Solved with Windsurf

Summary

In this video, the creator introduces Omar, a tool built to help users manage their Ollama models. Using the Rust programming language and Windsurf, the creator explains how they developed both a CLI and a GUI version of the tool. Omar allows users to track and manage unused models quickly. The video also dives into how the tool works by analyzing logs and manifests, highlighting various challenges in development. The creator reflects on their experience with Windsurf, contrasting it with other AI coding assistants and offering valuable tips on working with AI tools in development.

Takeaways

• 😀 Omar is a tool built to manage Ollama models, helping users identify unused models in their installation.
• 😀 The tool was developed in Rust, a language the creator was unfamiliar with, in just a few hours, with an added GUI in 20 minutes.
• 😀 Ollama users often download many models but may not know which ones to delete. Omar helps identify which models are underused.
• 😀 The CLI version of Omar is fast and displays model data such as name, last used date, usage count, and size.
• 😀 The tool provides easy identification of big models that haven't been used much and are good candidates for deletion.
• 😀 The GUI version of Omar allows for sorting models by name, usage count, size, and last used date, with a one-click deletion option.
• 😀 Omar combines data from two sources: Ollama's logs and its API, with the log file locations varying by operating system.
• 😀 Logs store the model usage, including timestamps, and manifest files contain model metadata, including model names and SHA values.
• 😀 Edge cases like 'Ghost Models', 'Silent Models', and 'Multi-Name Models' were discovered and handled during the development of Omar.
• 😀 Windsurf was used to assist in the development process, allowing the creator to work faster and learn Rust while building the tool.
• 😀 Unlike other AI coding assistants, Windsurf actively participates in the development process, writing and fixing code with minimal user intervention.

Q & A

What is Omar and what problem does it solve?

Omar (Ollama Model Report) is a tool designed to help users manage their Ollama models by providing insights into unused or seldom-used models. It solves the problem of not knowing which models to delete by scanning logs and the Ollama API to provide information on model usage.

Why did the creator build Omar in Rust, and how long did it take?

The creator built Omar in Rust because it was a language he barely knew, and he wanted to challenge himself. It took him a couple of hours to develop the core functionality of the tool and an additional 20 minutes to add a GUI using Windsurf.

What are some of the key features of Omar’s CLI version?

The CLI version of Omar is fast and provides an overview of all models in Ollama. It lists active models, showing details like the model name, last used date, usage count, and size. It helps identify models that haven’t been used much and could be candidates for deletion.

How does Omar’s GUI differ from the CLI version?

The GUI version of Omar is more interactive and allows users to sort models by name, last used date, usage count, or size. It also provides a button to easily delete models, making the process of managing models more user-friendly compared to the CLI version.

What are 'Ghost Models' and how are they handled in Omar?

'Ghost Models' are models that appear in the logs but are not listed in any manifest, often because they were deleted. Omar handles this by identifying and reporting these models even if they are no longer part of the active model list.

What are the challenges with 'Silent Models' in Omar?

'Silent Models' are models that appear in the manifests but never show up in the logs. These typically arise from custom builds or experimental features that don't write to the logs. Omar is able to identify and handle these models despite the lack of log data.

How does Omar deal with 'Multi-Name Models'?

'Multi-Name Models' are cases where multiple model names share the same weights file. Omar addresses this by recognizing that models with the same weights file might have different names or parameters, but only displays the weights file in the logs, not the specific model names.

How did Windsurf assist in building Omar?

Windsurf played a crucial role by acting as an AI coding assistant, helping the creator build the tool without prior experience in Rust. Windsurf helped by writing code, resolving errors, and offering suggestions to improve the functionality and user interface.

What is the key difference between Windsurf and Cursor?

Windsurf is integrated directly into the development process, providing a seamless experience as a coding assistant. In contrast, Cursor feels more like a plugin bolted onto VS Code, and its features, though useful, often feel like an afterthought compared to Windsurf's more deliberate design.

What challenges did the creator face while working with AI coding assistants like Windsurf?

One of the main challenges the creator faced was ensuring the AI assistant didn’t take the project in conflicting directions. It was important to clearly define the overall goal for the app to avoid adding unnecessary features. Additionally, some technical issues, such as AI-generated code errors, needed careful handling.