LLMs

Some tasks are easier to improve via system design. While LLMs appear to follow remarkable scaling laws that predictably yield better results with more compute, in many applications, scaling offers lower returns-vs-cost than building a compound system. For example, suppose that the current best LLM can solve coding contest problems 30% of the

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Overview

Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more

Memory Considerations

Since co-occurrence matrices are square, they grow exponential with the number of entities being embedded. For 50k entities and a 32-bit data format, a dense matrix will already be at 10GB. 100k entities puts it at 40GB.

If you are trying to embed even more entities than that or have limited RAM available, you may need to use a... See more

memary: Open-Source Longterm Memory for Autonomous Agents

memary demo

Why use memary?

Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve... See more

The context size of the input is too small for when you want to analyse CSV's with 1000's of rows and embedding doesn't really work because it loses context.

First time here? Go to our setup guide

Features

• 🤖 Multiple model integrations: OpenAI, transformers, llama.cpp, exllama2, mamba


• 🖍️ Simple and powerful prompting primitives based on the Jinja templating engine


• 🚄 Multiple choices, type constraints and dynamic stopping


• ⚡ Fast regex-structured generation


• 🔥 Fast JSON generation following a JSON schema

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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

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API wrappers, general-purpose AI tools and third-party AI tools for big platforms.

API wrappers have a weak moat.

General AI tools try to be the jack-of-all-trades.

Big platforms will eat up small apps by adding similar AI features themselves.