Know your
next move
The decision layer between your AI coding agents and production.
Remyx helps you identify the next improvement worth making, filter out what doesn't apply, and learn from every result.
Your next big win is out there
Every week adds another promising idea. Most will never justify the engineering time. The few that do are easy to miss.
The next change worth making
Remyx ranks candidate improvements against your codebase, constraints, and past results, then recommends the highest-confidence move or explains why to hold.
- matched to a call site in your code
- gated on fit, reachability, and license
- across prompts, retrieval, tools, routing
Remyx sits upstream of your coding agents and your evaluation stack. It decides which change is worth their time.
What it opened on real repos
Real draft PRs Outrider opened on well-known public repos, with the gates it checked in plain sight. Open any to read the selection reasoning and the diff.
Riemannian-preconditioned LoRA optimizer
Implemented, tested, benchmarked, refined through maintainer review, and merged upstream, with AI assistance disclosed and a human approving every step.
"this PR is already quite mature"peft maintainer · first review
+401 / −3 · 6 files · 5 commits · 9 tests · 33 days open to mergeSuper-Tuning & Supra · frozen-weight adaptation
Ports two adaptation methods from a 2026 paper behind PEFT's standard tuner interface, following the coordination path the maintainer confirmed before implementation began.
The paper's first author co-authored commits on the branchcoordination issue #3450 · maintainer-confirmed before implementation
+1309 / −3 · 24 files · opened Aug 5, 2026EcotReasoningModule · dense chain-of-thought supervision
Adapts the annotation pipeline to emit embodied chain-of-thought reasoning traces, wired into LeRobot's existing module contract. First-round review comments were addressed the same day.
AI assistance disclosed per LeRobot's AI_POLICY.mdtested end-to-end with an open VLM via an OpenAI-compatible endpoint · no cloud API key required
2 commits · 11 files · 3 unit tests + an end-to-end dataset runSteers RL fine-tuning toward the tokens the model is least sure about, so each update lands where it teaches the most.
The Outrider run that started the chain. Validated on our fork, then coordinated and reshaped into huggingface/peft #3382.
Turns repeated user corrections into a runtime check, so the agent stops making the mistakes you already corrected.
Swaps the hard clip in RL fine-tuning for a smooth penalty that gives rare tokens more room to move.
Lets a robot policy learn from imperfect demonstrations instead of throwing the messy data away.
# fork validations demonstrate the pipeline on real codebases. upstream statuses reflect maintainer decisions we don't control. skips happen on most runs by design.
Idea to production, systematically
Remyx closes the loop across your stack, recommending what to try next and learning from every result so the next recommendation is sharper. Hover a step to trace it through the cycle.
Every result compounds what your team learns
Every evaluation, experiment, and production outcome becomes evidence. Recommendations build on your real results, so each cycle starts sharper than the last.
- your eval suite, offline and A/B
- promote, comment, or stay silent, on your policy
- observe-only by default, earns autonomy on results
# you set the policy. starts in observe-only.
Own your evaluation system
Define quality using the criteria your team already trusts.
Remyx stores your criteria, results, and decision history in your repo and runs them consistently against every change. Remyx handles orchestration and reproducible execution.
Works with your stack
The tools you already use, in one experiment record. More ship every month.
plan & ship
# planned, shipped, reviewed
measure & learn
# offline + online results
build & run
# implemented + executed
# bring your own provider key: Anthropic, Z.ai GLM, Moonshot Kimi, and more.
Prove it on one repository
You leave with an evaluation system your team owns, running on your repository, and the results to decide what comes next.
One repository, one technical owner, one AI product or subsystem. Success criteria agreed in writing before the pilot starts.
A scoped install, evaluation criteria reviewed by your team and committed to your repo, an agreed set of changes run through the full workflow, and a final evidence report. Refundable if the agreed criteria are not met.
Founder-led. Runs in your GitHub Actions by default, with the option to run entirely in your infrastructure. The pilot fee credits toward your first annual agreement.
Security that fits how you already work
Remyx provisions server-side through a scoped GitHub App. Access is per repo and revocable, your keys stay in your own repo secrets, and a human gates every merge.
For teams building AI systems
The best AI teams don't stop at shipping. They measure, evaluate, and refine. Remyx turns evaluation results, experiment history, and production outcomes into a shared system for identifying and prioritizing the improvements most likely to drive better results.
Run better experiments
Remyx carries forward what your team has learned, helping you evaluate ideas faster and focus on the changes most likely to improve results.
- recommendations informed by prior outcomes
- context on every experiment
- faster validation of new ideas
Make better decisions
Remyx turns experiment results into organizational knowledge, helping teams prioritize work based on evidence instead of isolated findings.
- visibility across experiments and outcomes
- evidence behind every decision
- a shared system of record for improvement
Built by practitioners, for teams shipping AI in production
Mathematicians and award-winning ML practitioners, a decade applying AI in robotics, healthcare, recommendation, and enterprise data.
Salma Mayorquin
ceo & co-founder
Applied Math - UC Berkeley. Former Databricks Solutions Architect, startups to Fortune 500. Recognized by NVIDIA's developer community.
Terry Rodriguez
cto & co-founder
Mathematics - UC Berkeley, UNC Chapel Hill. 10+ years of production ML at Riot Games, Tubi, and Robust.AI. Open-source tools cited by Google DeepMind.
Ready to decide with evidence?
Start free with Outrider and get your first recommendation in minutes. We're in early access with a first group of teams shipping AI in production.
# your next move, with evidence.