idk feels a bit overstated. CUDA’s moat isn’t just writing kernels, it’s the whole ecosystem + hard earned perf intuition. AI helps write code but doesn’t replace that.
switching costs are def going down though, so CUDA feels more like the default vs the only option.
real moat is still ops. getting stuff to run is easy, getting it stable at scale isn’t. so yeah, not gone, just more like a tax now.
None of that talking about the cost of running such an attack and what models were involved during which phases. Seems like you can use Anthropic now as a proxies bot net
There's some fascinating research relating to this happening at UC Berkeley's Almeida Lab (https://nature.berkeley.edu/almeidalab/) on grapevine diseases, particularly relevant to the wine industry in Northern California.
1. The lab, led by Professor Rodrigo Almeida, is studying economically important grape diseases, focusing on Grapevine leafroll disease and Grapevine red blotch disease [1].
2. They're developing an AI tool for fast and accurate disease identification in vineyards, which could be a game-changer for disease management.
3. Their work combines molecular biology, ecology, and bioinformatics, using advanced techniques like genomics.
4. A recent study led by Kai Blaisdell showed that mealybugs efficiently transmit Grapevine leafroll-associated virus 3 under field conditions, with disease symptoms appearing throughout the plant one year after infection [2].
5. Their focus seems to be more on understanding and managing plant diseases using various molecular and ecological approaches [3].
This research is crucial for the wine industry, especially in regions like Northern California. It's not quite "genomics on the brink of the discovery what was penicillin through crispr", but it's still cutting-edge work that could have significant impacts on grape cultivation and wine production.
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This RouteLLM framework sounds really promising, especially for cost optimization. It reminds me of the KNN-router project ([https://github.com/pulzeai-oss/knn-router](https://github.co...), which uses a k-nearest neighbors approach to route queries to the most appropriate models.
What I like about these kinds of solutions is that they address the practical challenges of using multiple LLMs. Rate limits, cost per token, and even just choosing the right model for the job can be a real headache.
KNN-router, for example, lets you define your own logic for routing queries, so you can factor in things like model accuracy, response time, and cost. You can even set up fallback models for when your primary model is unavailable.
It's cool to see these kinds of tools emerging because it shows that people are starting to think seriously about how to build robust, cost-effective LLM pipelines. This is going to be crucial as more and more companies start incorporating LLMs into their products and services.
Which Gemini was used is important too btw. - just tried Gemini-1.5-pro and it was working just fine. So I really think the newer versions of LLMs are able to catch this.
That's an interesting prompt I tried on our Pulze.ai platform Spaces and we nailed it with automatically choosing the right model for this type of question gpt-4-turbo: "Yes, you can infuse garlic into olive oil without heating it up, but it requires caution due to the risk of botulism, a potentially fatal illness caused by Clostridium botulinum bacteria. These bacteria can thrive in low-oxygen environments and can produce toxins in food products like garlic-infused oil if not prepared or stored correctly."
I think that is one advantage of not just blindly trusting one model but finding consensus among many top rated models within one interface that allows you to quickly cross-check.
That’s an interesting use case, we thought of a composition of expert router with specialized models like code-generation models to handle coding tasks. Currently we encourage Users to experiment with their own data, and we're happy to help. If you're interested in applying this to smaller LLMs and specialized adapters, let's connect!
This just becomes interesting if electricity can be produced from reflected photons by the moon such as at night energy production is possible. Other than that I believe in fusion although the giant fusion reactor does help during the day. Instead of making photovoltaic more efficient they should do this with batteries
“What are LLMs but humans with extreme amnesia and no central coherence?”
LLMs are nothing alike to compare them with anything close to what’s going on in a brain. It’s all probabilistic and the closest comparison I can think of is comparing a bird with a plane - both fly, but well…
I think the comments on great api design got me thinking of a world in which you can have multiple of these frameworks orchestrate together. I could see use in adding this to a platform I’m currently building to overcome some issues llamaindex eg introduces.
Open source AI - OSAI - does not necessarily mean it is open. Take the latest OSAI models and try to run them. If you really want to support the idea of open source you’d need to build a model in a way that it is not just open but also accessible to everyone. There needs to be something like Napster that distributes those models in the hands of everyone not just a few folks gate keeping access because they can spend 150$k / month on h100 GPUs
I think smaller expert models will dominate the majority of applications. there is an optimum and fine balance to strike when it comes to size and usability. There will be many mechanisms like demonstrated in the post to find that optimum and realize it.
switching costs are def going down though, so CUDA feels more like the default vs the only option.
real moat is still ops. getting stuff to run is easy, getting it stable at scale isn’t. so yeah, not gone, just more like a tax now.