LLMs

Document search and synthesis

Scores of organizations want to harness generative AI so employees can easily find the most relevant documents through improved search results and summaries. For example, your organization can reduce the time it takes employees to find answers to common HR- and process-related questions. Internal manuals and sites are... See more

How do models represent style, and how can we more precisely extract and steer it?

A commonly requested feature in almost any LLM-based writing application is “I want the AI to respond in my style of writing,” or “I want the AI to adhere to this style guide.” Aside from costly and complicated multi-stage finetuning processes like Anthropic’s RL with... 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

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

GPT-4 Turbo can accept images as inputs in the Chat Completions API, enabling use cases such as generating captions, analyzing real world images in detail, and reading documents with figures. For example, BeMyEyes uses this technology to help people who are blind or have low vision with daily tasks like identifying a product or navigating a store.... See more

we’re in a capability overhang - the AI tech that already exists has huge potential impact, whether you engage or not, so get ahead by exploring

the appropriate approach is pathfinding which uses experiments to learn and, critically, artefacts to tell the organisation what to do next.

Mem0: The Memory Layer for Personalized AI

Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.

Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the embedchai

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Langfuse is an open source observability & analytics solution for LLM-based applications. It is mostly geared towards production usage but some users also use it for local development of their LLM applications.

Langfuse is focused on applications built on top of LLMs. Many new abstractions and common best practices evolved recently, e.g. agents,... See more

When it comes to identifying where generative AI can make an impact, we dig into challenges that commonly:

• Involve analysis, interpretation, or review of unstructured content (e.g. text) at scale


• Require massive scaling that may be otherwise prohibitive due to limited resources


• Would be challenging for rules-based or traditional ML approaches