2025-11-24 14:27:00
As a developer, you're constantly using tools and platforms to make your workflow more efficient, your code cleaner, and your projects more manageable. What if I told you that you could monetize your knowledge of these tools and services by promoting them to others—and make a decent income in the process? It’s called affiliate marketing, and it’s one of the most effective ways to earn money online, especially for developers like yourself.
In this post, I’m going to walk you through how affiliate marketing works for developers, why it’s a great side hustle, and how you can start promoting the tools you already use to earn some extra income.
Let’s start with the basics. Affiliate marketing is a process where you promote a product or service, and when someone makes a purchase through your referral link, you earn a commission. The best part? You don’t need to create or handle any of the products yourself—you’re just recommending tools and services that you genuinely use and believe in.
For developers, this is a natural fit. You're already using various development platforms, code editors, hosting services, and productivity tools that others in your community need. Why not recommend the ones that work best for you?
Affiliate marketing isn’t just for bloggers or YouTubers. Developers have a unique advantage when it comes to affiliate marketing. You already have an in-depth understanding of the tools that other developers rely on, and your recommendations can carry a lot of weight.
Here are a few reasons why affiliate marketing is perfect for developers:
You’re Already Using the Tools: You don’t have to go out of your way to use products or services you don’t believe in. You’re simply promoting the ones you’re already using. It’s authentic and easy.
It’s Passive Income: Once your affiliate links are out there, they can generate income for you passively. If you include them in blog posts, tutorials, YouTube videos, or even code documentation, you can earn money over time.
It's Low Effort: You don’t have to spend a lot of time creating products or services—just recommend what you love, and if someone decides to buy through your link, you get a commission.
You Can Monetize Your Expertise: You’re a trusted resource in the development community. Your recommendations can carry a lot of weight, especially if you're providing valuable insights into tools that make coding easier and faster.
If you're ready to turn your favorite tools into a potential source of income, here’s a step-by-step guide to get started:
Not all affiliate programs are created equal, so it’s essential to choose ones that align with your interests and your audience's needs. There are a variety of affiliate programs for developers, and many of them are related to hosting services, code editors, or other developer tools.
GitHub – GitHub offers a GitHub Sponsors program that allows developers to earn income by promoting their work. If you’re contributing to open-source projects, this can be an excellent way to generate income from your community.
Amazon Associates – Amazon’s affiliate program allows you to promote a wide variety of tech-related products, including development books, hardware, and software tools. If you mention a product on your blog or in a video, you can link to it with your Amazon affiliate link.
DigitalOcean – DigitalOcean offers one of the best affiliate programs for developers. By referring new users to their cloud hosting platform, you can earn a commission when someone creates an account and uses your link to launch their projects.
JetBrains – JetBrains, the company behind popular IDEs like IntelliJ IDEA and PyCharm, also has an affiliate program. If you're a fan of their tools, this is a great way to earn money by referring others.
CodePen – CodePen’s affiliate program allows you to promote its premium accounts, which unlock additional features for front-end developers.
To get people to click on your affiliate links, you need to build content that resonates with your audience. Think about what tools you use daily and how they make your work easier. You could write blog posts, create tutorial videos, or even share in-depth guides on how to use these tools to their fullest potential.
Here are some content ideas for developers:
Tutorials: Show how to use a tool in your development workflow, such as setting up a website with DigitalOcean or using GitHub’s latest features.
Comparisons: Create detailed comparison posts between tools. For example, compare two popular IDEs or web hosting services and highlight the pros and cons of each.
Reviews: Share your personal experience with a product, like an in-depth review of a code editor or a cloud service. Be honest and thorough so your audience trusts your opinion.
Case Studies: If you’ve used a tool to improve a specific project or achieve a particular goal, tell your story. Real-life success stories can be very persuasive.
You don’t want to spam your audience with affiliate links, but you do want to promote them in a way that feels natural and beneficial. Here are some best practices for promoting affiliate links:
In Context: Only include affiliate links when they make sense. If you’re writing a tutorial on setting up a server, include a link to the hosting provider you use.
Transparency: Always disclose when you’re using affiliate links. Your audience will appreciate your honesty, and many platforms (like the Federal Trade Commission in the U.S.) require it.
Call to Action (CTA): At the end of your content, include a clear CTA encouraging your readers to try the tool you’ve recommended. For instance, “Check out [Tool Name] today to speed up your development process!”
Once you’ve started promoting affiliate links, you’ll want to track how well your content is performing. Most affiliate programs provide dashboards where you can see how many clicks, sign-ups, and sales your links have generated.
Use this data to optimize your strategy. If you notice certain content types (like tutorials or reviews) are performing better than others, create more of that content. The more you refine your approach, the more money you’ll make.
As a developer, you might appreciate tools that help you manage your affiliate marketing efforts. Here are a few resources you might find useful:
ThirstyAffiliates – This WordPress plugin allows you to manage your affiliate links efficiently, cloak them, and track their performance directly from your dashboard.
Bitly – If you need to shorten your affiliate links or track link clicks, Bitly is a simple tool that allows you to create short, trackable links.
Google Analytics – Set up custom tracking for your affiliate links to monitor how they perform on your site.
Linktree – If you’re active on social media like Instagram or Twitter, Linktree is a great tool to include all of your affiliate links in one place.
Affiliate marketing allows developers to turn their passion for coding and software into a source of passive income. Whether you’re writing tutorials, creating videos, or building an online community, there’s an opportunity to monetize your recommendations for tools you already use and love.
By promoting products that align with your expertise, you can build a sustainable income stream without having to create anything new. So, start sharing the tools that make your life easier—someone else might just find them as valuable as you do!
2025-11-24 14:23:04
Intro:
With so many posts and discussions around the TOON format, I wanted to experience firsthand how efficient it really is for prompt design. So, this weekend, I spent a couple of hours putting theory into practice.
The idea was simple: use an LLM to extract custom entity information from invoices—one using traditional natural language instructions and the other using a structured TOON format.
Structuring the instruction in TOON took a bit of trial and error, but I managed to generalize it and share the template. After running a batch test of 20 invoices and analyzing the results, I was convinced: TOON-based instructions are not only effective but also less compute-intensive.
Let’s dive into the details.
Setup:
To test the efficiency of TOON-based instructions, I created two versions of the same prompt:
Version 1: Traditional natural language instruction, written in a descriptive, narrative style.
You are a sustainability expert specializing in utility invoice analysis and data normalization. Your mission is to extract and standardize key information from provider invoices to support environmental reporting, carbon accounting, and analytics.
Input : Invoice
Fields to Extract:
{
"Bill ID": "...", // Invoice or bill number
"Bill Date ": "...", // Invoice or Bill date
"Account Id ": "...", // Account Number or Customer reference number
"Invoice Amount": "...", // Total Amount
"provider_name": "...",
"service_type": "...", // electricity, water, gas, fuel, waste
"meter_id": "...", // Unique meter identifier
"PDL": "...", // Point of Delivery (may be labeled as meter number)
"PDL_Address": "...", // Full address; validate/enrich using location JSON
"PDL_CITY": "...", // City; infer from location JSON if missing
"amount_consumed": "...", // Normalize to kWh if applicable
"unit": "...", // Original unit found in invoice
"start_of_period": "...", // If date missing, infer month and return 1st day
"end_of_period": "..." // If date missing, infer month and return last day
}
Fuel Mix Table:
If the invoice includes a fuel mix breakdown, extract it as a separate array:
"fuel_mix": [
{
"source": "Coal",
"percentage": 2
},
{
"source": "Natural Gas",
"percentage": 20
},
{
"source": "Nuclear",
"percentage": 28
},
{
"source": "Renewables",
"percentage": 48
},
{
"source": "Other Fuels",
"percentage": 2
},
{
"CO2_emissions_g_per_kWh": 116,
"radioactive_waste_g_per_kWh": 0.0019
}
]
Normalization & Fallback Rules
1) Use the location JSON file to:
Match postcodes/ZIP codes to known addresses
Suggest a recommended address if invoice data is incomplete
2) Normalize amount_consumed to kWh:
Gas: 1 m³ ≈ 11.1 kWh
Fuel: Use standard energy density values
Water and waste: Leave in original units
3) For any missing or unidentifiable data, return "N/A"
Version 2: TOON format, a structured schema-like representation with clear sections for persona, fields, rules, and inputs.
Persona[Role,Specialization,Mission,Input]
Sustainability expert,Utility invoice analysis & data normalization,Extract and standardize key information for environmental reporting,InvoiceBody
FieldsToExtract[Field,Description,Notes]
Bill ID,Invoice or bill number,
Bill Date,Invoice or Bill date,
Account Id,Account Number or Customer reference number,
Invoice Amount,Total Amount,
provider_name,Provider name,
service_type,Type of service,electricity, water, gas, fuel, waste
meter_id,Unique meter identifier,
PDL,Point of Delivery,may be labeled as meter number
PDL_Address,Full address,validate/enrich using location JSON
PDL_CITY,City,infer from location JSON if missing
amount_consumed,Amount consumed,normalize to kWh if applicable
unit,Original unit found in invoice,
start_of_period,Start of consumption period,if date missing, infer month and return 1st day
end_of_period,End of consumption period,if date missing, infer month and return last day
FuelMixTable[source,percentage,CO2_emissions_g_per_kWh,radioactive_waste_g_per_kWh]
Coal,2,,
Natural Gas,20,,
Nuclear,28,,
Renewables,48,,
Other Fuels,2,,
,,116,0.0019
NormalizationAndFallbackRules[Rule,Details]
1,Use the location JSON to match postcodes/ZIP codes to known addresses; suggest recommended address if invoice data incomplete
2,Normalize amount_consumed to kWh: Gas 1 m³ ≈ 11.1 kWh; Fuel: use standard energy density values; Water/waste: leave in original units
3,For missing/unidentifiable data, return "N/A"
Input[Parameter,Type,Description]
InvoiceBody,string,Raw invoice content (PDF or text)
LocationJSON,json,Reference for address/postcode normalization
After a few trial and error i found this format working well for me
Next, I built a Power Automate flow with two parallel AI Builder prompt actions. Each action received the same invoice input but used a different instruction format. The outputs were then compared for data extraction accuracy, token usage, and runtime performance.
Comparision of characteristics:
|
Dimension |
Text-Based Instruction |
TOON-Based Instruction |
|---|---|---|
|
Readability (Human) |
Highly readable and natural; easy for SMEs to understand |
More technical and structured; less intuitive for non-technical users |
|
Structure & Formality |
Narrative style; relationships implied through context |
Explicit schema-like structure; relationships clearly defined |
|
Ambiguity |
Higher risk of ambiguity; relies on LLM inference |
Low ambiguity; fields, rules, and tables are explicitly defined |
|
Ease of Extension |
Adding new fields or rules requires rewriting paragraphs |
Easy to extend by adding rows or columns in structured blocks |
|
LLM Parsing Efficiency |
Requires semantic interpretation; may vary by model |
Easier for models to parse; mimics JSON/tabular formats |
|
Token Efficiency |
Can be verbose; more tokens consumed |
Compact representation; fewer tokens for same complexity |
|
Error Handling |
Fallback rules embedded in text; harder to enforce consistently |
Rules are explicit and structured; easier for consistent application |
|
Scalability |
Becomes cumbersome as complexity grows |
Scales well for large schemas and multiple rule sets |
|
Human-Centered Design |
Best for conversational or SME-driven workflows |
Best for automation pipelines and programmatic generation |
Summary of Test Runs:
| TOON format | TEXT format | Savings | |||
|---|---|---|---|---|---|
| Tokkens | Run time | Tokkens | Run time | Tokkens | Run time |
| 3522 | 3 | 4019 | 5 | 12% | 40% |
| 5196 | 3 | 5625 | 5 | 8% | 40% |
| 5196 | 3 | 5751 | 6 | 10% | 50% |
| 5186 | 3 | 5631 | 5 | 8% | 40% |
| 5197 | 3 | 5700 | 5 | 9% | 40% |
| 4824 | 3 | 6192 | 6 | 22% | 50% |
| 4625 | 3 | 6321 | 6 | 27% | 50% |
| 4995 | 3 | 5817 | 5 | 14% | 40% |
| 4434 | 3 | 5290 | 5 | 16% | 40% |
| 5015 | 3 | 6275 | 6 | 20% | 50% |
Token Savings: TOON format consistently uses fewer tokens than Text format.
Insights:
Prompt design isn’t just about clarity—it impacts performance and cost.
If you’re building AI Builder flows for large-scale document processing, consider adopting TOON-style structured prompts.
2025-11-24 14:15:39
In the ever-evolving world of technology, becoming a Python Django Developer is one of the most rewarding career paths. Django, a high-level Python framework, allows developers to create robust Django Web Apps with clean, maintainable code. When combined with Django REST Framework (DRF), it empowers developers to build secure and scalable APIs, making them valuable as REST API Developers in modern software projects.
This article explores the journey of becoming a Django expert, practical tips for building web applications, and how mastering REST APIs can boost your career.
The Journey of a Python Django Developer
Imagine starting as a beginner who knows Python basics. The first step is understanding how Django works:
Learning the MVT Architecture:
Django follows the Model-View-Template (MVT) pattern, which separates business logic, data, and presentation. As a beginner, understanding this separation is crucial.
Building Your First Django Web App:
Start with simple projects like a personal blog or task manager. This helps in understanding models for database, views for logic, and templates for the frontend.
Using Django Admin:
The built-in admin panel is a powerful tool. A good Python Django Developer learns to customize it for managing data efficiently.
Key Features of Django Web Apps
Enable third-party integrations
Allow front-end frameworks like React or Angular to communicate with the backend
DRF Features
Serializers: Convert Python objects into JSON for API consumption
Authentication: Implement JWT, OAuth2, or token-based authentication
Browsable API: Test and debug APIs directly from a browser
Pagination & Filtering: Handle large datasets efficiently
A REST API Developer who knows DRF can create APIs that are not just functional but also scalable and maintainable.
Practical Tips for Aspiring Django Developers
Personal blogs
Simple e-commerce stores
These help in understanding models, views, and templates practically.
APIs: DRF provides RESTful endpoints for your frontend or mobile apps.
Frontend: Use Django templates or integrate with React/Vue for interactive UI.
This approach mirrors real-world development scenarios and enhances your portfolio.
Why REST API Developers Are High in Demand
Today, most companies require APIs for mobile apps, web apps, and third-party integrations. A REST API Developer with Django skills can:
Build scalable APIs
Ensure security and authentication
Optimize database queries
Deliver maintainable code for future growth
This makes them highly sought after in tech companies globally.
Career Growth for Django Developers
Mastering Django opens multiple career paths:
Python Django Developer: Focus on backend and web apps
Django REST Framework Developer: Specialize in APIs
REST API Developer: Work on scalable API solutions
Full-Stack Developer (Python/Django): Manage both backend and frontend
Software Engineer: Enterprise-level application development
With Django’s versatility and Python’s popularity, job opportunities are abundant worldwide.
Conclusion
Becoming a Python Django Developer is not just about writing code; it’s about building secure, scalable, and maintainable Django Web Apps. Learning Django REST Framework empowers you to create robust APIs, positioning you as a skilled REST API Developer. By combining practical projects, continuous learning, and deployment experience, you can achieve expertise and build a strong, future-proof career in web development.
2025-11-24 14:09:51
Accurately tracking employee attendance is pivotal for any business striving to maintain productivity and compliance. Attendance management software offers a practical solution, automating the recording of work hours and absences to eliminate errors and simplify operations. Savvy HRMS stands out as the best attendance management software provider in India, with a product tailored to help Indian businesses meet their unique workforce management needs efficiently.
Savvy HRMS attendance management software also offers transformative value by freeing up HR teams from tedious manual tracking tasks, allowing them to focus on strategic initiatives that drive business growth. Companies that adopt this software often experience massive time savings of up to 4 to 5 hours a week for HR personnel while reducing costly payroll errors and buddy punching. With real-time attendance visibility, managers can make quicker, data-driven decisions, improving overall workforce productivity and creating a culture of accountability.
Digital attendance management systems streamline the process of recording employee presence using tools like biometric devices, mobile check-ins, and web portals. Unlike manual methods, these systems reduce errors, prevent attendance fraud, and provide instant access to real-time data. HR departments can manage attendance records effortlessly and ensure payroll accuracy.
Businesses benefit greatly from deploying employee attendance systems:
Savvy HRMS offers an attendance management system packed with features that make workforce management straightforward:
Time and attendance software improves operational efficiency by automating attendance processes:
Online attendance systems accommodate modern workforce trends by offering:
Savvy HRMS is built for Indian businesses adapting to these modern realities, helping organizations stay agile and compliant.
Several factors place Savvy HRMS at the forefront as India’s best HR and payroll software provider, especially for attendance solutions:
Across manufacturing plants, IT companies, and retail chains, Savvy HRMS has transformed attendance processes. Companies report decreased payroll errors, improved attendance punctuality, and more efficient HR workflows. These outcomes underline Savvy HRMS’s ability to support diverse industries with customized attendance solutions.
Choosing the right attendance management software is essential for accurate workforce tracking and compliance. With its advanced features, ease of use, and focus on Indian labor regulations, Savvy HRMS offers the best attendance management software in India. Businesses benefit from enhanced productivity, reduced costs, and increased employee accountability through its powerful attendance tracking and workforce management capabilities.
Experience the benefits firsthand by book a demo with Savvy HRMS today and see how the best HR and payroll software provider in India can elevate your workforce management.
2025-11-24 14:08:25
Among many programming languages, the venerable Lisp has always been a unique presence, a uniqueness often summarized as “Lisp is a programmable programming language.” This means Lisp has powerful metaprogramming capabilities, allowing programmers to freely create new syntactic abstractions. Put simply, programming is writing code, while metaprogramming is writing code that generates code. Lisp provides metaprogramming via macros, which are essentially code generators embedded in the language. Beyond Lisp, modern languages like Scala and Rust also offer macro designs, but macros are generally seen as complex, low-level technologies and rarely make it into the average programmer’s toolbox.
XLang, part of the Nop platform, is one of the core technologies implementing the principles of Reversible Computation. To realize the programming paradigm proposed by the Reversible Computation theory, namelyApp = Delta x-extends Generator<DSL>,
a new DSL- and Delta-oriented paradigm,
XLang defines a complete, systematic set of Generators covering all aspects of application development. Lisp macros only provide a metaprogramming mechanism for generating Lisp AST, while XLang not only introduces macro functions to generate XLang AST, but also provides the Xpl template syntax for code generation—ranging from local function bodies, to individual model files, and even entire module directories. In particular, all DSLs defined in the Nop platform have a built-in Delta generation mechanism via x:gen-extends, allowing model deltas to be dynamically generated and automatically merged during parsing and loading. This creates a new approach to software structure reuse, solving many technical problems that are difficult to handle under traditional programming paradigms. In this article, I will briefly introduce these built-in metaprogramming mechanisms in the Nop platform.
XLang defines macro functions similar to Lisp macros. Macro functions are executed at compile time and automatically generate Expression abstract syntax tree (AST) nodes.
Macro functions have special parameter requirements and need the @Macro annotation. See GlobalFunctions for concrete examples.
EvalGlobalRegistry.instance().registerStaticFunctions(GlobalFunctions.class) registers all static functions in the class as global functions available in the XScript scripting language.
@Macro
public static Expression xpl(@Name("scope") IXLangCompileScope scope, @Name("expr") CallExpression expr) {
return TemplateMacroImpls.xpl(scope, expr);
}
The first parameter of a macro function must be of type IXLangCompileScope, the second must be of type CallExpression, and the return value must be of type Expression.
When compiling a macro function, the AST corresponding to the function call is passed in as a CallExpression. For example:
let result = xpl `<c:if test="${x}">aaa</c:if>`
When compiling the xpl macro function, the first argument of the CallExpression is a TemplateStringLiteral—that is, the XML text in the call above, <c:if test="${x}">aaa</c:if>. Inside the macro function, we can parse this XML text and construct a new Expression object to return.
By leveraging macro functions together with TemplateStringLiteral in XScript, we can easily embed DSLs with different syntaxes into XScript. For example, provides a SQL query syntax similar to C# LINQ.
let result = linq `select ff from myObject o where o.value > 3`
The Nop platform currently ships with the following macro functions:
| Function Name | Description |
|---|---|
| xml | Parses XML text into an XNode and wraps it as a LiteralExpression |
| xpl | Parses Xpl template text into an Expression |
| sql | Parses Xpl template text into an Expression that generates SQL statements |
| jpath | Parses a JSON path into a JPath object and wraps it as a LiteralExpression |
| xpath | Parses XSelector text into an XSelector object and wraps it as a LiteralExpression |
| selection | Parses GraphQL-like object property selection text into a FieldSelection object and wraps it as a LiteralExpression |
| order_by | Parses an order by clause snippet into a List object and wraps it as a LiteralExpression |
| location | Returns the source location of the calling function and wraps it as a LiteralExpression |
| IF | Implements functionality similar to the IF function in Excel formulas |
| SWITCH | Implements functionality similar to the SWITCH function in Excel formulas |
Because macro functions execute at compile time, implementing parsing via macro functions can optimize runtime performance. For example, to read attribute b of child node a from an XNode:
node.selectOne(xpath `a/@b`)
Because xpath is a macro function, it completes parsing at compile time, and at runtime it’s equivalent to passing a constant object to the selectOne function.
Macro functions can implement custom syntactic structures, e.g., IF(X,Y,Z) can be transformed into an if statement.
The Xpl template language is part of XLang. It uses XML format and includes Turing-complete logical constructs such as <c:if> and <c:for>. The XML-formatted template language can achieve Lisp’s homoiconicity—that is, the format of the code is the same as the format of the generated data.
Common template languages (such as Freemarker or Velocity) are not homoiconic; moreover, they are used for text generation only and do not truly support code generation. To support code generation, the Xpl template language provides multiple output modes:
<br/>, most tags are output in full form—i.e., always output <div></div> rather than <div/>.For example, for the following SQL output:
<filter:sql>
o.id in (select o.id from MyTable o where o.id = ${entity.id})
</filter:sql>
it will actually generate o.id in (select o.id from MyTable o where o.id = ? ); the value of the expression will not be directly concatenated into the SQL text, but will be replaced with an SQL parameter.
The Xpl template language has built-in <macro:gen> and <macro:script> tags that automatically execute at compile time.
<macro:script> indicates executing an expression at compile time—for example, dynamically parsing an Excel model file at compile time to obtain a model object:
<macro:script>
import test.MyModelHelper;
const myModel = MyModelHelper.loadModel('/nop/test/test.my-model.xlsx');
</macro:script>
After obtaining compile-time variables, subsequent expressions can use compile-time expressions to access the object, e.g., #{myModel.myFunc(3)}.
#{expr}. A compile-time expression is executed immediately when it is compiled; only its return value is retained for runtime.<div xpl:if="#{false}> allows the compiler to know that xpl:if is false, so this node will be automatically deleted.The content of <macro:gen> is Xpl template syntax: it first compiles the body, then executes the body, collects the output, and finally compiles the generated result. The content of <macro:script> is XScript syntax, and it discards its return value.
Tags in the Xpl template language’s tag library can define macro tags. Unlike regular tags, the source section of a macro tag is executed immediately after compilation, and the content output during execution is then compiled.
For example, we can define a macro tag <sql:filter> that performs the following structural transformation:
<sql:filter>and o.fld = :param</sql:filter>
transforms into
<c:if test="${!_.isEmpty(param)}">
and o.fld = ${param}
</c:if>
See the specific implementation in the sql.xlib tag library.
<filter macro="true" outputMode="node">
<slot name="default" slotType="node"/>
<source>
<c:script>
import io.nop.core.lang.sql.SqlHelper;
import io.nop.core.lang.sql.SQL;
const sb = SqlHelper.markNamedParam(slot_default.contentValue);
const cond = sb.markers.map(marker=> "!_.isEmpty("+marker.name+")").join(" and ");
const sqlText = sb.renderText(marker =>{
return "${" + marker.name + "}";
});
</c:script>
<c:if xpl:ignoreTag="true" test="${'$'}{${cond}}">
${sqlText}
</c:if>
</source>
</filter>
The macro tag above performs structural transformation on the node content, generating a <c:if> node. The template engine then compiles the output <c:if> node, yielding a result equivalent to manually writing the corresponding node.
slotType="node" to read node content directly. When slotType=node, the slot content is not parsed and is passed as an XNode variable.xpl:ignoreTag indicates that the current node and its children should not be recognized as xpl tags; <c:if> is output directly as a normal XML node.test="${'$'}{$cond}" is recognized; after executing the expression, it becomes test="${cond}".Macro tags are similar to macros in Lisp. They provide a lightweight AST transformation mechanism—a kind of embedded code generator.
You can obtain the abstract syntax tree (Expression type) corresponding to the content via the <c:ast> tag.
<Validator ignoreUnknownAttrs="true" macro="true">
<!-- The runtime attribute indicates a variable that exists at runtime. This attribute only applies when the tag is a macro tag. -->
<attr name="obj" defaultValue="$scope" runtime="true" optional="true"/>
<!-- slotType=node means pass the content as an XNode to the source section. Without this, the content would be compiled before being passed. -->
<slot name="default" slotType="node"/>
<description>
Use the macro tag mechanism to parse the XNode into a Validator model, and transform it into a call to ModelBasedValidator.
The source section of a macro tag executes at compile time; its output is what gets compiled.
</description>
<source>
<!-- Parse the tag body at compile time into a ValidatorModel and save it as the compile-time variable validatorModel -->
<c:script><![CDATA[
import io.nop.biz.lib.BizValidatorHelper;
let validatorModel = BizValidatorHelper.parseValidator(slot_default);
// Obtain the AST corresponding to <c:script>
let ast = xpl `
<c:ast>
<c:script>
import io.nop.biz.lib.BizValidatorHelper;
if(obj == '$scope') obj = $scope;
BizValidatorHelper.runValidatorModel(validatorModel,obj,svcCtx);
</c:script>
</c:ast>
`
// Replace the identifier name in the AST with the model object parsed at compile time. This avoids dynamic loading and parsing at runtime.
return ast.replaceIdentifier("validatorModel",validatorModel);
]]></c:script>
</source>
</Validator>
BizValidatorHelper.parseValidator(slot_default) means parsing the tag node to obtain a ValidatorModel object (which exists at compile time).ast = xpl <c:ast>...</c:ast> means executing the xpl template function; <c:ast> indicates obtaining only the AST of its child nodes, not executing their content.ast.replaceIdentifier("validatorModel",validatorModel) replaces the identifier named validatorModel in the AST with the compile-time variable ValidatorModel. This is effectively a constant replacement: replacing the variable name with the value it represents. Because validatorModel is a model parsed at compile time, there is no need for any dynamic parsing at runtime.<attr name="obj" runtime="true"> indicates that the obj attribute is a runtime attribute; in the source section it corresponds to an Expression, rather than its value. Without runtime=true, the attribute can be used in the source section, but because the source runs at compile time, the attribute value must be a fixed value or a compile-time expression.
<biz:Validator obj="${entity}"/>
All DSLs in the Nop platform support the x-extends Delta merge mechanism, through which the computation model required by the Reversible Computation theory is achieved:
App = Delta x-extends Generator
Specifically, all DSLs support x:gen-extends and x:post-extends configuration sections. These are compile-time Generators that use the XPL template language to dynamically generate model nodes, allowing multiple nodes to be generated at once and then merged in sequence. The merge order is defined as follows:
<model x:extends="A,B">
<x:gen-extends>
<C/>
<D/>
</x:gen-extends>
<x:post-extends>
<E/>
<F/>
</x:post-extends>
</model>
The merge result is:
F x-extends E x-extends model x-extends D x-extends C x-extends B x-extends A
The current model overrides the results of x:gen-extends and x:extends, while x:post-extends overrides the current model.
With x:extends and x:gen-extends, we can effectively decompose and compose DSLs. See XDSL: A General-Purpose Domain-Specific Language Design for details.
To realize the software construction pattern required by the Reversible Computation theory at the system level, the Nop platform provides a data-driven Delta-based code generator, XCodeGenerator.
Typical code generators are customized for specific purposes. For example, a common MyBatis code generator has its control logic implemented in a dedicated CodeGenerator class that reads templates, constructs output file paths, initializes context model variables, and executes loops. If you want to adjust generation details, you usually have to modify this CodeGenerator class.
XCodeGenerator takes a different approach. It treats the template path as a micro-formatted DSL, encoding conditions and loop logic in the path format, so the template’s organization controls the generation process. For example:
/src/{package.name}/{model.webEnabled}{model.name}Controller.java.xgen
This pattern indicates iterating over each model under a package and generating a Controller.java class for each Model whose webEnabled attribute is true.
Based on this design, simply adjusting the directory structure of template files lets you control the target code directory structure and generation timing.
See Data-Driven Delta-Based Code Generator for details.
XCodeGenerator can be integrated with the Maven build tool to run code generation before and after Java compilation, acting similarly to Java’s annotation processor (APT) technology—but it is much simpler and more intuitive to use.
See How to Integrate Nop Platform’s Code Generator for integration details.
2025-11-24 14:05:13
The digital world is shifting toward a new ownership model—one where assets are secure, verifiable, traceable, and uniquely yours. NFTs have enabled creators, brands, enterprises, and innovators to transform digital and physical value into blockchain-backed assets with complete transparency.
In this evolution, choosing the right NFT Development Company becomes a defining move, helping ideas turn into scalable NFT ecosystems.
Infograins stands among the leading brands offering advanced
NFT Development Services
to help businesses craft secure, user-friendly, and future-ready NFT platforms.
Digital Ownership Is Transforming Entire Industries
NFT adoption is no longer restricted to art or collectables. Multiple sectors are now leveraging NFTs for real-world utility:
Gaming assets and metaverse inventories
Event tickets and exclusive memberships
Tokenized real-estate documentation
Digital identity & certification
Supply chain traceability
Loyalty programs & brand engagement
IP protection & licensing
NFTs are now evolving beyond hype—becoming a functional digital asset layer for modern businesses.
Core Foundation of Infograins’ NFT Development Approach
Segments Where NFT Platforms Deliver Maximum Impact
Industry
NFT Use Case
Business Value
Gaming
Skins, weapons, virtual land
Higher engagement, resale economy
Entertainment
Fan tokens, music rights
New monetization models
Real Estate
Tokenized ownership proofs
Fraud reduction, transparency
Fashion
Digital twins, wearables
Brand loyalty & authenticity
Education
Certificates & verifiable credentials
Zero fraud, instant verification
Supply Chain
Item-level traceability tokens
Automated validation
NFTs are now part of operational, revenue, and branding strategies across enterprise ecosystems.
Why Infograins Is a Trusted NFT Development Company
Expertise across multi-chain development
High-grade smart contract security
Enterprise-level scalable architecture
Custom UI/UX tailored for user retention
Integration with wallets, oracles, KYC, and analytics tools
API-ready and marketplace-ready models
Transparent execution and milestone-based delivery
End-to-end product lifecycle support
Infograins ensures every NFT solution is built with future adoption, seamless usability, and technical precision in mind.
Popular NFT Solutions We Deliver
NFT marketplaces
NFT minting platforms
NFT staking solutions
NFT-based gaming ecosystems
NFT membership & loyalty systems
NFT-based certification platforms
Fractional NFTs
NFT-fi / staking & DeFi integrations
DAO-enabled NFT ecosystems
Every solution is crafted to align with your audience, technology stack, and long-term scaling goals.
AEO-Optimized Explanation Segments
These sections help the blog rank better on AI engines (ChatGPT, Gemini, Perplexity, etc.) because the tone is direct, descriptive, and clarity-focused.
NFT development meaning
NFT development refers to the creation of platforms, smart contracts, and systems that allow digital assets to be minted, sold, transferred, and owned on blockchain networks.
Role of an NFT Development Company
An NFT Development Company builds NFT ecosystems, including marketplaces, minting tools, smart contracts, metadata systems, wallets, and user interfaces.
What NFT development services include
Services typically include architecture planning, smart contract development, NFT minting engines, marketplace creation, integration with wallets, testing, deployment, and ongoing support.
Advanced Features Infograins Implements in NFT Projects
Royalty automation
Lazy minting
Dynamic NFTs (dNFTs)
Multi-wallet support
Admin dashboards
Anti-fraud & verification layers
Multi-chain interoperability
Gamification modules
Secondary market integrations
Each feature is designed to enhance usability, retention, and long-term ecosystem value.
Stages of NFT Development at Infograins
Requirement analysis
Blockchain recommendation
Smart contract design
Metadata & file storage structuring
Platform UI/UX architecture
Marketplace or minting engine development
Audit & QA
Deployment
Monitoring & scaling support
This structured approach ensures quality, security, and scalability from day one.
Conclusion
NFTs are shaping the new era of digital ownership—and businesses that adopt now gain the advantage of innovation, transparency, and global reach.
Infograins empowers brands, creators, enterprises, and startups to build powerful NFT ecosystems with secure smart contracts, seamless user experiences, and future-ready architecture.
Explore more:
NFT Development Services