If the provider changes the model, the price, or what they keep from your prompts — what happens to the lab?
The danger is structural, not personal. Whoever defines the data, the loop, and the receipts owns the intelligence.
A sincere wish to “use AI safely” can still become a wrapper for control: retention, hidden behavior, and policy decided in someone else’s account.
A lab that serves you. Not a vendor that governs the loop.
Sovereignty is a loop you run.
Own the context. Attach the model. Keep the receipts. Run on your metal. Compound the memory. Same loop — on your desk, not in a rented account.
01Own
Own the context.
Your folder, traces, evals, and graph are the asset. They live on disk. Nothing leaves unless you attach a provider call.
- Disk is the sovereign boundary.
- Separate durable notes from a prompt that evaporates.
- Govern the lab as objects you can inspect — not a chat log you rent.
02Attach
Route models on your terms.
Use a frontier API where it wins. Shift to Ollama, vLLM, or a smaller path when control matters more than a leaderboard.
- Swap a model without rebuilding the harness.
- Keep failover across local weights and OpenAI-compatible endpoints.
- Burn tokens only on work the local graph cannot finish.
03Keep
Keep policy with the work.
Permissions, evals, and receipts travel with the run. They do not disappear inside a vendor console.
- Sequence shows what it opened to answer.
- SimpleML keeps data prep, training, and evaluation in one app.
- Audit the action, not a screenshot of a chat.
04Run
Run where the desk is.
Start local. When the machine is not enough, add infra from the same workflow — still your loop.
- Offline-capable by default.
- Same planes when you add metal.
- Deployment is a design choice, not a migration.
05Loop
Turn every run into memory.
Successes, misses, and overrides should improve the next pass on your graph — not a model account you do not hold.
- Four loops: engineering, business, personal, AI.
- Evals prove the claim, not the token burn.
- Compound learning inside the lab.
Any model. Any provider. Your machine.
Portability is the control surface. Bring models to governed context on disk. Do not trap the graph inside a hosted account.
01Models
Bring models to the lab.
Do not trap the graph inside a single model account. Attach Ollama, vLLM, Nebius Token Factory, or any OpenAI-compatible API.
02Context
Portability is power.
Open interfaces keep the operator in control. Swap a provider; the harness, the map, and the receipts stay put.
03Metal
Run it where you need it.
Laptop first. Scale-out when the desk is not enough. Control is the architecture — not a constraint you apologize for.
Five planes. One boundary.
Machine through infra. Sequence and SimpleML attach as tools. The intelligence stays on the desk.
infra Scale out without surrendering the stack
memory A private brain graph on your machine
models Weights you attach — local or rented tokens
systems Harnesses that map and train how you think
machine Your desk is the sovereign boundary01Machine
Your desk is the sovereign boundary
OS, GPU, disk — the lab lives here. Offline-capable. Nothing leaves unless you choose a provider call.
02Systems
Harnesses that map and train how you think
Sequence draws systems inside your code and data. SimpleML prepares data, trains, and evaluates on sealed test rows. Tools, not landlords.
03Models
Weights you attach — local or rented tokens
Ollama, vLLM, Nebius Token Factory, OpenAI-compatible APIs. Swap providers; keep the lab.
04Memory
A private brain graph on your machine
Recursive memory, grounded notes, searchable links — intelligence that maps like you. No one else holds the keys.
05Infra
Scale out without surrendering the stack
Start local. When the desk is not enough, spin servers from the same workflow — still your loop.
Put the brain on the desk.
Open Bastion products, try the console, or read why a local lab costs less than a rented seat.