Herror: piping errors to Claude

I have a confession: I hate red error lines.

Not because errors themselves are particularly bad, but because of how they're presented. A terminal throws a wall of stack traces at you, most of which you don't immediately understand, and somewhere between line 3 and line 147 lies the actual piece of information you need.

Then comes the scrolling. So much scrolling.

Honestly, scrolling should be forbidden. Haha.

During a company hackathon, I decided I had had enough of this experience and started working on a small utility called Herror.

The idea

The concept was straightforward: instead of forcing developers to interpret enormous error outputs manually, why not pipe them directly to Claude?

Fortunately, the company had unlimited access to Claude, so I had a perfect opportunity to experiment without worrying about API costs.

Herror would take an error output from the terminal, send it to Claude, and ask it to:

1. Classify the error.
2. Extract the relevant information.
3. Summarize what actually went wrong.
4. Suggest a potential solution.
5. Return the explanation directly to the terminal.

The goal wasn't to replace debugging or make developers blindly trust an LLM. It was to reduce the friction between encountering an error and understanding what to investigate next.

The forgotten solutions problem

There was another feature I particularly liked.

How many times have you encountered an error, spent twenty minutes figuring it out, fixed it, and then completely forgotten the solution a week later?

I certainly have.

So I wanted Herror to do more than simply explain errors. It would also collect them under identifiable tags, together with their proposed solutions.

Essentially, a small searchable knowledge base of problems and fixes.

Instead of solving the same problem repeatedly, developers could look up an error they had already encountered and retrieve the explanation.

I liked this aspect because it shifted the project from being just another AI wrapper into something that could potentially accumulate value over time.

Simple idea, useful result

Technically, the project was relatively straightforward to develop. The interesting part wasn't building a particularly sophisticated system, but identifying a small everyday frustration and using an available AI tool to improve it.

It was also a good example of how LLMs can be integrated into existing developer workflows without necessarily introducing a massive infrastructure overhead.

The result was a tool I genuinely enjoyed using.

The human factor

There was, however, one limitation that had very little to do with technology.

The project was developed during a hackathon, and while I found it useful, getting an entire team to adopt another developer's personal workflow improvement is a different challenge altogether.

Teams have established habits, priorities, and plenty of existing tooling. Even when an idea makes sense, introducing something new requires buy-in, maintenance, and a reason for everyone to change their routine.

And that's perfectly understandable.

Still, Herror was a fun reminder that some of the most interesting projects don't necessarily come from trying to solve massive technical challenges.

Sometimes, they come from being annoyed by something small every single day and deciding to do something about it.