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r/learnjavascriptu/demirb6 years agoPosts

Is there any service like LogRocket but where I can record canvas actions?

85% match
Hello, I'm working on a web game made in HTML5 Canvas using Pixi.js. I installed log rocket and it records the lobby very well. But when it comes to game play I'm not able to see anything printed on c
logrocket alternative
r/SaaSu/frontend_edy14 days agoPosts

Is anyone else suffering from LogRocket/FullStory bill shock?

60% match
Hey everyone, I've been using LogRocket (and previously FullStory) on a couple of my side projects and client apps. The tool itself is a lifesaver for debugging production issues and watching where u
logrocket alternative
r/azuredevopsu/MikFox734 months agoPosts

How do you actually debug a production bug that spans multiple API calls?

60% match
I'm researching how engineering teams handle debugging in production — specifically bugs that only appear as a sequence of API calls, not a single endpoint failure. Curious about a few things: Whe
reproduce production bugs
r/mendixu/thisisBrunoCosta5 months agoPosts

Does your team track how long it takes to reproduce production bugs vs actually fixing them?

60% match
I've been thinking about this and I'm wondering if other Mendix teams see the same pattern. A Priority 1 issue fires. Everyone scrambles. But the actual debugging doesn't start for hours because nobody can reproduce the issue locally. The data in dev is completely different from production, so the bug just doesn't show up. By the time someone finally gets it to reproduce, the fix is almost always quick. The investigation is what ate all the time. I expect something like 60-70% of total resolution time was just trying to see the bug, not fixing it. If we have a 4-hour SLA, that means we're betting investigation takes less than 2 hours. And for data-dependent bugs that bet almost never pays off. Anyone here tracking reproduction time as a separate metric from MTTR (mean time to resolution)? Or have you found ways to get production-representative data into your dev environment faster? Especially interested in how teams with complex integrations and larger datasets handle this. Thanks!
reproduce production bugs
r/QualityAssuranceu/Odd-Masterpiece-9070a year agoPosts

How effective have the session replay tools proved for debugging?

60% match
There are a few session replay tools available in the market to provide pinpoint information of customer issues/bugs. They don't do screen recordings but construct the replays from DOM interactions, l
session replay for debugging
r/devopsu/Potential_Force_41369 days agoPosts

Why are intermittent production bugs so hard to reproduce?

58% match
I had one of those bugs last month that made the whole team question reality for about four days. A checkout service would randomly throw 500s, maybe 3-4 times a day, always on the same endpoint, neve
intermittent production bug
r/OutSystemsu/thisisBrunoCosta5 months agoPosts

How much of your P1 incident time is actually spent reproducing the bug?

55% match
Something I've been tracking informally across a few OutSystems teams and it's kind of eye-opening. When a production incident hits, the fix itself is usually fast. 30 minutes, maybe an hour. But get
reproduce production bugs
r/OutSystemsu/thisisBrunoCosta5 months agoPosts

How much of your P1 incident time is actually spent reproducing the bug?

55% match
Something I've been tracking informally across a few OutSystems teams and it's kind of eye-opening. When a production incident hits, the fix itself is usually fast. 30 minutes, maybe an hour. But getting to the point where you can actually see the bug in a controlled environment? That's where the hours go. The pattern I keep seeing: alert fires, engineer gets paged, starts pulling logs. Tries to reproduce locally. Can't, because dev data looks nothing like production. Adds more logging, deploys to staging, waits. Eventually brings in senior engineers. 8-16 hours later someone finally reproduces it. Fix takes 45 minutes. For teams with SLA commitments this is brutal. Your four-hour resolution window is basically eaten by the investigation phase alone. Has anyone here started tracking reproduction time separately from total resolution time? Curious if OutSystems teams with enterprise-scale data have found ways to speed up that reproduction step, or if everyone just accepts it as the cost of doing business.
reproduce production bugs
r/alphaandbetausersu/gnapps5 months agoPosts

Testers wanted: AI agent debug tool for long sessions

53% match
While building AI agents for our clients we realised that agents running for 10 minutes feel good but were impossible to debug, so we built our evals product. We needed a way to understand what agents
session replay for debugging
r/sreu/DiamondLatter18422 days agoPosts

Why do ai agents lack production context today?

50% match
We had an incident recently that made it clear how "production-blind" our ai coding agents still are. One customer-facing api endpoint started timing out intermittently only under real production traffic, with a specific feature flag state. Staging was clean, local runs were clean and it smelled like one of those classic "only prod is cursed" bugs. We asked our internal ops copilot, hooked into repos, ci, basic metrics and tickets, what was going on. It pointed to a recent refactor and a deploy where p95 latency jumped then suggested optimizing a couple of functions none of that was the real issue. The actual root cause was a subtle combination of a downstream rate limiter, a feature flag rollout and an old retry policy that only triggered under a specific load shape. The ai agent had no idea about live flag state, infra throttling or how those policies interacted at runtime. It was guessing from static code and git history, nothing more. The pattern i'm seeing across ai coding agents and ops copilots: they usually don't get real-time distributed traces and request context, feature flag and config state at the moment of the incident, a timeline of what changed in the last few minutes around the service or past incident and postmortem knowledge about similar production failures. So they act like very smart static analyzers, not like someone who's oncall watching a live system fail. For anyone using ai agents around incident response or production debugging: how are you feeding them real production context like distributed traces, feature flags, and infra events?
debug intermittent bugs
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