We weren’t slow at fixing bugs — we were slow at understanding them
In our previous post, we talked about a simple realization:
We weren’t slow at fixing bugs.
We were slow at understanding them.
That led us to a deeper question:
What does a “good” bug log actually look like?
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Most bug reports fail before debugging even starts
A typical report looks like this:
- “It’s broken”
- “Webhook not working”
- “App doesn’t respond”
From a user’s perspective, that’s completely reasonable.
But from a developer’s perspective, it’s missing everything we need:
- Where did it happen?
- What did the user do before that?
- What failed exactly?
- Can we reproduce it?
So the real process becomes:
- Support asks follow-up questions
- Devs try to guess
- Time gets lost before any fix even begins
The problem isn’t debugging.
It’s the input we start with.
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A useful bug is not a message — it’s a reconstructable event
After running into this repeatedly, we started thinking differently.
A bug report shouldn’t be something you read.
It should be something you can reconstruct.
At minimum, a useful log should answer:
- Where did this happen? (URL / screen)
- What failed? (request, error, response)
- What led to it? (user actions, event sequence)
- Under what conditions? (device, browser, network)
- When did it happen? (timestamp)
Once you have that, debugging changes.
You’re no longer asking:
“What might have happened?”
You’re asking:
“Why did this specific sequence lead to failure?”
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Why user-reported steps are not enough
One of the most fragile parts of debugging is reproduction.
We usually rely on:
- users
- or support teams
to describe steps.
But by the time that happens:
- details are forgotten
- steps are incomplete
- or slightly inaccurate
Even small differences can make a bug impossible to reproduce.
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Reproduction should come from the system, not memory
So we stopped asking users for steps.
Instead, we derive them from the actual session.
We look at what really happened:
- page navigation
- clicks and interactions
- network requests
- state changes
From that, we reconstruct a simplified sequence of events leading up to the issue.
Not a perfect script — but usually enough to trigger the same failure again.
That turns reproduction from guesswork into something much closer to replay.
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Bugs are rarely universal
Another thing we kept seeing:
The same issue doesn’t affect everyone.
Sometimes it only happens:
- on a specific browser
- on a specific device
- for a specific user
- under certain network conditions
Without that context, bugs feel random.
With it, patterns start to emerge.
That’s why environment data matters just as much as the error itself.
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What we actually log now
Over time, our logs evolved into something closer to a structured issue.
For each bug, we capture:
- The failure itself
- request URL
- method
- status code
- response body
- Where it happened
- page URL
- screen / feature
- What led to it
- sequence of user actions
- navigation flow
- The environment
- browser
- OS
- device type
- network conditions
- When it happened
- precise timestamp
- Reproduction context
- a reconstructed path to trigger the issue again
At that point, a bug stops being a vague report
and becomes something you can actually work with immediately.
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What changed for us
The biggest shift wasn’t logging more.
It was logging the right things.
Before:
- we had errors
- but no context
Now:
- we have context
- and the error becomes obvious
We spend less time asking:
“What happened?”
And more time on:
“How do we fix it?”
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What we’re still figuring out
Even with all of this, it still feels incomplete.
There are still cases where:
- everything looks technically correct
- but the outcome is wrong for the user
No error. No exception.
Just a mismatch between what the system did and what the user expected.
Those are harder to capture.
And it raises a bigger question:
What does a truly complete bug log look like?
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If you’ve run into similar cases, I’d really appreciate your perspective.
We’re still building this and learning from real-world usage:
👉 https://flashlog.app
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