SCTY

Lab

Built in the open.
Folded into the work.

The lab is where the work gets tested first.

Some of it ships as open source. Some stays private. The point is simple: client work should be easier to trust, easier to run, and harder to break.

Tested under load

This is where workflow, memory, briefing, and QA ideas get used early, broken, and cleaned up before they reach client work.

Open when it can be

Public repos where that makes sense. Internal patterns where it does not.

Useful beats impressive

The goal is work that still holds together a month later, not a good-looking demo.

Why the lab matters

Where ideas earn their keep.

This is where new ideas get used early, broken, and tightened up.

Some of the best pieces are not public repos. They are the quiet parts underneath the work: state, memory, briefings, and anti-rot checks.

Open work matters because you should not have to trust a black box to get useful automation.

Four entry points

Four entry points.
Same working logic.

Client work does not inherit every repo. It gets the parts that hold up: execution, memory, briefings, and checks.

Living Knowledge Systems

For turning raw source material into memory a team can reuse.

Briefing Systems

For daily synthesis, leadership updates, and decision support.

nightly loop

A scheduled loop that gathers activity, resolves loose ends, and turns the day into a usable brief.

persona briefs

Briefs that keep priorities, continuity, and relationship context intact from week to week.

brandOS

Interfaces for seeing search and model signals side by side instead of guessing what the machines see.

field reports

Public essays that turn research and field notes into something sharper than a recap.

Assurance Systems

For monitoring, evals, and checks that catch drift before anyone else does.

tripwire

Checks for freshness, delivery failures, source health, and the quiet breaks that rot a workflow over time.

autoresearch

An experiment loop that tests changes, measures the result, and keeps the ones worth keeping.

critbench

Benchmarking for reasoning quality when “looks smart” is not enough.

givecare-bench

Benchmarking for high-stakes AI where tone, memory, and judgment all matter.

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