LLMs

The need for better AI or LLM-specific infrastructure, along with the host of problems that come with non-deterministic of LLMs, means that there’s more software work ahead of us, not less. Abstraction layers like LLMs create more possibilities and thus, more work.

Is this a good thing or a bad thing? I’m not sure.

A great example of this is frontend... See more

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

In some applications, such as inline code suggestions, the best AI models are too expensive, so tools like Github Copilot use carefully tuned smaller models and various search heuristics to provide results. In other applications, even the largest models, like GPT-4, are too cheap!

Matei Zaharia, Omar Khattab, Lingjiao Chen, et al. • The Shift From Models to Compound AI Systems

First of all, I'd say you have a bigger problem where your company is trying to find nails with a hammer. That is where your sentiment comes from, and could be an obstacle for both you and the company. It's the same deal when I see people keep on talking about RAG, and nowadays "modular RAG", when really, you could treat everything as a software... See more

r/MachineLearning - Reddit

Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.

We identified 30 types of tasks that UX professionals used generative AI tools for in their work. We grouped these tasks under four roles: content editor, research assistant, ideation partner, or design assistant.

Content editor : Generating and editing text, from microcopy to social media posts, based on specifications or copy given by UX

Mingjin Zhang • AI as a UX Assistant

The OpenAI Assistants API offers more than a simple prompt-sharing interface; it provides a sophisticated framework for AI interactions. It allows for persistent conversation sessions with automatic context management (Threads), structured interactions (Messages and Runs), integration with various tools for enhanced capabilities, customization... See more

Discord - A New Way to Chat with Friends & Communities

A new v0.4.0 release of lm-evaluation-harness is available !

New updates and features include:

Internal refactoring

Config-based task creation and configuration

Easier import and sharing of externally-defined task config YAMLs

Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource

More advanced configuration

GitHub - sqrkl/lm-evaluation-harness: A framework for few-shot evaluation of language models.

Amplify Partners was running a survey among 800+ AI engineers to bring transparency to the AI Engineering space. The report is concise, yet it provides a wealth of insights into the technologies and methods employed by companies for the implementation of AI products.

Highlights

👉 Top AI use cases are code intelligence, data extraction and workflow... See more

Paul Venuto • feed updates

To do this, we employ a technique known as AI-assisted evaluation, alongside traditional metrics for measuring performance. This helps us pick the prompts that lead to better quality outputs, making the end product more appealing to users. AI-assisted evaluation uses best-in-class LLMs (like GPT-4) to automatically critique how well the AI's... See more