latemedium

We need to know if big AI labs are explicitly training models to generate SVGs of pelicans on bicycles. I wouldn't put it past them. But it would be pretty wild in they did!

I think part of the reason why just a few people write custom CUDA / triton kernels is that it's really hard to do well. Languages like Mojo aim to make that much easier, and so hopefully more people will be able to write them (and do other interesting things with GPUs that are too technically challenging right now)

There's an important difference between Gemini and Claude that I'm not sure how to quantify. I often use shell-connected LLMs (LLMs with a shell tool enabled) to take care of basic CSV munging / file-sorting tasks for me - I work in data science so there's a lot of this. When I ask Claude to do something, it carefully looks at all the directories and files before doing anything. Gemini, on the other hand, blindly jumps in and just starts moving stuff around. Claude executes more tools and is a little slower, but it almost always gets the right answer because it appropriately gathers the right context before really trying to solve the problem. Gemini doesn't seem to do this at all, but it makes a world of difference for my set of problems. Curious to see if others have had the same experience or if its just a quirk of my particular set of tasks

I hacked something together a while back - a hotkey toggles between standard terminal mode and LLM mode. LLM mode interacts with Claude, and has functions / tool calls to run shell commands, python code, web search, clipboard, and a few other things. For routine data science tasks it's been super useful. Claude 3.7 was a big step forward because it will often examine files before it begins manipulating them and double-checks that things were done correctly afterwards (without prompting!). For me this works a lot better than other shell-integration solutions like Warp

My experience is starkly different. Today I used LLMs to:

1. Write python code for a new type of loss function I was considering

2. Perform lots of annoying CSV munging ("split this CSV into 4 equal parts", "convert paths in this column into absolute paths", "combine these and then split into 4 distinct subsets based on this field.." - they're great for that)

3. Expedite some basic shell operations like "generate softlinks for 100 randomly selected files in this directory"

4. Generate some summary plots of the data in the files I was working with

5. Not to mention extensive use in Cursor & GH Copilot

The tool (Claude 3.7 mostly, integrated with my shell so it can execute shell commands and run python locally) worked great in all cases. Yes I could've done most of it myself, but I personally hate CSV munging and bulk file manipulations and its super nice to delegate that stuff to an LLM agent

edit: formatting

I'm reminded of the metaphor that these models aren't constructed, they're "grown". It rings true in many ways - and in this context they're like organisms that must be studied using traditional scientific techniques that are more akin to biology than engineering.