LLM companies like Anthropic & OpenAI are really caught in a catch 22.
Their models are commoditized so quickly that they likely can’t even recover the cost of training them.
The answer would seem to be to build defensible product around those models.
The problem is their products are intrinsically linked to their models. The commoditization of the model thus devalues the product.
That leaves an opening for model agnostic products to “cross their product moat.”
I’ve just released a dataset and 7B chat model to bring OpenAI-style AI functions to open source LLMs and the quasi-open Llama family of LLMs. I’ve set up a HuggingFace Space for people to try it out. I’ve been working on this project since Llama-2 was released roughly 2 weeks ago. I’ve been experimenting with utilizing AI functions to help structure reasoning and control flow in autonomous and quasi-autonomous agents over the last few months and I thought it would be interesting to see how much of that functionality I could bring to smaller models that were more open and commercially-flexible than OpenAI’s proprietary API.
A recent conversation with a colleague crystallized one of the most challenging aspects of engineering management: providing clear guidance for promotion to senior roles. Their direct report was seeking a roadmap to senior engineer, and my colleague struggled to articulate that path in a way that felt satisfying.
After years of leading engineering teams across different organizations and scales, I’ve observed that the most successful teams consistently embody a core set of values and principles. These values not only shape how we work together but also directly impact the quality of our products and the satisfaction of our teams. I’m sharing these principles to articulate what I believe makes engineering teams truly exceptional.
