Tom Smykowski is a Poland-based senior software engineer with nearly two decades of product delivery experience, specializing in setting up optimized, AI-enhanced development environments for remote teams across the EU, UK, and US. For founders, CTOs, and executives looking to boost developer productivity and adopt AI-native workflows, Tom provides the technical leadership and hands-on engineering to configure tools like Claude Code, Cursor, and GitHub Copilot, establish quality gates, and integrate AI agents into your SDLC.
The Strategic Value of an AI-Optimized Development Environment
An AI-enhanced development environment is more than just installing a plugin. It represents a fundamental shift in how software is built, tested, and delivered. For remote teams, especially those distributed across time zones, a well-configured setup ensures consistency, reduces onboarding friction, and elevates code quality. It transforms individual productivity gains into a scalable, team-wide advantage, making your engineering output more predictable and your product development cycle faster.
Tom approaches this setup with a product-minded perspective, honed through roles like Staff Software Engineer at Croissant and leading frontend at industrial SaaS companies. He understands that the goal is not just technical configuration, but enabling your team to build better products. His work with clients like Printify, where he contributed to a design system used by over 60 frontend engineers serving 10M+ merchants, demonstrates his ability to implement systems that scale. An AI dev environment built by Tom is engineered for maintainability, collaboration, and long-term growth.
For international teams, a standardized, AI-assisted workflow is particularly critical. It bridges the gaps created by asynchronous communication and different working styles. By establishing clear conventions for AI tool usage, prompt libraries, and automated quality checks, Tom ensures that every engineer, whether in the EU, UK, or US East Coast, operates from the same empowered baseline. This reduces cognitive load and allows the team to focus on solving complex business problems rather than tooling inconsistencies.
Core Components of a Modern AI Development Setup
A comprehensive AI dev environment setup involves several interconnected layers. The foundation is the choice and configuration of AI coding agents. Tom evaluates tools like Cursor, Claude Code, and GitHub Copilot against your team's existing stack, which may include Vue.js, TypeScript, or React, and your specific workflow needs. The configuration goes beyond default settings to include custom instructions, context management, and integration with your codebase to maximize relevance and accuracy of AI suggestions.
Next, this setup must be integrated into the existing Software Development Lifecycle (SDLC). Tom architects workflows where AI agents assist in code generation, test creation, documentation, and even security scanning. He establishes quality gates, such as automated AI-assisted code reviews or linting rules tuned for AI-generated code patterns, to ensure maintainability and security are baked in from the start. This is where his experience in building compliant systems at Croissant and scalable platforms at Websoul, which served 32,000+ businesses, proves invaluable.
The environment also encompasses the broader developer experience (DevEx). This includes configuring CI/CD pipelines to leverage AI for test generation and analysis, setting up knowledge bases with optimized prompts for common tasks, and creating onboarding playbooks for new team members. The outcome is a cohesive, AI-native software factory that accelerates development without sacrificing code quality or team cohesion, a necessity for remote teams requiring robust, async-friendly processes.
Tailoring the Setup for Remote and Cross-Time-Zone Teams
Remote collaboration, especially across EU, UK, and US time zones, introduces unique challenges that an AI dev environment can directly address. Tom, based in Poland and experienced in remote work with international teams, designs setups that enhance asynchronous workflows. A key focus is on documentation and knowledge capture. By configuring AI tools to generate consistent, high-quality code comments, commit messages, and pull request descriptions, he ensures context is preserved and easily accessible for team members working in different hours.
Another critical aspect is standardization. Disparate tool configurations can lead to "works on my machine" issues, which are magnified in distributed teams. Tom creates unified configuration files for linters, formatters, and AI agents that are committed to the repository. He sets up pre-commit hooks and shared editor configurations to ensure every engineer, regardless of location, benefits from the same AI-assisted checks and productivity boosts. This level of consistency was crucial in his work on the Printify design system, supporting a large, distributed engineering team.
Finally, the setup fosters collaborative learning and continuous improvement. Tom implements mechanisms for teams to share effective prompts, curate examples of excellent AI-assisted code, and collectively refine the tooling setup. This creates a living system that evolves with the team's needs, turning the AI environment into a shared asset that improves over time. His background in technical writing, public speaking, and mentoring ensures these practices are communicated clearly and adopted effectively.
From Audit to Implementation: A Proven Process
Tom's engagement typically begins with an assessment phase. He conducts a codebase and architecture audit to understand your current stack, team structure, and pain points. This audit, informed by his deep experience with clients ranging from GE Healthcare and Unilever to startups like Wicket and Base Camp, identifies the highest-impact opportunities for AI integration. He evaluates everything from code quality and test coverage to existing DevOps pipelines and team communication patterns.
Following the audit, he collaborates with leadership to define a phased implementation roadmap. This might start with piloting a specific AI agent with a small team, establishing metrics for productivity and code quality impact. Tom then leads the hands-on configuration, working alongside your engineers to integrate the tools into their daily workflow. He provides the necessary training and documentation, drawing on his experience in corporate AI training and workshops, to ensure smooth adoption and address any initial resistance or learning curves.
The process culminates in the establishment of ongoing governance and iteration cycles. Tom helps set up channels for feedback on the AI tools, schedules regular check-ins to tune configurations, and plans for scaling the setup as the team grows. His goal is to transfer ownership and knowledge to your internal team, leaving you with a self-sustaining, optimized development environment. This end-to-end ownership, from discovery through production support, is a hallmark of his approach to product delivery.
Integrating AI Setup with Broader Engineering Excellence
An AI development environment does not exist in a vacuum. It must complement and enhance your overall engineering standards. Tom's work is grounded in principles of scalable architecture, accessibility, and user-focused design, evidenced in his projects for the Marshal Office of the Silesian Voivodeship and the Polish Tourism Organisation. He ensures that AI tooling promotes these principles, for instance, by configuring agents to suggest accessible HTML patterns or to follow established design system tokens.
Furthermore, the setup is aligned with business objectives. Whether the goal is accelerating MVP development for a founder, modernizing a legacy Angular or React application for an enterprise like Bunge, or improving the reliability of a system like those he worked on at the Institute of National Remembrance, the AI tools are configured to prioritize tasks that deliver direct value. Tom's product thinking ensures the environment setup is a strategic investment, not just a technical novelty.
Finally, this integration extends to team culture and safety. Tom establishes guidelines for responsible AI use, addressing concerns around code ownership, security, and bias. He configures security scanners and approval workflows to mitigate risks associated with AI-generated code. By proactively addressing these concerns, he builds trust in the new tools and fosters a culture of confident, ethical innovation within your remote engineering team.
When This is a Good Fit
Engaging Tom for your AI dev environment setup is an ideal fit if you are a founder, CTO, or engineering lead of a remote team in the EU, UK, or US. It is especially valuable if you are looking to systematically boost developer productivity, onboard AI tools effectively, and create a scalable, modern workflow for your product engineering efforts. His staff-level expertise provides the strategic oversight and hands-on skill to turn AI potential into a tangible, production-ready advantage for your team.