ExperimentOps for AI Development
Remyx helps AI teams to systematically discover, test, and deploy new techniques while closing the evaluation loop with controlled online experiments.Quick Start: Run Your First Experiment
Complete guide — 5 minutes to first results →
The Problem
You can implement ideas faster than you can validate them. With AI-assisted coding, writing code takes hours. But knowing if that code improves your application for real users? That takes weeks. Three specific bottlenecks: 1. Offline metrics don’t predict online success A team improved MRR by +18% offline. In production, user satisfaction only improved +7.9%. Test sets don’t match production, benchmarks don’t capture user behavior, and LLM-as-judge can be gamed. 2. Discovery is slow and noisy Social media surfaces hyped papers weeks after publication. Relevant work from unknown labs never gets discussed. You miss 99% of potentially useful papers. 3. Reproducibility is broken You spend 2-5 days debugging environments before you can test if a paper, blog, codebase method actually works.How Remyx Solves This
1
Discover papers in minutes
Semantic search over arXiv, matched to your engineering challenges. Pre-built Docker images for each paper.
Start Discovering
2
Test ideas systematically
Kanban board tracks experiments from hypothesis to results.
Create Experiment
3
Validate with real users
Integrate with A/B testing platforms. Measure actual user impact.
Deploy & Validate
4
Build institutional knowledge
Track which offline metrics predict online success. Each experiment makes the next one smarter.
Core Workflow
- Discovery
- Implementation
- Experimentation
- Validation
Find relevant papers fast
- Semantic search over daily arXiv papers
- Get personalized recommendations matched to your interests
- Pre-built Docker images eliminate environment setup
- Papers within hours of publication
Resources Search
Tools
Explore
Search arXiv papers with semantic understanding. Pre-built Docker images for reproducibility. Codebases, huggingface resources, and more coming soon!
Ideate
GitRank generates PRs implementing paper methods in your repos.
Experiment
Kanban board for tracking experiments with agent copilots.
Curate
Generate and score datasets with Data Composer and rubrics.
Train
Fine-tune models with LoRA (SFT and DPO strategies).
Evaluate
Compare models with MyxMatch and standard benchmarks.
Why Learning Compounds
Traditional approach: Each experiment starts from scratch. ExperimentOps: Each experiment builds on the last.| Experiment | Offline Prediction | Online Reality | Learning |
|---|---|---|---|
| #1 | Guess MRR matters | +18% MRR → +7.9% satisfaction | Correlation: 0.44 |
| #2 | Use MRR based on E1 | +22% MRR → +10.2% satisfaction | Correlation: 0.46 |
| #10 | High confidence | +15% MRR → predict +6.8% | Within 5% of actual |
Access Remyx
- Studio
- CLI
- API
Visual interface for interactive work
- Experiment board with drag-and-drop
- Paper viewer with chat
- Team collaboration
Open Studio
Learn More
Quick Start
5-minute guide to first experiment
ExperimentOps Concepts
Deep dive into methodology
Case Studies
How teams adopt systematic experimentation
Community
Experiment 2025 — Oct 30, San Francisco
Join researchers, engineers, and builders shaping the future of AI development.
What to expect:
Co-create the agenda — opening circle where attendees decide topics, trade practical insights you won’t find in docs or blogs
Breakout sessions — 30-minute deep dives on discovery, hypothesis generation, experiment design, feedback loops, post-mortems, and scaling
Real lessons from practitioners — what broke, what worked, what you’d do differently
Closing circle — share learnings and takeaways from the day
Join the community of researchers, engineers, and product builders who want to move faster from ideas to production. Come discuss what operationalizing learning really means, share what’s working (and what’s not).
What to expect:
Co-create the agenda — opening circle where attendees decide topics, trade practical insights you won’t find in docs or blogs
Breakout sessions — 30-minute deep dives on discovery, hypothesis generation, experiment design, feedback loops, post-mortems, and scaling
Real lessons from practitioners — what broke, what worked, what you’d do differently
Closing circle — share learnings and takeaways from the day
Join the community of researchers, engineers, and product builders who want to move faster from ideas to production. Come discuss what operationalizing learning really means, share what’s working (and what’s not).
Register Now
Connect with the community:
Questions?
Email contact@remyx.ai