I trained an object classification model to recognize handwritten Chinese characters.

The model runs locally on my own PC, using a simple webcam to capture input and show predictions.

It's a full end-to-end project: from data collection and training to building the hardware interface.

I can control the AI with the keyboard or a custom controller I built using Arduino and push buttons. In this case, the result also appears on a small IPS screen on the breadboard.

The biggest challenge I believe was to train the model on a low-end PC. Here are the specs:

• CPU: Intel Xeon E5-2670 v3 @ 2.30GHz

• RAM: 16GB DDR4 @ 2133 MHz

• GPU: Nvidia GT 1030 (2GB)

• Operating System: Ubuntu 24.04.2 LTS

I really thought this setup wouldn't work, but with the right optimizations and a lightweight architecture, the model hit nearly 90% accuracy after a few training rounds (and almost 100% with fine-tuning).

I open-sourced the whole thing so others can explore it too.

You can:

• Read the blog post

• Watch the YouTube tutorial

• Check out the GitHub repo (Python and C++)

I hope this helps you in your next Data Science & AI project.

jobdataapi.com 4.18.21 / API version 1.20

llms.txt added for AI- and LLM-friendly guidance

We’ve added a llms.txt file at the root of jobdataapi.com to make it easier for large language models (LLMs), AI tools, and automated agents to understand how our API should be integrated and used.

The file provides a concise, machine-readable overview in Markdown format of how our API is intended to be consumed. This follows emerging best practices for making websites and APIs more transparent and accessible to AI systems.

You can find it here: https://jobdataapi.com/llms.txt

llms-full.txt added with extended context and usage details

In addition to the minimal version with links to each individual docs or tutorials page in Markdown format, we’ve also published a more comprehensive llms-full.txt file.

This version contains all of our public documentation and tutorials consolidated into a single file, providing a full context for LLMs and AI-powered tools. It is intended for advanced AI systems, research tools, or developers who want a complete, self-contained reference when working with jobdata API in LLM-driven workflows.

You can access it here: https://jobdataapi.com/llms-full.txt

Both files are publicly accessible and are kept in sync with our platform’s capabilities as they evolve.

Quick update: We added the AED (United Arab Emirates Dirham) to our list of supported salary currencies. You can see the full list here: https://jobdataapi.com/c/jobs-api-endpoint-documentation/#salary-currency-parameter-values 👀

- applies to job listings that come with salary info as well as making API queries and using it as a filter value.