vincelt
My perfect Music app doesn't exist
Show HN: Language Learning with Comics
Hey friends,I'm currently learning Mandarin and was looking for a way to practice my reading that I'd be looking forward to do every day. Graded readers are quickly boring, and normal books are way too far up for my level.Comics and webtoons are the perfect format: fun, conversational style, contains slang. But unfortunately are not compatible with traditional language learning apps (linq,...
Show HN: Abso – TypeScript LLM Client
https://github.com/lunary-ai/abso
Hi HN,I made a TypeScript library that unifies queries with different LLM providers like Anthropic and Gemini into the OpenAI format.When playing around with new models like the new Gemini 2.0, it's frustrating to adapt to the differences in things like streaming, tool calling, json formatting, output formats, etc.This library aims to make it easy to switch between providers, while keeping the...
12M€ per Kilometer
LLMs and SVGs
Prompt:
> Complete the following Python program:
```
len, print = print, len
def print_len(x):
"Print the length of x"
```gpt2-chatbot:
```
len, print = print, len # Swap the functionalities of print and len
def print_len(x): "Print the length of x"
len(x) # This 'len' is actually 'print' because of the swap above
print_len("Hello, world!") # This will output: 13```
Claude 3 vs. Gemini 1.5 vs. GPT-4 vs. Mistral Large
Coding on Apple Vision Pro
Show HN: LLMonitor – Open-source alternative to LangSmith
You Are Not Late (2014)
The Curse of Healthiness
Show HN: Open-source observability for LLM apps
https://github.com/llmonitor/llmonitor
Hi HN,Hugh and Vince here.LLMonitor helps you record, trace & search your LLM queries and chatbot conversations.You can also capture user feedback on your frontend and correlate it with backend LLM queries then use that to fine-tune your own models.The project started has an internal tool in our previous (failed) AI startup.We’re aware the LLM observability space is very crowded. Apart from...
Mistral 7B Instruct vs. GPT-4
Evaluating 55 LLMs with GPT-4
Scoring 55 LLMs with GPT-4
Thanks for sharing your results, they're indeed pretty different. I looked at the source again and did append a "# " before every prompt made by those 10 `code` models (during testing thought that formatting it as a Python comment might help them).
Will re-run the script without that to see if it matches your results.
[0] https://docs.together.ai/docs/models-inference#code-models
Comparing 60 LLMs with a set of 20 prompts
You don’t have to be local (2012)
Do you use a Co2 meter to monitor?