Terry Rodriguez
terry-remyx
AI & ML interests
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Recent Activity
reacted to salma-remyx's post with 👀 about 5 hours ago
Your coding agent is waiting on you to decide what to try next.
It doesn't originate that decision on its own.
What's usually missing is a way to generate that decision systematically, grounded in something more than the random paper that came across someone's feed that week.
Outrider starts from research with code and data behind it to scope a change applying the core method in your own codebase. A feature branch gets gated on your own evaluation methods before it reaches you in review.
The result is tied to what actually happened in your system, not to a model's read on its own output.
Here's what a code recommendation system looks like end to end. reacted to salma-remyx's post with 🚀 1 day ago
There's plenty of AI coding tools now. None help you decide what to build next.
Every week brings a flood of new methods, models, and techniques. Nobody has time to read everything, figure out what applies to their codebase, let alone test it all.
We've open sourced Outrider to systematically explore new ideas for improving your AI system. In a few steps, you can set up the Outrider GitHub Action to:
- continuously search for relevant advances
- evaluate whether they fit your codebase
- open draft PRs when it finds worthwhile improvements
so you never stop discovering what's next.
Outrider: https://github.com/remyxai/outrider
Try it at https://studio.remyx.ai
Docs at https://docs.remyx.ai reacted to salma-remyx's post with 🔥 4 days ago
It's like Christmas in July for frontier coding models.
There's a lot of talk about AI costs, but I've managed to increase the rate of code production by orders of magnitude while reducing the average cost per feature branch over the past month.
Models like GLM-5.2 and Kimi-k3 have been a big part of that, but so has better automation.
I don't keep confirming prompts or redirecting my agent to check out the CONTRIBUTING.md to apply repo conventions, that's baked in. Boris Cherny was just posting about how to build the abstractions to push the limits to your attention in AI coding: https://x.com/bcherny/status/2077929390806073807
If that agent isn't proactive, it's just a chatbot!
I already know what repos I'm building on today, I know I'm going to check out what's new in the arXiv, look for code and licenses, smoke-test the repo, adapt the method to my own application, apply my own org's conventions to shape up a PR.
Now I orchestrate agents to work this out, when an idea makes it through the gates, I'm notified about a PR to review.
And I'm seeing more throughput by scheduling AI coding runs in Github Actions, get outrider: https://github.com/remyxai/outrider
It's a bit like moneyballing vibecoding.
We're using AI to filter the firehose of methods published to the arXiv, matching against your repo and development history, to meet the challenges to improving your application.
Way beyond AI chat with arXiv, the coding agents ground the recommendations and implementations on actual code, using verification and testing to gate which proposals get ramped up to PRs for review.
Check out this Outrider contribution, making its way upstream into the libraries we all use: https://github.com/huggingface/peft/pull/3382Organizations
