Key insights of today’s newsletter: This week Anthropic launched Claude 3.5 Sonnet and it is outperforming all the other state-of-the-art language models on common benchmarks. Not only is the model more powerful, it’s also cheaper and faster to use, which is great news for those building generative AI applications.
I’ve found Artifacts really helpful for seeing fully formed code and the result, and back tracking to see how it was made! An interesting part of the learning process.
When I want to read clear, straight to the point analyzes with original perspectives regarding updates in the field of LLMs and in general everything related to the 'behind the scenes' of AI, Teaching computers how to talk is undoubtedly among my choices . I had been curious for a long time to know how Anthropic and its new model were attracting so much attention. Thanks for this analysis.
Artifacts may be the most interesting element here, as there has been so little UX innovation with generative AI thus far. This is surprising to say given the significantly improved performance and the fact that GPT-4o, which amazed so many people, has a legitimate rival along many dimensions. We now have very good general purpose models from OpenAI, Anthropic, and Google at increasingly competitive price points. The performance/quality improvements have expanded the practical use cases. As price falls further and enterprises get past some of their internal hang-ups, we are likely to see adoption acceleration.
I’ve found Artifacts really helpful for seeing fully formed code and the result, and back tracking to see how it was made! An interesting part of the learning process.
When I want to read clear, straight to the point analyzes with original perspectives regarding updates in the field of LLMs and in general everything related to the 'behind the scenes' of AI, Teaching computers how to talk is undoubtedly among my choices . I had been curious for a long time to know how Anthropic and its new model were attracting so much attention. Thanks for this analysis.
Artifacts may be the most interesting element here, as there has been so little UX innovation with generative AI thus far. This is surprising to say given the significantly improved performance and the fact that GPT-4o, which amazed so many people, has a legitimate rival along many dimensions. We now have very good general purpose models from OpenAI, Anthropic, and Google at increasingly competitive price points. The performance/quality improvements have expanded the practical use cases. As price falls further and enterprises get past some of their internal hang-ups, we are likely to see adoption acceleration.