# uv Published articles for uv. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing ChatGPT Images 2.5 DevFeed: [Introducing ChatGPT Images 2.5]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-08T22:46:33Z Content type: release Language: en Sources: [Simon Willison's Weblog]() Topics: [ChatGPT](), [OpenAI](), [Generative AI](), [text-to-image](), [Command-line interface]() Tags: [ai](), [ai-2-236](), [chatgpt](), [generative-ai](), [generative-ai-1-982](), [openai](), [openai-463](), [text-to-image](), [text-to-image-47](), [tools](), [tools-78](), [uv](), [uv-100]() ### AI overview OpenAI's ChatGPT Images 2.5 release adds improved multi-turn instruction following, faster responses, and better preservation of subjects in reference photos. The API introduces the gpt-image-2.5-sunburst and gpt-image-2.5-flare model IDs, with Sunburst positioned for precise editing and Flare for faster everyday generation. ### Source excerpt Introducing ChatGPT Images 2.5 OpenAI's image generation models are apparently used "more than 3 billion images across ChatGPT Images and the GPT-Image models in the API". This latest release improves their instruction-following ability across multiple turns, responds faster, and "is better at preserving the subjects in your reference photos". There are two new model IDs in the API: gpt-image-2.5-sunburst and gpt-image-2.5-flare. Based on this I think Sunburst is the stronger option: Choose Sunburst for workflows where editing precision matters most, and Flare for fast, high-quality everyday image generation. I upgraded my openai_image.py CLI tool to support passing in one or more reference images, so now this works: uv run https://tools.simonwillison.net/python/openai_image.py \ 'add a raccoon scientist studying the chart thoughtfully' \ -i https://static.simonwillison.net/static/2026/openai-agent-usage.webp \ -m gpt-image-2.5-sunburst This is the original image, and here's what I got back from that prompt to "add a raccoon scientist studying the chart thoughtfully": Tags: tools, ai, openai, generative-ai, uv, text-to-image ## Track LLM API Costs with genai-prices DevFeed: [Track LLM API Costs with genai-prices]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-09-01T11:16:32Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Large Language Model](), [API](), [Inference](), [Library](), [Python](), [Binance]() Tags: [api](), [cost](), [example](), [genai](), [inference](), [install](), [library](), [llm](), [openai](), [pricing](), [python](), [report](), [request](), [tips](), [token](), [uv]() ### AI overview A tutorial introducing the genai-prices Python library for estimating the cost of calling LLM inference APIs. It explains that the library can avoid maintaining a custom pricing table and can report token counts and cost for each request, with an OpenAI installation and usage example. ### Source excerpt Tip: Use the genai-prices Python library to calculate the estimated cost of calling LLM inference APIs. This avoids maintaining your own pricing table and lets you report token counts and cost for each request. Below is an example: # Install: uv add openai genai-prices from openai import OpenAI from ## Installing CLIs with uv DevFeed: [Installing CLIs with uv]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-07-31T07:00:05Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Command-line interface](), [Python](), [Tool]() Tags: [cli](), [install](), [python](), [run](), [tips](), [tool](), [uv]() ### AI overview A short tutorial explains how to install a Python CLI as a standalone uv tool so it can be called directly without repeatedly using uv run, a virtual environment, or a prefix. ### Source excerpt 🐍 Python Tip! Tired of typing uv run every time? Install any Python CLI as a standalone uv tool and call it directly from anywhere, with no venv and no prefix. # Before: prefix every call with `uv run` uv run mytool --help uv run mytool serve # After: install once... uv ## How to Run Python Tools Without Installing Them DevFeed: [How to Run Python Tools Without Installing Them]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-04-07T07:00:59Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Python](), [developer tooling](), [Code]() Tags: [dependencies](), [development](), [development-tools](), [how-to](), [python](), [tips](), [uv]() ### AI overview This tutorial explains how to use uvx to run Python development tools without adding them to project dependencies. uvx runs tools in isolated, cached environments, helping keep pyproject.toml focused on the project's actual dependencies. The supplied excerpt mentions formatting and linting code without modifying pyproject.toml. ### Source excerpt Use uvx to run development tools without adding them to your project dependencies. uvx runs each tool in an isolated, cached environment, keeping your pyproject.toml clean and focused on actual dependencies. Here's an example: # uvx (alias for uv run tool) # Format and lint code without modifying pyproject.