My first “experiment” is now 1.5 years old, which feels like a lifetime in AI.
To get a sense of where we are with Copilot today, I decided to revisit that project. The goal was to upgrade the one-page web app into a proper Angular application using TypeScript.
I also took on the “Copilot challenge“: use only Copilot – No manual edits.
Here’s what I learned this time:
Refactoring with AI works
I was able to split code into Angular components, convert interactions to RxJs, move logic around, add proper typing, convert promises to async functions, extract services, remove dead code, and clean up naming. Copilot handled all of this well.
It handles technical code reliably
Parsing data, building user interfaces, caching with local storage, customizing chart behavior, adding compression: Copilot handles this also very well. It got confused with API behavior across chart library versions, but a real developer might too as well. It made UI iteration fast and smooth.
Agent mode is far better than edit mode
Edit mode often produced code that wouldn’t compile or had broken imports. Agent mode fixed those issues automatically. Not having to think about the context that much is also a relief. Using only edit mode first helped me see how much better agent mode is for real-world use.
Feels like working with a junior developer
Copilot gets things done, but may take shortcuts. Sometimes it ties logic too closely to rendering or makes structural choices that aren’t ideal. It helps, but you still need to guide it.
The code quality is generally good
Its output is usually clean, readable, and idiomatic. Not always how I’d write it, but solid. It consistently handles edge cases like null values. Over time, though, consistency degrades. One feature might follow one style, another a different one. You still need to set and enforce coding standards. Comments appear inconsistently, sometimes helpful, sometimes missing.
Mixed experience with CSS styling
Copilot is good at suggesting layout ideas, but maintaining a consistent visual style across the app was difficult.
It can write basic business logic
If your specification is clear and specific, it can generate useful logic. But for the code to match your expectation, you need to put the effort to give a precise specification. I didn’t investigate the generation of the test accordingly, this is for sure a subject to investigate further.
Temporal data types remains a weak spot
Handling dates was frustrating. I started by converting strings to date objects, thinking it would be more robust. But JavaScript’s Date isn’t well suited for this. Copilot didn’t flag the issue. Only later, when I asked directly, did it suggest sticking with strings. It often confused timestamps and date objects.
Data manipulation is a strong point
Tasks like changing the structure of JSON files or merging them worked well. No major issues here.
Copilot enables much faster iteration, significantly lowering the cost of programming and shifting the work towards software design only. The improved reliability of the agent mode compared to the edit mode provides a major cognitive relief. Iterating through chat, or even “negotiating” a solution before asking Copilot to implement it, feels fundamentally different from classic development.
Programming is an activity that tax your short-term memory. Usually, if I have less than half an hour, I won’t engage in programming. It’s usually too short to switch context and produce some working code. Something interesting happened during this second experiment: even if I had on 15-20 min, I could quickly try out a new idea with copilot.
There’s no question that it’s a more productive way to work.
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