Build path

Build AI workflows with execution access.

Begin with guided Dev Schooling, receive your keys and SDK, then move into controlled M13 execution.

1 / 6 — Dev Schooling

Begin with Dev Schooling.

Dev Schooling is a guided onboarding session inside your work area. It walks you through the complete developer setup and tells you exactly which commands to run in VS Code.

Follow the commands. Receive your keys. Download the SDK. Start building.

Guided developer setup
Ready to begin
01Open session
02Run commands
03Receive keys
04Download SDK
05Start locally
What you do
VS CodeRun the command Dev Schooling gives you.
01Open session

Start the guided workflow and stay inside one clear onboarding path.

02Run commands

Copy the commands you receive and run them in VS Code.

03Receive keys

Get API key, private key and public key reference prepared for developer execution.

04Download SDK

Receive your SDK package at the end of the guided setup.

05Start locally

Use the SDK start commands and test with Dev Credits when ready.

Session log
[✓]

Dev Schooling session opened.

[✓]

VS Code command handoff prepared.

[✓]

Developer keys provisioned.

[✓]

SDK download unlocked.

[✓]

Local start path ready.

Start Schooling WorkflowGet Dev Credits →
2 / 6 — Access
Access Readiness

Check your developer state.

After Dev Schooling, this console shows workspace, schooling, API keys and Dev Credits so you know whether execution is ready.

Start Schooling WorkflowGet Dev Credits →

Dev Credits are required for API calls, SDK tests and workflow execution.

3 / 6 — RITA

Keep the workspace oriented.

RITA turns local workspace knowledge into four orientation artifacts: Python source index, JSON contract index, Markdown system index and runner index. Development starts from a bounded map of files, anchors, summaries and execution entrypoints.

Artifact tabs
semantic_workspace_index.json
1{2  "artifact_file": "semantic_workspace_index.json",3  "index_family": "python_source",4  "map_entries": [5    {6      "source_ref": "steps/RITA08_SummarizeAdmittedSourcesPY.py",7      "semantic_summary": "Builds clean semantic summaries for admitted source files.",8      "anchors": [9        "function: build_prompt lines 274-275",10        "function: validate_semantic_summary lines 293-309"11      ]12    }13  ]14}
Command tabs
rita_run.trigger.json
1{2  "command": "rita run <workspace_path>",3  "triggers": [4    "bind workspace identity",5    "initialize workspace-local state",6    "run RITA core life",7    "write orientation artifacts"8  ],9  "outputs": [10    "semantic_workspace_index.json",11    "semantic_workspace_index-json.json",12    "semantic_workspace_index-md.json",13    "semantic_workspace_index-runner.json"14  ],15  "runtime_state": "active"16}
RITA impact layer
01

Energy and token cost reduction

RITA prepares compact workspace orientation before expensive model work starts, reducing repeated context loading and wasted token movement.

02

Development acceleration

RITA preserves orientation plateaus, indexes and change awareness so development resumes faster and with fewer blind retrieval cycles.

03

IP protection

RITA stays workspace-local. It exposes controlled indexes and targeted retrieval so proprietary context remains bounded to the active workspace contract.

4 / 6 — M13 API Bridge

Call M13 when the workflow needs pressure.

Route local workflow moments into the right M13 pressure mode: fast orientation, deep reasoning, compact summary, hard check, challenge pressure and attack-level reality testing.

fast

Rapid orientation and direct next action.

reasoning

Deep combined derivation when the task needs C-level thinking.

summary

Compact state transfer for continuation and handoff.

check

Hard KPI judging before a result moves forward.

challenge

Benchmark pressure against a stronger alternative.

attack

Reality pressure before the workflow enters production context.

Open API details →
5 / 6 — SDK

Enter the local machine room.

Explore the SDK as a complete local execution workspace: files, runner, workflow, artifacts, API bridge and RITA inside one machine room.

Your local execution workspace — explore every file, then run it.

Twelve tiles. Three are live-runnable. Every run leaves inspectable artifacts.
Machine Room
Machine Room ›Local Runner

This is where the extension becomes real.

Run the extension from terminal and watch the full flow pass.

execution-space/engine/terminal_extension_runner.pyexecution-space/bin/run.shexecution-space/bin/run_action_builder.sh
[✓]python3 execution-space/engine/terminal_extension_runner.py run
[✓]Starting local execution: run_eu_ai_act_diagnostic_pipeline
[✓]Run path: execution-space/runs/terminal_extension_run_<timestamp>
[✓]TERMINAL EXTENSION RUN PASSED
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Start the Schooling Workflow first. Keys and SDK package unlock together at the end of the guided setup.Start Schooling Workflow
6 / 6 — Publish / Payout

Publish workflows and earn from execution.

Beta

After a workflow becomes useful, it can move toward publishing. Developers can receive up to 30% of generated tokens as Payout Tokens when published workflows create usage.

01

Publishable workflow

A workflow needs a controlled execution path, clear artifacts and a reason to be used repeatedly.

02

Payout Tokens

The payout layer connects developer contribution to generated execution value.

03

Up to 30%

Eligible published workflows can return up to 30% of generated tokens as Payout Tokens.