AI-Assisted Coding: How Our Developers Are Reimagining Software Development
Artificial Intelligence has become one of the most disruptive forces in software engineering. What started with autocomplete suggestions in integrated development environments (IDEs) has now matured into a new way of coding—where developers collaborate with AI-powered assistants capable of writing, refactoring, and even reasoning about code.
At Mahisoft, we’ve seen firsthand how these tools are reshaping the way teams build software. To explore this shift in depth, we organized an AI-Assisted Coding Workshop, split into two sessions. Several of our developers shared their experiences experimenting with leading tools like Cursor and Windsurf, reflecting on both their promise and their pitfalls.
The conversations were insightful, pragmatic, and refreshingly candid. Rather than hype, what emerged was a balanced picture: AI-assisted coding is not a silver bullet, but when used wisely, it can dramatically boost productivity and change the way we think about software development.
This article distills the workshop discussions into a comprehensive guide. We’ll cover the evolution of AI in coding, practical experiences with Cursor and Windsurf, developer anecdotes, and a set of actionable recommendations for teams exploring these tools.
We would like to thank our developers Vittorio Adesso and Antonio Alvarez for putting this together and all those who participated in the live session.
The Evolution of AI in Coding
The idea of “smarter code editors” isn’t new. For decades, developers have had tools that tried to guess their intent. But the leap to AI-assisted coding is something entirely different.
Early Days: Rule-Based Autocomplete
In the early 2000s, IDEs like Eclipse and IntelliJ introduced autocomplete systems. These weren’t intelligent—they relied on static analysis, simple rules, and libraries to suggest possible methods or variable names. Useful, yes, but limited.
GitHub Copilot and the Rise of LLMs
The game changed in 2021 with GitHub Copilot, powered by OpenAI’s Codex. Suddenly, autocomplete wasn’t just about suggesting the next method—it could predict entire functions, infer intent from comments, and even generate boilerplate code. Competitors like TabNine followed, giving developers a first taste of what it feels like to code alongside a machine learning model.
From Chat to Agents
The next step was embedding chat-style prompts into coding workflows. Instead of writing every line, developers could highlight code and ask the AI: “Refactor this into smaller methods” or “Generate unit tests for this function.”
And now we’ve entered the era of agentic coding. Tools like Cursor and Windsurf go beyond autocomplete. They act as agents that can interact with the file system, run commands, and even pull external data—bringing us closer to a world where coding becomes a collaborative dialogue between human and machine.
Vibecoding: A Temptation and a Risk
A popular term floating around is “vibecoding.” It refers to letting the AI generate code blindly, approving results without much scrutiny. While this might be fun for experiments, our developers were unanimous: in professional environments, vibecoding is risky. AI outputs require human review, or you risk shipping elegant-looking but fundamentally broken code.
Inside the Workshop: Cursor in Action
The first session of our workshop focused on Cursor, an IDE that builds on the familiar Visual Studio Code experience but integrates AI at its core. Developers described it as lightweight, stable, and powered by advanced models like Claude and GPT.
Strengths of Cursor
- Smarter Autocomplete
Cursor’s autocomplete goes beyond single-line suggestions. It generates entire functions and utilities, often with better accuracy than earlier tools. Developers noted fewer hallucinations—instances where the AI confidently suggests code that doesn’t exist. - Context Awareness
Cursor doesn’t just complete functions; it understands where they belong. For example, if a developer writes a validateEmail function, Cursor might suggest inserting it directly into an existing getUserEmail workflow. - Prompt-Based Refactoring
Highlighting code and giving instructions yields surprisingly clean results. Developers used this to split large methods into smaller, modular pieces—improving readability without rewriting everything by hand. - Interactive Agents
Cursor agents can scan entire repositories, explain code functionality, or generate new features. This was particularly useful for onboarding—helping developers understand unfamiliar codebases faster.
Weaknesses of Cursor
- Multi-File Struggles
When asked to modify several files at once, Cursor often stalled. It generated code, ran into type errors, tried to fix itself, and sometimes spiraled into endless attempts before failing. - Model Selection Trade-Offs
Cursor offers “Max” modes powered by Claude Opus or GPT-O3. These models handle complex tasks better but are slower and more expensive. Choosing the right model is critical—use lightweight ones for exploration, and heavyweights only when necessary. - Unpredictability
Because outputs are stochastic, the same prompt may yield different results. This makes Cursor powerful but unreliable for live demos or deterministic workflows.
Developer Anecdotes
- Incremental Tasks Win
One engineer compared working with Cursor to processing tickets in Jira: break requests into small, incremental tasks. For example, instead of “build me a user management system,” ask for “add validation for empty user IDs.” - SQL Queries with a Twist
Another developer used AI to write SQL queries. Sometimes it generated overly complex subqueries, but other times it introduced efficient window functions that saved hours of manual tweaking. - UI Generation Pitfalls
When asked to generate React interfaces, the AI often inserted unnecessary containers that broke layouts. Over time, the developer learned to spot these recurring mistakes and adjust prompts accordingly. - Choosing Models Matters
For bug hunting, deep-thinking models like GPT-O3 performed better. But for quick refactors or brainstorming, smaller models were faster and cheaper.
Inside the Workshop: Windsurf in Action
The second session focused on Windsurf, created by the team behind Codium. While Cursor has become a popular experimental IDE, Windsurf was praised for its stability and enterprise focus.
Strengths of Windsurf
- Stability and Familiarity
Developers found Windsurf less glitchy than Cursor, especially on Linux. Its compatibility with Visual Studio Code plugins made it easy to adopt. - Context-Aware Autocomplete
Like Cursor, Windsurf uses repository-wide context. Its autocomplete suggestions were described as precise and contextually intelligent. - Command Mode
By pressing Ctrl+I, developers could open a command window, highlight code, and give instructions—similar to Cursor’s prompt bar. - Agentic Chat
Windsurf’s chat interface (opened with Ctrl+L) allowed developers to scaffold projects, install dependencies, or edit files interactively. In some cases, it autonomously scaffolded full applications with minimal input. - API Integrations
A standout feature was its ability to generate service classes directly from OpenAPI schemas, streamlining backend-to-frontend integration.
Weaknesses of Windsurf
- Prompt Ambiguity Risks
One developer asked Windsurf to “fix tests”. The AI rewrote every test to return true. Technically, all tests passed—but the logic was meaningless. - Overzealous Refactors
Without strict boundaries, Windsurf sometimes “improved” unrelated parts of the code. Guardrails were essential to prevent unintended changes.
Developer Anecdotes
- Project Scaffolding
A developer cloned a Django e-commerce framework (Oscar) and asked Windsurf to scaffold new features. With minimal prompting, Windsurf created services, set up endpoints, and even validated inputs—demonstrating how quickly prototypes can be spun up. - Internationalization Made Easy
Another engineer used Windsurf to add multi-language support to a project. By simply prompting it to implement translations in Spanish, German, French, and Italian, Windsurf handled much of the repetitive work—though the developer still needed to review for accuracy. - Checkpoints Save the Day
Like Cursor, Windsurf automatically creates versioned checkpoints. This proved invaluable when an overly ambitious prompt caused chaos, allowing developers to roll back safely.
Building a Practical Playbook for AI Coding
From both sessions, clear best practices emerged. Our developers distilled their experiences into a playbook for using AI coding tools effectively:
- Plan Before You Prompt
Don’t outsource thinking. Define what you want to build before asking AI for help. - Engineer Your Prompts
Be explicit: state the goal, constraints, expected output, and what not to change. - Start Small
Break tasks into incremental steps. Large, vague requests often produce disappointing results. - Choose the Right Model
Lightweight models are faster and cheaper for iterative work. Save heavyweights for complex, high-stakes tasks. - Review Everything
Never accept AI-generated code blindly. Always review, test, and refine. - Avoid Repository-Wide Refactors
AI tools struggle with massive, multi-file changes. Keep the scope local. - Respect Privacy and Compliance
Many tools log prompts and outputs. For sensitive projects, read the fine print and follow client requirements. - Stay Mentally Engaged
AI isn’t an excuse to stop thinking. Developers remain responsible for architecture, logic, and long-term maintainability.
The Impact: Time Savings and Changing Roles
One of the most compelling workshop insights was the measurable productivity boost.
- Developers estimated 20–30% time savings on repetitive or boilerplate tasks.
- Tasks like generating utility functions, writing unit test skeletons, or scaffolding UI components were significantly accelerated.
- In languages like JavaScript and Python, gains were substantial. In lower-level languages like C++, AI was less effective—offering ideas but rarely usable implementations.
Beyond raw productivity, the role of developers is evolving. Instead of writing every line, they spend more time curating, reviewing, and guiding AI outputs. As one developer put it:
“We’re moving from being code typists to being code reviewers and architects.”
This shift mirrors trends in other fields where AI augments, rather than replaces, human expertise.
Broader Implications: Education, Hiring, and Compliance
The workshop also touched on broader issues:
- Education and Competitive Programming
Some participants noted that AI challenges academic integrity. If students rely too heavily on AI, they may skip critical learning stages. Similarly, competitive programming events are debating whether to ban AI tools. - Hiring and Skills
As AI reduces the need for boilerplate coding, the value of developers who can engineer prompts, review critically, and architect systems increases. These “AI-fluent engineers” are already in demand. - Compliance and Legal Risks
Privacy concerns are real. Some tools log everything, which may violate client agreements. Developers must be vigilant when using AI on sensitive codebases.
The Future of AI and Software Development
The workshop reinforced a central truth: AI is not replacing developers, but it is reshaping the way we work.
The future of software development belongs to those who treat AI as a teammate, not a replacement. The best outcomes come when developers guide the AI with well-crafted prompts, validate its outputs, and focus on higher-level design.
As one of our developers summarized:
“AI won’t replace humans, but humans who use AI will replace those who don’t.”
In the near future, developers who adopt AI responsibly will not only work faster but also smarter—leaving those who ignore it at risk of falling behind.
Why Mahisoft Is Ready?
At Mahisoft, we believe the future of software engineering is human + AI, not human versus AI. Our developers—handpicked from the top 3% of talent in Latin America—are already experimenting, adapting, and integrating these tools into their workflows.
For U.S. companies, this means two things:
- You can access world-class engineers who understand both traditional development and the new AI-assisted paradigm.
- You gain partners who not only know how to write great code but also how to use AI responsibly, efficiently, and compliantly.
With nearshore advantages—time zone alignment, cultural fluency, and seamless communication—Mahisoft helps U.S. engineering leaders build teams that aren’t just keeping pace with the AI revolution, but leading it.
Ready to build your future with Mahisoft’s top-tier AI-fluent engineers? Get in touch with us today.
