LLMs
What is Substrate?
Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models – tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more
Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models – tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more
Nextra: the next docs builder
MLServer aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface, fully compliant with KFServing's V2 Dataplane spec. Watch a quick video introducing the project here.
- Multi-model serving, letting users run multiple models within the same process.
- Ability to run inference in parallel for vertical
GitHub - SeldonIO/MLServer: An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
The next-generation command line.
The source of truth for your team’s secrets, scripts, and SSH credentials.
The source of truth for your team’s secrets, scripts, and SSH credentials.
Fig
We consider these aspects of our problem:
- Latency : How fast does the system need to respond to user input?
- Task Complexity : What level of understanding is required from the LLM? Is the input context and prompt super domain-specific?
- Prompt Length : How much context needs to be provided for the LLM to do its task?
- Quality : What is the acceptable
Developing Rapidly with Generative AI
What’s the best way for an end user to organize and explore millions of latent space features?
I’ve found tens of thousands of interpretable features in my experiments, and frontier labs have demonstrated results with a thousand times more features in production-scale models. No doubt, as interpretability techniques advance, we’ll see feature maps... See more
I’ve found tens of thousands of interpretable features in my experiments, and frontier labs have demonstrated results with a thousand times more features in production-scale models. No doubt, as interpretability techniques advance, we’ll see feature maps... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Generative AI can automate simple tasks
By automating simpler, tedious tasks (generating boilerplate code, fixing linter errors, generating unit tests, etc.), generative AI can help engineers focus on more complex tasks.
Generative AI can improve quality & reliability
Since generative AI models are trained on large codebases, they have the potential... See more
By automating simpler, tedious tasks (generating boilerplate code, fixing linter errors, generating unit tests, etc.), generative AI can help engineers focus on more complex tasks.
Generative AI can improve quality & reliability
Since generative AI models are trained on large codebases, they have the potential... See more
Adam Huda • The Transformative Power of Generative AI in Software Development: Lessons from Uber's Tech-Wide Hackathon
no reason to build any kind of software product these days that doesn't have a significant UX/domain knowledge component
Discord - A New Way to Chat with Friends & Communities
So right now, LLMs (Large Language Models) are all the rage. But in the future, it’s possible that the way we get things done is composing things with a combination of LLMs, SMMs (Small, Mighty Models), agents and tools.
It’s what I call Cognitive Composition (because it sounds cool and I have a longtime love affair with alliteration).
This is how we... See more
It’s what I call Cognitive Composition (because it sounds cool and I have a longtime love affair with alliteration).
This is how we... See more