News Aggregator

GraphQL vs REST — Which Is Better?

Aggregated on: 2026-01-27 20:23:00

GraphQL and REST are two of the most popular API frameworks currently in use, with GraphQL seeing increasing adoption. GraphQL was developed by Facebook to address key challenges in REST APIs, particularly in reducing latency for mobile applications. The debate over which is better continues within the developer community, with strong arguments on both sides. The choice between GraphQL or REST depends on the type of application being built. GraphQL addresses latency issues caused by over-fetching and under-fetching, and provides built-in support for real-time updates. On the other hand, REST leverages HTTP constructs for caching, rate control, and telemetry, allowing these concerns to be handled at the platform layer rather than the application layer.

Building an Internal Document Search Tool with Retrieval-Augmented Generation (RAG)

Aggregated on: 2026-01-27 19:23:00

Why RAG Matters Now Large language models (LLMs) have shown how far generative systems can go. They draft text, answer questions, and even support software development. Yet they have a clear weakness. Models trained on public data often hallucinate and almost always lack access to company-specific knowledge (Ji et al., 2023). Relying only on pre-trained knowledge is risky when answers must be exact, such as in finance, healthcare, or HR policies. Retrieval-Augmented Generation, or RAG, has emerged as a practical solution. Instead of expecting the model to know everything, RAG connects the model to external sources of truth (Lewis et al., 2020). A user query is matched with relevant documents, and the model generates a response grounded in those documents. This approach closes the gap between general intelligence and domain expertise. The open question for many developers is whether RAG is just a patch for hallucination, or if it is the foundation for enterprise-ready AI.

Cost-Aware GenAI Architecture: Caching, Model Routing, and Token Budgets That Don’t Explode

Aggregated on: 2026-01-27 18:23:00

Shipping GenAI is easy. Shipping it without a surprise bill, latency spikes, and “why did it call the big model for that?” incidents is the hard part. This article is a practical architecture pattern for cost control as a first-class system requirement — built around three levers:

Designing Mathematical Software for Humans

Aggregated on: 2026-01-27 17:23:00

When we build developer tools, we tend to optimize for performance and API consistency. When we build mathematical tools, we have to optimize for something rarer: clarity of reasoning. Writing software for mathematicians, researchers, and educators requires a different mindset. It’s not just about whether the code runs fast; it’s about whether the code thinks the way the user does. This realization struck me while designing a Python package for combinatorial graph theory called chipfiring. What began as a niche research tool soon became an exercise in human-centered software design. The lessons, however, apply far beyond graph theory. Whether you’re writing a symbolic algebra engine, a geometry toolkit, or a probability simulator, the same principles can help you craft mathematical software that people actually understand.

Implementing HTTPS Two-Way Authentication in Android Using Delphi XE10.x

Aggregated on: 2026-01-27 16:23:00

I have an HTTPS cloud server. After a mobile app sends a request, it receives the content returned by the server. The server stores a self-made CA and a server certificate. Vue.js Component const https = require('https'); var fs = require('fs'); var options = { key: fs.readFileSync("./myserver.key"), cert: fs.readFileSync('./myserver.crt'), ca: fs.readFileSync('./MyCARoot.crt'), requestCert: true, rejectUnauthorized:true };

Versioning Lies: A Date Contract Is a Promise That Never Breaks

Aggregated on: 2026-01-27 15:23:00

URI versioning, such as /v1/foo, is one of the most used methods for versioning APIs. Clients can easily comprehend it, but as your API develops, it may become challenging to manage. However, a date versioned header-based API versioning can be a little more opaque for clients, but when done properly, it provides flexibility and maintainability. Let’s get a gist of both approaches.

Edge-First AI Architecture: Designing Low-Latency, Offline-Capable Intelligence

Aggregated on: 2026-01-27 14:23:00

Most mobile AI features silently depend on a “good enough” network. That’s fine on your office Wi-Fi. It’s not fine: On spotty 3G In the subway In a warehouse with terrible coverage When your cloud endpoint is down or throttled If your “AI feature” turns into a spinner or a generic error in those cases, users will stop trusting it.

The Rise of Platform Engineering: How Internal Developer Platforms Are Replacing Traditional DevOps

Aggregated on: 2026-01-27 13:23:00

Platform engineering has shed its experimental veneer. What began as isolated attempts by infrastructure teams to tame cloud sprawl — ServiceNow tickets piling up, Terraform modules copy-pasted across repositories, engineers slacking each other at 11 PM about which IAM role grants ECR access — has calcified into something defensible, something you can budget for. Internal Developer Platforms aren’t theoretical anymore. They’re the substrate on which organizations either scale gracefully or collapse under their own tooling weight. The shift feels less like a revolution and more like exhausted pragmatism. You build platforms because the alternative — letting every squad maintain its own Jenkins pipeline, its own Ansible playbooks, its own mental model of how secrets propagate from Vault to pods — becomes untenable somewhere between your third acquisition and your fiftieth microservice.

An Introduction to the Four Pillars of Observability

Aggregated on: 2026-01-27 12:23:00

It is a quiet Tuesday afternoon until the latency spikes begin. In the world of modern software engineering, we have moved far beyond the era of simple server monitoring. We no longer just “build and hope.” Instead, we strive for Continuous Reliability, a state where our systems are designed to be interrogated, understood, and improved in real time. This is the essence of Observability. To truly master a running system, we must look through four distinct lenses, often called the pillars of telemetry. Each provides a different chapter of the story, and together, they offer a level of visibility that transforms production from a “black box” into an open book.

Building an AI Agent Traffic Management Platform: APISIX AI Gateway in Practice

Aggregated on: 2026-01-26 20:07:59

Introduction: The Turning Point from Dispersed Traffic to Intelligent Governance Since early 2025, within a leading global appliance giant, multiple business lines have introduced numerous large language models (LLMs). The R&D department needed coding assistants to improve efficiency, the marketing team focused on content generation, and the smart product team aimed to integrate conversational capabilities into home appliances. The variety of models rapidly expanded to include both self-built solutions like DeepSeek and Qwen, as well as proprietary models from multiple cloud service providers. However, this rapid expansion soon exposed new bottlenecks: fragmented inference traffic, chaotic scheduling, rising operational costs, and uncontrollable stability issues.

Prompt Injection Is the New SQL Injection: How Hackers Are Breaking into AI Systems

Aggregated on: 2026-01-26 19:07:59

Why Prompt Injection Is the New Surface Attack and So Difficult by Design In December 2023, a Chevrolet dealership made headlines when users coaxed its ChatGPT-powered chatbot into “agreeing” to sell cars for $1. Just months earlier, in February, Microsoft’s Bing had exposed its hidden “Sydney” persona, venting irritation at users and sparking widespread alarm about AI behavior. These are some examples of prompt injection in action. OWASP now ranks prompt injection as the #1 vulnerability in their LLM Top 10. The thing that makes this challenging is that, since the SQL injection vulnerability took three decades to mature, we have now developed multiple methods to prevent it.

When Agile Teams Drown in Reports: How to Eliminate Noise and Build a Lean Reporting System

Aggregated on: 2026-01-26 18:08:00

Agile teams rely on data to make informed decisions, improve delivery flow, and maintain transparency across roles and ceremonies. Metrics provide visibility into how work progresses, where bottlenecks emerge, and whether the team is on track to meet its goals. Yet in many organizations, teams unconsciously fall into a reporting trap: they generate far more reports than they actually need. Dashboards, charts, spreadsheets, widgets, and analytics multiply over time: each created with good intentions, but often without clear ownership or a defined decision-making purpose. What begins as a simple attempt to track progress evolves into a sprawling reporting ecosystem that teams struggle to navigate.

Engineering Habits for Building Resilient Software

Aggregated on: 2026-01-26 17:07:59

Software engineering is changing quickly. Just as the internet and cloud computing changed how we build and launch apps, artificial intelligence (AI) is now speeding up the move from a minimum viable product (MVP) to large-scale systems. Tasks that used to take days, such as hackathons, now take only hours. Companies use AI agents for more than code completion. AI agents help with documentation, testing, and managing workflows. But adding AI to the software development lifecycle (SDLC) is not just about speed. It also means building good engineering habits to keep systems resilient, secure, and high-quality.

HAIP 1.0 for Verifiable Presentations: Securing the VP Flow

Aggregated on: 2026-01-26 16:07:59

In my previous article, I covered DPoP for securing the credential issuance (VCI) flow. This follow-up focuses on the Verifiable Presentation (VP) flow, in which a wallet presents credentials to a verifier. The VP Security Challenge Before HAIP, VP flows had significant vulnerabilities:

The Anatomy of an AI Agent and How to Build One With Docker Cagent

Aggregated on: 2026-01-26 15:07:59

Artificial intelligence (AI) agents are systems that understand their environment, reason, plan, and take actions using large language models (LLMs) with minimal human interaction. Today, we have agents that read your calendar, inbox, and event write and execute code. As part of this tutorial, we will explore the different constructs that make up an AI agent and learn how to build one using the Docker cagent. Unlike traditional software, which executes predefined logic, an AI agent can autonomously drive toward a goal with minimal human interaction. And at a very high level, every AI agent consists of seven core components.

How to Create AI-Enhanced Code Review Systems

Aggregated on: 2026-01-26 14:22:59

One of the main frustrations most engineers face when doing code reviews (PRs) is that they are often inconsistent. In some cases, PRs will receive in-depth reviews where each line of code is reviewed, while others may only have a summary review stating “Looks Good to Me” (LGTM), not because the developer did not care about the code being reviewed, but rather because they are coming off of a busy day, got tired, are overloaded with work context, etc. I have been through this situation multiple times with several startups, and as we continue to grow and develop into larger teams, the code bases we are developing continue to expand, and how we perform code reviews is becoming more chaotic, longer, and more subjective. Considering new members joining the development team, it is difficult for them to learn architectural patterns and guidelines, and typically, senior-level engineers become the bottleneck in code reviews.

The A3 Framework: Assist, Automate, Avoid — A Decision System for AI Delegation

Aggregated on: 2026-01-26 13:22:59

TL; DR: The A3 Framework The A3 Framework categorizes AI delegation before you prompt: Assist (AI drafts, you actively review and decide), Automate (AI executes under explicit rules and audit cadences), or Avoid (stays entirely human when failure would damage trust or relationships). Most AI training teaches better prompting. The A3 Framework teaches the prior question: Should you be prompting at all? Categorize first, then prompt.

Building Fault-Tolerant Data Pipelines in GCP

Aggregated on: 2026-01-26 12:22:59

After years of building data pipelines on Google Cloud Platform, I’ve learned fault tolerance isn’t optional; it’s essential from day one. What sets a production-ready pipeline apart isn’t the architecture diagram but how it handles out-of-order changes, backlog, crashes, and quota limits. This article distills lessons from real-world challenges: debugging issues at 3 a.m., handling unexpected load, and ensuring pipelines keep running through failures. The advice here is practical, not theoretical.

Secure AI Architecture for Payments: From Risk Signals to Real-Time Decisions

Aggregated on: 2026-01-23 21:11:09

Most users tap Pay on Android and assume it’s simple. Behind that one tap, your app is juggling PCI rules, device security, fraud checks, and 3DS — usually inside a tight latency budget measured in hundreds of milliseconds. If all of that logic lives in scattered checks, random API calls, and duplicated business rules, it will eventually break in subtle (and expensive) ways.

PostgreSQL Trigram Similarity vs. Pattern Matching: A Performance Comparison

Aggregated on: 2026-01-23 19:26:09

A previous article presented a performance comparison between PostgreSQL Full-Text Search (FTS) and plain pattern matching, especially in cases where a given piece of text is searched across multiple attributes of multiple entities. In brief, the conclusion was that FTS is significantly faster, while pattern matching offers greater flexibility, a wider range of applicability, and higher precision. The present article aims to continue this analysis, which is worthwhile when implementing global search solutions, in order to increase objectivity regarding the applicability of these methods and to help programmers make informed choices more easily.

Reimagining Innovation: How Citizen Application Development is Reshaping the Modern Enterprise

Aggregated on: 2026-01-23 18:26:09

Enterprise technology is shifting faster than ever. Innovation used to live behind a ticket. Business ideas waited in line while central teams balanced priorities, budgets, and release windows. Citizen application development changes that rhythm. With modern low-code and no-code platforms, people closest to the problem can create secure, integrated apps and automations that move work forward. The result is faster cycles, clearer ownership, and a pipeline of improvements that does not stall when bandwidth is tight. According to Gartner, citizen application development platforms accelerate digital transformation by empowering business users to independently build applications beyond traditional IT boundaries.

Vibe Coding Part 3 — Building a Data Quality Framework in Scala and PySpark

Aggregated on: 2026-01-23 17:26:09

A couple of years ago, I developed a class-based Data Quality (DQ) framework in Scala. After some time, I became part of an accelerator framework that dealt with many data engineering tasks, with data quality being one set of activities. After considering multiple factors, it was decided that the framework would be built using Python. Given that we wanted to handle large datasets and ensure scalability, we decided to implement the data quality framework in PySpark. Using the Scala-based DQ framework as a stepping stone and foundation, I developed an equivalent DQ framework in PySpark. The framework was successful, with the DQ functionality implemented as a Python library and packaged as a wheel.

How a Standardized Logistics Protocol Can Unlock AI's Full Potential in Supply Chain

Aggregated on: 2026-01-23 16:26:09

The logistics industry stands at an inflection point. While artificial intelligence promises to revolutionize supply chain operations — from real-time route optimization to autonomous fleet coordination — a fundamental bottleneck prevents these innovations from reaching their full potential: the lack of a standardized protocol for logistics providers to communicate. The NxM Problem in Modern Logistics Consider this scenario:

Securing AI/ML Workloads in the Cloud: Integrating DevSecOps with MLOps

Aggregated on: 2026-01-23 15:26:09

The security engineer's face went pale when she pulled up the access logs. Her team had deployed a fraud detection model to production three weeks earlier — standard stuff, containerized inference running on Kubernetes. Except someone had been quietly exfiltrating the model weights for the past eleven days through an API endpoint they'd forgotten to lock down. The attacker got everything: training architecture, parameter files, even the feature engineering pipeline. Six months of competitive advantage, gone. This happened at a Series C fintech in San Francisco last April. I know because I helped them write the incident report.

Docker Runtime Escape: Why Mounting docker.sock Is Worse Than Running Privileged Containers

Aggregated on: 2026-01-23 14:26:09

Most DevOps engineers know that running containers with the privileged flag is dangerous. It's one of those things we all learn early on - never use privileged mode in production unless you absolutely have to. Security teams flag it, auditors complain about it, and for good reason. But here's what scared me recently: there's something much worse that's being mounted into production containers every single day, and most teams don't realize the risk. I'm talking about mounting the Docker socket into containers.

Design and Implementation of Cloud-Native Microservice Architectures for Scalable Insurance Analytics Platforms

Aggregated on: 2026-01-23 13:26:09

Objective Statement This study proposes a scalable, modular, and cloud-native microservice architecture tailored for the insurance industry. The goal is to enable rapid enterprise-wide analytics adoption, seamless AI integration, and real-time data processing through containerization, orchestration, and service-based deployment models that enhance scalability, agility, and system resilience. Problem Context Although insurers are among the earliest adopters of artificial intelligence, fewer than 10% have successfully scaled AI initiatives beyond pilot programs.

The Future of AI Integration: Why Model Context Protocol Is the New API

Aggregated on: 2026-01-23 12:26:09

Introduction: From Custom-Code Chaos to Connection Harmony For decades, connecting software systems meant one thing: custom code. Every new integration required a new connector, another adapter, and a fresh set of maintenance nightmares. The same pain has now reached the world of AI. Developers are writing endless glue code to make large language models (LLMs) talk to databases, APIs, and business tools, and every integration is slightly different.

Scaling PostgreSQL Reads: Implementing Read-Your-Write Consistency Using WAL-Based Replica Routing

Aggregated on: 2026-01-22 20:26:09

At a previous company, we hit a point where our PostgreSQL database was clearly becoming a bottleneck. The primary instance was responsible for every user-facing search request, including some heavy queries from long-time users with years of historical data. CPU usage kept climbing, and before long, we were already running on the largest instance size available. Vertical scaling had officially hit its limit. Naturally, the next step was to introduce read replicas. On paper, it sounded straightforward. In reality, it wasn’t.

Feature Flags and Safe Rollouts With Azure App Configuration for Large SPA Teams

Aggregated on: 2026-01-22 19:26:09

When a new application starts, everything feels simple: features look straightforward, the architecture is clean, and launch day goes smoothly. Then the real work begins — a steady flow of new features, refactors, and bug fixes. You need to ship critical fixes fast without revealing unfinished pages or risky changes to customers. As the scope grows — multiple teams, shared dependencies, and large features — coordination gets harder. This is where feature flags help. Feature flags reduce risk by decoupling deployment from release. Azure App Configuration and Key Vault let you control rollouts safely, target by tenant/segment, and audit changes — without exposing secrets to the browser.

Agentic AI with Bedrock and DynamoDB Integration

Aggregated on: 2026-01-22 18:26:09

Introduction Modern banking is rapidly moving toward intelligent, context-aware automation. Instead of relying solely on static rule-based chatbots or hard-coded APIs, today’s systems need to understand intent, reason across multiple data sources, and deliver personalized insights in real time. This is where Agentic AI — autonomous systems capable of planning, reasoning, and taking action — is transforming customer interactions. In this article, we’ll explore how to build a customer service agent that leverages Amazon Bedrock and Amazon DynamoDB, powered by Boto3, to deliver dynamic responses such as:

Efficient Sampling Approach for Large Datasets

Aggregated on: 2026-01-22 17:26:09

Sampling is a fundamental process in machine learning that involves selecting a subset of data from a larger dataset. This technique is used to make training and evaluation more efficient, especially when working with massive datasets where processing every data point is impractical However, sampling comes with its own challenges. Ensuring that samples are representative is crucial to prevent biases that could lead to poor model generalization and inaccurate evaluation results. The sample size must strike a balance between performance and resource constraints. Additionally, sampling strategies need to account for factors such as class imbalance, temporal dependencies, and other dataset-specific characteristics to maintain data integrity.

Automating Visual Brand Compliance: A Multimodal LLM Approach

Aggregated on: 2026-01-22 16:26:09

In the domain of corporate marketing, “brand consistency” is the golden rule. However, enforcing that rule is often a manual, tedious nightmare. Marketing teams spend countless hours reviewing PDFs and slide decks to ensure the logo has enough padding, the fonts are correct, and the color gradients align with the style guide. For developers, this looks like a solvable problem. With the advent of multimodal large language models (LLMs) capable of processing text and images simultaneously, we can now build pipelines that “see” a document and “read” a brand rulebook to perform automated audits.

Why Semantic Layers Matter in Analytics: A Deep Dive into RAG Design

Aggregated on: 2026-01-22 15:26:09

Most analytic teams in companies are entering a new demand phase where stakeholders don't just want dashboards; they want a conversation that delivers fast answers, follow-ups, and breakdowns. Some examples are:

Data Engineering: Strategies for Data Retrieval on Multi-Dimensional Data

Aggregated on: 2026-01-22 14:26:09

You’ve likely heard about the benefits of partitioning data by a single dimension to boost retrieval performance. It’s a common practice in relational databases, NoSQL databases, and, notably, data lakes. For example, a very common dimension to partition data in data lakes is by date or time. However, what if your data querying requirements involve multiple dimensions? Let’s say you wish to query your data by field A and also by field B, or sometimes by field A but other times by field B. In this post, I’ll go over several common options for such a case.

MCP Isn't Just Convenience; It's a Security Problem and a Governance Opportunity

Aggregated on: 2026-01-22 13:26:09

The first time I wired an agent to real enterprise systems, it felt like I'd unlocked a cheat code. I had a clean, repeatable pattern: the model asks, a server answers, and suddenly the agent can do things. Then the uncomfortable thought hit: If I can plug in a new tool in an afternoon, an attacker can too.

Refactoring a Legacy React Monolith With Autonomous Coding Agents

Aggregated on: 2026-01-22 12:26:09

I've been wrangling React codebases professionally for well over ten years now, and honestly, the story is always the same in 2026: teams inherit these massive, everything-in-one-place apps built back when Create React App felt like the future. All the logic — auth, shopping cart, product lists, user profiles — lives in a handful of giant files. Props get drilled six levels deep, the state is scattered, and nobody wants to touch it because one wrong move brings the whole thing down. Last year, I led a refactor on a five-year-old dashboard exactly like that. We managed to break it into proper feature slices and even laid the groundwork for microfrontends. The thing that made the biggest difference? A multi-agent AI setup that did a lot of the heavy lifting for us. It wasn't magic — it still needed human eyes — but it turned a three-month nightmare into something we wrapped in five weeks.

The Future of Data Streaming with Apache Flink for Agentic AI

Aggregated on: 2026-01-21 20:26:08

Agentic AI is changing how enterprises think about automation and intelligence. Agents are no longer reactive systems. They are goal-driven, context-aware, and capable of autonomous decision-making. But to operate effectively, agents must be connected to the real-time pulse of the business. This is where data streaming with Apache Kafka and Apache Flink becomes essential. Apache Flink is entering a new phase with the proposal of Flink Agents, a sub-project designed to power system-triggered, event-driven AI agents natively within Flink’s streaming runtime. Let’s explore what this means for the future of agentic systems in the enterprise.

An Automated Inventory Pattern for Managing AWS EC2

Aggregated on: 2026-01-21 19:26:08

In the hybrid cloud era, managing infrastructure visibility is a constant battle. We spin up EC2 instances for testing, leave them running, and forget about them. Security groups become bloated, and cost management turns into a guessing game. While high-end tools like Datadog or CloudHealth offer solutions, they often come with significant licensing costs and integration overhead. Sometimes, you just need a lightweight, customizable way to see exactly what is running in your environment.

Where AI Fits and Fails in Workday Integrations

Aggregated on: 2026-01-21 18:26:08

Workday integrations sit at the heart of enterprise HR and finance systems, connecting Workday with myriad external applications. As artificial intelligence (AI) makes inroads into enterprise software, Workday engineers are exploring how AI can augment integration development and operations. From mapping data fields to detecting anomalies, AI promises to reduce manual effort and improve reliability. Yet amid the excitement, it is critical to distinguish where AI adds clear value versus where it overpromises or introduces risk. This strategic overview examines both sides, providing a balanced perspective for technically fluent Workday professionals. Workday itself has signaled a strong commitment to AI, embedding machine learning and automation into its platform. The goal is to weave intelligence into the flow of work rather than create standalone AI silos. For integration teams, this means new tools and features are emerging to streamline workflows. At the same time, seasoned engineers know that complex integrations require human insight. As we will see, AI will likely serve as an enhancer — not a replacement — for the expertise and judgment of Workday integration developers. With that context, let’s explore specific use cases where AI fits in Workday integrations and where it fails to live up to the hype.

RAG Architectures AI Builders Should Understand

Aggregated on: 2026-01-21 17:26:08

Large language models are exceptionally good at producing fluent text. They are not, by default, good at staying current, respecting boundaries of private knowledge, or documenting the sources of an answer. That gap is exactly where most AI products fail: the demo looks impressive, but the system is not trustworthy when users rely on it. Retrieval-augmented generation (RAG) closes the gap by designing an evidence path. Instead of letting the model “reason from memory,” you route the request through retrieval, enforce access rules, collect supporting sources, and then ask the model to answer from those sources with citations. In practice, RAG is less about prompting and more about engineering: a data pipeline, a contract, and an operational loop.

The No-Buffering Strategy: Streaming Search Results

Aggregated on: 2026-01-21 16:26:08

The "Buffering" Problem Let’s draw a parallel to video streaming. Modern protocols break the video into small, ordered chunks. This allows the client to render content immediately while the rest buffers in the background. The total data and the download time stay roughly the same, but the perceived speed improves dramatically. Complex search engines can be architected in a similar streaming fashion.

MERGE and Liquid Clustering: Common Performance Issues

Aggregated on: 2026-01-21 15:26:08

As a Spark support engineer, I still encounter many cases where MERGE or JOIN operations on Delta tables do not perform as expected, even when liquid clustering is used. While liquid clustering is a significant improvement over traditional partitioning and offers many advantages, people still sometimes struggle with it. There is often an assumption that enabling liquid clustering will automatically result in efficient merges, but in practice, this is not always true, and the reason is a lack of understanding. Here are the most common issues when executing a merge on a liquid clustering table.

Why High-Availability Java Systems Fail Quietly Before They Fail Loudly

Aggregated on: 2026-01-21 14:26:08

Most engineers imagine failures as sudden events. A service crashes. A node goes down. An alert fires, and everyone jumps into action. In real high-availability Java systems, failures rarely behave that way. They almost always arrive quietly first. Systems that have been running reliably for months or years begin to show small changes. Latency creeps up. Garbage collection pauses last a little longer. Thread pools spend more time near saturation. Nothing looks broken, and dashboards stay mostly green. Then one day, the system tips over, and the failure suddenly looks dramatic.

Multimodal AI Architecture: Unifying Vision, Text, and Sensor Intelligence

Aggregated on: 2026-01-21 13:26:08

Most Android AI features today are still single-modal A camera screen that does object detection. A chat screen that calls an LLM. A sensor-driven feature that runs in the background. The real fun starts when you combine these: camera, text, sensors, history, and context. That’s where multimodal AI shines — and where architecture makes or breaks your app.

AI-Driven Autonomous CI/CD for Risk-Aware DevOps

Aggregated on: 2026-01-21 12:26:08

Currently, the software development process relies on integrating development and operations (DevOps) to accelerate delivery without compromising quality. When the system becomes very complex, it becomes risky and delays the manual control of the continuous integration or continuous deployment (CI/CD) processes. AI-based autonomous pipelines manage the entire process by automating decisions, optimizing, and eliminating human errors. Continuous risk-aware DevOps involves monitoring and signaling issues, as well as predicting failures. The self-healing mechanisms handle the whole thing in a way that minimises disruption and improves system stability across different deployments.

Automating Traceability with Generative AI

Aggregated on: 2026-01-20 20:26:08

In the world of software engineering, we have robust CI/CD pipelines that ensure code traceability. We know exactly which commit caused a build failure. However, in Infrastructure Systems Engineering (Infrastructure SE), traceability is often broken. The documentation says one thing, the server configuration says another, and the test specification says a third. Verifying that the design intent matches the actual state is usually a manual process involving screenshots, spreadsheets, and human eyeballing.

Enterprise LLM Architecture Patterns, From RAG to Agentic Systems

Aggregated on: 2026-01-20 19:26:07

Large language models (LLMs) have rapidly moved from experimentation to production across enterprises, startups, and regulated industries. In this article, I present a set of 11 core LLM architecture patterns that have emerged as industry standards. These patterns are not mutually exclusive. In practice, high-quality LLM applications combine multiple patterns to achieve robustness, observability, and governance readiness.

Docker Hardened Images for Container Security

Aggregated on: 2026-01-20 18:26:08

In 2024, a staggering 87% of container images were found to have at least one vulnerability, and a measurable fraction of them have been targeted to compromise the production infrastructure. With cloud and container orchestration adoption not slowing down, the percentages are expected to increase. While organizations strive to keep their containers secure, security often takes a back seat to feature development. This is where Docker Hardened Images (DHI) can help, serving as a pivotal step towards container supply chain security.

The Messaging Challenges No One Talks About in Regulated, Air-Gapped, and Hybrid Environments

Aggregated on: 2026-01-20 17:26:08

The modern platform engineering mandate is clear: adopt Kubernetes, embrace microservices, and accelerate velocity. In theory, this leads to efficiency; in practice, if you operate within highly regulated sectors — Finance, Utilities, Defense, Healthcare, etc. — the journey often slows down due to significant networking and compliance requirements.

Why High Performance Storage is Important for AI Cloud Build

Aggregated on: 2026-01-20 16:26:07

The AI cloud market is experiencing exceptionally rapid growth worldwide, with the latest reports projecting annual growth rates between 28% and 40% over the next five years. It may reach up to $647 billion by 2030, according to various analyst reports. The surge in AI cloud adoption, GPU-as-a-service platforms, and enterprise interest in AI “factories” has created new pressures and opportunities for product engineering and IT leaders. Regardless of which public cloud or private cluster you choose, one key differentiator sets each AI and HPC solution apart: the performance of storage. While leading clouds often use the same GPUs and servers, the way data flows — between compute, network, storage, and persistent layers —determines everything from training speed to scalability. Understanding storage fundamentals will help you architect or select the right solution. We have previously covered how to build AI cloud solutions, and with hands-on experience in this space, we would like to share our thoughts in this article.