underlines
Joined 14 years ago
Joined 14 years ago
indeed as the author mentioned, LLMs can greatly help in areas where there is a long tail of not so important, easy to solve problems, that humans just don't have the time or priority to focus on. But combine this long tail of problems that can be solved: accumulated this might still be very beneficial as a sum of things.
another best practices every single solution using LLMs/agents should implement is "never trust the llm".
How I understand it, without really reading into it:
- Thomson Reuters did not "create" a frontier model, they gave some money, maybe a bit of their data to Imperial College London, and took Alibaba's Qwen 3.6 35B A3B Model for a basic fine tune.
- "They" (some undergrads at Imperial College London) used an existing ablation framework to undo some of the topic alignment of the original model.
- "They" fine tuned on some domain knowledge trying to preserve general knowledge - here maybe, just maybe some data came from Thomson Reuters.
As a result: one of 100's of Qwen 3.6 35B A3B sparse model fine tunes, just for publicity, to write overstating headlines like "X created their own frontier model"
Sadly they seem to not be releasing a sparse 35b A3b or anything inbetween "too large to host for mortals" and "fits into a consumer rtx". Probably not to eat away their profits on their API serving. 120b - 300b is a dead space right now, very few good releases in that size range. (I know there are, but the big labs aren't releasing stuff here)
This means we don't need a centralized maintainer for smaller tools. the facebook ad blocking cat and mouse game is something I cna very well imagine to just be something my computer would solve in real time, updating the uBlock filter/userscript to adapt to any fb changes in near real-time.