This thread is for anyone working on personal projects to share their progress, and hold themselves somewhat accountable to a group of peers.
Post your project, your progress from last week, and what you hope to accomplish this week.
If you want to be pinged with a reminder asking about your project, let me know, and I'll harass you each week until you cancel the service
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Notes -
I've been tinkering with LLMs recently.
I work as an engineer and I have been trying to build a second-brain repository of information in a database that I can use as context for an LLM to query. Something like Perplexity.ai, but on a local machine because I'd be working with company data. In the last week or so I've uploaded PDFs of whitepapers, books, and industry standards. So far, I've found that the LLMs are miles ahead of plain text search. The platform provides citations so the underlying references can be found quickly.
I am using a standard install of Open WebUI and ollama on a Linux machine. I've tried various smaller models (Deepseek-r1, Phi-3, Phi-4) and have been generally successful, but for larger models find that I just don't have enough computing power. I am comfortable installing and setting up software in a terminal, but I have no formal coding/software development background. So if I can do this, you can too.
Next week I plan to upload several years' worth of e-mails into the database, and see if I can run queries against it.
I wonder how much information can I upload into a database before I start running up against constraints?
If you're using any sort of real database (sqlite / mysql / postgres / mongo / etc) to store the documents, you'll never run into speed-related constraints. You might start running into semantic constraints about limitations of the models' ability to differentiate different topics, but that's unlikely with domain-specific applications like you have.
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