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Towards Data Science

144

Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models

Time series analysis can be made simpler by starting from basics and focusing on intuition behind concepts.
Understanding time series involves identifying trends, seasonality, and noise to make informed predictions.
Baseline models like Naive Forecast, Seasonal Naive Forecast, and Moving Average provide simple yet effective forecasting.
Baseline models like Moving Average can provide around 80% accuracy for business planning, making them valuable.
Decomposing time series into trend, seasonality, and residuals is crucial for selecting appropriate forecasting models.
Additive model in time series decomposition assumes that trend, seasonality, and residuals combine linearly.
The stability of seasonal patterns over time indicates that an additive model is suitable for decomposition.
Multiplicative models are preferred when seasonal effects scale with trend, capturing proportional changes.
Implementing a Seasonal Naive model based on decomposition shows the forecasting accuracy and limitations.
Evaluation metrics like MAPE are used to assess forecasting model performance and set benchmarks for future improvements.

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Tech Radar

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Pico just updated its best VR headset feature – and now I'm even more jealous my Meta Quest 3 doesn't have it too

Pico has introduced a new motion tracker for the waist, enhancing its VR headset.
The waist tracker is priced at 39.99 and offers accurate tracking for supported games.
Pico's foot trackers were a standout feature of the Pico 4 Ultra headset.
While Pico's motion tracking is impressive, it faces stiff competition from Meta Quest 3.

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Dev

114

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Hi everyone, nice to meet you!

Liram is a 16.5-year-old undergraduate student studying Computer Science at the Academic College of Management.
Liram is passionate about learning and experimenting with programming languages, algorithms, and software development.
Liram is excited about integrating into the job market and continuing to grow personally and professionally.
Liram is looking forward to connecting with people in the tech industry for collaboration and innovation.

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Mountaingoatsoftware

114

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Retrospective Training Bundle: Fix Broken Retros for Good

Many teams find sprint retrospectives uninspiring and ineffective, leading to low morale and stalled improvement.
Issues in retros often include surface-level discussions and a lack of meaningful insights or actionable outcomes.
Despite attempts to improve retros, such as changing templates or encouraging participation, many teams still struggle.
Retrospectives fail not due to format but because of losing sight of their purpose as the heart of team improvement.
A Better Retrospectives Bundle is introduced, comprising courses on enhancing retros and fixing common problems.
The Better Retrospectives course focuses on creating valuable retros through effective facilitation and design.
The Retrospectives Repair Guide helps troubleshoot issues like quiet teams, recurring problems, and lost action items.
This bundle aims to make retrospectives engaging, productive, and crucial for continuous improvement.
Retrospectives are vital for enhancing communication, morale, delivery, and quality within teams when executed effectively.
By obtaining the bundle, teams can transform retros from mundane rituals to drivers of team performance.
The offer for the Better Retrospectives Bundle is available until April 17, 9 PM PDT, providing a cost-effective solution for teams seeking retrospective improvement.

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Medium

405

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Privacy is a luxury — Dmail gets it. No more breaches, no more risk, just total security.

Dmail is a secure Web3-based alternative to Gmail that allows users to control their communication and data.
Dmail was created as a response to the vulnerability and lack of control in Web2 platforms.
By using Dmail, users don't have to worry about security breaches or compromising their privacy.
Dmail offers a decentralized, secure, and ownership-focused approach to digital communication.

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Medium

342

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Building Composite Configuration Sources in Spring Boot

Spring Boot collects configuration values from various sources to customize behavior in different environments without changing code.
The Environment object in Spring Boot holds a list of sources like files, system variables, and startup arguments.
Configuration sources are converted into PropertySource objects and prioritized based on a predefined order.
Spring Boot processes configuration sources in a specific order, with command-line arguments taking precedence over other sources.
External configuration files can be loaded using parameters like spring.config.location or through environment variables.
Spring Boot uses loaders to convert application.yml or application.properties files into key-value pairs for easier access.
Property sources are scanned and loaded using property loaders implemented based on file types.
Spring Boot resolves property conflicts by following a 'first one wins' process, picking the first occurrence of a property.
Property lookup in Spring Boot is consistent across different sources, including YAML files, environment variables, and command-line flags.
Spring supports placeholder syntax for referencing properties within other properties, aiding in clean configuration.

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Javacodegeeks

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The Best VS Code Extensions for Multi-Language Development in 2025

1. Dev Containers (by Microsoft) - Best for teams working across Python, Node.js, Java, or C++ where dependency conflicts are common.
2. Nix & Devbox - Best for teams that need version-pinned dependencies across languages.
3. Dependabot (GitHub) & Renovate - Best for projects with frequent dependency changes or security-sensitive applications.
4. Bazel (by Google) - Best for monorepos with mixed C++, Java, Go, or TypeScript.

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Towards Data Science

268

Mining Rules from Data

Mining rules from data is a crucial part of working with products, involving the implementation of rules based on practical examples to address business problems like fraud detection, customer segmentation, and cost management.
Rules can be established based on factors like user behavior, transaction patterns, and customer profiles to make informed decisions without relying solely on complex machine learning models.
Implementing static rules can be beneficial in scenarios where speed, interpretability, and compliance are essential considerations, such as during fraud waves or cost optimization in transactional businesses.
The article dives deep into using Decision Tree Classifier to extract rules from data, covering topics like building the model, fitting it, extracting rules, and exploring categorical encoding options.
A practical example using the Bank Marketing dataset demonstrates the process of creating rules based on decision tree splits and interpreting the resulting segments for targeted business strategies.
Various functions and logic are defined to parse decision tree structures, extract rule conditions, and apply them to identify customer segments with higher conversion probabilities for targeted actions.
The article emphasizes the importance of testing heuristics on validation sets to validate their effectiveness before deployment, especially in decision-making processes where limited operational resources are involved.
Exploring high cardinality categories and techniques like count encoding, target encoding, and smoothing highlight the challenges and strategies for handling categorical variables effectively in rule-based decision-making.
By combining data-driven rules with practical business insights, businesses can make informed decisions efficiently, optimize resource allocation, and enhance customer interactions based on segmented targeting.
The article provides a comprehensive guide on mining rules from data, showcasing the power of simple heuristics in driving business decisions and the significance of continuously updating and refining rules for evolving data landscapes.
It's essential to strike a balance between simplicity, interpretability, and accuracy when leveraging rule-based decision-making approaches, ensuring that rules remain relevant and effective in guiding business actions.

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Hackernoon

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With AI, Code Is No Longer the Hard Part

Large Language Models (LLMs) are revolutionizing software development, making coding faster and easier by generating optimized code quickly in various languages.
AI streamlines tasks like API development, debugging, and performance improvements, freeing engineers from tedious coding work.
While AI enhances coding efficiency, the true challenges for engineers lie in understanding business logic, designing architectures, and managing technical aspects.
AI augments developers, assisting in debugging, scaffolding generation, and refactoring, while engineers focus on higher-level tasks and oversight.
Junior developers now require skills in AI literacy, critical thinking, and systems design due to AI's ability to automate basic coding tasks.
Senior engineers evolve into master strategists who orchestrate AI workflows, optimize AI-generated code, and manage AI anomalies to ensure system efficiency.
The future of software development entails AI handling code generation and optimization, while engineers emphasize design, security, and AI governance.
Engineers who excel will be those who can leverage AI effectively, design scalable systems, and address AI limitations, rather than those with high code volumes.
Software engineers are becoming abstraction engineers, AI trainers, and system architects, necessitating a shift in skill sets for successful adaptation to AI-driven development.
The imperative for engineers is to evolve rapidly to keep pace with AI advancements in coding, as AI transforms the development landscape.
The decision lies in whether engineers lead the shift towards AI-led development or risk being surpassed in the evolving realm of software engineering.

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Medium

355

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Rule Number Four: If You Own It, They Own It

To foster ownership in a development team and ensure accountability, it is important to provide the context and explain the purpose behind a feature.
Sharing insights and data that demonstrate the business need can help developers understand the urgency and take responsibility for timely delivery.
Assigning the feature to a team member and actively supporting them, as well as giving them visibility and recognition, can foster a sense of ownership and pride in their work.
Creating an environment where open discussions are encouraged, even around controversial decisions, empowers individuals to take ownership of their work and fosters real accountability.

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Sdtimes

291

New Okta Platform innovations extend Identity Security Fabric to non-human identities in an agentic AI future

Okta introduced new capabilities on its Platform to secure non-human identities like AI agents with the same level of visibility and control as human identities.
The growth of non-human identities is predicted to increase significantly, leading to potential security risks due to lack of proper authentication methods and static credentials.
Okta emphasizes the importance of implementing an identity security fabric to manage the complexity and identity sprawl of human and non-human identities.
The Okta Platform offers comprehensive security solutions for all types of identities, including measures for Identity Security Posture Management and Okta Privileged Access.
New features like Separation of Duties, Secure Device Features, and Secure Identity Integrations aim to enhance security for non-human identities and applications.
The Okta On-prem Connector allows integration of on-premises apps with Okta Identity Governance for better visibility and management of application entitlements.
Organizations are urged to focus on securing all identities across applications and environments by leveraging deeper and secure identity integrations offered by Okta.
These innovations aim to provide a robust security framework for the future where both human and non-human identities play vital roles.
The importance of securing non-human identities is underlined as organizations increasingly adopt automation tools and AI agents in their operations.
The Okta Platform seeks to address the challenges posed by the growing number of non-human identities and the associated security vulnerabilities.
By prioritizing identity security, companies can effectively safeguard against potential threats and maintain compliance in an evolving digital landscape.

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Sdtimes

347

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Google goes all in on agent development, Gemini at Google Cloud Next 25

Google announces new tools for building AI agents, including the Agent Development Kit (ADK) and Agent2Agent (A2A) protocol.
Google updates Agentspace platform with new features, such as a no-code Agent Designer and access to two new agents: Deep Research and Idea Generation.
Google introduces the 7th generation TPU, Ironwood, designed for inference purposes, with improvements in performance/watt and interconnectivity.
Google adds new capabilities to Vertex AI, including Lyria text-to-music model, and announces new Gemini capabilities in Google Workspace to enable agentic workflows.

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Medium

114

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5 Essential System Design Questions Every Developer Should Know — II

WebSockets - Pros and Cons
Periodic Polling - Pros and Cons
Recommendation for using WebSockets and polling
Challenges in implementing end-to-end encryption and ensuring low latency

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Dev

177

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Headless CMS vs Traditional CMS: What Works Best in 2025?

Headless CMS platforms offer flexibility, performance, omnichannel readiness, security, and collaboration, making them more popular for developers and enterprises in 2025.
Traditional CMS platforms are suitable for smaller projects and quick launches, but may become less efficient when scaling.
Enterprises are shifting to headless CMS to future-proof their digital infrastructure, integrate with custom stacks and workflows, and achieve speed and scalability.
When choosing a headless CMS in 2025, consider pricing, demos, and dev-first tools, and prioritize flexibility and performance.

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Andrew Chen

154

Image Credit: Andrew Chen

Every marketing channel sucks right now

The article discusses the challenges faced by startups in the current marketing landscape.
Common marketing channels like SEO, influencer marketing, PR, email marketing, viral loops, referral/affiliate marketing, and big launches on social media are critiqued for various reasons.
The Law of Shitty Clickthroughs is referenced, indicating that over time, all marketing strategies result in declining effectiveness.
Startups are advised to focus on 'Little Channels' rather than traditional, mature marketing strategies which may already be saturated.
The importance of having a good product is emphasized, as even the best marketing strategies cannot compensate for a poor product.
Embracing novelty, taking risks with branding, and leveraging new technologies for marketing are encouraged as ways to stand out in a competitive market.
The article concludes with a hopeful tone, suggesting that innovation and creativity can help navigate the challenges of the evolving marketing landscape.

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