LLMs

core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.

Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more

OpenAI is treating its new marketplace seriously now: The brand new GPT store will come with REVENUE SHARING.... (missing in the Plugins launch)

and launching a Stateful Assistants API:

- Persistent Threads (/api/openai/threads)

- Built in Retrieval (chunking etc done for you)

- Code Interpreter (RIP Adv Data Analysis?)

- Speech to Text and Text to... See more

Principles for growable tools

There are three critical pieces to building a tool that can grow around its users over time.

1. Design around play . Sometimes I call this design around experimentation . Using the tool for day-to-day work should involve playing and experimenting with what’s possible with the tool. Whether that’s writing small programs to

... See more

Menlo Ventures released a report on ‘The State of Generative AI in the Enterprise’ and found that adoption is trailing the hype. Details below:

Generative AI still represents less than 1% of cloud spend by surveyed enterprises, including just an 8% increase in 2023.

Safety and ROI continue to be prime concerns, and the tangible advantages of being... See more

The exact metrics we use depend on the application — our main goal is to understand how users use the feature and quickly make improvements to better meet their needs. For internal applications, this might mean measuring efficiency and sentiment. For consumer-facing applications, we similarly focus on measures of user satisfaction - direct user... See more

Clean & curate your data with LLMs

databonsai is a Python library that uses LLMs to perform data cleaning tasks.

Features

• Suite of tools for data processing using LLMs including categorization, transformation, and extraction


• Validation of LLM outputs


• Batch processing for token savings


• Retry logic with exponential backoff for handling rate limits and

... See more

You can think your way into solving a deterministic system, but you cannot think your way into solving a probabilistic system.

The first thing that I want to call out is that deterministic software has edge cases, while probabilistic software has long tails.

I find that a lot of junior folks try to really think hard about edge cases around... See more

LLM-PowerHouse: A Curated Guide for Large Language Models with Custom Training and Inferencing

Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers,... See more