Forem: alingse

How I Used Claude and Gemini to Build a Commercial Company Search Tool

alingse — Wed, 03 Sep 2025 14:55:12 +0000

A few weeks ago, a friend of a friend reached out to me asking for help finding logistics companies in Casablanca, Morocco. He wanted me to guide him through writing a Google Maps API program using AI. I suggested he download VSCode and install Cline, but he got stuck on not knowing how to use Python. In the end, I helped him complete the task. It only took a few minutes. He got a CSV with 60 entries, was very satisfied, and paid me 100 yuan for my time.

This made me realize there's a real market need for this kind of service. So I spent two weeks, 40 conversations, 356 instructions, and 110 commits to build it. And here it is: https://company.jsonutil.online

Website Features

The tool is based on Google Maps APIs. After getting a list of places using the Places API, we use another tool to extract email addresses from their websites (that's another story somday I will write). All this data is returned to users, who can download it in CSV or JSON format, including address, name, phone, website, and email. This allows B2B users to conduct targeted marketing for their valuable products.

Let me tell you how I built it:

Starting with that initial Python script that generated in just minutes
Building a user interface with index.html
Extracting core functionality from the Python script and creating server.py
Using SQLite for the database - simple is enough
Adding caching functionality - same search conditions always return the same data
Website email extraction - we integrated another tool's API (that's another story)
Working on introductions and SEO - having Gemini generate articles, guides, and intro pages
Adding i18n support
Some minor performance optimizations

My AI Conversations

Let's take a look at some of my AI conversations during development.

> Please fix the data export functionality.
> I need real-time log printing in each concurrent task.
> You did a great job, it's now taking shape. Can you check if email extraction is concurrent?
> It's not convenient to view on mobile. I want you to support mobile email display effects. Please adjust it. It's better to have fewer large images and large fonts, and provide the most core functionality directly.
> I found the current API is a bit slow. Not suitable for real-time user waiting. You need to add more information in the /stream endpoint.
> Okay, please commit the code.
> Very good, I also need you to clear the previous table data when users click on popular searches.
> You did a great job. But the table display is not very aesthetic now. We want to highlight email information. It's best to have one complete email per row without collapsing. I suggest you compress the table width for ratings and types.

( these conversations is record by ai-cli-log )

The familiar blue-purple theme - this is really AI-generated aesthetics, but we liked it and kept it.

First, you enter what you want to search for - yoga studios, hardware stores, swimming pools, restaurants, anything works. Then select a city - we chose Phoenix for this example. Click search.

The system provides a progress bar showing what the server is doing (yes, I had it implement SSE). First, it locates the city, then searches using the API, adds some details, and finally extracts emails from websites.

When complete, users get a perfect table like this:

And that's it!

Most Impressive Lessons from Development

If you ask me what was most impressive during development:

Don't optimize too early - MVP is MVP, if it works, it works. Although I hear people say AI-created tools are just a few Python files pieced together, I want to say: yes, that's exactly right.
Simple is better than complex - enough said.
Provide a local environment for AI validation - but AI often can't debug because it doesn't know the server's status, like when it was looking for nginx processes on my local machine.
Everything as code - put all configurations in the same repository (like nginx.conf it remove the buffer config for sse api response). It can better debug the full picture. I guess this is what they call context engineering.

Conclusion

That's all for now. I hope people will use it. If it proves useful, I'll add registration, login, task management, and history features. Of course, I ultimately hope to help users, and maybe some will even pay to use it.

What do you think? Have you built similar tools using AI assistants? I'd love to hear about your experiences in the comments!

Supercharge Your CLI Workflow: Never Lose a Genius AI-Powered Idea Again with ai-cli-log

alingse — Tue, 15 Jul 2025 00:39:26 +0000

We've all been there. You're deep in a terminal session, wrestling with a complex problem. You turn to an AI assistant like Google's Gemini or Anthropic's Claude directly in your CLI. After a few prompts, you strike gold—a perfect code snippet, a brilliant debugging strategy, or a complex command you'll need again.

Then... you close the terminal. Or you scroll up, but the buffer is gone. That stroke of genius is lost to the digital ether. Frustrating, right?

What if you could have a perfect, replayable, and—most importantly—intelligently-named log of every single one of these sessions?

That's exactly why I created ai-cli-log.

What is ai-cli-log?

ai-cli-log is a command-line tool that wraps your terminal commands, seamlessly recording everything from your input to the AI's rendered output. It's designed to be a "fire-and-forget" solution to a problem many developers face daily.

It’s built with Node.js and TypeScript, and it leverages some clever tech to ensure the logs are not just text dumps, but faithful representations of what you actually saw on screen.

The Magic: Not Just Logging, but Smart Logging

Here’s what makes ai-cli-log more than just another script redirecting stdout to a file.

1. Lossless, Interactive Session Capture

Modern CLIs are complex. They have spinners, progress bars, and dynamic prompts that rewrite the screen. A simple > redirect misses all of that.

ai-cli-log uses node-pty to create a pseudo-terminal (pty) and @xterm/headless to run a headless terminal emulator. This means it interprets all the ANSI escape codes for colors, cursor movements, and screen clearing. The final log file is a pristine record of the rendered output, looking exactly like it did in your live session.

2. AI-Powered Filenames

This is the killer feature. A folder full of session-20250715-103000.txt isn't very helpful. ai-cli-log can use an AI to summarize your session and create a descriptive, human-readable filename.

Imagine turning this:
gemini-20250715-103215.txt

Into this:
gemini-20250715-103215-refactor-database-schema-with-migrations.txt

Now you can find the exact session you're looking for with a simple file search.

Getting Started in 60 Seconds

Ready to try it? It's simple.

1. Installation

npm install -g ai-cli-log

2. Basic Logging
Just prefix the command you want to run with ai-cli-log run.

ai-cli-log run gemini

After you exit the Gemini session, you'll find a new log file in a .ai-cli-log directory in your project folder.

╭───────────╮
│  > /quit  │
╰───────────╯

╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│                                                                                                                                │
│  Agent powering down. Goodbye!                                                                                                 │
│                                                                                                                                │
│  Performance                                                                                                                   │
│  Wall Time:                  4.7s                                                                                              │
│  Agent Active:               0s                                                                                                │
│    » API Time:               0s (0.0%)                                                                                         │
│    » Tool Time:              0s (0.0%)                                                                                         │
│                                                                                                                                │
│                                                                                                                                │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯


Session logged to .ai-cli-log/gemini-20250715-083847.txt

3. Enable AI Summaries
To get those smart filenames, use the -s (--with-summary) flag.

# Use the default summarizer
ai-cli-log -s run gemini

# Specify a summarizer (more on this below)
ai-cli-log --by gemini-pro run gemini

Now, when your session ends, ai-cli-log will send the transcript to your configured AI, get a summary, and use it in the filename.


(Session content long, sampling first and last 50 lines for summary)

Summary by ollama (took 13.9s): "interaction-summary"

Session logged to .ai-cli-log/gemini-20250715-083306-interaction-summary.txt

Configuration: Make It Your Own

ai-cli-log is designed to be flexible. You can configure different AI backends, prompts, and models.

Run the interactive setup to get started:

ai-cli-log init

This command will scan for installed AI tools (gemini, ollama, sgpt, etc.) and create a config.json file for you, either globally (~/.config/ai-cli-log/) or locally (./.ai-cli-log/).

Here’s a peek at what the configuration looks like:

{
  "summarizer": {
    "default": "gemini-pro",
    "summarizers": [
      {
        "name": "gemini-pro",
        "tool": "gemini",
        "prompt": "You are a log summarizer. Your response MUST be a valid JSON object with one key: "summary" (a 3-5 word, lowercase, filename-friendly phrase). Example: {"summary": "refactor-database-schema"}. The session content is:",
        "maxLines": 100
      },
      {
        "name": "ollama-llama3",
        "tool": "ollama",
        "model": "llama3",
        "prompt": "..."
      }
    ]
  }
}

You can define multiple "summarizers" and easily switch between them using the --by <name> flag.

Final Thoughts

ai-cli-log was born from a personal need, but it solves a universal problem for anyone working with AI in the terminal. It’s a small utility that has a massive impact on workflow, turning transient conversations into a permanent, searchable knowledge base.

Give it a try and never lose a great idea again.

Check out the project on GitHub: https://github.com/alingse/ai-cli-log

Happy coding!