Here is short info about post: Retrieval-Augmented Generation (RAG) has become the backbone of reliable AI assistants, search systems, and contextual chat experiences. Instead of relying purely on a large language model’s internal knowledge, RAG systems retrieve relevant external information and inject it into the model’s prompt, ensuring answers are more factual, explainable, and grounded in real data. On Android, however, RAG faces unique constraints. Mobile devices must operate under limited memory, intermittent connectivity, strict latency requirements, and battery considerations. A naïve cloud-only RAG approach introduces ... How Local Vector Cache Plus Cloud Retrieval Architecture for RAG on Android Keeps Responses Fast, Fresh, and Grounded

Here is short info about post: The rapid adoption of Large Language Models (LLMs) in enterprise applications has created a new class of architectural challenges. Developers are no longer only concerned with business logic and data persistence, but also with prompt construction, context management, safety, observability, and governance. Spring AI, as part of the broader Spring ecosystem, introduces Advisors as a powerful abstraction to address these cross-cutting concerns when interacting with LLMs. Interestingly, the conceptual foundation of Spring AI Advisors aligns very closely with Aspect-Oriented Programming ... How Spring AI Advisors Work and How Aspect-Oriented Programming Concepts Can Be Applied When Interacting With LLMs

Here is short info about post: Machine Learning (ML) systems are rapidly becoming core components of modern software products, powering everything from fraud detection and recommendation engines to autonomous vehicles and medical diagnostics. However, while ML promises transformative capabilities, it also introduces a fundamentally new security attack surface—one that traditional application security and DevSecOps practices are not designed to handle. Unlike conventional software systems that rely on deterministic logic and static rules, ML systems learn behavior from data, adapt over time, and often operate as opaque ... Why Machine Learning Systems Are Uniquely Vulnerable to Security Attacks and How MLSecOps Closes Gaps in Data, Models, and Pipelines

Here is short info about post: Modern applications generate massive volumes of logs that are invaluable for debugging, monitoring, auditing, and security analysis. However, logs often contain sensitive information such as email addresses, phone numbers, API keys, authentication tokens, credit card numbers, or personally identifiable information (PII). Persisting such data in plain text logs introduces serious compliance, privacy, and security risks. In Spring Boot–based systems, logs are typically emitted at very high throughput and across many threads. This makes it impractical to sanitize logs using naive ... How To Use Aho-Corasick Algorithm And Deterministic Tokenization In Spring Boot To Intercept Logs In Real Time And Remove Sensitive Values

Here is short info about post: In modern distributed systems, ensuring that operations behave correctly under concurrent access is one of the most challenging aspects of backend development. When multiple requests reach the same service—either due to retries, network failures, or parallel processing—systems must guarantee that business operations are executed exactly once, or at least produce the same result no matter how many times they are executed. This property is known as idempotence. Spring Boot applications deployed in microservice architectures are especially vulnerable to concurrency issues ... Implementing Idempotence in Distributed Spring Boot Applications Using MySQL Row-Level Locking and Transactions

Here is short info about post: Digital advertising has long depended on identifiers that were never designed for modern, privacy-aware, multi-device ecosystems. IPv4, cookies, mobile ad IDs, and probabilistic fingerprinting once formed the backbone of attribution, reach, and frequency measurement. However, fragmentation across devices, the explosive growth of Connected TV (CTV), and privacy regulations have exposed deep flaws in the legacy measurement model. IPv6 is emerging not merely as a networking upgrade, but as a structural shift in how digital identity and measurement can function at ... How IPv6 Is Disrupting the Digital Ad Measurement Model, Restoring Accuracy Across CTV and All Channels

Here is short info about post: The evolution of blockchain wallets has moved far beyond simple externally owned accounts (EOAs). As decentralized applications grow more complex and mainstream adoption accelerates, wallets must become programmable, user-friendly, and secure by design. ERC-4337 introduces Account Abstraction without protocol-level changes, enabling a new generation of smart wallets that redefine how users interact with Ethereum and EVM-compatible networks. This article explores how to build next-generation smart wallets using ERC-4337, covering architecture, core concepts, implementation steps, and practical coding examples. By the ... How To Build Next-Gen Smart Wallets With ERC-4337

Here is short info about post: Large Language Models (LLMs) have transformed how applications are built, enabling conversational interfaces, intelligent search, summarization, code generation, and much more. However, these capabilities come at a cost—both financially and operationally. Each inference call consumes compute resources, introduces latency, and increases expenses when scaled across thousands or millions of users. One of the most effective techniques to mitigate these issues is semantic caching, and Redis LangCache has emerged as a powerful tool for implementing it. By storing and retrieving LLM ... How To Use Redis LangCache To Semantically Cache LLM Prompts And Responses, Reducing Inference Costs And Improving Performance

Here is short info about post: Modern digital organizations face a paradox: the demand for faster delivery has never been higher, yet the risks associated with speed—security vulnerabilities, cost overruns, operational instability, and governance gaps—have never been greater. Traditional scaling approaches focused primarily on infrastructure elasticity or team expansion are no longer sufficient. To scale delivery safely and efficiently, organizations must adopt a holistic, AI-native operating model that integrates AI agents, semantic layers, platform engineering, supply-chain security, observability, and FinOps into a cohesive system. This article ... How To Scale Delivery Safely And Efficiently Using AI Agents, Semantic Layers, Platform Engineering, Supply-Chain Security, Observability, And FinOps

Here is short info about post: Modern API security has evolved far beyond simple bearer tokens. While OAuth 2.0 bearer access tokens are widely used, they suffer from a fundamental weakness: whoever possesses the token can use it. If an attacker intercepts the token—via a compromised network, browser extension, proxy, or log leak—the token can be replayed until it expires. Demonstrating Proof-Of-Possession (DPoP) was introduced to address this problem by cryptographically binding an access token to a specific client-held key. In this article, we explore how ... How Demonstrating Proof-of-possession (dpo P) Binds Access Tokens To A Client Key In Spring Boot

Here is short info about post: Computer vision models are now deployed in high-stakes environments such as autonomous driving, medical imaging, surveillance, robotics, and quality inspection. In these domains, raw performance on a clean benchmark dataset is not enough. A vision model must demonstrate high accuracy under real-world conditions, where backgrounds vary, noise is unavoidable, lighting changes unpredictably, and visual clutter can distort object appearance. This article explores testing methodologies for vision models with a strong focus on accuracy, background robustness, and noise resilience. We examine ... Tests for Vision Models Focused on High Accuracy, Backgrounds, and Noise

Here is short info about post: Artificial intelligence agents are no longer simple function callers or prompt responders. They are increasingly autonomous systems tasked with planning, decision-making, tool usage, and long-term interaction with dynamic environments. However, many AI agents today are fragile: they fail silently, repeat the same mistakes, forget lessons learned, and lack mechanisms for introspection. Transforming fragile AI agents into resilient systems requires more than better prompts or larger models. It demands architectural changes that enable agents to reflect on failures, store experiential knowledge, ... How To Transform Fragile AI Agents Into Resilient Systems That Reflect on Failures and Retain Knowledge to Avoid Repeating Mistakes