Nicolay Gerold

@nicolaygerold

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Data-Juicer: A One-Stop Data Processing System for Large Language Models

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. This project is being actively updated and maintained, and we will periodically enhance and add more features and data recipes. We welcome you to join us in... See more

alibaba • GitHub - alibaba/data-juicer: A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷为大语言模型提供更高质量、更丰富、更易”消化“的数据！

Tools

Think about emotions as elements of the brain and body made of these three elements. Around these center most theories of emotion.

Andrew Huberman • The Science of Emotions & Relationships

Could you have GPT-4 ask questions to the user rather than force the user to do prompt engineering? An experiment says this gave superior results. My read is that they tested this on easy cases where we already knew it would work, so we did not learn much.

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

Where Should Your Company Start with GenAI?

We call it WINS Work: the places where tasks, functions, possibly your entire company or industry are dependent on the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). Heart surgeons and chefs are knowledge workers but not WINS workers. Software programmers, accountants, and marketing professionals are WINS workers. GenAI has the potential to be power tools for WINS work. Every WINS task, subprocess, and end-to-end process within your enterprise (and in many cases the entire enterprise) should be evaluated for potential leverage with GenAI. Plotting your company on a 2×2 matrix, ask yourself where it falls. The top-level categorization can be accomplished by looking at the cost base of the firm overall and then by function, and estimating the percentage of work that is WINS work. For example, software development, customer service, marketing, and R&D are just four areas with high levels of WINS work.

Introducing Wren Engine

The advent of Trend AI agents has revolutionized the landscape of business intelligence and data management. In the near future, multiple AI agents will be deployed to harness and interpret vast amounts of internal knowledge stored within databases and data warehouses. To facilitate this, a semantic engine is crucial. This... See more

Introducing Wren Engine | WrenAI

RAG and Cool Projects / Repos

Depending on your use case, batching may help . If you are sending multiple requests to the same endpoint, you can batch the prompts to be sent in the same request. This will reduce the number of requests you need to make. The prompt parameter can hold up to 20 unique prompts. We advise you to test out this method and see if it helps. In some... See more

OpenAI Platform

Prompting

ReadySet is a transparent database cache for Postgres & MySQL that gives you the performance and scalability of an in-memory key-value store without requiring that you rewrite your app or manually handle cache invalidation. ReadySet sits between your application and database and turns even the most complex SQL reads into lightning-fast lookups.... See more

readysettech • GitHub - readysettech/readyset: Readyset is a MySQL and Postgres wire-compatible caching layer that sits in front of existing databases to speed up queries and horizontally scale read throughput. Under the...

Data Storage

An agent can be thought of as a logical wrapper around an LLM, allowing us to add several features to our AI systems, primarily:

Tool usage, such as calling APIs for info, executing code,

Internal thoughts over multiple generation steps

Ability to use various tools and reasoning steps to answer more complex queries.

Parallel agents can go and complete

James Briggs • LLMs Are Not All You Need | Pinecone

T3 promotes the consumption, conversion and utilization of energy. It acts on bones, organs and muscles.