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From Fundamentals to Factor Buckets : A K-Means Clustering Approach to Stock Styles

Investors often face information overload, leading to analysis paralysis and sub-optimal decisions.
The mismatch between individual investors' thinking and how data is presented by screeners is a significant issue.
A K-Means clustering approach was used to categorize S&P 500 companies into intuitive 'styles' like Undervalued, High Growth, etc.
The project focuses on capturing five core factor themes: Value, Growth, Quality, Defensive, and Momentum & Risk.
Data cleaning steps involved imputing sector medians, transforming skewed distributions, sector-neutral robust scaling, and winsorizing outliers.
Feature selection was based on Spread Ratio, and metrics with spread ratio > 4% were kept for clustering.
The number of clusters for each factor bucket was determined using elbow analysis, Silhouette score, and Davies–Bouldin index.
Clusters were labeled based on their characteristics, making the model output actionable and easy to interpret for investors.
The model's performance was tested using hypothetical investor personas and equal-weighted mini-portfolios.
The results suggested that the factor buckets captured distinct investment styles with varying performance.

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Medium

620

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Secure APIs Without JWT: Alternatives You Should Know About

JWT is commonly used for API authentication but can create more problems than it solves.
Developers often struggle with JWT's quirks, security pitfalls, and algorithm complexity.
There are excellent alternatives to JWT that may be better suited for specific use cases.
Exploring API authentication beyond JWT is crucial to understanding when to use other methods.
JWT, while not inherently bad, has weaknesses like algorithm confusion.
Many developers have fallen victim to attacks due to JWT's flexibility, such as the 'algorithm switching' attack.
Instances of leaked secrets like API keys due to JWT misconfigurations have been significant.

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Dev

373

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Behind the Scenes of Netflix: Unpacking Its World-Class Infrastructure

Netflix's world-class infrastructure handles over 260 million subscribers and billions of hours of content monthly, relying on cloud-native architecture and a custom CDN for global delivery.
Evolution: Originating as a DVD rental service, Netflix transitioned to AWS in 2008 for scalability and resilience, moving to a microservices-based ecosystem.
Architecture: Netflix operates entirely on AWS, utilizing services like EC2, S3, RDS, DynamoDB, and CloudFront for deployment, storage, and content delivery.
Microservices: Netflix runs over a thousand microservices that communicate through REST, gRPC, and Kafka for scalability and decoupling.
Video Pipeline: Netflix's streaming pipeline encodes videos in various formats, optimizing content delivery through a custom CDN called Open Connect for low latency.
Personalization: Netflix leverages machine learning for personalized recommendations, conducting extensive A/B testing to enhance user experience.
Observability: Netflix ensures reliability through chaos engineering tools like Chaos Monkey, and monitoring systems like Atlas, Zipkin, and ELK Stack.
Global Availability: Netflix deploys across AWS regions, smartly routing requests to nearest servers for low latency and adaptive streaming.
Developer Tooling: Netflix empowers developers with Spinnaker and Titus for safe deployments, and follows strict security measures for data protection.
Conclusion: Netflix's infrastructure showcases cloud-native engineering excellence, providing insights for building scalable and intelligent systems.

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Dev

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Unlock Your React Potential: 20 Must-Have Resources Beyond the Obvious!

This article presents 20 exceptional resources to enhance React.js skills beyond the basics, including courses, interactive platforms, GitHub repositories, and essential tools for React developers.
Courses like 'The Complete Intro to React v9' by Brian Holt, 'Epic React' by Kent C. Dodds, and freeCodeCamp's React Course offer in-depth learning experiences.
GitHub repositories like 'Awesome React' and 'React Patterns' provide curated lists and examples for enhancing React knowledge and understanding design patterns.
Useful resources like 'React Testing Library' for testing, 'TanStack Query' for data management, and 'Reactiflux Discord Community' for networking are highlighted.
The article concludes with recommendations for essential tools and communities every React developer should explore.

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Logrocket

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How to build and deploy a web app with Bolt

Building a web app with Bolt.new, an AI-powered web development agent, makes creating full-stack applications quick and effortless.
Bolt.new eliminates the need for extensive tool setup, offering a complete development environment accessible through simple conversational prompts.
Frontend developers can use Bolt.new to prototype big ideas quickly, even without advanced coding skills.
The article provides insights on utilizing Bolt.new, from token usage and AI prompt enhancement to best practices for effective prompts.
Detailed prompting with Bolt.new ensures better results and minimizes token consumption.
The process of building and deploying a web application with Bolt includes specifying design features, tech stack, functionalities, and code structure.
Integrating Supabase for authentication, database queries, and protected routes is discussed for extending the functionality of the web app.
The article also mentions Bolt.new features like live coding, code highlighting, and deployment options.
Bolt.new's pricing tiers and alternatives like Lovable and v0 by Vercel are outlined for comparison.
In conclusion, Bolt provides frontend developers with an efficient tool to bring their app ideas to life swiftly through detailed and structured prompts.

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Self-Learning-Java

416

Auto-Refresh for Materialized Views in BigQuery: Configuration and Best Practices

Materialized views in BigQuery improve query performance by precomputing query results.
Auto-refresh is essential for maintaining data freshness in materialized views.
Materialized views refresh within five minutes of base table changes in BigQuery.
BigQuery imposes a minimum refresh interval of 30 minutes to protect performance.
Auto-refresh can be enabled during view creation or using the ALTER statement.
Custom refresh intervals can be set between 1 minute and 7 days.
Shorter refresh intervals are suitable for real-time accuracy requirements.
Frequent refreshes for large tables can increase costs.
Monitoring job history in BigQuery helps in tracking refresh frequency.
Scheduled queries can provide more control than auto-refresh for materialized views.
Configuring auto-refresh optimally ensures data freshness without compromising performance.

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Self-Learning-Java

254

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Manually Refreshing Materialized Views in BigQuery: When and How to Do It

Materialized views in BigQuery help optimize performance by precomputing and storing query results.
BigQuery offers a way to manually trigger a refresh of materialized views for immediate data updates.
Reasons for manual refresh include needing to reflect recent changes, running tests, or verifying fixes.
To manually refresh a materialized view in BigQuery, use the system procedure BQ.REFRESH_MATERIALIZED_VIEW.
Example syntax: CALL BQ.REFRESH_MATERIALIZED_VIEW("project_id.dataset_id.materialized_view_name");
Manually refreshing updates the view with the latest data.
To check the last refresh time of a materialized view: SELECT last_refresh_time FROM INFORMATION_SCHEMA.MATERIALIZED_VIEWS WHERE table_name = 'your_view_name';
Materialized views enhance performance, and manual refresh provides control over updates when necessary.

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Self-Learning-Java

348

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What Are Labels in Prometheus Alerts?

Labels in Prometheus alerting rules are key-value pairs that provide additional information to alerts, helping categorize, route, and filter them.
Labels can be used to categorize alerts based on severity, route alerts to specific teams or channels, and group alerts in tools like Alertmanager, Grafana, or Slack.
Alert rules in Prometheus include labels like severity: critical to indicate the importance level of an alert.
Multiple labels can be added to an alert rule to make it more descriptive and informative, such as team and region information.
Labels assist in alert routing by specifying where to send alerts (email, Slack, PagerDuty, etc.), whether to silence or group alerts, and which team should respond.
Examples of label-based alert routing include sending critical alerts belonging to the infra team to the slack-infra channel.
In Prometheus alerting rules, labels are crucial for indicating the severity, team responsible, and environment context of alerts.
By using labels effectively, users can manage alert notifications, routing, and responses more efficiently within Prometheus.

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Logrocket

114

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How agile became a checkbox exercise, and how to get unstuck

Agile was born with the promise of making teams faster, more adaptive, and more aligned, but has lost its edge over time.
Many organizations treat agile as a checkbox exercise, leading to stagnation and lack of progress.
Rituals and ceremonies in agile can become hollow when driven more by habit than reflection.
Symptoms of process fatigue include backlog bloat, fake velocity, low morale, and ceremony saturation.
Diagnosing the root causes of agile dysfunction involves addressing process inertia, misunderstood metrics, and detached ceremonies.
Treating the agile process as a product can lead to improved outcomes and a focus on learning over metrics.
Reimagining agile involves prioritizing outcomes over outputs, learning over velocity, and clarity over cadence.
The team's involvement and co-creation of the agile process are essential for its success.
Resetting agile in phases through auditing, prioritizing, experimenting, and scaling can lead to improvement.
Agile should prioritize impact over imitation, focusing on solving real problems and delivering meaningful products.
The key is to approach agile with intentionality, fostering a culture of reflection, honesty, and adaptability.

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Medium

2.6k

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The Future of Software: When Apps Are Born from Words, Not Code

Software development is evolving to allow apps to be created from plain English instructions, rather than traditional coding, making it more accessible for non-developers.
New tools like Bolt.dev, Cursor, and v0.dev enable users to generate fully functional apps quickly by pasting plain English 'recipes' into the platforms and deploying them.
Apps are shared as intentions rather than code, allowing for customization and personalization based on individual preferences.
Users can remix apps by adding prompts for additional features like Stripe payments, analytics dashboards, or Slack integration, creating unique versions with AI assistance.
AI models like GPT-4o, Claude 3 Opus, DeepSeek, and LLaMA 3 interpret prompts to build apps with different styles and functionalities tailored to users' needs.
This revolution extends beyond software development to reshaping the creation of various content like images, music, and films by describing vision instead of coding.
Clarity of thought and articulating ideas effectively through prompts are becoming the new essential skills for rapid app creation.
The future of software development emphasizes idea articulation over memorizing code, enabling anyone with curiosity and clarity to participate in app creation.
While the concept may seem futuristic, the progression of tools like GitHub, no-code platforms, and AI advancements is making the vision of creating software with simple conversations more feasible.
The future of software creation is facilitated by the ability to input prompts to generate apps, marking a shift towards a more accessible and inclusive development process.

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Dev

561

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Android Developer Mode Guide: How to Enable It, Best Settings, and Tips to Become an Android Developer

Android Developer Mode is a hidden feature on Android devices that unlocks advanced tools primarily used by developers.
Enabling Developer Mode involves tapping 'Build number' 7 times in the 'About phone' section of Settings.
Key developer options to explore include USB Debugging, Stay Awake, Limit Background Processes, Show Taps & Pointer Location, Force GPU Rendering.
The Android operating system was developed by Android Inc., and later acquired by Google in 2005, leading to the widespread use of Android globally.
To become an Android developer, one needs to learn Java or Kotlin, install Android Studio, understand the Android SDK & API, build small projects, and publish apps on Google Play Store.
Joining tech communities like Reddit, GitHub, and Stack Overflow can further enhance learning and networking opportunities for aspiring Android developers.
Understanding Android Developer Mode is crucial whether you're curious about Android's inner workings or aiming to create your own apps.

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Dev

1.3k

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What is No-Code App Building?

No-code app building has revolutionized the tech world by allowing people to create apps without traditional programming code.
Using visual tools, individuals can create fully functional applications by clicking, connecting, and configuring components.
No-code platforms like Bubble, Airtable, Glide, and Adalo have democratized app development, making it more accessible and intuitive.
Benefits of no-code app building include faster development, reduced need for hiring engineers, and affordability of tools.
Real-world examples of no-code use cases include mobile apps for lead capturing, scheduling tools for schools, and gamified learning platforms.
Popular tools in the no-code landscape include Bubble for web apps, Glide for mobile apps, and Airtable for database logic.
Limitations of no-code include potential performance bottlenecks under scaling and vendor ecosystem dependencies.
AI integration is speeding up the no-code development process, aiming to make software creation even more accessible.
The cultural impact of no-code app building is shifting towards a world where building is as common as posting online, emphasizing creativity over coding skills.
No-code app building empowers individuals to bring their ideas to life without being hindered by a lack of coding expertise, marking a new era where everyone can be a builder.

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Medium

369

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I Tried 15+ Web Scraping Tools — Here’s the Only One I’d Trust for Big Data Projects

Web scraping tools have become essential for obtaining data, especially for AI-related projects.
Illegal data scraping is not permissible, necessitating the use of legitimate tools.
Bright Data is highlighted as a standout web scraping tool suitable for various purposes.
The recommended steps for utilizing Bright Data include signing up on their website for free credits.
Bright Data offers comprehensive functionalities and resources for efficient data scraping.
The tool is particularly useful for eCommerce, SERP tracking, AI model data, and market research.
Documentation and resources like webinars and videos aid in understanding how to use Bright Data.
Users can start for free with available credits and later manage funds for extended usage.
Bright Data's Web Scraper API library is instrumental in scraping specific data from platforms like LinkedIn, Instagram, and Amazon.
The 'Agent Browser' product facilitates running and controlling browsers online with advanced features.
Bright Data automates processes such as solving CAPTCHAs, handling detection avoidance, managing headers and cookies.
For non-coders, Bright Data offers user-friendly tools and no-code solutions, making it accessible to all.
Bright Data maintains compliance with legal standards and is used by notable Fortune 500 companies.
Customers can access ready-to-train data, ideal for AI projects, and benefit from regular updates.
Support, including 24/7 chat assistance, is available for users encountering scraping issues.
Overall, Bright Data is highly recommended for business growth and AI model training endeavors.

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Medium

110

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IoT Architectures Under Pressure

Despite the readiness of technology and clear benefits, the anticipated boom in IoT integration has not fully materialized.
The cost of smart devices poses a significant barrier to adoption, with expenses extending throughout the lifecycle.
Challenges like connectivity, interoperability, and high prices hinder the seamless integration of smart devices.
Exploration of alternative solutions and shared resources in IoT development can enhance efficiency and usability.
Reimagining smart device development by pooling resources and adopting edge computing could streamline operations and reduce redundancy.
The proposed firmware-less approach aims at consolidating resources and improving device cohesiveness.
Implementing centralized control, shared UI frameworks, and edge computing could optimize smart home management.
Transitioning to thin clients connected to a central hub for processing mirrors the concept of cloud gaming, enhancing efficiency.
The approach focuses on sustainability, efficiency, and utilizing existing tools for a seamless IoT architecture.
The article series 'IoT Architectures Under Pressure' explores practical implementations of efficient smart device development strategies.

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Alvinashcraft

174

Dew Drop – June 19, 2025 (#4443)

GitHub Copilot introduces Copilot Spaces to bring contextual suggestions.
Uno Platform Team discusses building Cross-Platform Media Applications in .NET with MediaPlayerElement.
MSSQL Extension for VS Code now supports Local Containers and GitHub Copilot Agent Mode.
Azure Developers delve into AI Agents with MCP.
Windows 365 and Azure Virtual Desktop enhance business resilience and security.
TypeScript introduces support for the new class Iterator at the type level.
Pulumi ESC now allows Bring Your Own Keys.
Deno 2.3 adds support for Local NPM Packages.
EF Core Query Hacks are shared to boost app performance.
OCI Artifacts are chosen by Docker for AI Model Packaging.

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