Ollama is an open-source AI tool designed to enable users to run and deploy large language models (LLMs) locally. Its goal is to provide a convenient and efficient way for developers to use models like GPT on their local machines without relying on cloud services. Ollama supports multiple models and focuses on optimizing performance, allowing even resource-constrained devices to smoothly run these models.
It seems like another year has passed, and the biggest change at work is a significant increase in AI participation. Previously, switching between different development languages required developers to be familiar with various language-specific API interfaces. Now, these basic code snippets can all be generated by AI, which is a huge blessing for developers.
As early as 2023, I’ve written two simple introductory articles about it. Now it’s been 25 years – how to put this… I haven’t felt a significant improvement. It still needs to develop its own cognition, be able to reasonably break down tasks, and, of course, most importantly, identify whether AI-generated code contains bugs.
Two years ago, I added a copy function to the site, which took me half a day of tinkering. Ultimately, the rendering effect wasn’t quite satisfactory, and as a somewhat mediocre frontend developer, I didn’t plan on improving it – if it worked, it was good enough. This year, I used AI to develop a mini-program, which has also made me more familiar with frontend development. I refactored it (the AI redesigned it).
Just as we needed to learn the techniques of searching engines back then, we also need to learn some techniques for communicating with AI, providing reasonable and sufficient constraints, and efficiently obtaining the answers we need.
If you look at it from a different angle, current AI is like a very good student with excellent memory – it has the ability to memorize everything. What we need to do is learn how to communicate with AI correctly, effectively, and precisely describe our needs to help AI generate the expected results.
“GitHub Copilot” was released less than two years ago, and “ChatGPT” has appeared. I don’t fully understand the underlying principles; I’ve been using them for a while. The two tools’ levels of assistance are completely different, but they have both achieved a significant increase in productivity.