Sabrina wrote an interesting write up on solving a math problem with gpt-4o. It turned out the text-only, chain-of-thought approach was the best performing, which is not what I would have guessed.
It’s was cool to see Simon dive into LLM-driven data extraction in using his project datasette in this video. Using multi-modal models for data extraction seems to bring a new level of usefulness and makes these models even more general purpose.Nostalgia: https://maggieappleton.com/teenage-desktop.
I wish I had done something like this.
Maybe I can find something on an old hard drive.I’m looking into creating a Deno serve that can manage multiple websocket connections and emit to one after receiving a message from another. A simple way to implement this is to have a single server id and track all the ongoing connections to websocket clients. I’m learning more about approaches that could support a multi-server backend.I take an irrational amount of pleasure in disabling notifications for apps that use them to send me marketing.I enjoyed reading Yuxuan’s article on whether Github Copilot increased their productivity. I personally don’t love Copilot but enjoy using other AI-assisted software tools like Cursor, which allow for use of more capable models than Copilot. It’s encouraging to see more folks adopting a more unfiltered thought journal.I read this post by Steph today and loved it. I want to try writing this concisely. I imagine it takes significant effort but the result are beautiful, satisfying and valuable. It’s a privilege to read a piece written by someone who values every word.llama 3-400B with multimodal capabilities and long context would put the nail in the coffin for OAI
— anton (@abacaj) May 6, 2024 Having gotten more into using llama 7b and 30b lately, this take seems likes it could hold water. Model inference still isn’t free when you scale a consumer app. Maybe I can use llama3 for all my personal use cases, but I still need infra to scale it.I read Jason, Ivan and Charles’ blog post on Modal about fine tuning an embedding model. It’s a bit in the weeds of ML for me but I learn a bit more every time I read something new.I played around with trying to run a Temporal worker on Modal. I didn’t do a ton of research upfront – I just kind of gave it a shot. I suspect this isn’t possible. Both use Python magic to do the things they do. This is what I tried.
import asyncio import os import modal from temporalio import activity, workflow from temporalio.client import Client, TLSConfig from temporalio.worker import Worker @activity.defn async def my_activity(name: str) -> str: return f"Hello, {name}!I read this interesting article by Gajus about finetuning gpt-3.5-turbo. It was quite similar to my experience fine tuning a model to play Connections. A helpful takeaway was that after finetuning the model, you shouldn’t need to include system prompt in future model inference, so you can save on token cost. I also liked the suggestion to use a database to store training data. I had also been wrangling jsonl files.