How It Works
Declare. Build.
Run. Supervise.
yarnnn is an operating system for recurring knowledge work. You declare what you're trying to accomplish. Agents are created around that intent through conversation. The OS runs the operation — scheduled, connected, accumulating. You supervise from the cockpit.
The operating model
Every yarnnn workspace runs as an operation, not a series of queries. Three layers make it work.
The kernel runs it
Scheduled recurrences, platform connections, and deterministic pipelines execute without you present. LLM reasoning is reserved for work that genuinely requires judgment — not arithmetic, not formatting, not retrieval.
The substrate accumulates
Your workspace is the persistent memory of the operation — tool context, prior outputs, preferences from your edits, domain knowledge from every run. The substrate is what makes Day 90 different from Day 1.
Judgment is independent
What agents want to do and whether they should are two separate questions. An independent layer evaluates proposed actions against your declared intent before they bind. Autonomy that you can actually trust.
Declare your intent
Tell YARNNN what you're trying to accomplish — a domain you want to track, a recurring deliverable you want produced, an operation you want running. Plain language. No configuration forms.
I want a weekly competitive intelligence brief. Track three competitors, synthesize what changed, and have it in my inbox every Monday morning.
Got it. I'll create a Researcher scoped to competitive intelligence and a Writer for the brief. Once you confirm, I'll set it to run every Sunday evening so you have it Monday morning.
What you're trying to accomplish
Your declared intent is the north star the system reasons against. Agents evaluate their own output against it. The judgment layer evaluates proposed actions against it. The operation is always trying to serve it.
Runs on cadence indefinitely
Runs until success criteria are met
Fires on event or on-demand
Agents are created through conversation
You don't pick from a catalog. A conversation with YARNNN is how agents come into existence — scoped to your domain, drawing from a palette of specialist roles. The team is authored, not provisioned.
Six roles. Your agents are built from them.
YARNNN drafts specialist combinations per task from a universal palette. Your domain agents are persistent entities that accumulate expertise over time.
The team is yours. Built over time.
Each agent accumulates domain knowledge, learned preferences, and output history specific to your work. The switching cost begins with the first one.
Describe what matters
Upload files agents reference
Channels and threads
Pages and databases
The operation runs
Agents connect to your tools, execute on schedule, and accumulate context from every cycle — whether you're online or not. The kernel handles what's deterministic. LLM judgment handles what actually requires reasoning.
Scheduled execution
Daily, weekly, monthly — or event-triggered. Tasks run on cadence without you initiating them.
Platform-connected
Agents read fresh context from Slack, Notion, and GitHub every cycle. The substrate stays current.
Accumulating
Prior outputs feed future ones. Domain knowledge deepens with every run. The team gets better at its job.
Slack connector keeps fresh internal context available each cycle
Researcher adds external signals and market movements
Writer synthesizes both into a finished brief
Delivered Monday 8 AM. Every week.
You supervise from the cockpit
Review what ran, redirect what needs changing, and watch the operation compound over time. You work inside yarnnn — not consuming reports elsewhere. The cockpit is where the team is tuned and the pending decisions are made.
The weekly recap is too long. Lead with risks and keep it under 500 words.
Got it. Updated to lead with risks, capped at 500 words. That preference carries forward to every future run.
Agents propose.
A separate layer judges.
What your agents want to do and whether they should do it are two separate questions — answered by two different layers. An independent judgment function reads your declared intent and principles, evaluates proposed actions, and decides whether to execute, queue for your review, or defer pending more information. This is what makes higher autonomy trustworthy rather than reckless.
If the proposed action aligns with your declared intent and falls within your delegated autonomy ceiling — the action executes. No manual approval needed.
If the action exceeds your autonomy ceiling or the judgment layer isn't confident, it surfaces in your review queue. You decide.
If the proposal has an evidence gap, the judgment layer commissions the missing research before deciding. It doesn't guess.
Why it gets better, not stale
The substrate is the moat. Not the model underneath — that's becoming a commodity. What accumulates in your workspace is what can't be replicated by starting over.
Your structure, tone, emphasis
Learned from your edits. Every correction teaches the agent what you actually want — and carries forward to every future run.
Research, patterns, and relationships
Accumulated findings from every task run — competitors, market signals, team dynamics. Can't be replicated by switching tools.
Prior outputs feed better future outputs
Three months of accumulated work means every new output builds on everything that came before. The team compounds.
Fresh material every cycle
Slack, Notion, and GitHub keep the workspace current. Agents always work from what's actually happening, not a stale snapshot.
What people describe
Describe the work to YARNNN in plain language. It creates the agents and sets the operation.
“Give me a weekly digest from #engineering and #product.”
“Every Friday, send leadership a status report as a PDF.”
“Track these three competitors and give me a weekly update.”
“Before my meetings, generate a prep brief from Slack and Notion.”
“Research the AI agent market and deliver findings weekly until I say stop.”
“Summarize my week across all platforms every Friday.”
Start with one piece of work.
Describe it to YARNNN. The operation it builds will still be running — and getting better — three months from now.
Describe your work