> ## Documentation Index
> Fetch the complete documentation index at: https://docs.remyx.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Build Your First Project
> An end-to-end walkthrough of the experimentation cycle in Remyx. Connect a repo, set up shared context, run an experiment, and review the result.
This series walks through the full experimentation cycle on a real open-source AI project. You'll connect a codebase, set up the shared context that grounds future work, scope a new experiment from a recommendation, implement it, evaluate it against a pre-committed standard, and track the result back into your project's history.
By the end, you'll have run a closed-loop experiment in Remyx and have a working pattern you can repeat on your own repos.
## What you'll learn
The series has seven tutorials grouped into five phases. Each tutorial is short and focused on a single user-facing question.
| Phase | Tutorial | Time | Outcome |
| ------------- | --------------------------------------------------------------------------------------------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Setup** | [Create your project](/tutorials/get-started/create-your-project) | \~3 min | A Remyx project linked to your repo, with experiment history extracted from merged PRs |
| **Setup** | [Set up your discovery feed](/tutorials/get-started/set-up-your-discovery-feed) | \~3 min | A daily discovery digest ranked against your team's actual work |
| **Setup** | [Define how progress gets measured](/tutorials/get-started/define-how-progress-gets-measured) | \~10 min | A locked eval template and decision policy. Your team's bar for shipping is written down before any variant runs |
| **Discovery** | [Scope an experiment from a recommendation](/tutorials/get-started/scope-an-experiment) | \~5 min | A scoped experiment with hypothesis, target metric, and tags, ready to be implemented |
| **Build** | [Implement an Experiment](/tutorials/get-started/implement-an-experiment) | \~10 min | A PR that implements the technique, generated by Claude Code reading your project context via MCP |
| **Decide** | [Run an evaluation](/tutorials/get-started/run-an-evaluation) | \~15 min | A variant scored against the locked eval template on Modal, with the decision logged |
| **Reflect** | [Stay in the loop](/tutorials/get-started/stay-in-the-loop) | \~5 min | A read on the project's velocity, the signals emerging across directions, the rationale behind every decision, and the lineage between related experiments |
Each tutorial is self-contained, but they're designed to compose. The artifact you produce in one tutorial is the input to the next. Following the whole series end-to-end leaves you with a real experiment closed in Remyx.
## The example used throughout
The series follows along on [remyxai/VQASynth](https://github.com/remyxai/VQASynth), an open-source synthetic-data pipeline for spatial-reasoning vision-language models. It's a public repo you can clone, fork, or just read alongside the tutorials.
You don't need to use VQASynth. It's a stand-in for whatever AI repo you actually care about. Wherever you see VQASynth mentioned, you can substitute your own repo URL. The flow works the same way on a retrieval stack, an LLM application, a multi-stage agent, a model-training pipeline, or anything in between.
Using a single example throughout means each tutorial's artifact carries forward to the next without re-establishing context.
## Concepts you'll meet along the way
A few terms recur across the series. Here's what they mean upfront:
* **Project.** A workspace that scopes a set of related experiments. Carries shared context (history, eval template, decision policy, integrations) so individual experiments don't have to re-establish it.
* **Experiment.** A single tracked change with a hypothesis, a target metric, and an outcome. Can be backfilled (auto-extracted from a merged PR) or created manually.
* **Eval template.** A saved configuration that says how to score a variant against a baseline.
* **Decision policy.** A saved set of rules that says when a variant counts as shipping, when it counts as rejecting, and when the team should iterate.
* **Variant.** A candidate alternative to the baseline that gets evaluated.
* **Baseline.** Whatever the variant gets compared against. The form depends on what your project produces (a model checkpoint, a dataset, a system version).
Each tutorial defines these inline as they come up. This list is here for orientation.
## Prerequisites
**Required for the full series.**
* A Remyx account
* A connected GitHub integration (see [Connectors](/platform/manage/connectors))
* Claude Code installed (for the implementation tutorial)
**Required for the evaluation tutorial.**
* A Modal account, with billing enabled
* A Hugging Face token, set as a project secret
## Start the series
Connect a repo and watch Remyx extract structured experiment history from your merge log. About three minutes.
## Or skip ahead
Daily discovery digests ranked against your team's work
Lock in your eval template and decision policy
Turn a paper into a structured experiment
Generate a PR via the MCP integration
Score the variant on Modal, log the decision
Velocity, signal patterns, decision rationale, and lineage views