Remyx

DocsSign inJoin us at Experiment 2025The bottleneck isn't building. It's knowing what works. Remyx gives your team a systematic way to find approaches worth testing, validate them against your system, and compound each improvement into the next.Try it freeRead the DocsFeatured by the AI communityHow it worksIdea to Production, SystematicallyExperiment with confidence. Integrate discovery, building, and validation.DiscoverFind approaches worth testing for your use case and spin up ready-to-run environments automatically.BuildMeasure changes against your production workload. Generate evidence for which approaches to pursue next.ValidateLink every change to a measurable outcome. Know what differences matter.How Remyx HelpsLearn what works.Remyx helps engineers test more ideas and helps leads know which ones drive improvements.For ML EngineersThere's a new technique every week. But without a way to test it against your system, you won't know if it's better than what you have or what to try next.Searching through ideas → Approaches surfaced from evidenceStuck debugging implementations → Pre-built, ready to test against your systemUnsure what moved the needle → Measurable results you can build onFor Team LeadsYour team is shipping changes fast, but you have no shared record of what's been tried, what worked, and where to double down next.Ad-hoc changes with no record→ Structured and repeatable testsOverreliance on intuition → A shared history of what's been tried and what workedOne-off tests→ Every team building on what the org has already learned Know What's Next Each experiment lends more evidence to refine your next hypothesis The space of possible improvements is too large to try everything. Remyx helps your team start from relevant prior work and build on what you learn. First hypothesis You find a technique relevant to your problem. Remyx sets up a pre-built environment so you can test it against your application directly, instead of rebuilding it from scratch. Narrowing the search space Your past results inform what to try next. Your next experiment follows from evidence, from what your team has already learned, so you're building on real results, not reacting to hype or following hunches. Shared decision record Every technique tested, every result measured, every dead end recorded. New team members onboard from real experiment history. Leadership reviews the reasoning behind decisions alongside the outcomes. EXP 1 First hypothesis You find a technique relevant to your problem. Remyx sets up a pre-built environment so you can test it against your application directly, instead of rebuilding it from scratch. EXP 3 Narrowing the search space Your past results inform what to try next. Your next experiment follows from evidence, from what your team has already learned, so you're building on real results, not reacting to hype or following hunches. EXP 10 Shared decision record Every technique tested, every result measured, every dead end recorded. New team members onboard from real experiment history. Leadership reviews the reasoning behind decisions alongside the outcomes. Your team's experiment history becomes the foundation for every decision after it. Learn how ExperimentOps works From the Founders Built by practitioners, for teams building at the frontier A team of mathematicians and award-winning ML innovators with a decade of experience applying AI in robotics, healthcare, content recommendation, and enterprise data/ml infrastructure. Salma MayorquinCEO & Co-Founder Applied Mathematics, UC Berkeley. Former Solutions Architect at Databricks advising MLOps strategy from startups to Fortune 500. Award-winning ML innovator recognized by NVIDIA's developer community. Enterprise MLData InfrastructureAI Strategy LinkedIn → Terry RodriguezCTO & Co-Founder UC Berkeley. 10+ years applying ML in healthcare, robotics, and content recommendation at Riot Games, Tubi, Robust.AI. Open-source tools cited by Google DeepMind and used in peer-reviewed research. RoboticsHealthcare AIContent Rec LinkedIn → Talks, Pods & Writing Conference talks, podcast conversations, and field notes on how AI teams go from experiment to production. 🎙Podcast · HeavybitExperimentOps: Why MLOps Alone Isn't EnoughSalma unpacks the shift from MLOps to ExperimentOps with stories from Netflix, Yelp, and Stripe on bringing foundation models into production.Salma MayorquinListen → 🎤Talk · MLOps CommunityBeyond Benchmarks: Measuring Success for AI InitiativesWhy offline metrics don't predict online success and how to design evaluation systems that align model development with business outcomes.Salma MayorquinWatch → ⚡Talk · AG2 CommunityGitRank: Automating Paper-to-PR with Agentic Code ExecutionTechnical deep-dive on how GitRank uses AG2 agents to discover, build, and test research implementations from arXiv to live experiments.Terry RodriguezWatch → 📰Feature · Cerebral ValleyRemyx: Your AI Production AssistantIn-depth interview on the mission behind Remyx, how the founders met at Berkeley, and their vision for the next generation of AI engineering tools.Salma MayorquinRead → ✍️Blog · SubstackVibes Don't ScaleWhy AI-assisted coding falls apart past the demo stage and what disciplined experimentation looks like when complex code changes are under-determined by simple prompts.Terry RodriguezRead → 🎙Podcast · HeavybitExperimentOps: Why MLOps Alone Isn't EnoughSalma unpacks the shift from MLOps to ExperimentOps with stories from Netflix, Yelp, and Stripe on bringing foundation models into production.Salma MayorquinListen → 🎤Talk · MLOps CommunityBeyond Benchmarks: Measuring Success for AI InitiativesWhy offline metrics don't predict online success and how to design evaluation systems that align model development with business outcomes.Salma MayorquinWatch → ⚡Talk · AG2 CommunityGitRank: Automating Paper-to-PR with Agentic Code ExecutionTechnical deep-dive on how GitRank uses AG2 agents to discover, build, and test research implementations from arXiv to live experiments.Terry RodriguezWatch → 📰Feature · Cerebral ValleyRemyx: Your AI Production AssistantIn-depth interview on the mission behind Remyx, how the founders met at Berkeley, and their vision for the next generation of AI engineering tools.Salma MayorquinRead → ✍️Blog · SubstackVibes Don't ScaleWhy AI-assisted coding falls apart past the demo stage and what disciplined experimentation looks like when complex code changes are under-determined by simple prompts.Terry RodriguezRead → Active in the AI Community Open Source We contribute open-source tools, datasets, and benchmarks across AI domains and the research community builds on them. 🌐From landmark paper to open infrastructureWe reproduced DeepMind's SpatialVLM data pipeline entirely with open-source tools, then trained models a fraction of the size, proving small, open models could learn spatial reasoning too. This enabled follow-on NeurIPS publications, new benchmarks, and a wave of open models across the field.Google DeepMind · CVPR 2024 → 🎓Datasets adopted by NeurIPS researchersOxford and UC Santa Cruz researchers trained and evaluated their models using Remyx open-source datasets.Oxford / UCSC · NeurIPS 2025 → 🛠️VQASynth: Open-source data synthesis pipelineA toolkit for composing multimodal training datasets, used and extended by researchers worldwide to advance model capabilities.GitHub · remyxai/VQASynth → 🤝Partner integration with AG2 (formerly AutoGen)Co-developed the RemyxCodeExecutor for AG2's agentic framework, enabling researchers to explore and test code from any paper.AG2 Official Docs → 📈30K+ downloads on community model releasesLessons learned from publishing open models on Hugging Face and what makes research artifacts actually get adopted by practitioners.Substack · Myx'd Results → 🌐From landmark paper to open infrastructureWe reproduced DeepMind's SpatialVLM data pipeline entirely with open-source tools, then trained models a fraction of the size, proving small, open models could learn spatial reasoning too. This enabled follow-on NeurIPS publications, new benchmarks, and a wave of open models across the field.Google DeepMind · CVPR 2024 → 🎓Datasets adopted by NeurIPS researchersOxford and UC Santa Cruz researchers trained and evaluated their models using Remyx open-source datasets.Oxford / UCSC · NeurIPS 2025 → 🛠️VQASynth: Open-source data synthesis pipelineA toolkit for composing multimodal training datasets, used and extended by researchers worldwide to advance model capabilities.GitHub · remyxai/VQASynth → 🤝Partner integration with AG2 (formerly AutoGen)Co-developed the RemyxCodeExecutor for AG2's agentic framework, enabling researchers to explore and test code from any paper.AG2 Official Docs → 📈30K+ downloads on community model releasesLessons learned from publishing open models on Hugging Face and what makes research artifacts actually get adopted by practitioners.Substack · Myx'd Results → Ready to stop guessing?Start improving your AI system in minutes.Try it freeRead the Docs --- DocsSign inAbout usProduction Ready AIWe're creating the tools that make AI development more intuitive, scalable, and impactful. Join usLearn moreOur teamMeet the TeamSalma MayorquinSalma Mayorquin is a seasoned machine learning engineer and co-founder of Remyx AI.Terry RodriguezTerry has a decade of experience applying machine learning in healthcare, robotics, and recommender systems.Join UsIf you're passionate about shaping the future of AI tooling and want to work on cutting-edge challenges, we’d love to hear from you.Email AddressWe will get back to you soon.Oops! Something went wrong while submitting the form.FaqsFrequently asked questionsDoes Remyx offer a free trial?The Remyx Dev Studio is free for developers to experiment building datasets, models, evaluations, and more. Authenticate using a Google or Github account to get started. Read more here. Do you currently have open positions?We’re always looking for talented people who are excited about building the future of AI development. If that sounds like you, reach out!What features does Remyx offer?Remyx provides a variety of tools around data curation, model selection, training, deployments, and evaluation. For all the latest releases, you can learn more in our docs.Do you have an enterprise offering?We're building the Remyx Studio deployable in your own cloud. If you're interested in early access or want to discuss your needs, get in touch! --- DocsSign inBlogNews & articlesCatch up on the latest Remyx updates, blogs, podcasts, and moreFeatureApr 1, 2025Remyx - Your AI Production Assistant 💡ProductMar 23, 2025Agile AI EngineeringProductMar 27, 2025Trustworthy AI ExperimentsProductMar 27, 2025The Agent for Experimentation CultureFeatureApr 1, 2025Remyx - Your AI Production Assistant 💡ProductMar 23, 2025Agile AI EngineeringProductMar 27, 2025Trustworthy AI ExperimentsProductMar 27, 2025The Agent for Experimentation Culture12345678Latest articlesAllFeatureAllNewsAllProductFollow us on social mediaStay up to date on the latest in AI Engineering newsFeatureApr 1, 2025Remyx - Your AI Production Assistant 💡ProductMar 23, 2025Agile AI EngineeringProductMar 27, 2025Trustworthy AI ExperimentsProductMar 27, 2025The Agent for Experimentation CultureProductApr 1, 2025Overcoming The Experimentation BottleneckProductApr 10, 2025Always Be EvaluatingStay TunedIf you're passionate about the future of AI engineering workflows, subscribe to the Monthly Myx!Email AddressThanks for joining our newsletter.Oops! Something went wrong while submitting the form. --- TALK TO USSee Remyx in actionBook a 30-minute walkthrough tailored to your team. We're working closely with early design partners and offer direct access to our founding team.Full nameEmail addressCompanyCompany websiteThank youPlease check your inbox to start your demoOops! Something went wrong while submitting the form.