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.

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

Most popular models on OpenRouter right now: https://openrouter.ai/models?categories=programming&order=mo...

Top 7 are all open models.

VSCode Extension:

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

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

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

  - 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.

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

- 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.

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!

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

Would love to have you join our Discord- https://discord.gg/XNXVD34P8

We'd love to have you join our Discord: https://discord.gg/XNXVD34P8

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

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

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.

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.

We do a lot of AST parsing - for both code and build configuration languages. Even then, we still have to rely on the LLM to figure out a lot of the details.

Making this work reliably for non-frontier models and codebases that don't have existing test harnesses is where a lot of the design work goes in.

FixBugs was built because while investigating prod incidents, I had an epiphany.

SWEs build tools to solve all types of problems, but we ourselves use the flakiest tools.

While working at Google for example I was surprised GDB support for any type of binary debugging was almost non-existent.

OSX is the best, we use the in-built (seatbelt) sandbox via sandbox-exec.

For Windows, we use WSL containers when available.

By default, if a safe sandbox environment is not available, we inform the user that a repro is not possible in the current conditions.

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

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