development

Supercharging LLMs: RoT Fuses Language Models with Decision Tree Search to Boost Reasoning Power

Supercharging LLMs: RoT Fuses Language Models with Decision Tree Search to Boost Reasoning Power

This is a Plain English Papers summary of a research paper called Supercharging LLMs: RoT Fuses Language Models with Decision Tree Search to Boost Reasoning Power. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview This paper explores a novel approach called "Reflection on Search Trees" (RoT) to enhance the capabilities of large language models (LLMs). RoT involves integrating tree search methods with LLMs to improve their reasoning and decision-making abilities. The paper presents the design and evaluation of the RoT system, demonstrating its effectiveness in outperforming traditional LLMs on various tasks.…
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Flutter vs. React Native

Flutter vs. React Native

Flutter vs. React Native: A Comprehensive 2024 Comparison When deciding on a framework for cross-platform mobile app development, it's essential to consider multiple factors. This detailed comparison between Flutter and React Native covers various aspects, including learning curve, ease of deployment, scalability, performance, popularity, community support, libraries, and more. By the end of this article, you'll have a clearer understanding of which framework might be best suited for your needs. 1. Learning Curve Flutter:Flutter uses Dart, a language designed by Google that's relatively easy to learn, especially for those with experience in object-oriented programming. Dart's syntax is straightforward, making it…
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Time Series in Data Science: Analysis of Bitcoin and Ethereum

Time Series in Data Science: Analysis of Bitcoin and Ethereum

Time series play a crucial role in Data Science, especially when analyzing financial data. The price variations of cryptocurrencies like Bitcoin and Ethereum offer an excellent opportunity to explore time series. In this article, we will analyze the price variations of Bitcoin and Ethereum in euros, using datasets ranging from 2012 to 2019 for Bitcoin and from 2015 to 2019 for Ethereum. We will also illustrate the use of some basic time series techniques with concrete examples and practical recommendations. Importing Libraries and Loading DataBefore diving into the analysis, we need to import the necessary libraries and load the datasets.…
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vivo Migrated from GlusterFS to a Distributed File System for AI Training

vivo Migrated from GlusterFS to a Distributed File System for AI Training

vivo is a multinational technology company that designs and develops smartphones, smartphone accessories, software and online services. We serve 400 million+ users in 60+ countries and regions. As a key module of vivo AI computing platform, VTraining is a general training platform built on Kubernetes clusters. It supports large-scale distributed training across multiple frameworks and is equipped with petabyte-scale distributed storage. Currently, VTraining serves 700+ users, covering mission-critical applications such as recommendations, speech, vision, NLP, and imaging. The platform boasts a GPU compute power of 300 PFLOPS, a cluster with 3,000+ nodes, and 5+ PB of storage data. In this…
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Creating a Smooth Transitioning Dialog Component in React: My Journey

Creating a Smooth Transitioning Dialog Component in React: My Journey

I've been working on a project to create a smooth transitioning dialog component in React, and I want to share my journey through the process, highlighting key steps and challenges encountered along the way. Part 1: Setting the Foundation In the first part of the series, I laid the groundwork by setting up the component structure. I created a context to manage the state and developed the main dialog component with its header, body, footer, and container. My primary goal was to ensure that the dialog could support minimisation and expansion while being adaptable to content changes. This foundation was…
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#29 — Group an Excel table and Store It as Multiple Files According to Part of The Values of Specified Cells

#29 — Group an Excel table and Store It as Multiple Files According to Part of The Values of Specified Cells

Problem description & analysis:The Excel table below is ordered by column A, whose values are strings separated by “-”. The first part of column A represents the category. Task: Store the table as multiple tab-separated txt files according to different categories. The file name format is Group_Category.txt.Group_AA.txt Group_BB.txt Group_CC.txt Solution:Use SPL IDE to execute the code: =T@b("data.xlsx").group(#1.split("-")(1);~).(T@b("Group_" / #1 / ".txt": #2)) Enter fullscreen mode Exit fullscreen mode Explanation:The T()function reads files as a table and writes a table to files; @ b means that column names are not included. group() function groups rows according to the specified rule and…
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Stay Compliant, Mitigate Risks: Understanding AML/KYC as a technologist

Stay Compliant, Mitigate Risks: Understanding AML/KYC as a technologist

Effective Anti-Money Laundering (AML) and Know Your Customer (KYC) strategies and processes are essential for financial institutions and other regulated entities. The regulatory landscape is rich with laws and enforcing agencies and organizations. As a software engineer, understanding these laws and requirements is crucial for implementing effectively compliant systems, safeguarding your organization from excessive regulatory auditing and penalization (including potentially steep fines). Key Activities Customer Due Diligence Intended to verify customer identities and assess their risk profiles, Customer Due Diligence (CDD) is critical to effective AML strategies. CDD employs systems and software providing for (1) identity verification and (2) risk…
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Creating Python Generators

Creating Python Generators

Introduction:Python generators are a type of function that allows us to create iterators. They are similar to regular functions in structure, but with a few key differences. Generators are useful for creating efficient and memory-friendly code, especially for working with large datasets or performing tasks repeatedly. In this article, we will explore the advantages, disadvantages, and features of creating python generators. Advantages of Python Generators: Memory Efficiency: Generators are memory-efficient as they do not store the entire sequence of values in memory, but rather generate and yield them one at a time. This is particularly useful when working with large…
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Getting the height of Android device independent of pixel densities

Getting the height of Android device independent of pixel densities

Table of contents TLDR (too long didn't read) Why does this matter? Resources My app on the Google play store My app's GitHub code TLDR (too long didn't read) When trying to get the height of a Android device, make sure you do it in an pixel density independent way: correct way to get height: val displayMetrics = Resources.getSystem().displayMetrics val heightPixels = displayMetrics.heightPixels //exact physical pixel amount(different on certain devices) val density = displayMetrics.density //density multiplier val pixelDensityIndependentHeight =heightPixels / density Enter fullscreen mode Exit fullscreen mode val height = Resources.getSystem().displayMetrics.heightPixels //as previously mentioned, this height will change depending on…
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