Are clients really paying for it? It’s really fascinating actually. Is it because the ”client” built it so they have sentimental value and sold it to themselves before you receive the request?
Love it. Are there any benchmarks available on how fast this is from file change, kernel event received, event dispatched? I would expect it to be really quick.
Fascinating, thanks for responding. If k may ask, what is your monthly (or any other interval) token usage? And are you finding a pi to be a bottleneck in regards of any performance?
Interesting, would you mind sharing your architectural setup? How does your index communicate to your agent server, what is the main agent framework/engine used?
Sounds like a cool concept to speak into your watch/wearable which automatically saves or performs tasks on the fly.
I am not affiliated with them at all, but https://getencube.com tries to remove a lot of friction for just these cases and reduce costs. I met their founder a couple of months back, great engineer and a really cool product
So how would you go and explain how an output of tokens can call a function, or even generate an image since that requires a whole different kind of compute? It’s still a layer between the model which acts as a parser to enable these capabilities.
Maybe “business” is a bad term for it, but the actual output of the model still needs to be interpreted.
Maybe I am way out of line here since this is not my field, and I am doing my best to understand these layers. But in your terms you are maybe speaking of the model as an application?
Is it the models tho? With every release (mutlimodal etc) its just a well crafted layer of business logic between the user and the LLM. Sometimes I feel like we lose track of what the LLM does, and what the API before it does.
This is great! I’ve been diving deep into local models that can run on this kind of hardware. Been building this exact same thing, but for complete recordings of meetings and such because, why not? I can even run a low-end model with ollama to refine and summaries the transcription. Even combining with smaller embedding models for a modern, semantic search. It has surprised me how well this works, and how fast it actually is locally.
Hopefully we will see even more locally run AI models in the future with a complete package.
I’ve used h3 for a game. Since they align with an unique hex, I can ensure that one cell grid aligns and is placed on the same place in the world, where players could then compete on.
Yes, I agree totally. But portfolios served a great purpose to expose what you can and what you know to the world and recruiters. Which would then lead to a opportunity to talk about the projects.
We might be going back to a more oldschool approach where talking directly and presenting themselves would be more of a value again. It have always been an higher value, but now it will be kinda more forced I believe.
Another route would be that portfolios become more blog-based, talking about different solutions and problems for each project, as you are saying.
I agree, that would also require engineers to become more invested into core domain problems, which would then lead to more specialised skills (deeper, not broader). My guess is that not everyone actually likes this, but as for now most of the current state points to that direction.
Are we seeing any conversational layer integrated with codex soon?