### Google Beam:
- Through Project Starline, Google Beam is a new 3D teleconferencing technology using a six-camera array and a custom light field display for realistic 3D representations during calls.

### Other Notable Announcements:
- Firesat: A partnership led by Earth Fire Alliance to use AI to create a breakthrough in wildfire detection. FireSat uses high-res multispectral satellite imagery and AI to provide near real-time insights on wildfires. Provides global high resolution imagery that is updates in every 20 minutes.
- SynthID Detector: A new portal to help identify AI-generated content in images, audio, video, and text by scanning for the SynthID watermark.
- Google AI Ultra: A new subscription plan offering the highest access to Google's most capable AI models and premium features.
- Personalized Smart Replies in Gmail: Gemini models can now generate smart replies tailored to your writing style by learning from your emails and Drive documents (with your permission).
- Real-time Translation in Google Meet: This feature can translate speech in near real-time, matching the speaker's voice, tone, and expressions. English and Spanish translation is rolling out in beta for Google AI Pro and Ultra subscribers.
- Chrome Password Manager Upgrade: It will automatically change passwords on accounts compromised in data breaches.

These were some of the major announcements from yesterday's Google I/O Keynote. The event continues today with more developer-focused sessions.

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Weak vs Strong Patterns

Types of Stablecoins

Today, we’ll tell you about the three main categories of stablecoins.

▪️ Stablecoins backed by fiat currencies

These coins are backed by real-life assets, fiat money, or paper money. Two examples of this stablecoin are Tether (USDT) and USD Coin (USDC). The companies issuing these coins own large reserves to support every issued coin; however, Tether has come under intense scrutiny in the past for this specific issue.

▪️ Stablecoins backed by cryptocurrencies

Some projects are so bold that they’re willing to back their stablecoin with other cryptocurrencies (not real assets or money). For example, a crypto-backed stablecoin with a value of $1 could be supported by a crypto asset worth $2. The logic here is that if the underlying asset’s value were to drop, the stablecoin would still be able to maintain its dollar peg.

The most famous crypto-backed stablecoin is Dai (DAI).

▪️ Algorithmic stablecoins

Algorithmic stablecoins are not backed by assets or fiat currencies, which makes it difficult to understand why or how they’re stablecoins in the first place. As their name indicates, the value of these coins is controlled by computer algorithms. If the stablecoin’s value is pegged to $1 but rises above $1, the code will automatically mint and release more coins into circulation to lower the stablecoin’s value back to $1. Conversely, if the value drops below $1, the algorithm will remove—or burn—coins from circulation to lift the value back up to $1. The amount of coins you hold will change, but they’ll always reflect the value you own.

Please note: Stablecoins are not dollars—they’re cryptocurrencies. Even when dealing with stablecoins, investing in crypto carries inherent risks—case in point the collapse of Terra’s algorithmic stablecoin TerraUSD.

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AI Tools

𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤

→ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦

Clearly outline the type of task:
↬ Classification: Predict discrete labels (e.g., cats vs dogs).
↬ Regression: Predict continuous values
↬ Clustering: Find patterns in unsupervised data.

→ 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚

Data quality is critical for model performance.
↬ Normalize and standardize features MinMaxScaler, StandardScaler.
↬ Handle missing values and outliers.
↬ Split your data: Training (70%), Validation (15%), Testing (15%).

→ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞

𝑰𝐧𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Number of neurons equals the input features.

𝐇𝐢𝐝𝐝𝐞𝐧 𝐋𝐚𝐲𝐞𝐫𝐬
↬ Start with a few layers and increase as needed.
↬ Use activation functions:
→ ReLU: General-purpose. Fast and efficient.
→ Leaky ReLU: Fixes dying neuron problems.
→ Tanh/Sigmoid: Use sparingly for specific cases.

𝐎𝐮𝐭𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Classification: Use Softmax or Sigmoid for probability outputs.
↬ Regression: Linear activation (no activation applied).

→ 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬

Proper weight initialization helps in faster convergence:
↬ He Initialization: Best for ReLU-based activations.
↬ Xavier Initialization: Ideal for sigmoid/tanh activations.

→ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧

↬ Classification: Cross-Entropy Loss.
↬ Regression: Mean Squared Error or Mean Absolute Error.

→ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐭𝐡𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫

Pick the right optimizer to minimize the loss:
↬ Adam: Most popular choice for speed and stability.
↬ SGD: Slower but reliable for smaller models.

→ 𝐒𝐩𝐞𝐜𝐢𝐟𝐲 𝐄𝐩𝐨𝐜𝐡𝐬 𝐚𝐧𝐝 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐳𝐞

↬ Epochs: Define total passes over the training set. Start with 50–100 epochs.
↬ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.

→ 𝐏𝐫𝐞𝐯𝐞𝐧𝐭 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠

↬ Add Dropout Layers to randomly deactivate neurons.
↬ Use L2 Regularization to penalize large weights.

→ 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠

Optimize your model parameters to improve performance:
↬ Adjust learning rate, dropout rate, layer size, and activations.
↬ Use Grid Search or Random Search for hyperparameter optimization.

→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞

↬ Monitor metrics for performance:
→ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
→ Regression: RMSE, MAE, R² score.

→ 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧

↬ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.

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🤖 AI wins debates against humans, with bonus points for knowing your personal details

New research reveals that AI can be just as persuasive as humans in debates, and even more so when armed with information about a person’s age, gender, ethnicity, and political views. Francesco Salvi, a researcher at the Swiss Federal Institute of Technology, warns that if persuasive AI can be deployed at scale, it could lead to armies of bots microtargeting undecided voters with tailored political narratives that feel authentic.

The study, which involved 600 debates, found that AI was most effective at shifting opinions on topics that people didn’t already have strong views on. This suggests that AI can exploit the undecided middle by providing personalised persuasion. Unlike your annoying uncle at Thanksgiving, AI’s debate style is analytical and structured, making points that feel crafted specifically for the individual.

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Remote Phala Network Ambassador Job at Phala Network

- Good for someone who is into Web 3 & decentralized Technologies

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Roadmap to becoming Data Analyst

Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

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Interview Process for Data Science Role at Amazon (https://t.me/datascienceresourcestp/85)

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From researching to booking: many travelers act on AI-generated tips

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OpenAI acquired io, the AI device startup co-founded by Jony Ive

Jony Ive was the designer of Iphone.

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Flutter vs. React Native A Comprehensive Comparison

When it comes to cross-platform mobile app development, two of the most popular frameworks are Flutter and React Native. Both have their unique strengths and can be the right choice depending on your project needs.

1. Overview
- Flutter: Developed by Google, Flutter is an open-source UI toolkit that allows developers to create natively compiled applications for mobile, web, and desktop from a single codebase. It uses the Dart programming language.
- React Native: Developed by Facebook, React Native is a popular framework for building mobile applications using JavaScript and React. It enables developers to create apps for both iOS and Android with a single codebase.

2. Performance
- Flutter: Known for its high performance, Flutter uses Dart's ahead-of-time (AOT) compilation to compile the code into native machine code, which results in faster execution and smoother performance.
- React Native: While React Native also offers good performance, it relies on JavaScript to bridge the gap between the app and the native components, which can sometimes lead to performance bottlenecks.
Optimize your App's Perfomance: t.me/mobiledevresourcestp/86

3. Development Experience
- Flutter: Flutter provides a rich set of pre-designed widgets and a hot reload feature, which allows developers to see changes in real-time without restarting the app. However, Dart is less commonly used compared to JavaScript, which might require a learning curve.
- React Native: React Native benefits from the vast ecosystem of JavaScript and React. It also supports hot reloading, making the development process faster and more efficient. The familiarity of JavaScript can be a significant advantage for many developers

4. Community and Ecosystem
- Flutter: Flutter has a growing community and is backed by Google, which ensures regular updates and improvements. The ecosystem is expanding, but it is still not as extensive as React Native's.
- React Native: With a larger and more mature community, React Native has a wealth of libraries, tools, and resources available. This extensive ecosystem can be very beneficial for developers looking for third-party integrations.

5. Use Cases
- Flutter: Ideal for projects that require a high level of custom UI and performance, such as gaming apps or applications with complex animations.
- React Native: Best suited for applications that need to be developed quickly and efficiently, especially if the development team is already familiar with JavaScript and React.

Conclusion
- Both Flutter and React Native are powerful frameworks for cross-platform app development. Your choice between the two should depend on your specific project requirements, team expertise, and performance needs. Flutter excels in performance and custom UI, while React Native offers a more extensive ecosystem and faster development with JavaScript.

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Data Science Roadmap

🌟 Unlock Your Potential with Free Online Courses! 🌟

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🏝 JetBrains announced a strategic partnership with Spring and Kotlin (https://blog.jetbrains.com/kotlin/2025/05/strategic-partnership-with-spring/)

Key areas of the partnership:
👉 Full null safety for Kotlin and Spring applications
👉 Official Spring training materials will use Kotlin for examples
👉 Will work on speeding up the kotlinx.reflect library (https://kotlinlang.org/api/core/kotlin-reflect/), since reflection is actively used in Spring
👉 Development of the Kotlin DSL for configuration

It is not clear what this will result in for Ktor Server, but clearly JB is trying its best to increase the popularity of the language beyond mobile and this is vital for Kotlin

App Dev Updates: https://t.me/mobiledevresourcestp

Confidence comes after we do difficult things.

Many people, no matter what field, do not want to try difficult things. Because they think that having confidence is a prerequisite for doing difficult things. Their thinking may seem right, but the reality is the opposite. A person who cannot face difficult things cannot have confidence because he cannot overcome them.

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AI Generated Movie _ This is where we are currently with image and video generation with AI

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Remote Software Engineer Job at Zencore (Short Term Contract)

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Waking up at 5 AM to train is hard .
Saying no to fast food is hard
Lifting heavy is hard..

But you know what's harder?

Joint pain from being overweight.
Health issues from eating bad .
Being frail and weak.

Choose your hard...🤞

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Top 10 Machine Learning Algorithms 👇👇

1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.

2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.

3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.

4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.

5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.

6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.

7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.

8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.

9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.

10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.

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