ok i promised screenshots- thread below. its interesting that the model does not attempt to one-shot everything top to bottom. it scaffolds out the structure with a bunch of TODOs at the end, implements basic testing, and then attempts to fix
CODE
-
JSON Mode and Client-Side Retries for Timeout Issues
By
–
its totally fair. i actually had the instructor js docs open this morning. i ultimately just wanted to 1) try json mode over function calling, and 2) for this weird usecase i wanted retries to be done clientside as i run into timeout issues otherwise
-
LLM Agents Challenge: Porting PyTorch GPT-2 Training to C
By
–
Btw writing the llm.c training code would imo be a very interesting, impressive, self-contained and very meta challenge for LLM agents. The prompt is: Take the PyTorch code train_gpt2.py
And write, compile and unit test a single .c file that reproduces the training: train_gpt2.c -
Extracting Prompts from Jupyter Notebooks Automatically
By
–
Yeah, it's probably not too hard to write code that scans through every .ipynb notebook in a folder and extracts the prompts and responses
-
PyTorch Layers Migration to C: Tutorial and Implementation Guide
By
–
I added a quick crappy tutorial on how PyTorch layers are moved to C, with a few possibly helpful pointers: https://
github.com/karpathy/llm.c
/blob/master/doc/layernorm/layernorm.md
… -
Using Long Context Models to Manage Multi-File Projects
By
–
With long context models like Claude you can send an entire project – spanning multiple files – in a single prompt and then ask questions about it or ask it to rewrite sections of the code for you
-
Using LLM Claude Opus to Complete Code with Prompts
By
–
My favourite prompt from this post is still the first one. cat files_to_prompt/cli.py | llm -m opus –system 'finish this code for me' Love that we have tools that can do this now!
-
Sweeps Training System for Axolotl Open Source
By
–
Built a simple sweeps system for training with Axolotl… would anyone want this/find this useful? cc @winglian
-
ChatGPT Web Interface and Code Interpreter Usage Preferences
By
–
I still do most of my casual LLM usage via the ChatGPT web interface partly out of convenience but also because that gives me Code Interpreter which I lean on for all sorts of things
-
Logging LLM Prompts to SQLite Database for Analysis
By
–
The thing I like most about using the terminal for this is that my LLM tool logs everything to a SQLite database – I have more than 2,000 logged prompt/responses now from over 100 different models Would be neat to get a notebook to log prompts globally like that as well