Real plugin. Well-built. Permanent in Claude Code setup now.

What it do

Caveman is Claude Code skill from Julius Brussee. Strip output to fragments — no articles, no filler, no pleasantries, no hedging. Technical content stay intact. Padding die.

Repo tagline: “why use many token when few token do trick.” Tells most of story.

Why worth doing

Reasons stuck:

1. Output compress hard. Public benchmark avg 65% fewer output tokens. Range 22–87% depending on prompt.
2. Brevity may help accuracy. March 2026 paper: constrain big models to brief responses, accuracy up 26 points on certain benchmarks. Less rope to hang model with.
3. Compress memory files too. Sub-skill Caveman Compress rewrite CLAUDE.md into caveman-speak. Cut ~46% off input tokens every session. Input side dominate real coding work.

Honest caveat: headline number misleading. Typical 100,000-token session, prose responses ~6,000 tokens. Compress save ~4% total. Real wins: readability, speed, compress-CLAUDE.md trick.

Install it

Two commands in Claude Code:

/plugin marketplace add JuliusBrussee/caveman
/plugin install caveman@caveman

Restart Claude Code, and the skill loads at the start of every session. Trigger with /caveman, or just say “caveman mode” or “less tokens please” — Claude understands.

There are three intensity levels: /caveman lite drops filler, /caveman full switches to fragments, /caveman ultra goes maximum terse. I live in full. Ultra is funny for about ten minutes.

The honest take

There’s a fair counter-argument floating around — that saving a couple hundred output tokens is negligible when coding sessions burn through hundreds of thousands of tokens. Fair. But for me the win isn’t really the dollar savings. It’s that responses are faster to read, my CLAUDE.md is half the size, and the model seems to get to the point quicker.

Install takes a minute. If it doesn’t click, uninstall is also a minute. Worth a try.

Wait — you done already?

Notice how fast the first half of this post went down? That was caveman. Same point, half the words, and your eyeballs are already at the bottom of the page wondering where the rest of the article went.

There is no rest of the article. Caveman ate it. Cave brain happy. Token meter happy. Your lunch break, miraculously, intact.

Links:

• Repo: github.com/JuliusBrussee/caveman

2024 was about prompt engineering—learning how to talk to AI. 2025 was about context engineering and agents—learning how to make AI useful. 2026 will be about something bigger: scaling AI across the entire company.

Not just for developers. Not just for data teams. Everyone.

I’m sharing three perspectives on what this means:

1. Multi-agent orchestration — The shift from single agents to coordinated teams of specialized agents working in parallel.

2. Enabling AI company-wide — Why CTOs must make safe, effective AI tools available to everyone—before shadow AI fills the gap.

3. Everyone builds their own agents — The most important shift: empowering every employee to create and own AI agents that automate their work.

These aren’t speculative. They’re pragmatic bets on where the value will come from in 2026.

1. Multi-Agent Orchestration

2024 was the year of prompt engineering. 2025 was the year of context engineering and AI agents.

My take: 2026 will be the year of multi-agent orchestration—multiple specialized agents working in parallel with shared memory and context.

Here’s what that means in practice:

An architect agent reasons deeply about a problem and creates a plan. It then delegates individual tasks to the right agents for the job. Writing straightforward code? A fast, lightweight agent. Testing whether a new feature actually works? That might need deep thinking and multimodal capabilities to understand the UI, the logs, and the expected behavior.

The skill isn’t just “use more agents.” It’s knowing which agent for which task—and orchestrating them effectively.

What this means for developers:

Your value shifts from writing code to delivering value faster. You’ll spend more time directing agents, verifying results, and catching what matters: bugs and security flaws.

Clean code aesthetics? They matter less now—AI can read and refactor messy code just fine. But buggy or insecure code is the real risk. When you’re not writing the code yourself, how do you catch the problems? That’s the skill to develop.

Legacy code you don’t understand? Just ask AI to explain it—or better yet, have it make the changes for you.

Software craftsmanship isn’t dead. But it’s no longer the differentiator. In 2026, what sets you apart is the combination of judgment, the courage to push limits, and the ability to compress time to value. The landscape is shifting fast—and the most valuable skill is constantly discovering what’s possible and helps to deliver value more effectively.

2. Enabling AI Across the Company

As a CTO in 2026, your job isn’t just to adopt AI for engineering—it’s to enable everyone in the company to use AI. Not optional. Not “nice to have.” This is an imperative.

Here’s the reality: 78% of AI users bring their own tools to work—and 52% won’t even admit they’re doing it (CloudSphere, 2025). They’re not being malicious. They’re trying to be productive. And if you don’t give them approved tools, they’ll find their own.

Shadow AI is already inside your company. The question is whether you’re going to fight it or channel it.

What this means for CTOs:

Give people tools they can safely use with company data. This doesn’t mean chasing whatever went viral on LinkedIn last week—it means providing tools that do the job well, with proper security and data governance. If the approved option is good enough, people won’t go looking elsewhere.

Rethink how you train people to use AI. Forget scheduled training sessions that are outdated by the time they happen. AI is moving too fast. Instead, build a culture of continuous knowledge sharing—colleagues learning from each other, pairing with AI-savvy team members, sharing what’s working in real time.

And set a goal: every employee tries to use AI to solve a problem at least once per quarter. Make it a company KPI. If it doesn’t work today, things will have progressed enough to solve it next quarter. The point is to keep pushing, keep experimenting, and keep learning—because standing still means falling behind.

This isn’t a one-time initiative. It’s ongoing. The tools will keep evolving, and your people need to evolve with them.

3. Everyone Builds Their Own Agents

Here’s my most important prediction for 2026: everyone in the company—not just developers—needs to be able to build and own their own AI agents.

Not “use AI tools someone else built.” Build their own. To automate the boring, repetitive tasks that drain their time and energy, so they can focus on creating value.

The numbers back this up: knowledge workers could automate 30–70% of their current tasks with AI (McKinsey). Organizations using workflow automation are reducing operational costs by up to 30% (Gartner, 2025). And by 2026, business-user developers will outnumber professional developers 4:1.

This isn’t about job loss. It’s about job evolution. AI agents won’t take over the world—but they might take over your inbox. And that’s a good thing.

What this means for CTOs:

You need to provide three things:

1. Tools and platforms — Give people no-code or low-code tools to create and orchestrate agents. Make it easy to start, with templates and guardrails built in.

2. Training and knowledge sharing — Help people figure out what to automate and how. This isn’t a one-time workshop—it’s continuous peer learning and idea sharing.

3. Expert support — Pair business users with AI-savvy team members who can help them set things up and solve problems. Not gatekeepers—enablers.

Yes, this creates governance challenges. Yes, you’ll need guardrails to prevent a mess of ungoverned automations. But the alternative—centralizing all AI development in IT—is too slow. The companies that win will be the ones that trust their people to build, with the right support and oversight in place.

Everyone creates. Everyone owns. That’s how you scale AI across the enterprise.

The Bottom Line

The companies that win in 2026 will be the ones that trust their people to build.

Not just use AI. Build with it. Own it. Push its limits.

The landscape is shifting fast. What works today won’t work in six months. The most valuable skill isn’t mastering any specific tool—it’s constantly testing what’s now possible.

2026 won’t reward the companies with the best AI strategy on paper. It will reward the ones that move fastest, learn continuously, and put AI in the hands of everyone.

That’s the bet I’m making. What’s yours?

A Picture Paints a Thousand Flavors

It began on a lazy Sunday evening when I stumbled upon an image of the ingredients of one of the best cocktails I ever tasted. The name? Señor Smokey. The ingredients were listed right there, but the proportions? That remained the drink’s tantalizing secret.

The listed ingredients were:

• Mezcal
• Grapefruit
• Lime juice
• Falernum
• Agave
• Chocolate bitters
• Egg white
• Chilli salt

Just reading the ingredients again had my taste buds tingling. But how could I unlock the perfect blend of these flavors?

Enter ChatGPT: My Digital Bartender

With the ingredient list in hand, I turned to ChatGPT, the digital marvel by OpenAI. I fed it the list, and voila! It swiftly provided a recipe that promised a smooth, smoky cocktail sensation.

Here’s the magic potion it crafted for Señor Smokey:

1. 2 oz Mezcal
2. 1 oz fresh grapefruit juice
3. 0.5 oz fresh lime juice
4. 0.5 oz falernum
5. 0.25 oz agave syrup (you can adjust to taste)
6. 1-2 dashes of chocolate bitters
7. 1 egg white (from a fresh egg)
8. Chili salt (for rimming the glass)

ChatGPT even provided the instructions:

1. Glass Prep: Begin by rimming your glass. Wet the rim with a grapefruit or lime wedge, then dip into the chili salt to coat the rim. Set aside.
2. Dry Shake: In a shaker, combine the egg white, mezcal, grapefruit juice, lime juice, falernum, agave syrup, and chocolate bitters. Shake without ice first (this is known as a “dry shake”) to emulsify the egg white and create a foamy texture.
3. Wet Shake: After the dry shake, add ice to the shaker and shake again until well chilled. This is referred to as a “wet shake.”
4. Strain & Serve: Strain the mixture into your prepped glass. You should see a nice, foamy layer on top from the egg white.
5. Garnish (Optional): If desired, you can add a small piece of grapefruit or lime peel or wheel for visual appeal.
6. Enjoy responsibly!

The combination of ingredients you provided suggests a delightful balance of smoky, tangy, sweet, and bitter flavors with a silky texture from the egg white. The chili salt adds an extra layer of spicy kick to each sip. Enjoy your Señor Smokey!

The First Sip

With bated breath, I followed the concoction’s steps, and as I took the first sip, a smoky, tangy, sweet symphony played on my palate. The Señor Smokey was every bit as divine as I remembered!

The Power of Technology (and a Good Palate)

My journey with Señor Smokey taught me that with the right tools and a little determination, the world of mixology is right at our fingertips. Whether you’re a budding bartender, a cocktail connoisseur, or just someone who loves a good drink story, never underestimate the power of technology paired with human curiosity.

So the next time you’re eyeing a drink on social media, remember my tale of Señor Smokey. And who knows? You might just find your next favorite cocktail. Cheers! 🍹

Lean Startup and Design Sprint

In the early stages of a startup, the ability to rapidly prototype, test, and pivot is vital. The Lean Startup methodology encourages iterative development, allowing businesses to validate their ideas and learn quickly from mistakes. In parallel, running design sprints facilitates the rapid prototyping of ideas and solutions. Applying these methodologies effectively is skill that can be taught hands-on.

Tech 101 for Non-Tech Founders

The tech landscape can be daunting for non-technical founders. However, having a basic understanding of tech concepts and terminologies is crucial for the effective management of a tech-oriented business. I would provide non-technical founders with essential knowledge to bridge the communication gap with their technical team and make informed decisions.

Building a Minimum Viable Product (MVP)

The essence of a startup lies in its product. An MVP serves as the heart of a startup’s offer, allowing the company to bring a product to market quickly for user testing and feedback. I would focus on how to strategically choose features, set priorities, and leverage Agile methodology for product development.

Understanding and Implementing Agile Methodology

Agile development emphasizes flexibility, customer satisfaction, and cross-functional collaboration. Understanding and implementing Agile, including Scrum, can enhance project management efficiency and product quality. I would guide startups on how to effectively manage and plan their software development projects.

Choosing the Right Tech Stack

A startup’s tech stack is a significant determinant of its scalability, performance, and growth potential. Factors such as the startup’s industry, team expertise, and product features all play into this decision. With proficiency in AWS, Java, React, Next.js, and serverless technologies, I can guide startups on how to make the best tech stack choices to suit their specific needs.

Introduction to Data Science and AI

Data is the lifeblood of today’s businesses. Understanding how to leverage data through AI and machine learning is increasingly becoming a competitive advantage in the startup world. I would introduce the basics of these powerful technologies and show how startups can leverage them.

TL;DR

These topics were identified based on my experience in technology and leadership roles. They’re intended to equip startups with the skills and knowledge they need to develop, validate, and refine their business ideas. By instilling a robust understanding of these key areas, startups should be well-prepared to start generating revenue and scaling their models effectively.

Let me know what other topics would be essential from a tech perspective!

Asking the right questions early in the process can help you assess potential risks and make informed decisions about AI service providers. Here are ten questions to ask, along with some potential red flags to look out for:

Data Security and Privacy

Question: Is the AI service provider compliant with GDPR and other applicable data protection and privacy regulations? What mechanisms do they have in place to protect data during transmission and at rest?

What to look for: You’ll want to see evidence of robust data protection measures, such as data encryption and secure data transfer methods. Compliance with relevant regulations and standards is non-negotiable.

Data Usage

Question: Will the AI service provider have access to our data? If so, how will this access be controlled? Will our data be used to train or improve the provider’s AI models?

What to look for: Clear policies about how your data will be used and controlled are crucial. Beware of providers who might use your data to train models that could be used by competitors, which might lead to leakage of your company’s proprietary knowledge.

Data De-identification

Question: How does the provider ensure that data used to train or improve AI models is properly de-identified or anonymized?

What to look for: The provider should have robust procedures for de-identifying data, reducing the risk of data being re-identified later. If a provider can’t assure you of this, it could pose a significant risk.

Bias and Fairness

Question: What measures does the provider take to ensure that the AI models are fair and do not exhibit or perpetuate bias?

What to look for: The provider should be transparent about their methods for preventing and detecting bias in their models. AI models that are biased can lead to unfair outcomes and potential legal issues.

Transparency and Explainability

Question: How transparent is the AI model’s decision-making process? Can the provider offer insights into how the model makes decisions or predictions?

What to look for: Transparency and explainability are essential for trust and accountability. Providers should be able to explain in understandable terms how their models work.

Accountability

Question: Who is responsible if the AI service makes a decision that leads to harm or violates laws or regulations?

What to look for: The provider should be clear about accountability. If they avoid taking responsibility for their model’s decisions, that’s a red flag.

Review and Auditing

Question: Can the provider’s data handling and AI practices be audited? Does the provider have mechanisms in place for regular review and improvement of its AI practices?

What to look for: You’ll want a provider who is open to external audits and has a commitment to continual improvement.

Exit Strategy

Question: If the contract with the AI service provider ends or if the provider goes out of business, how will our data be handled? Can we easily retrieve or delete our data?

What to look for: Ensure there is a clear exit strategy that includes retrieving or securely deleting your data.

Training Data

Question: Is the data used to train the AI model ethically sourced and free of copyright restrictions?

What to look for: The provider should be able to confirm that they have the necessary rights to use the training data and that it was obtained ethically.

Quality Assurance

Question: How does the provider ensure the accuracy and reliability of the AI model?

What to look for: Look for providers with robust quality assurance processes that include regular testing and validation of their models.

TL;DR

By asking these questions and understanding what to look for in the answers, you’ll be well equipped to navigate the complex landscape of generative AI integration with data security and privacy in mind. Remember, a good AI provider should be able to answer these questions to your satisfaction, demonstrating their commitment to data security, privacy, and overall ethical AI practices.

Let me know if I am missing any essential questions!

For Founders: Fine-tuning the Pitch

A startup founder recently used ChatGPT in an intriguing way. They asked the AI to act as a business angel, with a focus on three key elements: the team, cash flow generation, and sustainable growth. Here’s how the conversation unfolded:

Founder:

Pretend you are a business angel who's top 3 things he looks at when
deciding on investing are the team, cashflow generation, and 
sustainable growth. Can you interview me about my business idea and 
let me know if you think you would invest?

ChatGPT: Responded with a series of pointed questions, delving deep into each of the three focus areas.

In this exchange, ChatGPT helped the founder critically evaluate their business idea, providing valuable insight into what potential investors might look for. It served as a sounding board, enabling the founder to refine their pitch and anticipate possible questions.

For VCs and Business Angels: A First Filter

ChatGPT’s application isn’t limited to founders. VCs and business angels can use it to streamline their investment process. Given its ability to process and analyze a large volume of information quickly, it can be programmed to evaluate pitch decks and business plans against specific criteria. It can ask initial screening questions, providing a preliminary filter to manage the deluge of funding requests these investors often face.

ChatGPT’s ability to ask incisive questions and engage in meaningful dialogue can help investors gauge a startup’s potential. It can highlight key areas of concern or interest that investors may want to delve deeper into during face-to-face meetings.

Detailed Analysis with ChatGPT: A Closer Look

One of the key strengths of using ChatGPT in preparing a pitch deck or evaluating a startup proposal is its ability to provide detailed analysis based on the information shared. Let’s dive deeper into this with some concrete examples from the simulated conversation with a startup founder:

1. Cost Management: The founder shared that their strategy was to start with remote services, moving to hiring as needed. ChatGPT recognized this as a savvy approach to cost management. Startups need to be lean and agile, especially in their early stages. This strategy shows an understanding of that necessity, signaling that the team has a solid grasp on resource allocation—a critical factor for potential investors.

2. Revenue Streams: In the business model discussed, the startup relied solely on subscription revenue. While this might work well in the initial stages, ChatGPT highlighted a potential risk: over-reliance on a single revenue source. This observation is crucial as it encourages founders to consider diversifying their revenue streams to create a more robust business model. The founder’s consideration of additional offerings for the target customer group was recognised as a positive step in this direction.

3. Competitive Landscape: One of the striking claims from the founder was the lack of direct competition. In the ever-changing landscape of online content creation, this is a significant advantage. However, ChatGPT was quick to point out that while the lack of competition is a current strength, it might not remain so in a dynamic market. This realistic view can encourage founders to stay innovative and prepared for potential competitors entering the market.

These examples illustrate the depth of analysis possible with ChatGPT. It doesn’t merely accept the information presented but critically evaluates it, providing valuable feedback. For founders, this means they can gain a more comprehensive understanding of their business model’s strengths and potential weaknesses. For investors, it highlights key areas to probe deeper during face-to-face meetings.

In essence, ChatGPT can provide an additional layer of analysis, helping both founders and investors make more informed decisions.

Limitations and Advantages

Available around the clock, ChatGPT shines in its ability to handle multiple requests simultaneously, showcasing its impressive scalability. Founders will appreciate the low-pressure environment it offers, perfect for practicing and refining pitches. On the other hand, investors will find it handy as a first-level filter to manage the influx of proposals they receive regularly.

While ChatGPT has the ability to mimic investor-like responses and ask thought-provoking questions, we must remember that it’s still an AI. It doesn’t have the intricate understanding and business savvy of a seasoned investor. Despite this, its talent for processing information and delivering detailed responses makes it a formidable tool in refining pitches and initial screening.

TL;DR

ChatGPT can be a powerful tool for startup founders and investors alike. It can offer an objective analysis of a business idea, enabling founders to refine their pitches and prepare for investor meetings. For VCs and business angels, it can be an efficient tool for initial screening. By leveraging this innovative technology, both founders and investors can make the startup journey more streamlined and focused.

How do you think the incorporation of AI tools like ChatGPT might reshape the landscape of venture capitalism and angel investing?

Accelerating Repetitive Tasks

One of the most significant advantages of generative AI is its ability to handle repetitive tasks that have been completed by countless developers before. For instance, setting up and scaffolding a new Spring Boot project, creating CI/CD pipeline scripts, or generating DDL scripts for database tables can be tedious and time-consuming.

Generative AI systems can automate these tasks, learning from the patterns in existing codebases and generating new code that follows the same structure. This allows developers to bypass the mundane setup stages and dive straight into the core functionality of the project.

Reducing Errors

Developers are only human, and making errors is inevitable. However, generative AI systems can significantly reduce the number of errors that occur during the development process, especially in repetitive tasks. By using AI-generated code, developers can reduce the likelihood of mistakes caused by human oversight, ensuring that the final product is more robust and reliable.

Focusing on High-Value Work

By automating repetitive tasks, generative AI frees up developers to concentrate on the more interesting, valuable, and fulfilling aspects of their work. This enables developers to dedicate more time to designing innovative features, enhancing user experience, and solving complex problems – ultimately resulting in higher-quality software.

A Synergistic Approach

It’s important to note that generative AI isn’t a perfect solution that can entirely replace human developers. It still requires an experienced programmer to evaluate the generated solutions and make necessary adjustments. This synergy between human developers and generative AI systems can lead to more efficient and effective software development, as it combines the strengths of both parties. Developers can leverage the AI-generated code while applying their expertise to ensure the desired results are achieved.

Conclusion

Generative AI holds the potential to revolutionize the software development process, offering developers the chance to automate repetitive tasks, diminish errors, and focus on higher-value work. By embracing generative AI, developers can not only boost their productivity but also enhance their job satisfaction by engaging with more creative aspects of software development. The collaboration between human developers and generative AI systems carries immense promise, and the sooner developers adapt to this novel paradigm, the better prepared they will be to address the challenges of the ever-evolving tech landscape.

Prerequisites

1. AWS CLI installed and configured with the necessary permissions to create and update CloudFormation stacks and other resources.
2. An existing S3 bucket and CloudFront distribution created using CloudFormation. In this example, we’re using the stack name webclient-assets.

Update your CloudFormation template

Update your existing CloudFormation template to include the necessary resources for the Lambda function, S3 Bucket Notification, and Lambda Permission.

Here is the updated CloudFormation template including the new resources:

[...]
# Add the following resources to your existing CloudFormation template

# Lambda function
InvalidateCloudFrontFunction:
  Type: AWS::Lambda::Function
  Properties:
    FunctionName: InvalidateCloudFront
    Handler: index.lambda_handler
    Role: !GetAtt LambdaExecutionRole.Arn
    Runtime: python3.8
    Code:
      S3Bucket: REPLACE_WITH_YOUR_S3_BUCKET_NAME
      S3Key: lambda_function.zip

# S3 bucket notification configuration
ContentBucketNotification:
  Type: AWS::S3::BucketNotificationConfiguration
  Properties:
    Bucket: !Ref WebsiteBucket
    NotificationConfiguration:
      LambdaConfigurations:
        - Event: s3:ObjectCreated:*
          Function: !GetAtt InvalidateCloudFrontFunction.Arn

# Lambda execution role
LambdaExecutionRole:
  Type: AWS::IAM::Role
  Properties:
    AssumeRolePolicyDocument:
      Version: '2012-10-17'
      Statement:
        - Effect: Allow
          Principal:
            Service:
              - lambda.amazonaws.com
          Action:
            - sts:AssumeRole
    Policies:
      - PolicyName: LambdaExecutionPolicy
        PolicyDocument:
          Version: '2012-10-17'
          Statement:
            - Effect: Allow
              Action:
                - logs:CreateLogGroup
                - logs:CreateLogStream
                - logs:PutLogEvents
              Resource: 'arn:aws:logs:*:*:*'
            - Effect: Allow
              Action:
                - cloudfront:CreateInvalidation
              Resource: '*'

# Lambda invoke permission
LambdaInvokePermission:
  Type: AWS::Lambda::Permission
  Properties:
    Action: lambda:InvokeFunction
    FunctionName: !Ref InvalidateCloudFrontFunction
    Principal: s3.amazonaws.com
    SourceAccount: !Ref 'AWS::AccountId'
    SourceArn: !Sub 'arn:aws:s3:::${WebsiteBucket}'

Package and upload your Lambda function code

Create a file named ```index.py`` with the following Python code for the Lambda function:

import boto3
import uuid

def lambda_handler(event, context):
    distribution_id = 'REPLACE_WITH_YOUR_CLOUDFRONT_DISTRIBUTION_ID'

    cloudfront_client = boto3.client('cloudfront')
    caller_reference = str(uuid.uuid4()) # Using a UUID as CallerReference

    response = cloudfront_client.create_invalidation(
        DistributionId=distribution_id,
        InvalidationBatch={
            'Paths': {
                'Quantity': 1,
                'Items': ['/*']
            },
            'CallerReference': caller_reference
        }
    )

    return {
        'statusCode': 200,
        'body': json.dumps(response)
    }

Create a .zip archive containing the index.py file. Name the archive ```lambda_function.zip``.

Update your CloudFormation stack

Use the AWS CLI to update your CloudFormation stack with the updated template:

aws cloudformation update-stack \
  --stack-name webclient-assets \
  --template-body file://path/to/your/updated_template.yaml \
  --capabilities CAPABILITY_NAMED_IAM

Replace path/to/your/updated_template.yaml with the path to your updated CloudFormation template file.

Conclusion

Now, whenever you update the content in your S3 bucket, the associated CloudFront cache will be automatically invalidated. This will ensure that your users always see the latest content on your website.

If you need to make further changes to your CloudFormation stack or Lambda function, simply update the necessary files and repeat the steps to update the stack.

Happy coding!

First initialize the project with npm

Open your terminal, create a project directory, and cd into the new directory. Then initialize the project with npm by executing the following script:

npm init -y
npm install webpack webpack-cli webpack-dev-server postcss-loader css-loader html-webpack-plugin style-loader copy-webpack-plugin --save-dev

Next create some basic files to work with:

touch webpack.config.js
touch postcss.config.js
mkdir dist
mkdir src
touch src/index.html
touch src/index.js
touch src/style.css

Then open the webpack.config.js and copy the following content:

const path = require('path');
const HtmlWebpackPlugin = require('html-webpack-plugin');
const CopyWebpackPlugin = require('copy-webpack-plugin');

module.exports = {
  mode: 'development',
  entry: {
    bundle: path.resolve(__dirname, 'src/index.js'),
  },
  output: {
    path: path.resolve(__dirname, 'dist'),
    filename: 'bundle.js',
    clean: true,
    assetModuleFilename: '[name][ext]',
  },
  module: {
    rules: [
      {
        test: /\.css$/i,
        use: ['style-loader', 'css-loader', 'postcss-loader'],
      },
    ],
  },
  plugins: [
    new HtmlWebpackPlugin({
      template: 'src/index.html'
    }),
    new CopyWebpackPlugin({
      patterns: [
        {
          from: path.resolve(__dirname, 'src', '**', '*.{jpg,png,svg,ico,webmanifest}'),
          to({ context, absoluteFilename }) {
            const relativePath = path.relative(path.join(context, 'src'), absoluteFilename);
            return path.join(context, 'dist', relativePath);
          },
          context: __dirname,
          globOptions: {
            dot: false,
            ignore: ['**/node_modules/**'],
          },
        },
      ],
    }),   
  ],  
  devServer: {
    static: {
      directory: path.resolve(__dirname, 'dist'),
    },
    port: 3000,
    open: true,
    hot: true,
    compress: true,
    historyApiFallback: true,
  }
};

Add Tailwind CSS to the project

Install Tailwind CSS

npm install -D tailwindcss postcss autoprefixer
npx tailwindcss init

Next add Tailwind CSS to your postcss.config.js file

module.exports = {
    plugins: {
      tailwindcss: {},
      autoprefixer: {},
    }
  }

Configure your template paths in the tailwind.config.css

/** @type {import('tailwindcss').Config} */
module.exports = {
  content: ['./src/**/*.{html,js}'],
  theme: {
    extend: {},
  },
  plugins: [],
}

Next add Tailwind CSS directives to your style.css

@tailwind base;
@tailwind components;
@tailwind utilities;

and include the css in the ìndex.js

import './style.css';

Now create an initial HTML page and include the css

<!doctype html>
<html>
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
</head>
<body>
  <h1 class="p-3 text-center">
    Initial Webpack with Tailwind CSS
  </h1>
      <div class="w-64 py-16 mx-auto text-5xl text-red-700">
        <div x-data="{ show: false }">
          <button
            class="px-6 py-2 text-2xl text-white bg-blue-500 rounded shadow hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-400 focus:ring-opacity-50"
            @click="show = !show">
            Click me
          </button>
          <div x-show="show" class="my-16">
            <h1>Oh, hello!</h1>
          </div>
        </div>
      </div>  
  <script src="bundle.js"></script>
</body>
</html>

Finally add AlipineJS

Add the framework to the project:

npm i alpinejs --save-dev

Then you add AlpineJS to the ìndex.js

import Alpine from 'alpinejs';

window.Alpine = Alpine;

Alpine.start();

Run and build your app

First add scripts for this to your package.json:

"scripts": {
    "dev": "webpack serve",
    "build": "webpack"
  },

Then you can build once and create your dist/bundle.js

npm run build

In order to run your local development server with webpack

num run dev

Conclusion

In conclusion, this blog post offers a comprehensive guide on setting up a modern and efficient development environment using Webpack, Tailwind CSS, and Alpine.js. By following these steps, you will be able to create a streamlined and modular development process that takes advantage of the powerful features offered by these tools. As a result, you’ll be well-equipped to create scalable and maintainable web applications that are both visually appealing and highly interactive.

To help you get started, I’ve created a starter project on GitHub that includes all the necessary configuration files and dependencies to start building web applications with Tailwind CSS, AlpineJS, PostCSS, and Webpack. You can find the starter project here:

https://github.com/poornerd/tailwindcss-alpinejs-postcss-webpack-starter

Visualize and map dependencies

Use tools like dependency maps or program boards to visualize and manage dependencies. This will help teams identify and address them early in the development process, reducing delays and risks. Maintain these visual aids regularly to track progress and manage changes.

Establish cross-team communication channels

Encourage open communication between teams through regular cross-team meetings, shared chat platforms, or other collaboration tools. This will foster a culture of transparency and collaboration, making it easier to address dependencies as they arise.

Use a shared backlog

Create a shared backlog of items that have dependencies across teams. This will make it easier to prioritize and manage work across teams, while also ensuring that everyone is aware of the dependencies that need to be resolved.

Implement feature toggles

Feature toggles can help manage dependencies by allowing teams to work on features in isolation. This reduces the risk of broken builds and promotes independent development. When a feature is complete, it can be toggled on without impacting other teams.

Focus on API and contract-first development

By defining contracts and APIs early in the development process, teams can build their components in isolation while ensuring compatibility. This reduces the likelihood of unexpected issues when integrating components.

Appoint a dependency manager

Designate an individual or a small team to oversee dependency management across the organization. This role will be responsible for identifying, tracking, and resolving dependencies, as well as ensuring that teams have the resources they need to address them.

Prioritize dependency resolution in sprint planning

During sprint planning, prioritize resolving dependencies to minimize the risk of delays and blockers. This can include allocating time for cross-team collaboration, addressing known dependencies, and planning for potential issues.

Conclusion

Managing dependencies between scrum teams in a scaled agile approach is a challenging yet essential aspect of successful project delivery. By implementing these strategies, organizations can promote collaboration, increase efficiency, and ensure the timely delivery of value to stakeholders.