Portable inputs
Code, dependency manifests, assets, models, and configuration all have explicit entry points, with no hidden state tied to a local device.
Workflow in Practice
Connect a Cloud Mac to your workflow—not build another one
Every case follows the same structure: define the goal and existing blockers, explain how the machine, node, and tools are connected, then list deliverables and status that the team can verify directly. There are no scores, and vague performance claims never replace execution steps.
- 4 types
- Reusable workflows
- 3 tiers
- Physical configurations available
- 4
- Orderable nodes
Task Launch Checklist
Physical node assigned
Track every state from input to delivery
iOS release buildDependencies, signing checks, export
Daily · JP
Artifact ready
Mobile CI runnerDedicated labels, caching, cleanup
Monthly · SG
Task cleaned up
Long-running AI experimentCheckpoints, logs, remote monitoring
Quarterly · KR
Checkpoint saved
Batch media exportTranscoding, verification, final sync
Weekly · HK
Delivery synced
One consistent way to evaluate
See whether the workflow closes the loop before choosing a tier
All four cases use the same checklist, making it easy to map your own process step by step. Inputs must be portable, execution must be reproducible, results must have artifacts or logs, and credentials and temporary data must be cleaned up before handoff.
Reproducible execution
Install commands, tool versions, Runner labels, and task parameters go into scripts or records instead of relying on personal memory.
Verifiable results
Packages, build logs, test reports, checkpoints, or exported files have defined locations and can be traced to the relevant task.
Ready for handoff
After the task ends, remove temporary credentials, sync essential data, and record environment changes so the next team member can take over.
Indie developers
Keep the release pipeline—from commit to package export—on one dedicated physical Mac
Ideal for iOS or macOS projects that need a complete macOS GUI and command-line environment temporarily without occupying a personal computer long term. Enable the machine for the project term and keep dependencies, build records, and exports together.
- Task goal
- Install dependencies, build with Xcode, verify signing assets, and export the package.
- Existing blocker
- The local device also handles daily development, long archives consume resources, and environment changes are difficult to trace.
- How it connects
- Sync the repository, lockfiles, and build scripts to the Cloud Mac, then use a remote session for initial setup.
- Observable result
- Archive logs, signing results, export manifests, and package paths map back to the same commit.
01
Sync inputs
Check out the specified commit and verify package-manager lockfiles, build settings, and export options to avoid undocumented local dependencies.
02
Pin the toolchain
Record Xcode and command-line tool versions. Install dependencies, run a clean build, and confirm the project directory permissions are correct.
03
Verify signing
Check certificates, provisioning profiles, bundle settings, and export parameters against the project checklist. Never retain sensitive content in logs.
04
Export for delivery
Save archive logs, the export summary, and artifact verification data, then sync the package to the team’s agreed delivery location.
Definition of done
The specified commit builds successfully, signing checks pass, the package and logs are exported, and temporary credentials are removed.
Mobile CI teams
Connect a self-hosted runner with dedicated labels and put caching and cleanup rules in the pipeline
For teams that already use GitHub Actions, GitLab CI, or Jenkins and only need a stable macOS executor. Connect the Cloud Mac as a dedicated Runner while your existing pipeline remains responsible for scheduling.
- Task goal
- Route builds to the specified physical node by label and safely reuse dependency caches between tasks.
- Existing blocker
- Shared executors drift in configuration, concurrency limits are unclear, and processes or temporary files left by failed jobs affect later builds.
- How it connects
- Register a self-hosted runner and configure dedicated labels, a working directory, concurrency limits, cache directories, and exit hooks.
- Observable result
- Scheduling logs identify the Runner label, while cache hits and cleanup actions are recorded in the task log.
01
Register the Runner
Register the executor using the team’s naming convention, and put the operating system, chip architecture, and project purpose into readable labels.
02
Restrict scheduling
Only jobs that explicitly declare the dedicated label can run on the machine. Set concurrency according to its capacity to prevent contention.
03
Manage caches
Separate dependency caches from build artifacts. Include tool versions and the lockfile digest in cache keys so invalidation remains explainable.
04
Run cleanup
Whether a job succeeds or fails, stop leftover processes, remove temporary credentials, and clean sensitive files from the project workspace.
Definition of done
Jobs run only on the designated label, cache policies have version boundaries, failed jobs also trigger cleanup, and the Runner can accept the next job.
AI researchers
Run long experiments on the 64GB M4 Pro tier, with checkpoints and process logs stored separately
SoarMac M4 Pro includes an M4 Pro chip, 64GB RAM, and a 2TB SSD—well suited to memory-intensive, long-running experiment workflows. Use remote sessions to monitor and adjust work without keeping a personal computer connected continuously.
- Task goal
- Run long experiments, save checkpoints regularly, and review metrics and error logs through a remote session.
- Existing blocker
- Personal devices may be carried around or go to sleep, making it hard to know where to resume after an interruption.
- How it connects
- Fix the working directory, environment manifest, log location, and checkpoint naming convention on a dedicated physical Mac.
- Observable result
- Experiment parameters, run logs, checkpoints, and recovery records map to the task ID without depending on a continuously active remote session.
01
Establish a baseline
Record the code commit, dependency versions, input-data summary, and initial parameters. Run a short job first to validate the environment and output path.
02
Start the long-running job
Decouple the main process from the remote session, write standard output to a log file, and preserve a clear exit status on abnormal termination.
03
Save checkpoints
Write checkpoints by experiment stage while saving the parameter summary and current progress, rather than leaving only unidentified model files.
04
Validate recovery
Run at least one recovery test before delivery. Confirm that checkpoints are readable and logs are continuous, then sync essential results to team storage.
Definition of done
The experiment continues after the remote session disconnects, checkpoints include parameter context, recovery steps are validated, and key results have been exported.
Audio & video workflows
Split upload, batch processing, quality checks, and final sync into four handoff-ready stages
For teams using macOS-native tools and Apple Silicon acceleration for batch transcoding, rendering, or export. The goal is not to move every collaboration task into a remote desktop, but to define clear boundaries for inputs, queues, outputs, and sync locations.
- Task goal
- Receive media, run batch processing and exports, complete quality checks, and sync finished files to team storage.
- Existing blocker
- Local exports occupy creative workstations, media versions and export settings are scattered, and team members cannot tell which final file is ready to deliver.
- How it connects
- Define and record separate locations for uploads, the processing queue, temporary files, finished files, and the sync target.
- Observable result
- Each batch has an asset manifest, parameter summary, failed-item record, artifact verification data, and sync status.
01
Receive media
Generate a file manifest before upload. After arrival, verify counts, names, and checksums, and treat the source-media directory as read-only.
02
Run batch processing
Put resolution, codec, color, audio, and output naming into presets or scripts. Send failed items to a separate retry list.
03
Verify outputs
Spot-check duration, frame dimensions, audio tracks, and file integrity. Keep the source filename and processing log for any anomaly.
04
Sync finished files
Sync only verified deliverables and the delivery manifest. Confirm that team storage is readable before removing temporary files from the machine.
Definition of done
Source media remains intact, processing parameters are reproducible, failed items are recorded, final files are verified and synced, and temporary directories are cleaned.
From the team’s perspective
The real improvement is clearer task boundaries
The following quotes reflect typical user roles and focus on ways of working—not scores, rankings, or performance promises.
“Once I put the dependencies, Xcode version, and export settings into the project checklist, switching machines no longer meant guessing the environment from scratch. At the end of a release, the package and logs can go straight to the next collaborator.”
“We didn’t need another CI platform; we needed a Mac that our existing scheduler could locate precisely. Once dedicated labels, cache keys, and exit cleanup were built into the pipeline, the execution boundaries became clear.”
“Reliable long-running jobs depend on checkpoints, logs, and recovery steps—not on keeping a remote screen open. Once we standardized those rules, experiments became easier to review and hand off.”
Choose the closest case and complete one verifiable workflow first
Need to compare memory, storage, and rental terms? Start with the three tiers. Moving GUI or command-line tasks? Start with the remote connection guide. When you’re ready, choose a machine, term, and node in Singapore, Tokyo, Seoul, or Hong Kong. Actual availability is returned in real time by the console.