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Ansible TUI – a zero-dependency terminal UI for running playbooks

Hacker News

Ansible TUI – a zero-dependency terminal UI for running playbooks

I got tired of grepping YAML files to remember tag names and copy-pasting hostnames into --limit every time I ran a playbook. AWX/Semaphore solve this but require a database and a daemon. So I built a single-file TUI instead. Demo： https://raw.githubusercontent.com/congzhangzh/ansible-tui/re... It parses your inventory.yml and playbook.yml, gives you a split-pane selector for hosts and tasks, builds the ansible-playbook command live as you toggle checkboxes, then runs it with streaming output — all inside the terminal. The binary is ~100MB (Deno runtime embedded), but there's also a one-liner for Deno users that needs nothing local: deno run --allow-read --allow-run --allow-write --allow-env \ https://raw.githubusercontent.com/congzhangzh/ansible-tui/main/app.tsx Single file (~650 lines of TSX), MIT licensed. https://github.com/congzhangzh/ansible-tui

Visit productView on Hacker NewsFeb 26, 2026
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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: user, single, tasks · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
49%49% 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 · Strong signals: users · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
34%34% 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
20%20% 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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