AutoCrawler is a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding and aims to assist crawlers in handling diverse and changing web environments more efficiently. This work introduces a crawler generation task for vertical information web pages and proposes the paradigm of combining LLMs with crawlers, which supports the adaptability of traditional methods and enhances the performance of generative agents in open-world scenarios. Generative agents, empowered by large language models, suffer from poor performance and reusability in open-world scenarios.
This post explores the use and implications of temperature scaling in the language model output generation, specifically discussing its relationship to the beam search heuristic.
Jeff Geerling tests and compares the performance of the Turing RK1 and the Raspberry Pi 5 in this blog article. The RK1 consistently beat the Pi 5 in standard CPU benchmarks and performed well in real-world cluster benchmark tests. The author also discusses the compactness of the build and integrating it into a new mini rack.
Learn how to build an efficient pipeline with Hydra and MLflow
In this tutorial, we will build a RAG system with a self-querying retriever in the LangChain framework. This will enable us to filter the retrieved movies using metadata, thus providing more meaningful movie recommendations.
Microsoft revealed a new AI tool called Infra Copilot, which uses its existing GitHub Copilot to create infrastructure code.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
How to use Burr, an open source framework using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them.
Deep learning has been deployed in many tasks in NLP, such as translation, image captioning, and dialogue systems. In machine translation, it is used to read source language (input) and generate the desired language (output). Similarly in a dialogue system, it is used to generate a response given a context. This is also known as Natural Language Generation (NLG).
It all started as a joke. I was in a group chat with a few of my friends and we were talking about football (soccer for the American readers). I entered the chat during a mildly heated discussion about the manager of a team one of my friends supports. It was going on for a bit while with seemingly no end in sight...
