Wonderful Tech @Taiwan

AppWorks, a venture capital firm based in Taiwan, has invested in several companies during their Series A rounds. Notable investments include: 1. **Carousell** (2018, Series C): A classifieds marketplace for buying and selling items. 2. **Concept Art House** (2021): Specializes in NFTs and video game art creation. 3. **Cyber Sierra** (2021): Offers cyber risk solutions and insurance. 4. **Dapper Labs** (2019): Creator of NBA Topshot and the Flow blockchain. 5. **Dappio** (2022): Aggregates yields across DeFi and NFTs. 6. **Dcard** (2020, Series C): A forum-based social platform. 7. **Deep Sentinel** (2018, Series A): AI-based home protection. 8. **Docosan** (2021, Series Seed): A healthcare marketplace. 9. **Eden Farm** (2021, Series A): A B2B agritech startup. 10. **EMQ** (2017, Series A): A financial technology company focusing on remittances. 11. **Fandora** (2014, Series A): Online seller of illustration merchandise. For more information on AppWorks and their investments, you can v

 Designing an autocomplete feature for a search engine involves understanding user behavior, optimizing for speed and relevance, and ensuring the system can handle a large number of requests. Here's a step-by-step guide on how you might approach this: 1. **Requirement Gathering (需求收集)**    - **User Experience (用戶體驗)**: Understand the latency requirements. Autocomplete suggestions need to be fast, typically returning in under 100 milliseconds.    - **Scale (規模)**: Predict the number of requests per second during peak times. This will help determine infrastructure needs.    - **Relevance (相關性)**: Ensure the suggestions are relevant to the users. 2. **Data Collection (數據收集)**    - Gather a list of commonly searched queries from the search logs.    - Monitor user interactions with the autocomplete feature to refine and improve over time. 3. **Trie Data Structure (Trie數據結構)**    - Use a Trie (or Prefix Tree) which is especially efficient for this use case. As the user types, the system

Designing a video streaming server involves multiple technical aspects and intricate architectural decisions. Here's a high-level consideration and recommended steps from a system design perspective: 1. **Define Requirements (確定需求)**:    - **Throughput (吞吐量)**: How many client connections does your server need to support simultaneously?    - **Latency (延遲)**: How quickly should the streaming begin to play?    - **Video Quality (視頻質量)**: Are you supporting resolutions like 4K, 1080p, 720p, etc.?    - **Streaming Type (串流類型)**: Do you need to support live streaming or VOD (Video on Demand)? 2. **Choose Appropriate Protocols (選擇適當的協議)**:    - **HLS (HTTP Live Streaming)** and **DASH (Dynamic Adaptive Streaming over HTTP)** are popular streaming protocols today. Both support Adaptive Bitrate Streaming (ABR), adjusting video quality dynamically based on a user's network condition.    - **RTMP (Real-Time Messaging Protocol)**: Though less commonly used now, it remains important in ce

### HMD Global and Nokia: Assessing the Past, Present, and Future of Their Partnership Since 2016, Nokia and HMD Global have successfully maintained a collaborative relationship that has revitalized the Nokia brand through a range of mobile and tablet devices. The partnership has been structured around a brand licensing agreement that facilitates the global sale of devices bearing the Nokia brand. But recent developments suggest a potential shift in strategy for HMD Global, raising several questions about the future of the partnership. #### **Historical Background of the Partnership** The collaboration between Nokia and HMD Global blossomed out of a mutual objective to resurrect the once iconic Nokia brand. The agreement was founded on a well-defined business model encompassing brand licensing, shared technologies, and revenue-sharing parameters that aligned with the vision to rejuvenate the Nokia brand while ensuring a strategic business growth. #### **A New Era with HMD’s Own Brand**

### The Revival of Nokia: An Insight into the Nokia-HMD Global Partnership In a dynamic and fiercely competitive mobile device market, alliances and partnerships are not uncommon. One such notable collaboration is between Nokia, a veteran in the telecommunications industry, and HMD Global, a relatively new player aiming to make significant strides in the market. Here we delve deep into the partnership between these two Finnish companies, exploring the synergy that brings Nokia’s legacy into the modern smartphone era. #### **Background** **Nokia**, with its rich history stemming from a paper manufacturing venture to becoming a leader in the telecommunication equipment and mobile communications devices, was a global giant in the mobile manufacturing sector during the early 2000s. However, with the advent of smartphones, Nokia's market share gradually dwindled. **HMD Global**, founded in 2016, came into existence with the primary goal to create and market mobile phones and tablets und

### The Revival of Nokia: An Insight into the Nokia-HMD Global Partnership In a dynamic and fiercely competitive mobile device market, alliances and partnerships are not uncommon. One such notable collaboration is between Nokia, a veteran in the telecommunications industry, and HMD Global, a relatively new player aiming to make significant strides in the market. Here we delve deep into the partnership between these two Finnish companies, exploring the synergy that brings Nokia’s legacy into the modern smartphone era. #### **Background** **Nokia**, with its rich history stemming from a paper manufacturing venture to becoming a leader in the telecommunication equipment and mobile communications devices, was a global giant in the mobile manufacturing sector during the early 2000s. However, with the advent of smartphones, Nokia's market share gradually dwindled. **HMD Global**, founded in 2016, came into existence with the primary goal to create and market mobile phones and tablets und

Kaggle itself provides some great free resources and online courses, which can help you prepare for their contests. Here are a few: 1. **Kaggle Learn**: Kaggle's own "Learn" platform has a number of free, fast-paced courses designed to get you up to speed with the most important tools and techniques used in data science:    - **Python**: Teaches the basics of Python programming and data science.    - **Intro to Machine Learning**: Covers the fundamental concepts required to get started with machine learning.    - **Intermediate Machine Learning**: Introduces more complex machine learning models, including handling missing values and categorical variables, and using pipelines.    - **Data Visualization**: Teaches how to create beautiful and informative visualizations.    - **Pandas**: Introduces the Pandas library, which is fundamental for data analysis and manipulation in Python.    - **Deep Learning**: Covers the basics of deep learning, using the TensorFlow library.    

Yes, Kaggle is a popular platform for data science competitions, but there are several others that you might be interested in as well. Here are a few: 1. **Zindi**: Zindi is a platform for data scientists in Africa to connect and compete. They run competitions (both for profit and non-profit) focused on solving Africa's most pressing problems. 2. **DrivenData**: DrivenData hosts competitions that aim to address some of the world's biggest social challenges. The challenges range from health, education, climate, and more. 3. **CodaLab Competitions**: CodaLab is an open-source web platform that enables researchers, developers, and data scientists to collaborate. They have a section dedicated to competitions. 4. **CrowdANALYTIX**: CrowdANALYTIX hosts data science and AI-related contests and also has an active community of data scientists. 5. **DataScienceGlobal Impact Challenge**: This is an annual competition aimed at data scientists and non-profits. The goal is to showcase how da

Exploratory Data Analysis (EDA) is a critical step in any data science project. It involves understanding the data you're working with, discovering patterns, identifying anomalies, testing hypotheses, and checking assumptions using statistical summaries and graphical representations. Here's a bit more detail: 1. **Understanding the Data**: Start by checking what each column represents, the types of values (categorical, numerical, binary, etc.), and get a general sense of the data structure. 2. **Summary Statistics**: Pandas provides a `describe()` function that gives a useful summary of the numerical columns. It shows the mean, standard deviation, min, max, and quartiles. For non-numeric data, you can use the `value_counts()` method to see the distribution of categories. 3. **Visualizing the Data**: Graphical representations can help you understand the data better. Histograms and box plots are useful for visualizing distributions, scatter plots can show relationships between va

Participating in Kaggle competitions is a great way to learn and apply data science techniques. Here are some steps and tips to get you started: 1. **Create a Kaggle Account**: The first step is to create a Kaggle account, if you haven't already. 2. **Find a Competition**: Browse the Competitions section on Kaggle to find one that interests you. If you're a beginner, you might want to start with one of the "Getting Started" competitions, such as the "Titanic: Machine Learning from Disaster". 3. **Understand the Problem Statement**: Read the competition details carefully to understand the problem you need to solve, the data you have to work with, and the metric on which your solution will be evaluated. 4. **Download the Data**: Download the provided datasets. Kaggle competitions usually provide a training set, which includes the target variable, and a test set, which you'll use to make predictions for submission. 5. **Explore the Data**: Use techniques su