Nicolay Gerold
@nicolaygerold
Nicolay Gerold
Aversion and arousal as a push pull that drive emotions and is connected with an action (moving/leaning in or out).
Andrew Huberman • How Foods and Nutrients Control Our Moods
However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more
Understanding the Cost of Generative AI Models in Production
Sympathetic nervous system (responsible for attention) has a 24 hour cycle. We have altradian rhythms during the day:
Andrew Huberman • How Your Brain Works & Changes
The study leveraged large language models (GPT-3.5) to identify and analyze critical aspects of emerging corporate risks using earnings call transcripts — with respect to Political, Climate-related, and AI risks.
More than 69,000 earnings call transcripts were used in the study, and the GPT model was instructed to make human-readable risk summaries... See more
More than 69,000 earnings call transcripts were used in the study, and the GPT model was instructed to make human-readable risk summaries... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Alex G. Kim, Maximilian Muhn, Valeri V. Nikolaev (University of Chicago)
https://substack.com/redirect/eab21ccc-5d25-43c4-84a9-62cd22f40901?j=eyJ1IjoiODUwdnkifQ.SwtdSAkqrwd6thREgtV_qi7ujPFqTpWZMtKcVndUB1A
For Global State
There are a lot of different state managers in front-end development. Every time you blink a new state management is being open-sourced. But the one that works for me is MobX.
Some people don’t like it because a lot of “magic” happens behind the scenes out of your control. But for me, that is a feature, not a bug. I like to... See more
There are a lot of different state managers in front-end development. Every time you blink a new state management is being open-sourced. But the one that works for me is MobX.
Some people don’t like it because a lot of “magic” happens behind the scenes out of your control. But for me, that is a feature, not a bug. I like to... See more
Pavel Pogosov • Top 14 Libraries for Any React App
The solution: The ingestion service
To meet these unique demands, the Search Infrastructure team implemented the Ingestion Service to gracefully handle Twitter’s traffic trends. The Ingestion Service queues requests from the client service into a single Kafka topic per Elasticsearch cluster. Worker clients then read from this topic and send the... See more
To meet these unique demands, the Search Infrastructure team implemented the Ingestion Service to gracefully handle Twitter’s traffic trends. The Ingestion Service queues requests from the client service into a single Kafka topic per Elasticsearch cluster. Worker clients then read from this topic and send the... See more
Stability and scalability for search
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 • GitHub - PythonNest/PyNest: PyNest is a Python framework built on top of FastAPI that follows the modular architecture of NestJS
Amplify Partners was running a survey among 800+ AI engineers to bring transparency to the AI Engineering space. The report is concise, yet it provides a wealth of insights into the technologies and methods employed by companies for the implementation of AI products.
Highlights
👉 Top AI use cases are code intelligence, data extraction and workflow... See more
Highlights
👉 Top AI use cases are code intelligence, data extraction and workflow... See more
Paul Venuto • feed updates
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