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.

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