From GUI Honeymoon to CLI Power: Choosing a FOSS AI Workflow

Setting the Stage: Context for the Curious Book Reader

This entry captures a pivotal moment in a developer’s journey away from mainstream, GUI-centric AI coding tools towards a more sustainable and powerful command-line interface (CLI) workflow. The author, working on their project Pipulate, navigates the burnout of constant development and the allure of popular tools like Cursor AI, which leverages large language models from providers like Anthropic. This exploration is set against a backdrop of a rapidly commoditizing “Intelligence as a Service” market, where developers must weigh the benefits of proprietary, venture-backed tools against the long-term stability and flexibility of free and open-source software (FOSS).

The core of this deep dive demonstrates a practical solution for staying on the cutting edge of a rapidly evolving tool—the Gemini CLI—within the unique constraints of NixOS, an immutable Linux distribution. This isn’t just about using an AI assistant; it’s about reclaiming control, aligning one’s toolset with a personal development philosophy, and building a workflow that respects muscle memory and fosters a state of flow, free from the context-switching of mouse-driven interfaces.

The Burnout, the Sprint, and the Search for a Payoff

I can’t keep pushing steadily day after day, week after week and month after month on Pipulate — on top of the day-to-day client work to pay the bills. I have to give myself intermittent rests. And so I did through the month of August. I’m back on a roll but I can feel the need to pace myself. I also feel the need to behave like we’re in a sprint and not a marathon because we have to use the motivation while we have it and we have to get stuff out there when the timing is right. The timing is right. Things should feel more like cutting catapult ropes to release all the energy that I built up within the system rather than feeling like pouring more and more energy into it. I’ve got to get to the payoff.

The Pendulum Swings Back: Rejecting the VSCode Bandwagon

Well, the payoff is more easily visualized now. I had to force change in my approach. While I went through that same nutty honeymoon phase with AI agentic that it seems like everyone else is too based on the YouTube videos and even hopped onto the VSCode bandwagon in the form of Cursor AI in order to not miss the fun, the pendulum is swinging back to my kind of vibe. People with my vibe were left out in the cold, but this CLI-mode movement is righting the situation.

A Tale of Two Emails: The Breakup with Cursor AI

After I cancelled my Cursor subscription having proven myself as exactly the kind of customer they want — starting out on the $20/mo subscription and then 10x’ing my spend, I pulled back and then cancelled my subscription. Days later I received this email:

--------- Forwarded message ---------
From: Cursor <hi@cursor.com>
Date: Tue, Sep 9, 2025 at 8:09 PM
Subject: Canceled subscription
To: <********************>


Hi Mike,

We noticed you recently canceled your subscription and wanted to check in. If we
fell short for you, we want to learn why and make it right.

Could you share a sentence or two on what you disliked about Cursor? Or perhaps
a screenshot of where it performed poorly? This will help us improve the product
for future users.

We'd be grateful to understand your candid thoughts. Wishing you the best in any
case!

Best,
Cursor Team


If you don't want to hear from us again, please let us know.

…to which I simply answered:

https://mikelev.in/

…if they want to see the reasons, I have documented it copiously. Perhaps coincidentally 3 days later I receive:

--------- Forwarded message ---------
From: Michael from Cursor <********************>
Date: Fri, Sep 12, 2025 at 9:52 PM
Subject: Now shipped: Cursor CLI
To: <******************>


Hi,

You can now use Cursor Agent from the CLI or headless in any environment.

curl https://cursor.com/install -fsS | bash

If you're using Neovim, JetBrains, or other IDEs, you can now use the full power
of Cursor Agent alongside your favorite tools.

The CLI works with any model as part of your Cursor subscription. You can now
choose to use Cursor agent in the editor, or have multiple agents run in
parallel in the terminal or remotely.

Cursor CLI interface
What Cursor CLI can do:
Choose from any top AI model or use Auto for speed and efficiency

Work with the same rules and context as the Cursor IDE

Connect external tools via MCP (Model Context Protocol)

Run headless in scripts to automate code reviews, generate docs, and more

Run it anywhere: GitHub Actions, scripts, other IDEs, or any CI system

Try the CLI and let us know what features we should add.

Cursor's Logo
Cursor,
The AI code editor

Unsubscribe

Intelligence as a Service: Navigating a Commoditized Market

So intelligence has become a service. Too bad the “I” is already taken by Infrastructure as a Service (IaaS).

Now I’m not going to deny that the training details matter. Scaling is not all you need. Throwing more GPUs and more training material isn’t all it takes especially now with agentic mode where these things could really rock your world by going off half-cocked and willy nilly. But all the weirdness is going to settle down over time and any remaining disparity between Anthropic Claude and Google Gemini will dissolve away until intelligence as a service is a commodity. It probably is at that point already and I’m experimenting with that trying to break my Claude dependency with the Google 1000-prompts per day free.

I wasn’t about to switch from one insult to muscle memory, VSCode/Cursor, to another: Google’s Project IDX and their answer to VSCode. And no matter how much people were going gaga over Claude Code, after the subtle Cursor price hikes which was really passing on Anthropic costs in disguise I wasn’t about to switch to a command-line tool that was closed-source and couldn’t be wired to anything but Claude (named Claude Code as it is). Along comes OpenAI Codex with better licensing and I get to thinking. I’m going to have to try the CLI code assistants sooner or later but it was such a gargantuan readjustment for me to swallow the VSCode/Cursor pill.

Why Open Source Matters: The Gemini CLI Tipping Point

Then along comes Google pressing all the right buttons. Let me clarify that by this time and which you can more than confirm by going back over my tech journal, I’m a big fan of Google Gemini for coding. In fact even with Anthropic Claude as my main day-to-day workhorse coding assistant workhorse, the implementation plans that Claude was following almost all came from Gemini. I have the prompt_foo system built in Pipualte to show how I build XML payloads of my entire codebase so that I can do them in a single webform submit from the Web UI for Gemini Pro which I get as part of my GoogleOne subscription. And to this date is is the only model that lets me do that — payloads 130K tokens as measured by tiktoken. And the quality back was stunning. Gemini clearly knew how to code and I was only not using it in Cursor because I’d have to give it an API key and pay piecemeal when I felt I was already paying through GoogleOne and could use this roundabout method. Gemini for implementation plans through the Web UI and Claude for implementation through Cursor AI. It was golden… for awhile.

Now I know Google’s going to lower the quota on me sooner or later, hike the rates and try cooking the frog just like Cursor/Anthropic did. But like it or not as the global infrastructure juggernaut that Google is they can afford to give away a lot more for free to get you hooked, and that doesn’t bode well for companies that don’t have their already-built global datacenter infrastructures they can leverage to price-dump with as this stuff becomes more commoditized. Sorry, Anthropic. If it’s any consolation, OpenAI is in the same boat although they have a clever plan to get US taxpayers to pay for their Stargate datacenter. Either that or the UAE. But until that’s built out, OpenAI is in the same boat as Anthropic — can’t price-dump AIaaS ‘cause they’re not Google, Microsoft, Amazon or… drumroll please… xAI! Elon Musk paid his way into the global-scale datacenter game with their TWO colossal Colossus datacenters near Memphis Tennessee and indeed Grok has been free in Cursor AI for awhile now trying to lure you into that same addiction dependency.

Alright so obviously Google’s trolling for customers with the same first dose free with Gemini CLI intending to cut you off or make you pay just like everyone else. They’re legally obliged to do that sort of crap by virtue of being a publicly traded company answerable to shareholders. But after the CLI AI assistant tension has been mounting for months and I’ve been chomping at the bit to make the switch back and nothing has tempted me sufficiently, along comes Google with Gemini CLI and it being Apache 2 licensed! Now while it’s true it’s named Gemini CLI just like Claude Code is similarly named, the whole friggin codebase being in GitHub with the right license to fork makes a heck of a lot of difference. This means Google has no choice if people want to connect forked versions of Gemini CLI to other models. And indeed we already see a plugin community popping up to create new /command plugins for Gemini CLI. This pushed me over the edge. I’m now on Gemini CLI.

Let me demonstrate to you it in action. I’m going to post this entire article to Gemini CLI while I have my NixOS configuration.nix file accessible to it. For those following along who happen to be in super-geek mode, here’s the background.

Gemini CLI like so many things on the bleeding edge is being updated almost nightly, so there’s always a new version. However NixOS is a version of Linux that they say is immutable. In other words you can’t normally just install things onto it. You have to put it in the configuration.nix file so that when you build your system the Nix domain-specific language (DSL) for building systems does all the work. The advantages to this are so significant it will eventually kill Docker for all but the most extreme use cases where containerization is actually an advantage over declarative deterministic system builds. Trust me, it’s world-changing. The world just hasn’t caught up yet. However it does present some unique challenges on things that update nightly. From Gemini CLI’s GitHub page, the installations options are:

📦 Installation

Quick Install

Run instantly with npx

# Using npx (no installation required)
npx https://github.com/google-gemini/gemini-cli

Install globally with npm

npm install -g @google/gemini-cli

Install globally with Homebrew (macOS/Linux)

brew install gemini-cli

…but interestingly, it’s already in the Nix software repository so I could just put this in my configuration.nix:

environment.systemPackages = [
  pkgs.gemini-cli
];

…but it wouldn’t give me the nightly build. But the fact that this is already in the Nix repository speaks volumes. Anything that isn’t free and open source, or at least open source and available on GitHub wouldn’t be. Gemini CLI is FOSS with an excellent Apache 2 license, and so there it is! That is already quite amazing in its own right. But here’s the thing. I want the nightly builds.

A Practical Demonstration: Taming Nightly Builds on NixOS

Gemini is not only describing what is going on in my Nix configuration file to get the Gemini CLI nightly build, it actually did the work to make it that way though this instance doesn’t know that and is just describing what it sees.

Me: Gemini CLI, look at my configuration.nix and explain to the nice people what’s going on.

Gemini CLI:

╭────────────────────────────────╮
│ ✓  ReadFile configuration.nix  │
╰────────────────────────────────╯

Of course. I’ve reviewed your configuration.nix file. It’s quite a comprehensive setup.

Here’s the explanation of how you’re getting the nightly builds of gemini-cli:

You’ve cleverly sidestepped the issue of the gemini-cli in the main Nixpkgs repository being potentially outdated. Instead of directly installing pkgs.gemini-cli, you’ve created a custom shell script that acts as a wrapper.

This section is the key:

let
  gemini-cli-unstable = pkgs.writeShellScriptBin "gemini" ''
    #!${pkgs.stdenv.shell}
    echo "Running gemini-cli from nixpkgs/master..."
    exec ${pkgs.nix}/bin/nix run nixpkgs/master#gemini-cli -- "$@"
  '';

Here’s a breakdown for the nice people:

1. pkgs.writeShellScriptBin "gemini" ...: This Nix function creates a new script named gemini and places it in a location that your system’s PATH can find.
2. exec ${pkgs.nix}/bin/nix run nixpkgs/master#gemini-cli -- "$@": This is the important line.
   • Instead of running a locally installed version, it uses the nix run command.
   • nixpkgs/master#gemini-cli tells Nix to fetch the gemini-cli package directly from the latest master branch of the official Nixpkgs GitHub repository. This is how you get the “nightly” build.
   • – “$@” ensures that any arguments you pass to your local gemini command (like gemini –version) are forwarded to the gemini-cli executable that nix run executes.

Finally, by including gemini-cli-unstable in your environment.systemPackages, you make this wrapper script available globally.

So, every time you run the gemini command, you’re not running a version that was installed when you built your system. Instead, you’re telling Nix to go out, find the absolute latest version from the development branch, and run it on the fly. It’s a very effective way to use bleeding-edge tools on an otherwise stable and declarative system like NixOS.

Beyond Interactive Mode: Unleashing Headless and Agentic AI

Exactly! So if people are like “why FOSS” this is the answer. Now Cursor cleverly got around this with a pre-packaged AppImage. That’s a way for non-FOSS software to do this and there’s an example of that in my configuration.nix to that can be found documented elsewhere on my technical journal. But it’s better doing it the true FOSS way because it can’t be taken away from you.

The combination of being command-line based and FOSS means it’s safe to build your muscle memory up around. You can get into flow-state using it because it’s not point-and-click mouse-driven software like VSCode and other code editors modeled after the WYSIWYG Word Processor model. I don’t need my text editors to be like Microsoft Word where whenever you need to make an edit you have to take your hand away from the keyboard and knock yourself out of the zone re-orienting yourself where you are and what you’re doing switching between modes.

The vim text editor also has switching between modes. But instead of switching between point-and-click and typing mode, it’s always still typing mode. It’s just there’s one typing mode that’s for issuing commands and there’s another for editing. It takes awhile to get used to but once you do it’s nothing less than life changing. That’s why there’s a vim mode in almost everything. And no, having a vim mode in VSCode does not compensate. I spent almost a year trying to convince myself it could. It can’t. But once you’re good at vimming you can switch between vi, vim and nvim (NeoVim) with ease — three “levels” of the same basic core program, the first of which is part of the specification for Unix! It’s the ubiquitous text editor that’s always there. If you log into remote web servers like headless cloud servers, you’ll be right at home. It’s the bedrock of tech.

And so being in that same command-line interface (CLI) for your AI coding assistance has value. Clearly it does because even Cursor AI released their own Cursor CLI (the aforementioned email). But is Cursor CLI free and open source software (FOSS) and hosted at GitHub? No and no. So I have no buyer’s remorse going to Gemini CLI, not that I actually even bought anything. I’m spending less.

But now my mind goes to all the other advantages one should get from going to free and open source software for AI integration from the command-line. There should be all sorts of excellent tricks, the very least of which is simple prompt/response on the command line. But more advanced tricks would definitely include the sort of agentic tasks for which you might program something custom with LangChain. There’s running headlessly, GitHub Actions, CI and such.

But Does Gemini CLI Run Headlessly For Automation Scripts?

Hey Gemini, does Gemini CLI run headlessly? Answer in a way where I can use your response directly here as a continuation of this article. You can speak as yourself. And you can jump right into those code examples that assume we get past the authentication hurdle. Show me examples of what it looks like to call Gemini CLI in some other mode than the default interactive mode you get when you just type gemini. Show me an example of using it headlessly.

Gemini 2.5 Pro Web UI: Of course. It’s great to see you diving in and leveraging the command-line interface. You’re right—the real power of a CLI tool unlocks when you move beyond the interactive loop and integrate it into scripts and automated workflows. It’s built for exactly the kind of headless operation you’re thinking about.

Here’s a response you can use to continue your article, explaining how to use the Gemini CLI headlessly.

Yes, absolutely. The Gemini CLI is designed from the ground up to run headlessly. As a command-line tool, its natural habitat is the terminal, which means it excels in non-interactive scenarios perfect for scripting, automation, and integrating with your other favorite tools.

When you just type gemini and hit enter, you’re starting an interactive chat session. But to run it headlessly, you simply provide the prompt directly as an argument. The tool will process the request, print the response directly to standard output, and then exit. This makes it incredibly easy to pipe data in and out of it, just like any other well-behaved command-line utility.

Let’s look at some examples of what this looks like in practice.

Basic Non-Interactive Prompts

This is the most direct way to use the CLI headlessly. You simply follow the command with your prompt, enclosed in quotes. It’s perfect for quick questions or simple scripting tasks.

Syntax: gemini "Your prompt here"

Example: Let’s ask for a git command.

gemini "What is the git command to see a log of commits with their changes?"

Expected Output:

The command you're looking for is `git log -p`. The `-p` or `--patch` flag generates patch-like output showing the differences introduced in each commit.

Piping Content for Headless Use

This is where things get really powerful. You can use the standard Unix pipe (|) to send the output of one command to the Gemini CLI as its input. This is the cornerstone of headless automation.

Example 1: Summarizing a File

Imagine you have a file called README.md and you want a quick summary without opening it. You can use cat to print the file’s contents and pipe it directly to gemini.

Command:

cat README.md | gemini "Summarize this for me in one sentence."

Expected Output:

This project is a command-line interface tool that allows users to interact with Google's Gemini models for tasks like code generation, summarization, and general Q&A.

Example 2: Explaining a Piece of Code

You come across a complex script and want to understand what it does. Let’s say you have a file process_data.sh.

Command:

cat process_data.sh | gemini "Explain what this shell script does."

Expected Output:

This shell script appears to be an data processing pipeline. It first downloads a dataset using `curl`, then unzips it, processes the contents with an `awk` script to extract specific columns, and finally saves the cleaned data to a new CSV file.

Integrating with Other Developer Tools

You can seamlessly blend the Gemini CLI into your development workflow. For instance, you could ask it to generate code and immediately save it to a file.

Example: Creating a Python script from a prompt

Here, we’ll ask the CLI to write a simple Python web server and use a redirection (>) to save the output directly into a file named server.py.

Command:

gemini "Write a simple Python web server using the http.server module that serves files from the current directory on port 8000." > server.py

After running this command, you won’t see any output in your terminal. But if you check your directory, you’ll have a new, ready-to-run file:

server.py contents:

import http.server
import socketserver

PORT = 8000

Handler = http.server.SimpleHTTPRequestHandler

with socketserver.TCPServer(("", PORT), Handler) as httpd:
    print("serving at port", PORT)
    httpd.serve_forever()

These examples just scratch the surface. By treating intelligence as another command-line tool, you can integrate it into CI/CD pipelines for automated code reviews, use it in Git hooks to generate commit messages, or build complex scripts that leverage AI for processing text, code, and more. This is the FOSS, CLI-first approach you’ve been talking about in action.

CLI-based AI Assistants Aren’t Just Interactive Terminals

Me: Pshwew, this is a relief. I sort of assumed all that because coming out with something called a CLI tool for AI could not possibly just be the interactive session that launches when you just type the default command, which in this case is just gemini. The implication is you’re going to be able to do all the stupid data tricks, Unix piping, automated script integration and headless stuff that this implies, and this is excellent validation. I still have some questions about how to explicitly control agentic mode because in the interactive sessions you give it progressive permissions to do edits once or always and the like. So there’s got to be some way to manage multi-step long-running agentic tasks even in automated scripts. I (finally) jumped on the AI CLI bandwagon when the licensing, pricing and AI quality was all in line. But now I need to start doing my exploration beyond the interactive interface.

Gemini 2.5 Pro Web UI: You’re hitting on the exact next step and the most critical question for turning this from a cool assistant into a powerful automation engine. How do you handle agentic tasks that require permissions and multiple steps in a headless script?

The interactive prompts ([Y]es, once, [A]lways, [N]o, [V]iew) are a safety mechanism for interactive use, but they would completely halt any automated script.

The Gemini CLI solves this with a command-line flag.

Explicitly Controlling Agentic Mode with --auto

To pre-approve the tool’s actions and run it in a fully non-interactive agentic mode, you use the --auto flag (or its shorthand -a).

When you add --auto to your command, you are essentially telling the Gemini CLI: “I trust you. For this specific command, you have permission to perform any necessary actions like reading files, writing files, or executing shell commands without asking me first.”

This is the key to unlocking multi-step tasks in automated scripts.

Example: Headless Code Refactoring

Let’s take a practical example. Imagine you have a simple Python script called greeting.py that you want to refactor.

greeting.py (Before):

# A simple script to greet a user.
import sys

if len(sys.argv) > 1:
    name = sys.argv[1]
else:
    name = "World"

print(f"Hello, {name}!")

Now, you want to write a script that automatically refactors this to use a function. In a headless environment (like a GitHub Action or a deployment script), you can’t manually approve the file edit. This is where --auto comes in.

The Command:

You give it a clear, agentic prompt, provide the file as context, and add the --auto flag to grant it permission to write the changes.

gemini "Refactor this code to use a main() function that handles the logic." greeting.py --auto

What Happens Headlessly:

1. Gemini CLI starts.
2. It reads the content of greeting.py because it was provided as an argument.
3. The model formulates a plan: “I need to create a main function, move the logic inside it, and call that function.”
4. It identifies that this plan requires modifying the greeting.py file.
5. Because the --auto flag is present, it skips the interactive permission prompt and proceeds directly with the file modification.
6. The CLI overwrites greeting.py with the refactored code and exits.

greeting.py (After):

# A simple script to greet a user.
import sys

def main():
    """
    Gets a name from command-line arguments and prints a greeting.
    """
    if len(sys.argv) > 1:
        name = sys.argv[1]
    else:
        name = "World"
    
    print(f"Hello, {name}!")

if __name__ == "__main__":
    main()

Managing Multi-Step, Long-Running Tasks

For more complex, multi-step tasks, you’ll typically use an external script (like a Bash or Python script) to orchestrate multiple calls to the Gemini CLI. The CLI command itself is stateless—it executes and then exits. Your script provides the state and the logic.

Imagine a task: “Create a new project, add a boilerplate file, and install dependencies.”

Your orchestration script (setup_project.sh) might look something like this:

#!/bin/bash

PROJECT_NAME="my-new-app"
echo "Starting project setup for $PROJECT_NAME..."

# Step 1: Ask Gemini to create the directory and a main file.
gemini "Create a directory named $PROJECT_NAME and inside it, create a Python file named app.py with a simple Flask 'Hello, World!' app." --auto

# Check if the file was created before proceeding.
if [ -f "$PROJECT_NAME/app.py" ]; then
    echo "Project structure created."

    # Step 2: Ask Gemini to create a requirements.txt file.
    gemini "In the $PROJECT_NAME directory, create a requirements.txt file listing Flask as a dependency." --auto

    echo "Requirements file created. Project setup complete."
else
    echo "Project setup failed."
    exit 1
fi

In this workflow, you’re not running one long-running agentic task. Instead, you’re chaining together a series of discrete, headless agentic commands, using the script’s logic to guide the overall process. This gives you both the power of agentic AI and the robust control of scripting.

Book Analysis

This detailed analysis transforms the raw technical journal entry into structured material ready for a book, focusing on extracting core insights, identifying narrative threads, and preparing the content for discoverability.

Packaging the Insight: Book-Worthy Titles & SEO-Friendly Filenames

• Title Option 1: From GUI Honeymoon to CLI Power: Why I Chose an Open-Source AI Workflow
  • Filename: gui-to-cli-ai-workflow.md
  • Rationale: This title captures the central narrative arc of the entry—a journey of disillusionment and rediscovery. It appeals to developers contemplating a similar switch and uses keywords like “GUI,” “CLI,” and “AI workflow.”
• Title Option 2: Taming Bleeding-Edge AI: Running Nightly Gemini CLI Builds on NixOS
  • Filename: gemini-cli-nixos-nightly-builds.md
  • Rationale: This is a highly technical, problem-solution title that targets a niche but dedicated audience. It promises a specific, valuable technical solution for NixOS users and those interested in advanced AI tool management.
• Title Option 3: The Commoditization of AI: A Developer’s Playbook for Avoiding Vendor Lock-In
  • Filename: ai-commoditization-vendor-lock-in.md
  • Rationale: This title elevates the entry’s themes to a strategic level, appealing to team leads, senior developers, and those interested in the tech industry’s economics. It frames the personal choice as a universally applicable strategy.
• Title Option 4: Why FOSS and the Command Line Are Winning the AI Assistant War
  • Filename: foss-cli-ai-assistant.md
  • Rationale: A bold, opinionated title that hooks the reader with a strong thesis. It’s perfect for a chapter aiming to persuade and provoke discussion about the future of development tools.
• Preferred Option:
  • Title (plain text for YAML): From GUI Honeymoon to CLI Power: Choosing a FOSS AI Workflow
  • Filename: gui-to-cli-foss-ai-workflow.md
  • Rationale: This title is the strongest because it balances the personal journey (“GUI Honeymoon”) with the powerful, repeatable lesson (“CLI Power,” “FOSS AI Workflow”). It has broad appeal, clearly states the topic, and contains the most relevant keywords for both general and technical searches.

From Raw Material to Book Chapter: Potential & Polish

• Core Strengths (as Book Material):
  • Authentic Journey: It provides a transparent, first-person account of a developer’s evolving relationship with AI tools, capturing the frustration, analysis, and eventual solution.
  • Strategic Insight: The entry connects a personal tooling choice to broader industry trends like AI commoditization, vendor lock-in, and the economic power of infrastructure giants.
  • Practical, Niche Solution: It contains a highly specific and clever technical solution (running nightly builds on NixOS via a wrapper script) that is valuable and hard to find elsewhere.
  • Strong Philosophical Stance: It champions the enduring value of CLI, FOSS, and keyboard-centric workflows (vim) in an era dominated by GUI-based tools, offering a compelling counter-narrative.
• Leveraging for a Broader Audience (Constructive Polish):
  • Add a “Key Players” Box: Early in the chapter, include a small glossary or table briefly defining the key entities (Cursor, Anthropic, Gemini, NixOS, FOSS) for readers unfamiliar with the landscape.
  • Visualize the NixOS Solution: Add a simple diagram illustrating the flow: gemini command -> wrapper script -> nix run nixpkgs/master#gemini-cli. This would make the core technical solution instantly understandable.
  • Expand on the “Why”: Elaborate on the “insult to muscle memory” concept. Dedicate a paragraph to explaining the cognitive cost of switching between keyboard-driven and mouse-driven interfaces, making the argument for vim/CLI more concrete for those who haven’t experienced it.

The AI’s Take: Editorial Insights & Transformative Potential

This entry is a fantastic case study for a chapter on “Sustainable AI Tooling and Development Philosophy.” It transcends a simple “how-to” guide and becomes a narrative about technological sovereignty. The author isn’t just choosing a tool; they are architecting a workflow that aligns with deep-seated principles of openness, efficiency, and long-term viability. The interaction documented—using the Gemini CLI to explain its own installation on NixOS—is a powerful, meta-narrative that perfectly illustrates the tool’s utility and the author’s point.

The raw, journal-like nature of the entry is its greatest strength. When curated for a book, it will provide an authentic “ride-along” experience. Readers get to witness the entire thought process: the personal motivation (burnout), the market analysis (AI commoditization), the technical problem (nightly builds on NixOS), and the elegant solution. This is far more compelling than a dry, after-the-fact tutorial. The dynamic between the author’s exasperation with closed systems and their excitement for open, flexible tools forms a natural narrative tension that can anchor a discussion on the future of developer work in the age of AI.

Prompting the Next Stage: Ideas for Further AI Refinement

1. Task Suggestion 1: Generate a “Lessons Learned” summary.
   • Potential Prompt Snippet for Next AI: “Based on the provided journal entry, analyze the author’s journey and distill it into a bulleted list of 3-5 key takeaways for developers choosing an AI coding assistant. Focus on principles like vendor lock-in, open source, and workflow sustainability.”
2. Task Suggestion 2: Create an explanatory analogy.
   • Potential Prompt Snippet for Next AI: “Review the section on using a wrapper script to run nightly builds of Gemini CLI on NixOS. Develop a simple, non-technical analogy to explain this concept to a less technical reader. For example, compare it to ‘subscribing to a magazine’s daily online articles instead of buying the monthly print version from a newsstand.’”