Nicolay Gerold

Watching intense light between 10 and 4 at Night reduces the capacity to produce dopamine.

There is often a notable gap between state of the art research and what practitioners can reasonably use. However, I'm glad to say that attention sinks can be added to any pretrained LLM at near to no additional effort.

I have released the attention_sinks Python module, which acts as a drop-in replacement for the transformers API. This Python module... See more

Fatherhood reduces testosterone and cortisol caused by an increase in prolactin. The theory is the decrease in sleep requires a surplus of fat. The act of sex and abstinence from sex can increase testosterone levels.

Higher performance and lower cost than any single LLM

We invented the first LLM router. By dynamically routing between multiple models, Martian can beat GPT-4 on performance, reduce costs by 20%-97%, and simplify the process of using AI

Self-Operating Computer Framework

A framework to enable multimodal models to operate a computer.

Using the same inputs and outputs of a human operator, the model views the screen and decides on a series of mouse and keyboard actions to reach an objective.

Key Features

• Compatibility : Designed for various multimodal models.


• Integration : Currently

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Workers AI? It’s another building block that we’re adding to our developer platform - one that helps developers run well-known AI models on serverless GPUs, all on Cloudflare’s trusted global network. As one of the latest additions to our developer platform, it works seamlessly with Workers + Pages, but to make it truly accessible, we’ve made it... See more

There are endokrine signal involved to signal hunger and satiety. The archuit nucleus release chemicals that act as brakes and accelerators of hunger. - The PMOC neurons (system) produce a-MSH, which reduces appetite and get active while eating. They are activated by ultra-violate light to the eyes, which explains the lower hunger and appetite in... See more

• Query the RAG anyway and let the LLM itself chose whether to use the the RAG context or its built in knowledge


• Query the RAG but only provide the result to the LLM if it meets some level of relevancy (ie embedding distance) to the question


• Run the LLM both on it's own and with the RAG response, use a heuristic (or another LLM) to pick the best answer

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A Python API for Intelligent Visual Discovery

Lux is a Python library that facilitate fast and easy data exploration by automating the visualization and data analysis process. By simply printing out a dataframe in a Jupyter notebook, Lux recommends a set of visualizations highlighting interesting trends and patterns in the dataset. Visualizations... See more