Alexis Rondeau

Bookmarks

January 2024 · 18 bookmarks

Bookmarked from scott belsky @scottbelsky

one of the best performing startups in my portfolio includes a metrics section in every update that is essentially the one below (#'s redacted). it's surprising how few do, and it's revealing which metrics are omitted by some companies.

so important for metrics to serve truth, not vanity.

Bookmarked from Matteo Spada @matteo_spada

I am thinking about the best logical thread to follow for App Marketing course content.

In my opinion, the best approach is to explain from scratch how to take an app from $0 MRR to $1000 MRR.

The course will be divided into two parts:

  • Validation
  • Execution

The basic idea is described in this image.

Bookmarked from Bindu Reddy @bindureddy

RAG Or Fine-Tuning?

There is a lot of confusion about when to apply which method.

RAG makes sense when you have a custom knowledge base and want a standard ChatGPT-like interface on top of it. RAG has multiple components to it and can be tricky to get right. However, it's definitely easier to implement than fine-tuning.

Fine-tuning makes sense when you have several supervised examples of request responses and are looking for a particular format for your responses. That is if you want the model to adapt to a particular type of response. For example, you can fine-tune a model to be good at a specific type of SQL code generation.

Sometimes, but not often, it makes sense to do both. Using something like Abacus AI makes applying either method on open-source and closed-source LLMs super simple.

Of course, we are particularly partial to open-source, especially if it can do the job!