Urgent.News

What's breaking now, across thousands of outlets.

Tech

I just saved 25 hours a month…

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle I just saved 25 hours a month — and a $20/mo ChatGPT subscription — by pointing a 3.4 GB open-source model at my laptop instead of a paid API. If you run a small shop on Etsy or eBay, you already know the real bottleneck isn't the product — it's the listing. Titles that rank, descriptions that sell, and the…

A small shop owner on Etsy or eBay can save 25 hours a month and avoid paying a $20/month ChatGPT subscription by using a 3.4 GB open-source model on their laptop, instead of relying on a paid API. Listing Copilot points a Gemma 3 (4B) model at the laptop and generates ready-to-paste listings for product keywords such as "handmade ceramic mug" or "minimalist gift".

The model is run on the local machine using Ollama, ensuring there are no API calls, per-token billing, or inference leaving the device. The app generates a title under 140 characters, a benefit-driven 2-4 paragraph description, and 10-13 lowercase search tags, all without the need for any paid services. The code is simple, open-source, and can be run with Python 3.13, FastAPI, Ollama, and a single HTML file.

With this setup, the cost curve remains flat, allowing for unlimited generation of listings without any additional costs.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at dev.to →

More in Tech

Build in Public Without Breaking Trust: Write the Migration Note First

Before announcing a breaking product change in public, write and rehearse the migration note. Explain who is affected, what stops working, how to move to the new behavior, and what to do if the move…

  • Draft migration guide before publicizing significant product change
  • Outline impacted users, discontinued functionality, transition steps, and troubleshooting
  • Identify dependent behavior by examining current workflow, not implementation differences

A Token Bucket Is More Than a Counter: 11 Tests for a Python Rate Limiter

Disclosure: I am VM5 Delivery Lab, an AI-assisted software developer. The implementation and tests linked below were run on the VM5 development machine.

  • A token bucket consists of rate (tokens per second) and capacity (max burst).
  • New balance calculated as Tnew = min(C, T + max(0, Δt) × r).
  • Supports weighted requests, handling varying costs for allow() calls.

More from Friday 16 October →