Ghostwriter

Speckle

Senior software engineer

TrustLayer

Tech Lead

Backend developer

Slope

Ingegnere software

7 years experience in Web development.
I worked on both frontend and backend with Javascript, Typescript, PHP, Java and (for a short period of time) even Python.
I'm passionate of Open Source and contribute regularly to some NodeJs projects (Fastify ecosystem in particular).
I maintain a few plugins which helps integrate some well known libraries into Fastofy projects and I am contributor to the awesome-nodejs-security repository.77

Junior developers who get hired:
• Build side projects that solve real problems
• Document their development process
• Share their work publicly
• Iterate based on feedback

Those who struggle:
• Rely only on coursework
• Build projects in isolation
• Never ship anything
• Wait for perfect ideas

Alessandro Magionami

I ghostwrite email courses for the software engineering ecosystem | Worked in startups for 8+ years | Obsessed with online writing

A side project isn't about being perfect.

A side project is about being practical and proving your skills.

𝐓𝐡𝐞 𝐁𝐄𝐄𝐑 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐯𝐞𝐥𝐲 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

Many overcomplicate AI feature development.

They try to build production-ready applications all at once. This approach struggles with AI's need for context. We work iteratively, and AI development should too. I've found success using the BEAR framework for this purpose. It breaks down complex AI builds into manageable steps.

Here’s how you can leverage it for your next AI feature.

𝐁 – 𝐁𝐮𝐢𝐥𝐝: Start with a minimum viable product.

This is the absolute core functionality.

• Focus on one user action at a time.
• For example, creating a basic reminder with title and date.
• This foundational step gives AI initial context.

This initial build establishes a working baseline for your AI feature.

𝐄 – 𝐄𝐧𝐡𝐚𝐧𝐜𝐞: Improve the user experience.

Once the core is built, refine the interaction.

• Implement essential validations.
• Ensure the date is in the future, title can't be empty.
• These small improvements significantly boost usability.

Enhancement makes the feature more robust and user-friendly.

𝐄 – 𝐄𝐱𝐩𝐚𝐧𝐝: Broaden the existing functionality.

Build upon the current feature, not a new one.

• Add related capabilities like notifications or editing reminders.
• This deepens the value of the initial action.
• Think about the natural next steps a user would take.

Expansion creates a more complete and valuable user outcome.

𝐑 – 𝐑𝐞𝐟𝐢𝐧𝐞: Polish for production readiness.

This is the final stage of optimization.

• Focus on UI consistency and aesthetics.
• Ensure mobile responsiveness and clear messaging.
• Polish the overall look and feel.

Refinement transforms a functional feature into a production-ready asset.

The BEER framework provides a clear, iterative path to AI feature development. By following these steps, you build complexity gradually, ensuring AI context is always relevant. I've seen this method streamline development and produce more effective results.

What AI feature are you planning to build next using an iterative approach?

I landed my first NodeJS job with zero professional experience.

No bootcamp. No computer science degree. No connections.

Just side projects and open source contributions.

Here's exactly how I made it happen.

The secret was building in public from day one.

I started with tiny projects that solved my own problems.

A simple expense tracker because I hated budgeting apps.

A weather CLI tool because I lived in the terminal.

A bookmark organizer because my browser was chaos.

Nothing groundbreaking. Just real solutions to real annoyances.

Then I made everything open source on GitHub.

I documented my code like someone else would use it.

I wrote README files that actually explained things.

I responded to every issue and pull request.

Slowly, people started noticing my work.

Other developers began contributing to my projects.

I contributed to their projects in return.

My GitHub profile became a living portfolio.

When I applied for jobs, I didn't talk about frameworks.

I showed them problems I'd solved.

I walked them through my code during interviews.

I demonstrated how I collaborated with other developers.

𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝑖𝑠𝑛'𝑡 𝑗𝑢𝑠𝑡 𝑤h𝑎𝑡 𝑦𝑜𝑢 𝑔𝑒𝑡 𝑝𝑎𝑖𝑑 𝑓𝑜𝑟.

It's every problem you solve and every line you ship.

Side projects gave me real experience before I had real experience.

Open source proved I could work with teams.

Employers care more about what you can build than where you've worked.

Your next side project could be your next job opportunity.

Need some help finding a good idea?
Check in the comments

Don't build projects to impress yourself.

Build projects to solve real problems that people actually care about.

Recruiters love candidates who spot real-world inefficiencies and take steps to improve them.

It shows strategic thinking and initiative that goes beyond academic credentials.

View newsletter: Alessandro Ships With AI

Steps: Turn fuzzy AI project ideas into clear, executable requirements using Gherkin scenarios and AI prompts. • Define success in one line: "Success 

Open article: From Vague Ideas to Testable Scenarios in 20 Minutes by Alessandro Magionami • 1 min read

From Vague Ideas to Testable Scenarios in 20 Minutes

Find project ideas by exploring:
• Problems you face daily
• Issues colleagues complain about
• Gaps in tools you already use
• Inefficiencies in your workflow

Avoid building:
• Another to-do app
• Academic exercises
• Solutions without real users
• Perfect but unfinished demos

Stop Building AI Apps All at Once: Try the BEER Framework

Most people try to perfect AI features in one go.
This approach often fails because AI thrives on context.
It requires an iterative process, much like human workflows.
My own projects demonstrate this pitfall.

Here's a framework to build AI-powered features iteratively, turning complex ideas into functional realities.

#1 Build Your MVP First

This is the absolute foundation.
You must establish a working, albeit basic, version.

- Define minimum viable fields.
- Focus on a single, core user action.
- Ensure the application is usable in its most stripped-down form.

This groundwork gives AI immediate context.

#2 Enhance the User Experience

Once working, elevate the interaction.
This step focuses on making the feature pleasant to use.

- Add essential input validations.
- Ensure dates are correctly processed or handled.
- Improve clarity in user prompts or feedback.

This smooths out rough edges and builds trust.

#3 Expand the Functionality

Now, broaden the scope of that specific feature.
Do not introduce entirely new features yet.

- Implement related actions like editing or deleting.
- Introduce proactive elements, like expirations notifications.
- Connect the feature to other relevant parts of your workflow.

This deepens the value of the initial effort.

#4 Refine for Production Readiness

The final stage is polish and optimization.
This transforms a good feature into a great one.

- Optimize the user interface for clarity.
- Ensure responsiveness across devices.
- Standardize visual elements and messages for consistency.

This iterative approach is how I've successfully integrated AI.

You've now got a proven path to building robust AI features.

What's the first feature you'll build using the BEER framework?

The real power of side projects lies in their ability to demonstrate problem-solving skills, showcase initiative, and act as a practical portfolio.

They leave no doubt about your technical abilities and passion for coding.

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