Technical Articles / Blogs / Perficient

Language Mastery as the New Frontier of Software Development

Juan Pineda — Mon, 16 Feb 2026 17:23:54 +0000

In the current technological landscape, the interaction between human developers and Large Language Models (LLMs) has transitioned from a peripheral experiment into a core technical competency. We are witnessing a fundamental shift in software development: the evolution from traditional code logic to language logic. This discipline, known as Prompt Engineering, is not merely about “chatting” with an AI; it is the structured ability to translate human intent into precise machine action. For the modern software engineer, designing and refining instructions is now as critical as writing clean, executable code.

1. Technical Foundations: From Prediction to Instruction

To master AI-assisted development, one must first understand the nature of the model. An LLM, at its core, is a probabilistic prediction engine. When given a sequence of text, it calculates the most likely next word (or token) based on vast datasets.

Base Models vs. Instruct Models

Technical proficiency requires a distinction between Base Models and Instruct Models. A Base LLM is designed for simple pattern completion or “autocomplete.” If asked to classify a text, a base model might simply provide another example of a text rather than performing the classification. Professional software development relies almost exclusively on Instruct Models. These models have been aligned through Reinforcement Learning from Human Feedback (RLHF) to follow explicit directions rather than just continuing a text pattern.

The fundamental paradigm of this interaction is simple but absolute: the quality of the input (the prompt) directly dictates the quality and accuracy of the output (the response).

2. The Two Pillars of Effective Prompting

Every successful interaction with an LLM rests on two non-negotiable principles. Neglecting either leads to unpredictable, generic, or logically flawed results.

1. Clarity and Specificity

Ambiguity is the primary enemy of quality AI output. Models cannot read a developer’s mind or infer hidden contexts that are omitted from the prompt. When an instruction is vague, the model is forced to “guess,” often resulting in a generic “average response” that fails to meet specific technical requirements. A specific prompt must act as an explicit manual. For instance, rather than asking to “summarize an email,” a professional prompt specifies the role (Executive Assistant), the target audience (a Senior Manager), the focus (required actions and deadlines), and the formatting constraints (three key bullet points).

Vague Prompt (Avoid)	Specific Prompt (Corporate Standard)
“Summarize this email.”	“Act as an executive assistant. Summarize the following email in 3 key bullet points for my manager. Focus on required actions and deadlines. Omit greetings.”
“Do something about marketing.”	“Generate 5 Instagram post ideas for the launch of a new tech product, each including an opening hook and a call-to-action.”

2. Allowing Time for Reasoning

LLMs are prone to logical errors when forced to provide a final answer immediately—a phenomenon described as “impulsive reasoning.” This is particularly evident in mathematical logic or complex architectural problems. The solution is to explicitly instruct the model to “think step-by-step.” This technique, known as Chain-of-Thought (CoT), forces the model to calculate intermediate steps and verify its own logic before concluding. By breaking a complex task into a sequence of simpler sub-tasks, the reliability of the output increases exponentially.

3. Precision Structuring Tactics

To transform a vague request into a high-precision technical order, developers should utilize five specific tactics.

• Role Assignment (Persona): Assigning a persona—such as “Software Architect” or “Cybersecurity Expert”—activates specific technical vocabularies and restricts the model’s probabilistic space toward expert-level responses. It moves the AI away from general knowledge toward specialized domain expertise.

• Audience and Tone Definition: It is imperative to specify the recipient of the information. Explaining a SQL injection to a non-technical manager requires a completely different lexicon and level of abstraction than explaining it to a peer developer.

• Task Specification: The central instruction must be a clear, measurable action. A well-defined task eliminates ambiguity regarding the expected outcome.

• Contextual Background: Because models lack access to private internal data or specific business logic, developers must provide the necessary background information, project constraints, and specific data within the prompt ecosystem.

• Output Formatting: For software integration, leaving the format to chance is unacceptable. Demanding predictable structures—such as JSON arrays, Markdown tables, or specific code blocks—is critical for programmatic parsing and consistency.

Technical Delimiters Protocol

To prevent “Prompt Injection” and ensure application robustness, instructions must be isolated from data using:
• Triple quotes (“””): For large blocks of text.
• Triple backticks (`): For code snippets or technical data.
• XML tags (): Recommended standard for organizing hierarchical information.
• Hash symbols (###): Used to separate sections of instructions.

Once the basic structure is mastered, the standard should address highly complex tasks using advanced reasoning.

4. Advanced Reasoning and In-Context Learning

Advanced development requires moving beyond simple “asking” to “training in the moment,” a concept known as In-Context Learning.

Shot Prompting: Zero, One, and Few-Shot

• Zero-Shot: Requesting a task directly without examples. This works best for common, direct tasks the model knows well.

• One-Shot: Including a single example to establish a basic pattern or format.

• Few-Shot: Providing multiple examples (usually 2 to 5). This allows the model to learn complex data classification or extraction patterns by identifying the underlying rule from the history of the conversation.

Task Decomposition

This involves breaking down a massive, complex process into a pipeline of simpler, sequential actions. For example, rather than asking for a full feature implementation in one go, a developer might instruct the model to: 1. Extract the data requirements, 2. Design the data models, 3. Create the repository logic, and 4. Implement the UI. This grants the developer superior control and allows for validation at each intermediate step.

ReAct (Reasoning and Acting)

ReAct is a technique that combines reasoning with external actions. It allows the model to alternate between “thinking” and “acting”—such as calling an API, performing a web search, or using a specific tool—to ground its final response in verifiable, up-to-date data. This drastically reduces hallucinations by ensuring the AI doesn’t rely solely on its static training data.

5. Context Engineering: The Data Ecosystem

Prompting is only one component of a larger system. Context Engineering is the design and control of the entire environment the model “sees” before generating a response, including conversation history, attached documents, and metadata.

Three Strategies for Model Enhancement

1. Prompt Engineering: Designing structured instructions. It is fast and cost-free but limited by the context window’s token limit.

2. RAG (Retrieval-Augmented Generation): This technique retrieves relevant documents from an external database (often a vector database) and injects that information into the prompt. It is the gold standard for handling dynamic, frequently changing, or private company data without the need to retrain the model.

3. Fine-Tuning: Retraining a base model on a specific dataset to specialize it in a particular style, vocabulary, or domain. This is a costly and slow strategy, typically reserved for cases where prompting and RAG are insufficient.

The industry “Golden Rule” is to start with Prompt Engineering, add RAG if external data is required, and use Fine-Tuning only as a last resort for deep specialization.

6. Technical Optimization and the Context Window

The context window is the “working memory” of the model, measured in tokens. A token is roughly equivalent to 0.75 words in English or 0.25 words in Spanish. Managing this window is a technical necessity for four reasons:

• Cost: Billing is usually based on the total tokens processed (input plus output).

• Latency: Larger contexts require longer processing times, which is critical for real-time applications.

• Forgetfulness: Once the window is full, the model begins to lose information from the beginning of the session.

• Lost in the Middle: Models tend to ignore information located in the center of extremely long contexts, focusing their attention only on the beginning and the end.

Optimization Strategies

Effective context management involves progressive summarization of old messages, utilizing “sliding windows” to keep only the most recent interactions, and employing context caching to reuse static information without incurring reprocessing costs.

7. Markdown: The Communication Standard

Markdown has emerged as the de facto standard for communicating with LLMs. It is preferred over HTML or XML because of its token efficiency and clear visual hierarchy. Its predictable syntax makes it easy for models to parse structure automatically. In software documentation, Markdown facilitates the clear separation of instructions, code blocks, and expected results, enhancing the model’s ability to understand technical specifications.

Token Efficiency Analysis

The choice of format directly impacts cost and latency:

Markdown (# Title): 3 tokens.
HTML (Title): 7 tokens.
XML (...): 10 tokens.

Corporate Syntax Manual

Element	Syntax	Impact on LLM
Hierarchy	`# / ## / ###`	Defines information architecture.
Emphasis	`bold`	Highlights critical constraints.
Isolation	```	Separates code and data from instructions.

8. Contextualization for AI Coding Agents

AI coding agents like Cursor or GitHub Copilot require specific files that function as “READMEs for machines.” These files provide the necessary context regarding project architecture, coding styles, and workflows to ensure generated code integrates seamlessly into the repository.

• AGENTS.md: A standardized file in the repository root that summarizes technical rules, folder structures, and test commands.

• CLAUDE.md: Specific to Anthropic models, providing persistent memory and project instructions.

• INSTRUCTIONS.md: Used by tools like GitHub Copilot to understand repository-specific validation and testing flows.

By placing these files in nested subdirectories, developers can optimize the context window; the agent will prioritize the local context of the folder it is working in over the general project instructions, reducing noise.

9. Dynamic Context: Anthropic Skills

One of the most powerful innovations in context management is the implementation of “Skills.” Instead of saturating the context window with every possible instruction at the start, Skills allow information to be loaded in stages as needed.

A Skill consists of three levels:

1. Metadata: Discovery information in YAML format, consuming minimal tokens so the model knows the skill exists.

2. Instructions: Procedural knowledge and best practices that only enter the context window when the model triggers the skill based on the prompt.

3. Resources: Executable scripts, templates, or references that are launched automatically on demand.

This dynamic approach allows for a library of thousands of rules—such as a company’s entire design system or testing protocols—to be available without overwhelming the AI’s active memory.

10. Workflow Context Typologies

To structure AI-assisted development effectively, three types of context should be implemented:

1. Project Context (Persistent): Defines the tech stack, architecture, and critical dependencies (e.g., PROJECT_CONTEXT.md).

2. Workflow Context (Persistent): Specifies how the AI should act during repetitive tasks like bug fixing, refactoring, or creating new features (e.g., WORKFLOW_FEATURE.md).

3. Specific Context (Temporary): Information created for a specific session or a single complex task (e.g., an error analysis or a migration plan) and deleted once the task is complete.

A practical example of this is the migration of legacy code. A developer can define a specific migration workflow that includes manual validation steps, turning the AI into a highly efficient and controlled refactoring tool rather than a source of technical debt.

Conclusion: The Role of the Context Architect

In the era of AI-assisted programming, success does not rely solely on the raw power of the models. It depends on the software engineer’s ability to orchestrate dialogue and manage the input data ecosystem. By mastering prompt engineering tactics and the structures of context engineering, developers transform LLMs from simple text assistants into sophisticated development companions. The modern developer is evolving into a “Context Architect,” responsible for directing the generative capacity of the AI toward technical excellence and architectural integrity. Mastery of language logic is no longer optional; it is the definitive tool of the Software Engineer 2.0.

Kube Lens: The Visual IDE for Kubernetes

Vignesh Vasu — Mon, 02 Feb 2026 15:37:47 +0000

Kube Lens — The Visual IDE for Kubernetes

Kube Lens is a desktop Kubernetes IDE that gives you a single, visual control plane for clusters, resources, logs and metrics—so you spend less time wrestling with kubectl output and more time solving real problems. In this post I’ll walk through installing Lens, adding clusters, and the everyday workflows I actually use, the features that speed up debugging, and practical tips to get teams onboarded safely.

Prerequisites

A valid kubeconfig (~/.kube/config) with the cluster contexts you need (or point Lens at alternate kubeconfig files).

What is Lens (Lens IDE / Kube Lens)

Lens is a cross-platform desktop application that connects to one or many Kubernetes clusters and presents a curated, interactive UI for exploring workloads, nodes, pods, services, and configuration. Think of it as your cluster’s cockpit—visual, searchable, and stateful—without losing the ability to run kubectl commands when you need them.

Kube Lens features

Kube Lens shines by packaging common operational tasks into intuitive views:

Multi-cluster visibility and quick context switching so you can compare clusters without copying kubeconfigs.
Live metrics and health signals (CPU, memory, pod counts, events) visible on a cluster overview for fast triage.
Built-in terminal scoped to the selected cluster/context so CLI power is always one click away.
Log viewing, searching, tailing, and exporting right next to pod details — no more bouncing between tools.
Port-forwarding and local access to cluster services for debugging apps in-situ.
Helm integration for discovering, installing, and managing releases from the UI.
CRD inspection and custom resource management so operators working with controllers and operators aren’t blind to their resources.
Team and governance features (SSO, RBAC-aware views, CVE reporting) for secure enterprise use.

Install Lens (short how-to)

Kube Lens runs on macOS, Windows, and Linux. Download the installer from the Lens site,

After installing it, launch Lens and complete the initial setup, and create/sign in with a Lens ID (for syncing and team features)

Add your cluster(s)

Lens automatically scans default kubeconfig locations (~/.kube/config).
To add a cluster manually: go to the Catalog or Clusters view → Add Cluster → paste kubeconfig or point to a file.
You can rename clusters and tag them (e.g., dev, staging, prod) for easier filtering.

Main UI walkthrough

Overview shows your cluster health assessment. This is where you get visibility into node status, resource utilization, and workload distribution

Nodes show you data about your cluster nodes

Workloads will let you explore your deployed resources

Config will show you data about your configmaps, secrets, resource quotas, limit ranges and more

In the Network you will see information about your services, ingresses, and others

And as you can see, there are other options present, so this would be a great time to stay a couple of minutes in the app, and explore all the things that you can do.

As soon as there are changes happening in your cluster, Lens picks them and propagates them immediately through the interface. Pod restarts, scaling operations, and configuration changes appear without manual refresh, providing live insight into cluster operations that static kubectl output cannot simply match.

Example:

I will start with a basic nginx deployment that shows pod lifecycle management:

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.21
        ports:
        - containerPort: 80
        resources:
          requests:
            memory: "64Mi"
            cpu: "250m"
          limits:
            memory: "128Mi"
---
apiVersion: v1
kind: Service
metadata:
  name: nginx-service
spec:
  selector:
    app: nginx
  ports:
    - protocol: TCP
      port: 80
      targetPort: 80
  type: ClusterIP
---

apiVersion: v1
kind: ConfigMap
metadata:
  name: nginx-config
data:
  nginx.conf: |
    server {
        listen 80;
        location / {
            return 200 'Hello from Lens!\n';
            add_header Content-Type text/plain;
        }

Apply this using kubectl.

kubectl apply -f nginx_deployment.yaml

Now that we’ve created a couple of resources, we are ready to explore Lens.

Here are all the pods running:

By clicking on the 3 dots on the right side, you get a couple of options:

You can easily attach to a pod, open a shell, evict it, view the logs, edit it, and even delete it.

Here is the ConfigMap:

And this is the service:

Port-Forward to Nginx

Apart from everything that I’ve shown you until now, you also get an easy way to enable port forwarding through Lens.

Just go to your Network tab, select Services, and then choose your service:

You will see an option to Forward it, so let’s click on it:

You can choose a local port to forward it to, or leave it as Random, have the option to directly open in your browser

Helm Deploy:

Lens provides a built-in Helm client to browse, install, manage, and even roll back Helm charts directly from its graphical user interface (GUI), simplifying deployment and management of Kubernetes applications. You can find available charts from repositories (like Bitnami, enabled by default), customize values.yaml, and install releases with a few clicks, seeing all your Helm deployments in the dedicated Helm tab.

Access Helm: Click the “Helm” icon in Lens, then select “Charts” to see available options.
Browse & Search: Find charts from repositories (Artifact Hub, Bitnami, etc.) or add custom ones.
Install: Select a chart, choose a version, edit parameters in the values.yaml section, and click “Install”.
Manage Releases: View installed releases, check their details (applied values), and perform actions like rolling back.

Using built-in metrics and charts

Lens integrates cluster metrics (where available) for nodes and workloads.
Toggle charts in the details pane to get CPU/memory trends over time.

Tips and best practices

Keep kubeconfigs minimal per cluster and use named contexts for clarity.
Tag clusters (dev/stage/prod) and use color coding to reduce the risk of accidental changes.
Use Lens for exploration and quick fixes; keep complex automation in CI/CD pipelines.
For sensitive environments, restrict Lens access and avoid storing long-lived credentials locally.

Reference

https://docs.k8slens.dev/

Helm Unit Test: Validate Charts Before Deploy

Vignesh Vasu — Mon, 02 Feb 2026 06:51:17 +0000

Why Helm UnitTest?

Untested charts risk syntax errors, wrong resources, or missing configs that surface only during installs. Unit tests render templates locally, catch issues early, and ensure values like replicas or images work as expected.

CI/CD pipelines run these tests automatically, blocking merges with failures. Using helm unittest will lower prod incidents from template bugs.

Install Helm-Unittest
Prerequisites: Helm 3.8+, Git, basic YAML skills.

Install the plugin:

helm plugin install

https://github.com/helm-unittest/helm-unittest.git

helm unittest --help  # Verify installation

Chart Structure

Organize your chart like this:

my-app/
├── Chart.yaml
├── values.yaml
├── templates/
│ ├── deployment.yaml
│ └── service.yaml
└── tests/
└── basic_test.yaml

Write Your First Test

Create tests/basic_test.yaml:

suite: basic deployment tests
templates:
  - deployment.yaml
tests:
  - it: should have correct kind
    asserts:
      - isKind:
          of: Deployment

  - it: sets replicas correctly
    set:
      replicaCount: 3
    asserts:
      - equal:
          path: spec.replicas
          value: 3

  - it: uses correct image
    set:
      image.repository: nginx
      image.tag: "1.21"
    asserts:
      - equal:
          path: spec.template.spec.containers[0].image
          value: nginx:1.21

This suite renders deployment.yaml, applies value overrides, and verifies structure.

Assertion	Purpose	Example
`isKind`	Check resource type	`of: Deployment`
`equal`	Exact path match	`path: spec.replicas` `value: 2`
`matchSnapshot`	Full template validation	`{}`
`hasKey`	Field existence	`path: metadata.labels.app`
`matchRegex`	Pattern matching	`path: metadata.name` `value: "^my-app-.*"`
`contains`	Document count	`count: 1`
`isEmpty`	No documents	`{}`
`fileExists`	Template presence	`path: templates/deployment.yaml`

Test labels, resources, env vars, and volumes similarly.

Run Tests

From chart root:

helm unittest .                    # Default run
helm unittest . -v                 # Verbose output
helm unittest . --output-type junit > test-results.xml  # CI reports
Green output shows passes; failures list exact path mismatches.

Green output shows passes; failures list exact path mismatches.

Example verbose run:

✓ basic_test.yaml: "should have correct kind" 
✓ basic_test.yaml: "sets replicas correctly"

Advanced Testing

Multiple Values Files:

- it: works with prod values
values:
- ../../values-prod.yaml
asserts:
- greater:
path: spec.template.spec.containers[0].resources.limits.memory
value: 1Gi

Subcharts: Prefix with subchartName//.
Conditionals: Test if blocks with varied set values.

Snapshots for golden files:

- it: matches production snapshot
  set:
    env: production
  asserts:
    - matchSnapshot: {}

Update with helm unittest . -u.

CI/CD Integration

GitHub Actions workflow (.github/workflows/test.yaml):

name: Helm Test
on: [pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - uses: azure/setup-helm@v4
      with:
        version: v3.15.0
    - run: helm plugin install https://github.com/helm-unittest/helm-unittest
    - run: helm unittest . --output-type junit > junit.xml
    - uses: actions/upload-artifact@v4
      with:
        name: test-results
        path: junit.xml

Best Practices

Follow AAA: Arrange (set values), Act (render), Assert (check).
One assertion per test for clear failures.
Test defaults, overrides, and edge cases (replicaCount: 0).
Combine with helm lint and helm template --validate.
Version control tests/ with your chart.
Use descriptive it: names.

Common Pitfalls

PathCheck typos: Use JSONPath like spec.template.spec.containers.[0].name.
Snapshot drift: Review changes before -u.
Global values: Scope with global.* paths.

Start testing your charts today—your future self will thank you

Perficient Included in the IDC Market Glance: Healthcare Ecosystem, 4Q25

Alyssa Schottel — Thu, 22 Jan 2026 20:09:10 +0000

Healthcare organizations are managing many challenges at once: consumers expect digital experiences that feel as personalized as other industries, fragmented data in silos slows strategic decision-making, and AI and advanced technologies must integrate seamlessly into existing care models.

Meeting these demands requires more than incremental change—it calls for digital solutions that unify access to care, trusted data, and advanced technologies to deliver transformative outcomes and operational efficiency.

IDC Market Glance: Healthcare Ecosystem, 4Q25

We’re proud to share that Perficient has been included in the “IT Services” category in the IDC Market Glance: Healthcare Ecosystem, 4Q25 report (Doc# US54010025, December 2025). This segment includes systems integration organizations providing advisory, consulting, development, and implementation services, as well as products or solutions.

We believe this inclusion reinforces our expertise in leveraging AI, data, and technology to deliver intelligent tools and intuitive, compliant care experiences that drive measurable value across the health journey.

We believe this commitment aligns with critical shifts IDC Market Glance highlights in its latest report, which emphasizes how healthcare organizations are activating advanced technology and AI. IDC Market Glance shares, “Health systems and payers are moving more revenue into value-based care and capitated risk, pushing tech buyers to favor solutions that improve quality metrics, lower total cost of care, and help hit incentive thresholds.”

As the industry evolves, IDC predicts: “Technology buyers will likely favor vendors that align revenue models to customer risk arrangements, plug seamlessly into large platforms, and demonstrate human-centered design that supports clinicians rather than replacing them.”

To us, this inclusion validates our ability to help healthcare organizations maximize technology and AI to drive transformative outcomes, power enterprise agility, and create seamless, consumer-centric experiences that build lasting trust.

Intelligent Solutions for Transformative Outcomes

These shifts are actively transforming the healthcare ecosystem, challenging leaders to rethink how they deliver care and create value. Our partnerships with leading organizations show what’s possible: moving AI from pilot to production, building interoperable data foundations that accelerate insights, and designing human-centered solutions that empower care teams and improve the cost, quality, and equity of care.

Easing Access to Care With a Commerce-Like Experience

We helped Rochester Regional Health reimagine its digital front door to triage like a clinician, personalize like a concierge, and convert like a commerce platform—creating a seamless experience that improves access, trust, and outcomes. The mobile-first redesign introduced smart search, dynamic filters, and real-time booking, driving a 26% increase in appointment scheduling and saving $79K+ monthly in call center costs. As a result, this transformative work earned three industry awards, recognizing the solution’s innovation in accessibility, engagement, and measurable impact on patient care.

Consumers expect frictionless access to care, personalized experiences, and real-time engagement. Our recent Access to Care Report reveals more than 45% of consumers aged 18–64 have used digital-first care instead of their regular provider—and 92% of them believe the quality is equal or better. To deliver on consumers’ expectations, leaders need a unified digital strategy that connects systems, streamlines workflows, and gives consumers simple, reliable ways to find and schedule care.

Explore how our Access to Care research continues to earn industry awards or learn more about our strategic position on find care experiences.

Empowering Care Ecosystems Through Interoperable Data Foundations

We helped a healthcare insurance leader build a single, interoperable source of truth that turns healthcare data into a true strategic asset. Our FHIR‑enabled solution ingests, normalizes, and validates data from internal and external systems and shares a consolidated, reliable dataset through API connectors, gateways, and extracts, grounded in data governance. Ultimately, this interoperable data foundation accelerates time to market, minimizes downtime through EDI and API modernization, and ensures the right data reaches the right hands at the right time to power consumer‑grade experiences, while confidently meeting interoperability standards.

Discover our platform modernization and data management capabilities.

Accelerating Member Support With Human-Centered GenAI Innovation

We helped a leading Blue Cross Blue Shield health insurer transform CSR support by deploying a natural language Generative AI benefits assistant powered by AWS’s AI foundation models and APIs. The intelligent assistant mines a library of ingested documents to deliver tailored, member-specific answers in real time, eliminating cumbersome manual processes and PDF downloads that previously slowed resolution times. Beyond faster answers, this human-centered solution accelerates benefits education, equips agents to provide relevant information with greater speed and accuracy, and demonstrates how generative AI can move from pilots into core infrastructure to support staff rather than replace them.

Read more about our AI expertise or explore our human-centered design services.

Build Your Scalable, Data-Driven Future

From insight to impact, our healthcare expertise  equips leaders to modernize, personalize, and scale care. We drive resilient, AI-powered transformation to shape the experiences and engagement of healthcare consumers, streamline operations, and improve the cost, quality, and equity of care.

We have been trusted by the 10 largest health systems and the 10 largest health insurers in the U.S., and Modern Healthcare consistently ranks us as one of the largest healthcare consulting firms.

Our strategic partnerships with industry-leading technology innovators—including AWS, Microsoft, Salesforce, Adobe, and  more—accelerate healthcare organizations’ ability to modernize infrastructure, integrate data, and deliver intelligent experiences. Together, we shatter boundaries so you have the AI-native solutions you need to boldly advance business.

Ready to Turn Fragmentation Into Strategic Advantage?

We’re here to help you move beyond disconnected systems and toward a unified, data-driven future—one that delivers better experiences for patients, caregivers, and communities. Let’s connect  and explore how you can lead with empathy, intelligence, and impact.

Part 1: Mobile AI 2026: Why On-Device Intelligence is the New Standard

Mark Shen — Mon, 19 Jan 2026 20:15:36 +0000

Subtitle: From Critical Medical Hardware to the Apple Ecosystem, the future of mobile intelligence is local, instant, and unified.

We are standing at a hardware tipping point. For the last decade, “AI” on mobile effectively meant one thing: sending data to the cloud and waiting for an answer. Especially for those chatbots, adding AI to an app meant integrating a slow, spinning loading indicator while data traveled to a server, waited in a queue, and eventually returned text. Users are tired of waiting. They are overwhelmed by generic bots that feel disconnected from the app they are actually using.

But as we move toward 2026, the script is flipping. Phone manufacturers are shipping devices with neural engines (NPUs) so powerful they rival the desktop GPUs of just a few years ago. This shift isn’t just about faster chatbots or smoother animations; it is reshaping critical industries like healthcare and unifying the mobile ecosystem under a single dominant model family: Google Gemini.

The Hardware Revolution: The “Brain” in Your Pocket

The defining trend of the 2025-2026 cycle is the explosion of Hardware Acceleration. Modern mobile processors—whether it’s the latest Snapdragons powering Android flagships or the A-series chips in iPhones—are no longer just Central Processing Units (CPUs). They are dedicated AI powerhouses capable of “always-on” generative tasks.

This hardware leap means we can now run massive models (like Gemini Nano) directly on the device. The benefits are immediate and transformative:

Zero Latency: No network round-trips. The intelligence feels instantaneous.
Total Privacy: Sensitive data never leaves the phone’s secure enclave.
Offline Reliability: Intelligence works in elevators, basements, and airplanes.

The Critical Use Case: Android in Healthcare

Nowhere is this shift more vital than in the rapidly expanding world of Medical Devices. Android has quietly become the operating system of choice for specialized medical hardware, from handheld ultrasound scanners to patient vitals monitors.

Why is the edge critical here? Because medical environments are unforgiving. A doctor in a rural clinic or a paramedic in a speeding ambulance cannot rely on spotty 5G connections to process a patient’s vitals or analyze an X-ray.

Privacy Compliance: Processing sensitive patient data (like facial analysis for pain detection) strictly on-device removes complex regulatory cloud compliance hurdles. The data stays with the patient.
Reliability: An Android-based diagnostic tool must work instantly, 100% of the time, regardless of Wi-Fi status.
Adoption: We are seeing a massive surge in smart, connected medical tools that rely on commodity Android hardware to deliver hospital-grade diagnostics at a fraction of the cost.

The “One AI” Future: Gemini on iOS & Android

Perhaps the most compelling reason to bet on Gemini is the upcoming unification of the mobile AI landscape. Reports indicate that Apple is partnering with Google to integrate Gemini models into iOS 18 and macOS Sequoia for complex reasoning tasks and summaries, a rollout expected to mature by Spring 2026.

While Apple will handle basic tasks with its own on-device models, it is leaning on Gemini’s superior reasoning for the “heavy lifting.” This creates a unique opportunity for developers:

Unified Intelligence: Learning to engineer prompts and integrations for Gemini means you are effectively targeting the entire mobile market—both the Android medical devices and the premium iPhone user base.
Cross-Platform Consistency: A feature built on Gemini’s logic will behave consistently whether it’s running on a Samsung Galaxy Tab in a hospital or an iPhone 17 in a consumer’s hand.
Future-Proofing: With these updates expected shortly, building expertise in Gemini now puts us ahead of the curve when the feature goes mainstream across billions of Apple devices.

In Part 2, we will leave the strategy behind and dive into the code to see how we are already building this future today on iOS and Android.

Model Context Protocol (MCP) – Simplified

Saravanan Ponnaiah — Thu, 08 Jan 2026 07:50:15 +0000

What is MCP?

Model Context Protocol (MCP) is an open-source standard for integrating AI applications to external systems. With AI use cases getting traction more and more, it becomes evident that AI applications tend to connect to multiple data sources to provide intelligent and relevant responses.

Earlier AI systems interacted with users through Large language Models (LLM) that leveraged pre-trained datasets. Then, in larger organizations, business users work with AI applications/agents expect more relevant responses from enterprise dataset, from where Retrieval Augmented Generation (RAG) came into play.

Now, AI applications/agents are expected to produce more accurate responses leveraging latest data, that requires AI systems to interact with multiple data sources and fetch accurate information. When multi-system interactions are established, it requires the communication protocol to be more standardized and scalable. That is where MCP enables a standardized way to connect AI applications to external systems.

Architecture

Using MCP, AI applications can connect to data source (ex; local files, databases), tools and workflows – enabling them to access key information and perform tasks. In enterprises scenario, AI applications/agents can connect to multiple databases across organization, empowering users to analyze data using natural language chat.

Benefits of MCP

MCP serves a wide range of benefits

Development: MCP reduces development time and complexity when building, or integrating with AI application/agent. It makes integrating MCP host with multiple MCP servers simple by leveraging built-in capability discovery feature.
AI applications or agents: MCP provides access to an ecosystem of data sources, tools and apps which will enhance capabilities and improve the end-user experience.
End-users: MCP results in more capable AI applications or agents which can access your data and take actions on user behalf when necessary.

MCP – Concepts

At the top level of MCP concepts, there are three entities,

Participants
Layers
Data Layer Protocol

Participants

MCP follows a client-server architecture where an MCP host – an AI application like enterprise chatbot establishes connections to one or more MCP servers. The MCP host accomplishes this by creating a MCP client for each MCP server. Each MCP client maintains a dedicated connection with its MCP server.

The key participants of MCP architecture are:

MCP Host: AI application that coordinates and manages one or more MCP clients
MCP Client: A component that maintains a dedicated connection to an MCP server and obtains context from an MCP server for MCP host to interact
MCP Server: A program that provides context to MCP clients (i.e. generate responses or perform actions on user behalf)

Layers

MCP consists of two layers:

Data layer – Defines JSON-RPC based protocol for client-server communication including,
- lifecycle management – initiate connection, capability discovery & negotiation, connection termination
- Core primitives – enabling server features like tools for AI actions, resources for context data, prompt templates for client-server interaction and client features like ask client to sample from host LLM, log messages to client
- Utility features – Additional capabilities like real-time notifications, track progress for long-running operations
Transport Layer – Manages communication channels and authentication between clients and servers. It handles connection establishment, message framing and secure communication between MCP participants

Data Layer Protocol

The core part of MCP is defining the schema and semantics between MCP clients and MCP servers. It is the part of MCP that defines the ways developers can share context from MCP servers to MCP clients.

MCP uses JSON-RPC 2.0 as its underlying RPC protocol. Client and servers send requests to each other and respond accordingly. Notifications can be used when no response is required.

Life Cycle Management

MCP is a stateful protocol that requires lifecycle management. The purpose of lifecycle management is to negotiate the capabilities (i.e. functionalities) that both client and server support.

Primitives

Primitives define what clients and servers can offer each other. These primitives specify the types of contextual information that can be shared with AI applications and the range of actions that can be performed. MCP defines three core primitives that servers can expose:

Tools: Executable functions that AI applications can invoke to perform actions (e.g., API calls, database queries)
Resources: Data sources that provide contextual information to AI applications (e.g., file contents, API responses, database records)
Prompts: Reusable templates that help structure interactions with language models (e.g., system prompts, few-shot examples)

Notifications

The protocol supports real-time notifications to enable dynamic updates between servers and clients. For example, when a server’s available tools change – such as when new functionalities are added or existing functionality is updated – the server can send tool update notifications to all its connected clients about these changes.

Security in Data Accessing

While AI applications communicate with multiple enterprise data sources thgrouch MCP and fetch real-time sensitive data like customer information, financial data to serve the users, data security becomes absolutely critical factor to be addressed.

MCP ensures secure access.

Authentication and Authorization

MCP implements server-side authentication where each MCP server validates who is making the request. The enterprise system controls access through:

User-specific credentials – Each user connecting through MCP has their own authentication tokens
Role-based access control (RBAC) – Users only access data that the role permits
Session management – Time-limited sessions that expire automatically

Data Access Controls

MCP server acts as a security gateway that enforces the same access policies as direct system access:

- Users can only query data that they are authroized to access
- The server validates every request against permission rules
- Sensitive information can be masked or filtered based on user privileges

Secure Communication

- - Encrypted connections – All data transmissions uses TLS/HTTPS encryption
  - No data storage in AI – AI systems do not store the financial data it accesses; it only process it during the conversation session

Audit and Monitoring

MCP implementations in enterprise ecosystem should include:

- - Complete audit logs – Every data access request is logged with user, timestamp and data accessed
  - Anomaly detection – Engage mechanisms that monitor unusual access patterns and trigger alerts
  - Compliance tracking – All interactions meet regulatory requirements like GDPR, PCI-DSS

Architecture Isolation

Enterprises typically deploy MCP using:

- - Private network deployment – MCP servers stay within the enterprise secure firewall boundary
  - API gateway integration – Requests go through existing security infrastructure
  - No direct database access – MCP connects and access data through secure APIs, not direct access to database

The main idea is that MCP does not bypass existing security. It works within the same security as other enterprise applications, just showing a smarter interface.

MCP Implementation & Demonstration

In this section, I will demonstrate a simple use case where MCP client (Claude Desktop) interacts with “Finance Manager” MCP server that can fetch financial information from the database.

Financial data is maintained in Postgres database tables. MCP client (Claude Desktop app) will request information about customer account, MCP host will discover appropriate capability based on user prompt and invoke respective MCP tool function that can fetch data from the database table.

To make MCP client-server in action, there are three parts to be configured

- - Backend Database
  - MCP server implementation
  - MCP server registration in MCP Host

Backend Database

Postgres table “accounts” maintains accounts data with below information, “transactions” table maintains the transaction performed on the accounts

MCP server implementation

FastMCP class implements MCP server components and creating an object of it initialize and enables access to those components to create enterprise MCP server capabilities.

The annotation “@mcp.tool()” defines the capability and the respective function will be recognized as MCP capability. These functions will be exposed to AI applications and will be invoked from MCP Host to perform designated actions.

In order to invoke MCP capabilities from client, MCP server should be up & running. In this example, there are two functions defined as MCP tool capabilities,

- - get_account_details – The function accept account number as input parameter, query “accounts” table and returns account information
  - add_transaction – The function accepts account number and transaction amount as parameters, make entry into “transactions” table

MCP Server Registration in MCP Host

For AI applications to invoke MCP server capability, MCP server should be registered in MCP host at client end. For this demonstration, I am using Claude Desktop as MCP client from where I interact with MCP server.

First, MCP server is registered with MCP host in Claude Desktop as below,

Claude Desktop -> Settings -> Developer -> Local MCP Servers -> Click “Edit Config”

Open “claude_desktop_config” JSON file in Notepad. Add configurations in the JSON as below. The configurations define the path where MCP server implementation is located and instruct command to MCP host to run. Save the file and close.

Restart “Claude Desktop” application, go to Settings -> Developer -> Local MCP servers tab. The newly added MCP server (finance-manager) will be in running state as below,

Go to chat window in Claude Desktop. Issue a prompt to fetch details of an account in “accounts” table and review the response,

User Prompt: User issues a prompt to fetch details of an account.

MCP Discovery & Invoke: The client (Claude Desktop) processes the prompt, interacts with MCP host, automatically discover the relevant capability – get_account_details function in this case – without explicitly mention the function name and invoke the function with necessary parameter.

Response: MCP server process the request, fetch account details from the table and respond details to the client. The client formats the response and present it to the user.

Another example to add a transaction in the backend table for an account,

Here, “add_transaction” capability has been invoked to add a transaction record in “transactions” table. In the chat window, you could notice that what MCP function is being invoked along with request & response body.

The record has been successfully added into the table,

Impressive, isn’t it..!!

There are a wide range of use cases implementing MCP servers and integrate with enterprise AI systems that bring in intelligent layer to interact with enterprise data sources.

Here, you may also develop a thought that in what ways MCP (Model Context Protocol) is different from RAG (Retrieval Augmented Generation), as I did so. Based on my research, I just curated a comparison matrix of the features that would add more clarity,

Aspect	RAG (Retrieval Augmented Generation)	MCP (Model Context Protocol)
Purpose	Retrieve unstructured docs to improve LLM responses	AI agents access structured data/tools dynamically
Data Type	Unstructured text (PDFs, docs, web pages)	Structured data (JSON, APIs, databases)
Workflow	Retrieve → Embed → Prompt injection → Generate	AI requests context → Protocol delivers → AI reasons
Context Delivery	Text chunks stuffed into prompt	Structured objects via standardized interface
Token Usage	High (full text in context)	Low (references/structured data)
Action Capability	Read-only (information retrieval)	Read + Write (tools, APIs, actions)
Discovery	Pre-indexed vector search	Runtime tool/capability discovery
Latency	Retrieval + embedding time	Real-time protocol calls
Use Case	Q&A over documents, chatbots	AI agents, tool calling, enterprise systems
Maturity	Widely adopted, mature ecosystem	Emerging standard (2025+)
Complexity	Vector DB + embedding pipeline	Protocol implementation + AI agent

Conclusion

MCP Servers extend the capabilities of AI assistants by allowing them to interact with external services and data sources using natural language commands. Model Context Protocol (MCP) has a wide range of use cases and there are several enterprises already implemented and hosted MCP servers for AI clients to integrate and interact.

Some of the prominent MCP servers include:

GitHub MCP Server: Allows AI to manage repositories, issues, pull requests, and monitor CI/CD workflows directly within the development environment.

Azure DevOps MCP Server: Integrates AI with Azure DevOps services for managing pipelines, work items and repositories, ideal for teams withing the Microsoft ecosystem.

PostgreSQL MCP Server: bridges the gap between AI and databases, allowing natural language queries, schema exploration and data analysis without manual SQL scripting.

Slack MCP Server: Turns Slack into an AI-powered collaboration hub, enabling message posting, channel management

Bruno : The Developer-Friendly Alternative to Postman

Pawan Akre — Fri, 02 Jan 2026 08:25:16 +0000

If you’re knee-deep in building apps, you already know APIs are the backbone of everything. Testing them? That’s where the real magic happens. For years, we’ve relied on tools like Postman and Insomnia to send requests, debug issues, and keep things running smoothly. But lately, there’s a buzz about something new: Bruno. It’s popping up everywhere, and developers are starting to make the switch. Why? Let’s dive in.

What Exactly is Bruno?

Picture this: an open-source, high-performance API client that puts your privacy first. Bruno isn’t some bloated app that shoves your stuff into the cloud. “No,” it keeps everything right on your local machine. Your API collections, requests, all of it? Safe and sound where you control it, no cloud drama required.

Bruno is built for developers who want:

Simplicity without compromise
High performance without unnecessary extras
Complete freedom with open-source flexibility

It’s like the minimalist toolbox you’ve been waiting for.

Why is Bruno Suddenly Everywhere?

Bruno solves the pain points that frustrate us with other API tools:

Privacy First: No forced cloud uploads, your collections stay local. No hidden syncing; your data stays completely under your control.
Fast and Lightweight: Loads quickly and handles requests without lag. Perfect for quick tests on the go.
Open-Source Freedom: No fees, no lock-in. Collections are Git-friendly and saved as plain text for easy version control.
No Extra Bloat: Focused on what matters, API testing without unnecessary features.

Bottom line: Bruno fits the way we work today, collaboratively, securely, and efficiently. It’s not trying to do everything; it’s just good at API testing.

Key Features

Bruno keeps it real with features that matter. Here are the highlights:

Totally Open-Source

No sneaky costs or paywalls.
Peek under the hood anytime—the code’s all there.
A group of developers is contributing to GitHub, making it better every day. Wanna join? Hit up their repo and contribute.

Privacy from the Ground Up

Everything lives locally.
No accounts, no cloud pushes—your requests don’t leave your laptop.
Ideal if you’re handling sensitive APIs and don’t want Big Tool Company snooping.
Bonus: Those plain-text files integrate well with Git, so team handoffs are seamless.

Light as a Feather, Fast as Lightning

Clean UI, no extra bells and whistles slowing you down.
Starts up quickly and zips through responses.
Great for solo endpoint tweaks or managing large workflows without your machine slowing.

Getting Bruno Up and Running

Installing Bruno is simple. It works on Windows, macOS, and Linux. Just choose your platform, and you’re good to go.

#3. Quick Install Guide

Windows

Head to Bruno’s GitHub Releases page.
Grab the latest .exe file.
Run it and follow the prompts.
Boom—find it in your Start Menu.

macOS

Download the .dmg from Releases.
Drag it to Applications.
Fire it up and get testing.

Linux

Snag the .AppImage or .deb from Releases.
For AppImage: chmod +x Bruno.AppImage then ./Bruno.AppImage.
For .deb: sudo dpkg -i bruno.deb and sudo apt-get install -f.

GUI or CLI? Your Call

GUI: Feels like Postman but cleaner. Visual, easy-to-build requests on the fly.
CLI: For the terminal lovers. Automate tests, integrate with CI/CD, or run collections: bruno run collection.bru –env dev.

Build Your First Collection in Minutes

Bruno makes organizing APIs feel effortless. Here’s a no-sweat walkthrough.

Step 1: Fire It Up

Launch Bruno. You’ll see a simple welcome screen prompting you to create a new collection.

Step 2: New Collection Time

Hit “New Collection.”
Name it (say, “My API Playground”).
Pick a folder—it’s all plain text, so Git loves it.

Step 3: Add a Request

Inside the collection, click “New Request.”
Pick your method (GET, POST, etc.).
Enter the URL: https://jsonplaceholder.typicode.com/posts.

Step 4: Headers and Body Magic

Add the header: Content-Type: application/json.
For POSTs, add a body like:

JSON

{
"title": "Bruno Blog",
"body": "Testing Bruno API Client",
"userId": 1
}

Step 5: Hit Send

Click it, and watch the response pop: status, timing, pretty JSON—all right there.

Step 6: Save and Sort

Save the request, create folders for environments or APIs, and use variables to switch setups.

Bruno vs. Postman: Head-to-Head

Postman’s the OG, but Bruno’s the scrappy challenger winning hearts. Let’s compare.

Speed

Bruno: Lean and mean—quick loads, low resource hog.
Postman: Packed with features, but it can feel sluggish on big projects. Edge: Bruno

Privacy

Bruno: Local only, no cloud creep.
Postman: Syncs to their servers—handy for teams, sketchy for secrets. Edge: Bruno

Price Tag

Bruno: Free forever, open-source vibes.
Postman: Free basics, but teams and extras? Pay up. Edge: Bruno

Feature	Bruno	Postman
Open Source	Yes	No
Cloud Sync	No	Yes
Performance	Lightweight	Heavy
Privacy	Local Storage	Cloud-Based
Cost	Free	Paid Plans

Level up With Advanced Tricks

Environmental Variables

Swap envs easy-peasy:

Make files for dev/staging/prod.
Use {{baseUrl}} in requests.
Example:

{
"baseUrl": "https://api.dev.example.com",
"token": "your-dev-token"
}

Scripting Smarts

Add pre/post scripts for:

Dynamic auth: request.headers[“Authorization”] = “Bearer ” + env.token;
Response checks or automations.

Community & Contribution

It’s community-driven:

Repo: https://github.com/usebruno/bruno
Contribute: Fork, branch, PR. Report bugs, chat ideas—help build the future.

Conclusion

Bruno isn’t just another API testing tool; it’s designed for developers who want simplicity and control. With local-first privacy, fast performance, open-source flexibility, and built-in Git support, Bruno delivers everything you need without unnecessary complexity.
If you’re tired of heavy, cloud-based clients, it’s time to switch. Download Bruno today and experience the difference: Download here.

Microservices: The Emerging Complexity Driven by Trends and Alternatives to Over‑Design

Juan Quintana Gomez — Wed, 31 Dec 2025 15:13:56 +0000

The adoption of microservice‑based architectures has grown exponentially over the past decade, often driven more by industry trends than by a careful evaluation of system requirements. This phenomenon has generated unnecessarily complex implementations—like using a bazooka to kill an ant. Distributed architectures without solid foundations in domain capabilities, workloads, operational independence, or real scalability needs have become a common pattern in the software industry. In many cases, organizations migrate without having a mature discipline in observability, traceability, automation, domain‑driven design, or an operational model capable of supporting highly distributed systems; as a consequence, they end up with distributed monoliths that require coordinated deployments and suffer cascading failures, losing the benefits originally promised by microservices (Iyer, 2025; Fröller, 2025).

Over‑Design

The primary issue in microservices is not rooted in their architectural essence, but in the over‑design that emerges when attempting to implement such architecture without having a clear roadmap of the application’s domains or of the contextual boundaries imposed by business rules. The decomposition produces highly granular, entity‑oriented services that often result in circular dependencies, duplicated business logic, excessive events without meaningful semantics, and distributed flows that are difficult to debug. Instead of achieving autonomy and independent scalability, organizations create a distributed monolith with operational complexity multiplied by the number of deployed services. A practical criterion to avoid this outcome is to postpone decomposition until stable boundaries and non‑functional requirements are fully understood, even adopting a monolith‑first approach before splitting (Fowler, 2015; Danielyan, 2025).

Minimal API and Modular Monolith as Alternatives to Reduce Complexity

In these scenarios, it is essential to explore alternatives that allow companies to design simpler microservices without sacrificing architectural clarity or separation of concerns. One such alternative is the use of Minimal APIs to reduce complexity in the presentation layer: this approach removes ceremony (controllers, conventions, annotations) and accelerates startup while reducing container footprint. It is especially useful for utility services, CRUD operations, and limited API surfaces (Anderson & Dykstra, 2024; Chauhan, 2024; Nag, 2025).

Another effective alternative is the Modular Monolith. A well‑modularized monolith enables isolating functional domains within internal modules that have clear boundaries and controlled interaction rules, simplifying deployment, reducing internal latency, and avoiding the explosion of operational complexity. Additionally, it facilitates a gradual migration toward microservices only when objective reasons exist (differentiated scaling needs, dedicated teams, different paces of domain evolution) (Bächler, 2025; Bauer, n.d.).

Improving the API Gateway and the Use of Event‑Driven Architectures (EDA)

The API Gateway is another critical component for managing external complexity: it centralizes security policies, versioning, rate limiting, and response transformation/aggregation, hiding internal topology and reducing client cognitive load. Patterns such as Backend‑for‑Frontend (BFF) and aggregation help decrease network trips and prevent each public service from duplicating cross‑cutting concerns (Microsoft, n.d.-b; AST Consulting, 2025).

A key principle for reducing complexity is to avoid decomposition by entities and instead guide service boundaries using business capabilities and bounded contexts. Domain‑Driven Design (DDD) provides a methodological compass to define coherent semantic boundaries; mapping bounded contexts to services (not necessarily in a 1:1 manner) reduces implicit coupling, prevents domain model ambiguity, and clarifies service responsibilities (Microsoft, n.d.-a; Polishchuk, 2025).

Finally, the use of Event‑Driven Architectures (EDA) should be applied judiciously. Although EDA enhances scalability and decoupling, poor implementation significantly increases debugging effort, introduces hidden dependencies, and complicates traceability. Mitigating these risks requires discipline in event design/versioning, the outbox pattern, idempotency, and robust telemetry (correlation IDs, DLQs), in addition to evaluating when orchestration (Sagas) is more appropriate than choreography (Three Dots Labs, n.d.; Moukbel, 2025).

Conclusion

The complexity associated with microservices arises not from the architecture itself, but from misguided adoption driven by trends. The key to reducing this complexity is prioritizing cohesion, clarity, and gradual evolution: Minimal APIs for small services, a Modular Monolith as a solid foundation, decomposition by real business capabilities and bounded contexts, a well‑defined gateway, and a responsible approach to events. Under these principles, microservices stop being a trend and become an architectural mechanism that delivers real value (Fowler, 2015; Anderson & Dykstra, 2024).

References

Anderson, R., & Dykstra, T. (2024, julio 29). Tutorial: Create a Minimal API with ASP.NET Core. Microsoft Learn. https://learn.microsoft.com/en-us/aspnet/core/tutorials/min-web-api?view=aspnetcore-10.0
AST Consulting. (2025, junio 12). API Gateway in Microservices: Top 5 Patterns and Best Practices Guide. https://astconsulting.in/microservices/api-gateway-in-microservices-patterns
Bächler, S. (2025, enero 23). Modular Monolith: The Better Alternative to Microservices. ti&m. https://www.ti8m.com/en/blog/monolith
Bauer, R. A. (s. f.). On Modular Monoliths. https://www.raphaelbauer.com/posts/on-modular-monoliths/
Danielyan, M. (2025, febrero 4). When to Choose Monolith Over Microservices. https://mikadanielyan.com/blog/when-to-choose-monolith-over-microservices
Fowler, M. (2015, junio 3). Monolith First. https://martinfowler.com/bliki/MonolithFirst.html
Fröller, J. (2025, octubre 30). Many Microservice Architectures Are Just Distributed Monoliths. MerginIT Blog. https://merginit.com/blog/31102025-microservices-antipattern-distributed-monolit
Iyer, A. (2025, junio 3). Why 90% of Microservices Still Ship Like Monoliths. The New Stack. https://thenewstack.io/why-90-of-microservices-still-ship-like-monoliths/
Microsoft. (s. f.-a). Domain analysis for microservices. Azure Architecture Center. https://learn.microsoft.com/en-us/azure/architecture/microservices/model/domain-analysis
Microsoft. (s. f.-b). API gateways. Azure Architecture Center. https://learn.microsoft.com/en-us/azure/architecture/microservices/design/gateway
Moukbel, T. (2025). Event-Driven Architecture: Pitfalls and Best Practices. Undercode Testing. https://undercodetesting.com/event-driven-architecture-pitfalls-and-best-practices/
Nag, A. (2025, julio 29). Why Minimal APIs in .NET 8 Are Perfect for Microservices Architecture?. embarkingonvoyage.com. https://embarkingonvoyage.com/blog/technologies/why-minimal-apis-in-net-8-are-perfect-for-microservices-architecture/
Polishchuk. (2025, diciembre 12). Design Microservices: Using DDD Bounded Contexts. bool.dev. https://bool.dev/blog/detail/ddd-bounded-contexts
Three Dots Labs. (s. f.). Event-Driven Architecture: The Hard Parts. https://threedots.tech/episode/event-driven-architecture/
Chauhan, P. (2024, septiembre 30). Deep Dive into Minimal APIs in ASP.NET Core 8. https://www.prafulchauhan.com/blogs/deep-dive-into-minimal-apis-in-asp-net-core-8

Beyond the Version Bump: Lessons from Upgrading React Native 0.72.7 → 0.82

Godwin Pauldurai — Wed, 24 Dec 2025 08:39:47 +0000

Introduction

When I started investigating the React Native upgrade from 0.72.7 to 0.82, my initial goal was simple:

Check breaking changes and library compatibility. But very quickly, I realized this upgrade was not just a version bump. It was deeply tied to React Native’s New Architecture, especially Fabric UI Engine and TurboModules. This blog shares what I discovered, what changed internally, and why this upgrade matters in real-world apps, not just release notes.

Why I Started Digging Deeper

At first glance:

The app was already stable
Performance was “acceptable”
Most screens worked fine

Why should we even care about Fabric and TurboModules while upgrading?

The answer became clear when I compared how React Native worked internally in 0.72.7 vs 0.82.

The Reality in React Native 0.72.7 (Old Architecture)

In 0.72.7, even though the New Architecture existed, most apps were still effectively running on the old bridge model.

What I Observed

UI updates were asynchronous
JS → Native communication relied on serialized messages
Native modules were eagerly loaded
Startup time increased as the app grew

Performance issues appeared under:

Heavy animations
Large FlatLists
Complex navigation stacks

None of these were “bugs” they were architectural limitations.

What Changed in React Native 0.82

By the time I reached 0.82, it was clear that Fabric and TurboModules were no longer optional concepts they were becoming the default future. The upgrade forced me to understand why React Native was redesigned internally.

My Understanding of Fabric UI Engine (After Investigation)

Fabric is not just a rendering upgrade it fundamentally changes how UI updates happen.

What Changed Compared to 0.72.7

Synchronous UI Updates

Earlier:

UI updates waited for the JS bridge

With Fabric:

UI updates can happen synchronously
JS and Native talk directly through JSI
Result: noticeably smoother interactions

This became obvious in:

Gesture-heavy screens
Navigation transitions
Scroll performance

Shared C++ Core

While upgrading, I noticed Fabric uses a shared C++ layer between:

JavaScript
iOS
Android

This reduces:

Data duplication
Platform inconsistencies
Edge-case UI bugs

From a maintenance point of view, this is huge.

Better Support for Concurrent Rendering

Fabric is built with modern React features in mind.

That means:

Rendering can be interrupted
High-priority UI updates are not blocked
Heavy JS work doesn’t freeze the UI

In practical terms:

The app feels more responsive, even when doing more. TurboModules: The Bigger Surprise for Me, I initially thought TurboModules were just an optimization. After digging into the upgrade docs and native code, I realized they solve multiple real pain points I had faced earlier.

What I Faced in 0.72.7

All native modules are loaded at startup
App launch time increased as features grew
Debugging JS Native mismatches was painful
Weak type safety caused runtime crashes

What TurboModules Changed:

Lazy Loading by Default

With TurboModules:

Native modules load only when accessed
Startup time improves automatically
Memory usage drops

This alone makes a big difference in large apps.

Direct JS Native Calls (No Bridge)

TurboModules use JSI instead of the old bridge.

That means:

No JSON serialization
No async message queue
Direct function calls

From a performance perspective, this is a game-changer.

Stronger Type Safety

Using codegen, the interface between JS and Native becomes:

Explicit
Predictable
Compile-time safe

Fabric + TurboModules Together (The Real Upgrade)

What I realized during this migration is:

Fabric and TurboModules don’t shine individually they shine together.

Area	React Native 0.72.7	React Native 0.82
UI Rendering	Async bridge	Synchronous (Fabric)
Native Calls	Serialized	Direct (JSI)
Startup Time	Slower	Faster
Animations	Jank under load	Smooth
Native Integration	Fragile	Strong & typed
Scalability	Limited	Production-ready

My Final Take After the Upgrade Investigation

Upgrading from 0.72.7 to 0.82 made one thing very clear to me:

This is not about chasing versions. This is about adopting a new foundation.

Fabric and TurboModules:

Remove long-standing architectural bottlenecks
Make React Native feel closer to truly native apps
Prepare apps for future React features
Reduce hidden performance debt

If someone asks me now:

“Is the New Architecture worth it?”

My answer is simple:

If you care about performance, scalability, and long-term maintenance yes, absolutely.

How to Secure Applications During Modernization on AWS

Vijaya Bhaskar Reddy — Fri, 19 Dec 2025 06:40:17 +0000

Why Do We Need to Secure Our Applications?

Cloud environments are very dynamic and interconnected. A single misconfiguration or exposed API key can lead to:

Data breaches

Compliance violations

Costly downtime

Attackers often target application-level weaknesses, not just infrastructure gaps. If any application handles sensitive data, financial transactions, or user credentials, security is critical.

Common Mistakes Made When Building Applications

Hardcoding API keys and credentials

Ignoring dependency vulnerabilities

Skipping encryption/decryption for sensitive data

Essential Security Best Practices

1. Identity and Access Management (IAM)

Create dedicated IAM roles for your Lambda functions, EC2 instances, or ECS tasks instead of hardcoding access keys in your application.

We must regularly review who has permissions using the IAM Access Analyzer.

We must avoid using the root account for day-to-day operations/ any operations as a developer.

2. Don’t Store/Share Secrets in Your Code

Your appsettings.json is not the right place for secret keys. Storing API keys or database passwords.

We must use AWS Secrets Manager or Parameter Store to keep secrets safe.

Fetch keys at runtime by using AWS SDK for .NET or the AWSSDK.Extensions.NETCore.Setup configuration provider

3. Always Encrypt Data

Encryption is one of the best practices to encrypt our sensitive data

Enable HTTPS by default for all your endpoints.
Use AWS Certificate Manager (ACM) to issue and manage SSL/TLS certificates.
In your application, make sure that all traffic is redirected to HTTPS by adding app.UseHttpsRedirection();
AWS KMS to encrypt your S3 buckets, RDS databases, and EBS volumes.
If you’re using SQL Server on RDS, enable Transparent Data Encryption (TDE).

Encrypt & Decrypt API Key with KMS

4. Build a Secure Network Foundation

Must use VPCs with private subnets for backend services.

Control the traffic with Security Groups and Network ACLs.

Use VPC Endpoints to keep traffic within AWS’s private network

Use AWS WAF to protect your APIs, and enable AWS Shield to guard against DDoS attacks.

5. Keep Your Code and Dependencies Clean

Even the best infrastructure can’t save a vulnerable codebase.

Update your .NET SDK and NuGet packages regularly.

Use Amazon Inspector for runtime and AWS environment security, and tools like Dependabot for Development-time dependency security to find vulnerabilities early.

Add code review analysis tools (like SonarQube) in your CI/CD pipeline.

6. Log Everything and Watch

Enable Amazon AWS CloudWatch for all central logging and use AWS X-Ray to trace requests through the application.

Turn on CloudTrail to track every API call across your account.

Enable GuardDuty for continuous threat detection.

Getting Started with Python for Automation

Ponnarasi Marimuthu — Tue, 09 Dec 2025 14:00:21 +0000

Automation has become a core part of modern work, allowing teams to reduce repetitive tasks, save time, and improve accuracy. Whether it’s generating weekly reports, organizing files, processing large amounts of data, or interacting with web applications, automation helps individuals and companies operate more efficiently. Among all programming languages used for automation, Python is one of the most widely adopted because of its simplicity and flexibility.

Why Python Is Perfect for Automation

Python is known for having a clean and readable syntax, which makes it easy for beginners to start writing scripts without needing deep programming knowledge. The language is simple enough for non-developers, yet powerful enough for complex automation tasks. Another major advantage is the availability of thousands of libraries. These libraries allow Python to handle file operations, manage Excel sheets, interact with APIs, scrape websites, schedule tasks, and even control web browsers – all with minimal code. Because of this, Python becomes a single tool capable of automating almost any repetitive digital task.

What You Can Automate with Python

Python can automate everyday tasks that would otherwise require significant manual effort. Simple tasks like renaming multiple files, organizing folders, or converting file formats can be completed instantly using small scripts. It is also commonly used for automating Excel-based workflows, such as cleaning datasets, merging sheets, generating monthly summaries, or transforming data between formats. Python is equally powerful for web-related automation: collecting data from websites, making API calls, sending automated emails, downloading content, and filling out online forms. For more advanced uses, Python can also automate browser testing, server monitoring, and deployment processes.

Setting Up Your Python Automation Environment

Getting started is straightforward. After installing Python, you can use an editor like VS Code or PyCharm to write your scripts. Libraries required for automation can be installed using a single command, making setup simple. Once you have your environment ready, writing your first script usually takes only a few minutes. For example, a short script can rename files in a folder, send an email, or run a function at a specific time of the day. Python’s structure is beginner-friendly, so even basic programming knowledge is enough to start automating everyday tasks.

Examples of Simple Automation

A typical example is a script that automatically renames files. Instead of renaming hundreds of files one by one, Python can loop through the folder and rename them instantly. Another example is an automated email script that can send daily reminders or reports. Python can also schedule tasks so that your code runs every morning, every hour, or at any time you choose. These examples show how even small scripts can add real value to your workflow by reducing repetitive manual tasks.

Best Practices When Building Automation

As you begin writing automation scripts, it helps to keep the code organized and reliable. Using virtual environments ensures that your project libraries remain clean. Adding error-handling prevents scripts from stopping unexpectedly. Logging enables you to track what your script does and when it executes. Once your automation is ready, you can run it automatically using tools like Task Scheduler on Windows or cron on Linux, so the script works in the background without your involvement.

How Companies Use Python Automation

Python automation is widely used across industries. IT teams rely on it to monitor servers, restart services, and handle deployment tasks. Business teams use it to generate reports, clean data, update dashboards, and manage document workflows. Marketing teams use automation for scraping competitor information, scheduling social media posts, or tracking engagement. For developers, Python helps with testing, error checking, and system integration via APIs. Across all these areas, automation improves efficiency and reduces human error.

Conclusion

Python is an excellent starting point for anyone who wants to begin automating daily tasks. Its simplicity, combined with its powerful ecosystem of libraries, makes it accessible to beginners and useful for professionals. Even basic automation scripts can save hours of work, and as you grow more comfortable, you can automate more complex processes involving data, web interactions, and system management. Learning Python for automation not only makes your work easier but also adds valuable skills for professional growth.

Creators in Coding, Copycats in Class: The Double-Edged Sword of Artificial Intelligence

Juan Quintana Gomez — Thu, 04 Dec 2025 00:30:15 +0000

“Powerful technologies require equally powerful ethical guidance.” (Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014).

The ethics of using artificial intelligence depend on how we apply its capabilities—either to enhance learning or to prevent irresponsible practices that may compromise academic integrity. In this blog, I share reflections, experiences, and insights about the impact of AI in our environment, analyzing its role as a creative tool in the hands of developers and as a challenge within the academic context.

Between industry and the classroom

As a Senior Developer, my professional trajectory has led me to delve deeply into the fascinating discipline of software architecture. Currently, I work as a Backend Developer specializing in Microsoft technologies, facing daily the challenges of building robust, scalable, and well-structured systems in the business world.

Alongside my role in the industry, I am privileged to serve as a university professor, teaching four courses. Three of them are fundamental parts of the software development lifecycle: Software Analysis and Design, Software Architecture, and Programming Techniques. This dual perspective—as both a professional and a teacher—has allowed me to observe the rapid changes that technology is generating both in daily development practice and in the formation of future engineers.

Exploring AI as an Accelerator in Software Development

One of the greatest challenges for those studying the software development lifecycle is transforming ideas and diagrams into functional, well-structured projects. I always encourage my students to use Artificial Intelligence as a tool for acceleration, not as a substitute.

For example, in the Software Analysis and Design course, we demonstrate how a BPMN 2.0 process diagram can serve as a starting point for modeling a system. We also work with class diagrams that reflect compositions and various design patterns. AI can intervene in this process in several ways:

Code Generation from Models: With AI-based tools, it’s possible to automatically turn a well-built class diagram into the source code foundation needed to start a project, respecting the relationships and patterns defined during modeling.
Rapid Project Architecture Setup: Using AI assistants, we can streamline the initial setup of a project by selecting the technology stack, creating folder structures, base files, and configurations according to best practices.
Early Validation and Correction: AI can suggest improvements to proposed models, detect inconsistencies, foresee integration issues, and help adapt the design context even before coding begins.

This approach allows students to dedicate more time to understanding the logic behind each component and design principle, instead of spending hours on repetitive setup and basic coding tasks. The conscious and critical use of artificial intelligence strengthens their learning, provides them with more time to innovate, and helps prepare them for real-world industry challenges.

But Not Everything Is Perfect: The Challenges in Programming Techniques

However, not everything is as positive as it seems. In “Programming Techniques,” a course that represents students’ first real contact with application development, the impact of AI is different compared to more advanced subjects. In the past, the repetitive process of writing code—such as creating a simple constructor public Person(), a function public void printFullName() or practicing encapsulation in Java with methods like public void setName(String name) and public String getName()—kept the fundamental programming concepts fresh and clear while coding.

This repetition was not just mechanical; it reinforced their understanding of concepts like object construction, data encapsulation, and procedural logic. It also played a crucial role in developing a solid foundation that made it easier to understand more complex topics, such as design patterns, in future courses.

Nowadays, with the widespread availability and use of AI-based tools and code generators, students tend to skip these fundamental steps. Instead of internalizing these concepts through practice, they quickly generate code snippets without fully understanding their structure or purpose. As a result, the pillars of programming—such as abstraction, encapsulation, inheritance, and polymorphism—are not deeply absorbed, which can lead to confusion and mistakes later on.

Although AI offers the promise of accelerating development and reducing manual labor, it is important to remember that certain repetition and manual coding are essential for establishing a solid understanding of fundamental principles. Without this foundation, it becomes difficult for students to recognize bad practices, avoid common errors, and truly appreciate the architecture and design of robust software systems.

Reflection and Ethical Challenges in Using AI

Recently, I explained the concept of reflection in microservices to my Software Architecture students. To illustrate this, I used the following example: when implementing the Abstract Factory design pattern within a microservices architecture, the Reflection technique can be used to dynamically instantiate concrete classes at runtime. This allows the factory to decide which object to create based on external parameters, such as a message type or specific configuration received from another service. I consider this concept fundamental if we aim to design an architecture suitable for business models that require this level of flexibility.

However, during a classroom exercise where I provided a base code, I asked the students to correct an error that I had deliberately injected. The error consisted of an additional parameter in a constructor—a detail that did not cause compilation failures, but at runtime, it caused 2 out of 5 microservices that consumed the abstract factory via reflection to fail. From their perspective, this exercise may have seemed unnecessary, which led many to ask AI to fix the error.

As expected, the AI efficiently eliminated the error but overlooked a fundamental acceptance criterion: that parameter was necessary for the correct functioning of the solution. The task was not to remove the parameter but to add it in the Factory classes where it was missing. Out of 36 students, only 3 were able to explain and justify the changes they made. The rest did not even know what modifications the AI had implemented.

This experience highlights the double-edged nature of artificial intelligence in learning: it can provide quick solutions, but if the context or the criteria behind a problem are not understood, the correction can be superficial and jeopardize both the quality and the deep understanding of the code.

I haven’t limited this exercise to architecture examples alone. I have also conducted mock interviews, asking basic programming concepts. Surprisingly, even among final-year students who are already doing their internships, the success rate is alarmingly low: approximately 65% to 70% of the questions are answered incorrectly, which would automatically disqualify them in a real technical interview.

Conclusion

Artificial intelligence has become increasingly integrated into academia, yet its use does not always reflect a genuine desire to learn. For many students, AI has turned into a tool for simply getting through academic commitments, rather than an ally that fosters knowledge, creativity, and critical thinking. This trend presents clear risks: a loss of deep understanding, unreflective automation of tasks, and a lack of internalization of fundamental concepts—all crucial for professional growth in technological fields.

Various authors have analyzed the impact of AI on educational processes and emphasize the importance of promoting its ethical and constructive use. As Luckin et al. (2016) suggest, the key lies in integrating artificial intelligence as support for skill development rather than as a shortcut to avoid intellectual effort. Similarly, Selwyn (2019) explores the ethical and pedagogical challenges that arise when technology becomes a quick fix instead of a resource for deep learning.

References:

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. [Available at: https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-Publication.pdf]
Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.

Technical Articles / Blogs / Perficient

Language Mastery as the New Frontier of Software Development

1. Technical Foundations: From Prediction to Instruction

2. The Two Pillars of Effective Prompting

Title

Kube Lens: The Visual IDE for Kubernetes

Kube Lens — The Visual IDE for Kubernetes

Prerequisites

What is Lens (Lens IDE / Kube Lens)

Kube Lens features

Install Lens (short how-to)

Add your cluster(s)

Main UI walkthrough

Example:

Port-Forward to Nginx

Helm Deploy:

Using built-in metrics and charts

Tips and best practices

Reference

Helm Unit Test: Validate Charts Before Deploy

Why Helm UnitTest?

Install Helm-Unittest Prerequisites: Helm 3.8+, Git, basic YAML skills.

Install the plugin:

Chart Structure

Write Your First Test

Run Tests

Advanced Testing

CI/CD Integration

Best Practices

Common Pitfalls

Perficient Included in the IDC Market Glance: Healthcare Ecosystem, 4Q25

IDC Market Glance: Healthcare Ecosystem, 4Q25

Intelligent Solutions for Transformative Outcomes

Easing Access to Care With a Commerce-Like Experience

Empowering Care Ecosystems Through Interoperable Data Foundations

Accelerating Member Support With Human-Centered GenAI Innovation

Build Your Scalable, Data-Driven Future

Ready to Turn Fragmentation Into Strategic Advantage?

Part 1: Mobile AI 2026: Why On-Device Intelligence is the New Standard

The Hardware Revolution: The “Brain” in Your Pocket

The Critical Use Case: Android in Healthcare

The “One AI” Future: Gemini on iOS & Android

Model Context Protocol (MCP) – Simplified

What is MCP?

Benefits of MCP

MCP – Concepts

Participants

Layers

Data Layer Protocol

Life Cycle Management

Primitives

Notifications

Security in Data Accessing

Authentication and Authorization

Data Access Controls

Secure Communication

Audit and Monitoring

Architecture Isolation

MCP Implementation & Demonstration

Backend Database

MCP server implementation

MCP Server Registration in MCP Host

Conclusion

Bruno : The Developer-Friendly Alternative to Postman

What Exactly is Bruno?

Why is Bruno Suddenly Everywhere?

Key Features

Totally Open-Source

Privacy from the Ground Up

Light as a Feather, Fast as Lightning

Getting Bruno Up and Running

#3. Quick Install Guide

Windows

macOS

Linux

GUI or CLI? Your Call

Build Your First Collection in Minutes

Step 1: Fire It Up

Step 2: New Collection Time

Step 3: Add a Request

Install Helm-Unittest
Prerequisites: Helm 3.8+, Git, basic YAML skills.