Ollama-Swift

AI is a feature, not a product.

For instance - have your LLM create a tool for your framework of choice that pulls commits that are new in your local branch vs. the remote, to gather them up for a code review.

Then by adding that tool and supplying the right prompt, your LLM can give you a code review before you push it. No SaaS in sight, and no git-push in your submit/code-review cycle, let alone a human reviewer.

If you want to get more heavyweight you could deploy a lambda somewhere that responds to a webhook in github and posts that same review as a comment on each PR.

Sky’s the limit & you can build this stuff as easy as anyone.

This is genius. Extend it to photos as well, no need for DSC_0001 when you can quickly analyze the contents and metadata and group things into folders with intelligent names. Maybe combine it with spotlight tags. There's a lot of similar untapped potential: a feature to look through your downloads folder and group things into folders, bundling all the stuff that's "junk" (e.g. zip files, dmgs, etc.) from stuff that could be valuable. And importantly in all cases it's ok if there are some errors since nothing is actually destroyed, it's just misclassified but still searchable.

In fact, do one better: spotlight is great, but sometimes you don't remember the exact wording. You don't even need a full LLM to do semantic search. And if you're going there, might as well go all the way and add a DevonThink like feature to show related documents. There's so much untapped potential to truly make the computer a "bicycle for the mind", that I'm surprised Apple hasn't bought DevonThink, integrated with an LLM, and shipped it (DevonThink could be integrated into finder, DevonSphere is a souped up spotlight, and DevonAgent would be perfect if they ever launched a search engine).

https://www.nomic.ai/blog/posts/nomic-embed-text-v1

They then use cosine similarity to compare the user's query to retrieve a list of "top N" embeddings which point to the doc chunks, then shove those into the entire query which is sent off to the LLM. RAG is just a means of injecting "relevant" docs into your input context - no training or special LLMs are required.

To do what you're asking, you'd need to fetch a recent wikipedia dump, spend some time 'normalizing'/'sanitizing', and perhaps most importantly, figure out how to divide each article (some of which would well exceed the embedding size) into chunks that you could generate embeddings from. Then you'd need to store them into a vector database (unlike the article which does not persist them). I personally use Qdrant, there is also Postgresql(has a pgvector extension), lancedb, etc.