Why We Built CodeAndSystem.ai

A growing number of companies now explicitly allow AI tools during technical interviews. In those sessions, the default workflow is ChatGPT in a browser tab — and it is the wrong workflow. Alt-tabbing is visible on your screen share. Copy-pasting problem text is slow under time pressure. The output is an unstructured wall of text when you need code, complexity, and edge cases. And the same loop is painful weeks earlier during prep, just without the stakes.

We built CodeAndSystem.ai as the purpose-built tool for that moment. Same models as ChatGPT and Claude. Different workflow: one hotkey captures the problem from your screen, the app stays always-on-top over your editor, and Privacy Mode excludes the window from screen recordings at the OS level on Windows and uses an overlay on macOS/Linux. Your interviewer sees focused engineering — not a browser tab. The same hotkeys and workflow are used during prep, so there is zero switching cost from practice to performance.

The secondary loop: the context-switch tax on everyday AI-assisted coding

Every developer who uses AI for coding outside of interviews does the same dance. You hit an error. You open a browser tab. You navigate to ChatGPT. You paste the error message. You realize the error message alone is not enough context. You go back, copy the surrounding code, paste that too. You write a prompt: "I'm getting this error when I run this function, here's the code, I think the issue is..." You wait. You read the response. You switch back to your editor.

That loop takes 30 to 60 seconds. You do it dozens of times a day. It adds up to hours per week spent not on the problem itself, but on the mechanics of communicating the problem to an AI.

The same tool that works in a live AI-allowed interview kills this loop for everyday work too.

The insight: screenshots capture what text cannot

The thing that made this click for us was watching developers struggle to describe visual information in text. A stack trace has structure -- the line that matters is usually in the middle, surrounded by context that narrows the diagnosis. An architecture diagram has spatial relationships -- which services connect to which, where the bottlenecks are. Code formatting carries meaning -- indentation, grouping, comments next to specific lines.

When you copy-paste text into a chat window, you lose all of that. When you take a screenshot, you keep all of it.

AI vision capabilities made this practical. The models can read a screenshot of code and understand not just the text, but the layout, the context, the visual structure. They read a stack trace the way you read a stack trace -- scanning for the relevant frame, reading the surrounding code, connecting cause to effect.

So we built a tool that lets you capture any region of your screen and send it directly to AI for analysis. One hotkey. Select a region. Get a solution. That is the entire workflow.

The approach: desktop app, BYOK, speed

We made three technical decisions early that shaped the product:

Desktop app, not a browser extension. A browser extension can only see your browser. A desktop app can see your entire screen -- your IDE, your terminal, your diagramming tool, your video call where someone is sharing their screen. The capture surface is everything you can see, not just one application.

Bring Your Own Key. Your screenshots go directly from your machine to OpenAI or Anthropic using your own API keys. We never see your code, your errors, or your screenshots. This was non-negotiable. Developers are (rightfully) cautious about where their code goes, and we did not want to be in the business of handling anyone's proprietary data.

Speed above everything. We built with Tauri and Rust. The app is under 50MB installed, launches in under a second, and the full workflow from hotkey to solution takes 3-8 seconds. If the tool is not dramatically faster than switching to a browser tab, it does not justify existing.

What developers use it for

In testing, four workflows kept coming up:

Interview prep. Developers practicing on LeetCode capture the problem statement and get a solution with full explanation and complexity analysis without leaving the problem page.

Debugging. Capture an error message with the surrounding code and get a root cause analysis, not just a generic "try this" suggestion.

System design. Capture architecture diagrams -- even hand-drawn whiteboard sketches -- and get feedback on scalability, missing components, and trade-offs.

Code review. Screenshot unfamiliar code from a PR or shared screen and get a plain-language explanation of what it does and why.

Try the beta

CodeAndSystem.ai is available now with a free trial -- 5 solutions within a 30-minute window, no credit card required. It runs on macOS, Windows, and Linux.

If you spend time switching between your editor and a browser to get AI help with coding problems, give it 5 minutes. The difference between "describe your problem to an AI" and "show your problem to an AI" is larger than it sounds.

We are shipping fast and listening to feedback. If you have thoughts on what to build next, we are on GitHub and at support@codeandsystem.ai.