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
The quality of dataset is 95% of everything. The rest 5% is not to ruin it with bad parameters.
After 500+ LoRAs made, here is the secret
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]
Principles for growable tools
There are three critical pieces to building a tool that can grow around its users over time.
There are three critical pieces to building a tool that can grow around its users over time.
- Design around play . Sometimes I call this design around experimentation . Using the tool for day-to-day work should involve playing and experimenting with what’s possible with the tool. Whether that’s writing small programs to
Beyond customization: build tools that grow with us | thesephist.com
The Gemini API context caching feature is designed to reduce the cost of requests that contain repeat content with high input token counts.
When to use context caching
Context caching is particularly well suited to scenarios where a substantial initial context is referenced repeatedly by shorter requests. Consider using context caching for use cases... See more
When to use context caching
Context caching is particularly well suited to scenarios where a substantial initial context is referenced repeatedly by shorter requests. Consider using context caching for use cases... See more
Context caching guide | Google AI for Developers | Google for Developers
The OpenAI Assistants API offers more than a simple prompt-sharing interface; it provides a sophisticated framework for AI interactions. It allows for persistent conversation sessions with automatic context management (Threads), structured interactions (Messages and Runs), integration with various tools for enhanced capabilities, customization... See more
Discord - A New Way to Chat with Friends & Communities
We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable... See more
Developing Rapidly with Generative AI
Developers can now generate human-quality speech from text via the text-to-speech API. Our new TTS model offers six preset voices to choose from and two model variants, tts-1 and tts-1-hd . tts is optimized for real-time use cases and tts-1-hd is optimized for quality. Pricing starts at $0.015 per input 1,000 characters. Check out our TTS guide to... See more
.png?table=block&id=b4e186f9-aa38-4fce-b32e-8fdd8fc746ce&spaceId=996f2b3b-deaa-4214-aedb-cbc914a1833e&width=1260&userId=&cache=v2)