kirtivr

Congratulations on the launch. I love the idea and the execution.

When I looked into this a while back I explored using ptrace() to add breakpoints and even add functions at specific line numbers. But ptrace is so slow, and it doesn't work with bytecode-in-VM setups.

What were some of the requirements you guys had when building HyperProbe? I can see low latency was one.

Deepseek has always been better than people gave it credit for.

You have to be careful with the inference provider though, Chinese providers are subject to laws that mandate data sharing with their government.

One big reason I can think of is to avoid vendor lock-in. This is why most enterprises use a multi-cloud setup, despite the increased complexity.

FixBugs, an agent that ingests the rich context surrounding production bugs to reproduce them in a sandbox and generate verified fixes. It's available in the form of a self-hosted VSCode extension and as a Github app.

VSCode Extension:

  https://fixbugs.ai/go/vscode-extension

  - full code and data privacy.
  - zero data retention models opted out of training.

GitHub App:

  https://fixbugs.ai/go/github-app
  - we do access your code temporarily.
  - pick a repo to install FixBugs on.

Current capabilities:

  - Reproduce the bug.

  - Identify the root cause.

  - Generate a fix.

  - Verify the fix.

  - Review the generated code using multiple AI models to help catch potential regressions.

Do try it and let me know what you think!

to be fair, coding agent harnesses have been becoming more and more complex.

it's not an llm in a loop with tools anymore (as claude code was rumoured to be on HN).

A few different ways:

- Copilot may be better at implementing features. We're better at investigating bugs and fixing them.

- We handle huge context very easily. We specialize towards investigating large amounts of logs/metrics and traces.

- We do a lot of work to generate a non-trivial reproduction test case in a sandbox, which allows us to verify bug fixes. Ship confidently, not just based on the best available hypothesis.

- You can apply and remove code changes with the click of a button. We have an in-built VCS that allows you to add or remove sets of changes from a long coding session.

- Our pricing model is different. While with the standard Copilot developer plan you get $10 of AI usage (nothing if you're using Claude or GPT), we allow you unlimited triages on a fixed number of bugs. It's not about AI credits, it's about investigating and fixing complex bugs.

This is on our roadmap!

At this time we are focussing on evaluation benchmarks like SWE-bench (verified). This is a simpler benchmark and does not really map well to investigating alerts that have a huge amount of context. But its a start.

I am wondering if we can improve upon foundation models with our reproduction <-> hypothesis loop approach.

Foundation models tend to be precision first, and context limited, so they can get sidetracked by various things.

This is an interesting problem to be working on right now!

Thanks. As teams move faster with AI generated code, the focus on quality and reliability will have to increase as well.

And we will have to build better tools if we want to accelerate shipping velocity.

Thank you!

are you using any AI tools to debug productions bugs at this time?

Nginx, Caddy, Flink and Firefox are some applications where we we've managed to consistently fix and reproduce reported bugs.

Of course, we did not send the PRs to the repos seeing how they're already overloaded with them.

Nginx for example has 181 open PRs right now, but they only merge 2 or 3 in a day.

We either mock or fake out the interfaces.

I much prefer faking to mocking, because that still preserves a lot of the real-world behavior relevant to prod bugs.

A full prod reproduction would be a holy grail, but probably only attainable for complex distributed systems if we have access to a prebuilt staging-like environment.

We're traversing the bridge between mocks, fakes and staging at this time.

War story time? xD