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Three Ways AI Transforms Coding & What Makes the Best Developers

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Nevron io
April 10, 2025

Welcome to the thirteenth edition of our weekly newsletter.

We would like to thank every single one of you who read last week’s edition, gave us feedback and we encourage you to keep doing so.

Have any questions you want us to cover? Reply to this email and we will answer them in depth in each weekly edition!

This week we’ll dive into the following topics:

  • 8 Actionable Tips To Excel In Programming

  • Insights From DevRev Leadership Circle Amsterdam 2025

  • How “Search” Is Evolving in the Age of AI

  • The Emails That Shaped the Early Internet (1992-1998)

  • Why Don’t Companies Fix Bugs?

Matthias Endler, a Rust developer and open-source maintainer with 20 years of experience, recently shared a thoughtful analysis of traits that exceptional software developers share. For anyone looking to excel in programming, these insights are invaluable:

  • Read the reference documentation: The best developers go straight to the source instead of relying on Stack Overflow or guessing.

  • Master your tools: Great programmers understand the technologies they use at a fundamental level—their history, limitations, and ecosystem.

  • Carefully read error messages: Top engineers extract valuable information from error messages rather than getting frustrated by them.

  • Break down complex problems: Exceptional developers simplify difficult challenges until they become manageable, making seemingly impossible tasks achievable.

  • Get your hands dirty: The best programmers aren't afraid to dive into unfamiliar code and learn by doing.

  • Always help others: Despite busy schedules, great developers make time to support their colleagues.

  • Write well: There's a strong correlation between programming skill and communication ability—clear writing reflects clear thinking.

  • Never stop learning: The most impressive developers continue exploring new technologies throughout their entire careers.

You can read Endler's full post here: The Best Programmers I Know

Today (Thursday 10th of April) is the AI industry panel where our Director, Tony Kyriakidis, discussed how AI is transforming software development.

When asked about AI's impact on the Software Development Lifecycle (SDLC), he emphasized that implementation (step 3) has seen the most significant changes.

The SDLC refers to the process of creating software applications, consisting of six phases:

1-Plan

2-Design

3-Implement

4-Test

5-Deploy

6-Maintain

Based on our founder's presentation, here are the three standout ways AI is changing implementation:

1- Boilerplate code: Our projects share up to 50% of their code base, using similar philosophies and technologies. AI tools now help us quickly generate this repetitive portion, allowing developers to focus on building unique functionality and solving real problems instead of rewriting common elements.

2- No more code-block: "Being in flow" is one of the greatest feelings for developers. While we previously relied heavily on StackOverflow to find solutions when stuck, AI tools like ChatGPT and GitHub Copilot now help debug issues in real-time, allowing developers to maintain their productive flow state much more effectively.

3- Faster MVPs: AI tools enable much more experimentation by making it possible to create minimum viable precursor features (early test versions) with minimal time and resources. This lets companies quickly test if new features will have a positive business impact before committing to full development.

As AI becomes more sophisticated, the function of traditional search is shifting. While many people are beginning to use AI assistants for information gathering, search engines still serve a critical but more focused purpose.

Search engines primarily function as portals to specific resources. When you know something exists (like a programming language website or news about an event) but don't know the exact URL, you use search engines to find it.

For example, if you hear about a new programming language called "Frob," you might Google "Frob Language" to find its website.

Where Large Language Models (LLMs like ChatGPT) excel is in open-ended research—scenarios where you don't know in advance what information you need or where to find it.

The early to mid-1990s saw crucial developments in email technology that helped shape the internet we know today:

- 1992: MIME (Multipurpose Internet Mail Extension) transformed email by allowing multimedia attachments and supporting character sets beyond ASCII.

- 1992: The first WYSIWYG (What You See Is What You Get) editors appeared, making email formatting accessible to non-technical users.

- 1993-1996: Webmail emerged, with Hotmail and RocketMail (later Yahoo! Mail) pioneering free, browser-based email access.

- 1998: "Spam" entered the Oxford Dictionary, reflecting the growing problem of unwanted mass communications.

1992 Email Example

Check out the Cyberpunk Archives to read more

A recent article examines why software bugs often remain unfixed, even when solutions seem straightforward. The analysis goes beyond the simple "lazy developers" narrative to reveal systemic reasons:

- Requirements tyranny: If a bug fix isn't tied to a specific feature or requirement, it gets labeled as "tech debt" and pushed to the bottom of priorities.

- Staff turnover: As developers come and go, institutional knowledge fades, and bugs become orphaned issues with no clear owner.

- Risk aversion: Even small changes to legacy code can have unpredictable consequences, making developers hesitant to touch stable (but buggy) systems.

- Invisible ROI: Improving existing features often doesn't directly impact metrics that show up in quarterly reports, unlike new features or products.

Read the full article here

Thinking About Tackling a Big Project or Adopting New Tools?

We’d love to help you plan and make it happen. Let’s talk about what’s next: Reach Out to Us.

Thanks for reading! If you have ideas or topics you’d like us to cover, drop us a message.

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