Nicolay Gerold
@nicolaygerold
Nicolay Gerold
š What is Oxen?
Oxen is a lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
The interface mirrors git, but shines in many areas that git or git-lfs fall short. Oxen is built from the ground up for data, and is optimized to handle large... See more
Oxen is a lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
The interface mirrors git, but shines in many areas that git or git-lfs fall short. Oxen is built from the ground up for data, and is optimized to handle large... See more
Oxen-AI ā¢ GitHub - Oxen-AI/oxen-release: Official repository for docs and releases of the Oxen CLI
Data Structuring & Storage
You can get one-trial learning through strong valence in both directions. You can quicken it through an emotional high adrenaline or a solely high emotional state. But itās the presence of high adrenaline, cortisol and neuroprenephrine that leads to the stamping done of the memories.
Andrew Huberman ā¢ Understand & Improve Memory Using Science-Based Toolsļæ¼
Itās highly individual to genes, epigenetics and environment on what diet your body can thrive.
Andrew Huberman ā¢ How Foods and Nutrients Control Our Moods
Description
PyNest is designed to help structure your APIs in an intuitive, easy to understand, and enjoyable way.
With PyNest, you can build scalable and maintainable APIs with ease. The framework supports dependency injection, type annotations, decorators, and code generation, making it easy to write clean and testable code.
This framework is not a... See more
PyNest is designed to help structure your APIs in an intuitive, easy to understand, and enjoyable way.
With PyNest, you can build scalable and maintainable APIs with ease. The framework supports dependency injection, type annotations, decorators, and code generation, making it easy to write clean and testable code.
This framework is not a... See more
PythonNest ā¢ GitHub - PythonNest/PyNest: PyNest is a Python framework built on top of FastAPI that follows the modular architecture of NestJS
Unlike consumers, enterprises want control over how their data is used and shared with companies, including the providers of AI software. Enterprises have spent a lot effort in consolidating data from different sources and bringing them in-house (this article Partner integrations + System of Intelligence: Todayās deepest Moat by fellow Medium... See more
AI Startup Trends: Insights from Y Combinatorās Latest Batch
š¤ Create perpetual chatbots with self-editing memory!
šļø Chat with your data - talk to your SQL database or your local files!
š You can also talk to docs - for example ask about LlamaIndex!
šļø Chat with your data - talk to your SQL database or your local files!
š You can also talk to docs - for example ask about LlamaIndex!
cpacker ā¢ GitHub - cpacker/MemGPT: Teaching LLMs memory management for unbounded context šš¦
One of the focus areas at Together Research is new architectures for long context, improved training, and inference performance over the Transformer architecture. Spinning out of a research program from our team and academic collaborators, with roots in signal processing-inspired sequence models, we are excited to introduce the StripedHyena models.... See more
Paving the way to efficient architectures: StripedHyena-7B, open source models offering a glimpse into a world beyond Transformers
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
- You have access to a proprietary asset (like data) that others donāt have easy access to. In our āwrite job postingsā example, perhaps you have a corpus of thousands of job postings including some outcome scores (as to how well they did). You could use this data to create better job postings. Others donāt have ready access to this data. Note: The
Dharmesh Shah ā¢ How To Build a Defensible A.I. Startup
Protecting LLM products:
(1) Is hard to bootstrap. This already hints to existing customers or you need to get a bunch of your customers to co-develop (insurance model ā companies pooling their data to solve a problem they all have). This runs into a bunch of issues: competitive drive of the companies, data privacy and security.
(2) Reserved for existing companies. This is the co-pilot model.
(3) This might be the most sustainable one, but it is also the hardest one. I have not seen anything in that direction yet besides OpenAI.