Nicolay Gerold
@nicolaygerold
Nicolay Gerold
If I had to pick one state management library for all of my React projects in 2024, I would choose Zustand . It takes almost no time to learn how to use it. It also doesn’t require you to add a provider component to your application, which makes it very convenient to use in any component you like.
đź“š React Libraries You Should Use In 2024
Model Card for Zephyr 7B β
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that... See more
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that... See more
HuggingFaceH4/zephyr-7b-beta · Hugging Face
Powerful AI systems can help us interpret the neurons of weaker AI systems. And those interpretability insights often tell us a bit about how models work. And when they tell us how models work, they often suggest ways that those models could be better or more efficient. —Dario Amodei, Anthropic
Sarah Wang • What Builders Talk About When They Talk About AI | Andreessen Horowitz
It's a little bit of a conundrum. A model we do not understand explains another model we do not understand.
AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as... See more
r/singularity - Reddit
Chaining LLM Agents instead of LLM calls. Seems like a pretty heavy prompt engineering effort.
They are pushing for agents that are specialized in a certain tasks through RAG / finetuning, where CAMEL and other frameworks failed.
One interesting area for exploration might be finetuning LLMs for collaboration before finetuning them for tasks.
If your inhales are longer than your exhaled, you are speeding up your heart. When you inhale, the diaphram moves down and the volume of the heart grows. Then the heart moves slower in that bigger volume and the neurons register the slower blood flow. The brain then signals the heart to speed up the blood flow by increasing the heart rate. The same... See more
Andrew Huberman • Tools for Managing Stress & Anxiety
LLM’s responses can be volatile and unreliable, as slight changes in the prompt can lead to significant variations in the output. Occasionally, the LLM may generate irrelevant or nonsensical information, necessitating ongoing improvements. Additionally, the context window has limited capacity, which can impact our agents’ understanding of complex... See more
Introducing our work on general-purpose LLM Agents | GoodAI
Shreyas Doshi on Pre-Mortems, the LNO Framework, the Three Levels of Product Work, Why Most Execution Problems Are Strategy Problems, and ROI vs. Opportunity Cost Thinking
Lenny Rachitskylennysnewsletter.comThe study leveraged large language models (GPT-3.5) to identify and analyze critical aspects of emerging corporate risks using earnings call transcripts — with respect to Political, Climate-related, and AI risks.
More than 69,000 earnings call transcripts were used in the study, and the GPT model was instructed to make human-readable risk summaries... See more
More than 69,000 earnings call transcripts were used in the study, and the GPT model was instructed to make human-readable risk summaries... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Alex G. Kim, Maximilian Muhn, Valeri V. Nikolaev (University of Chicago)
https://substack.com/redirect/eab21ccc-5d25-43c4-84a9-62cd22f40901?j=eyJ1IjoiODUwdnkifQ.SwtdSAkqrwd6thREgtV_qi7ujPFqTpWZMtKcVndUB1A
Most commonly, ETL means moving data from some source system (e.g. a production database, Slack API) into an analytical data warehouse (e.g. Snowflake) where the data is easier to combine and analyze. Most data teams use a vendor like Fivetran or an orchestration platform like Airflow to do this.
Modal is a great solution for ETL if you are... See more
Modal is a great solution for ETL if you are... See more