Use AI to propose and implement refactors ranging from local function cleanup to coordinated cross-file code changes across large codebases, including bulk renames, signature migrations, API contract alignment, framework upgrades, parallel migration campaigns, and broader legacy modernization that benefits from repository context.
Engineers choose the refactor or modernization strategy, review diffs, run builds and tests, validate contract and pipeline impacts, confirm behavior and performance remain correct, assess architecture implications, and decide what is safe to merge.
AI updates call sites across the repo to match a renamed API or signature change while keeping formatting and imports consistent.
Claude Code / Sourcegraph CodyAI identifies duplicated logic across services and proposes a shared module/library extraction with incremental PR-sized changes.
Claude Code / GitHub Copilot ChatAI helps restructure a monolith into clearer layers (domain/service/adapters), generating diffs that engineers review and test thoroughly.
Claude Code / JetBrains AI AssistantI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
Design and build LLM-powered products and agentic systems
Go from idea to production with a clear implementation roadmap
Build AI with human-in-the-loop in regulated environments
Released May 28, the Claude Opus 4.7 upgrade beats its predecessor, GPT-5.5, and Gemini 3.1 Pro across almost all benchmarks. Mythos 1 and Sonnet 4.8 could be next.
Over the past few months, I’ve been creating projects/applications with AI-powered coding assistants, and the experience has been nothing short of transformative. What started as curiosity has evolved into a fundamental shift in how I approach software development. Here’s what I learned about the capabilities, limitations, and best practices of coding with AI.
I used AI every day for 30 days as a backend engineer.
Not for side projects or experiments — but in real work: debugging issues, writing code, understanding systems, and reviewing logic.
Some things became 10× faster.
Others actually made me worse.
Here’s what actually changed.
There is no shortage of AI tools aimed at developers right now: chat assistants, IDE completions, agents that promise to run your tests, and new products every month with overlapping features. I am not going to argue which one is “best.” Instead, here is what I am actually using today on hobby code: ChatGPT for quick, low-context questions, Cursor when the work needs my repository in the loop, and GitHub Copilot for fast inline help while I type. That trio might change, but it reflects how I have learned to spend money and attention in 2026.
The through-line is simple: match the tool to how much context the problem needs. That stops me from dumping half a repo into a browser tab for a vague design question, or firing up an editor assistant when I only wanted a two-paragraph explanation of something I could read in the docs.
This is not a product review. It is a snapshot of how I work, using the projects on my projects page as concrete examples.
I've been using Claude Code daily for 6+ months. It's replaced most of my routine work:
• To understand code base - yes true this is game changing for me. its creates nice tech documents.
• Boilerplate generation
• Refactoring large codebases
• Debugging edge cases
• Writing tests & automation scripts - top notch in UT's
It outperforms Cursor and GitHub Copilot for deep reasoning and architectural changes. Period.
⚠️ BUT — always validate. Review the code. Run your tests. You're still the final gatekeeper. AI hallucinates less here, but "less" ≠ "never."