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Use Natural Language to Run Terminal Commands (Ghost in the Shell)

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Use Natural Language to Run Terminal Commands (Ghost in the Shell)

Hi HN, I just released a very simple AI Shell / CLI tool that lets you convert natural language directly into executable shell commands. Built with python to simplify terminal workflows and help you quickly run commands without memorizing syntax. It has two main usage modes: One-shot mode: Quickly execute commands using natural language. Example: ai "find all PNG files larger than 5MB" The AI responds with the appropriate command and asks if you want to execute it. Interactive chat mode: Start a terminal chat session with the AI (ai -i) to iteratively build commands or scripts. Some common use-cases: * Quickly unzipping, untarring, or compressing files without Googling syntax. * Complex file searches and manipulations (e.g., filtering, renaming batches of files). * Managing Docker containers or Kubernetes deployments with plain English. *... It supports macOS, Linux, and Windows. It also integrates nicely with tools like tmux for split-pane workflows. I built this because I regularly found myself searching the web for simple commands I couldn’t remember. Feedback and feature ideas would be great! GitHub Repo: https://github.com/codingmoh/ghost_in_the_shell/ Install with pip using pip install ghost-in-the-shell Looking forward to your thoughts!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, dock · Missing: agents, agent, cursor
92%92% 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: supports · Missing: reddit linkedin, podcasting, created
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, ide, io · Missing: https docs, excited, exist
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 · Strong signals: host · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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, active · Missing: mrr, revenue, profit
14%14% 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.

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