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oesa

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Working on self-hostable developer environments at https://trailer.dev

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Ask HN: What Are You Working On? (July 2026) 9 days ago

Yeah, Kubeflow can be rough, we've been down that road too as well.

I really like Coder, but one thing for the life of me I couldn't figure out is that even with the self-hosted version, why does anything have to go through their infra?

With trailer.dev our explicit goal is to have a fulfilling user experience for the following scenario:

  - You have servers you own or rent

  - You have a team that want some persistent developer workspaces that are running on that server

  - You run our 30mb + the server side somewhere and boom, you can have your team(s) work on your project.
If you want we (the trailer team) can run the server component for you, but that's it.

I tried spinning up a coder instance just now again, with docker, and I'm being tunneled through US East Pittsburgh with 117ms of latency for a local dev environment tool :(

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Ask HN: What Are You Working On? (July 2026) 9 days ago

https://trailer.dev, a self-hostable development environment platform.

Been working on it on and off for 4 years now. I used to lead an ML consultancy and one thing I've learned is that data scientist DO NOT understand reverse proxies, nvidia drivers, docker and can not set up complex environments for their projects, especially ones that would support collaborative work.

It's been really fun to work on this, currently working on Kubernetes support.

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Jeff Dean responds to EDA industry about AlphaChip 2 years ago

In the tweet Jeff Dean says that Cheng at al. failed to follow the steps required to replicate the work of the Google researchers.

Specifically:

In particular the authors did no pre-training (despite pre-training being mentioned 37 times in our Nature article), robbing our learning-based method of its ability to learn from other chip designs

But in the Circuit Training Google repo[1] they specifically say:

Our results training from scratch are comparable or better than the reported results in the paper (on page 22) which used fine-tuning from a pre-trained model.

I may be misunderstanding something here, but which one is it? Did they mess up when they did not pre-train or they followed the "steps" described in the original repo and tried to get a fair reproduction?

Also, the UCSD group had to reverse-engineer several steps to reproduce the results so it seems like the paper's results weren't reproducible by themselves.

[1] https://github.com/google-research/circuit_training/blob/mai...

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