Nicolay Gerold

Model description

Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the Mixtral 8x7B MoE LLM.

The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.... See more

Who is your customer? Do you already know 5 actual people who are interested in your product? 1. Who in your network could be a potential customer? How could you quickly build a network?2. Who are your target customers, and where are they currently spending time, online or offline?3. What’s a unique and untold story you can pitch press?Find them... 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

My $0.02 is that a lot of the future research/work there will be figuring out how to identify effective sub-graphs to provide additional context, to avoid having to pass in the entire graph. As well as trying to identify ontology-less structures in real-time, which includes NER and RE, as well as named entity/relationship... See more

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

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HeimdaLLM

Pronounced [ˈhaɪm.dɔl.əm] or HEIM-dall-EM

HeimdaLLM is a robust static analysis framework for validating that LLM-generated structured output is safe. It currently supports SQL.

In simple terms, it helps makes sure that AI won't wreck your systems.

tests include:

• Unit tests for individual components and isolated functions.


• Integration tests for interactions between multiple components.


• End-to-end tests that assess the entire system’s functionality from a user’s perspective

Tests should be simple. It should be easy to identify what went wrong when reading a failing test.

Transform your E-Commerce Customer Service with AI

Empowering Shopify merchants with automated AI customer service, boosting agent productivity while slashing costs.

• How valuable is what I’m doing?


• What makes this hard for others to do?


• Could this get dramatically easier because of what others launch or how the industry evolves?


• If this does get easier, do I have a “Plan B” to create differentiated value?
  
  Note: An early version of this post appeared in my personal blog (still a work in progress). Decided to

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