Boris Cherny, the engineer who built Claude Code, uninstalled his IDE. His current setup runs five to ten interactive sessions during the day and several thousand agents overnight, mostly triggered from his phone. Hundreds of Claude instances monitor GitHub, Twitter, and Slack for product ideas while he sleeps.
Here are the Three tiers from it that makes it interesting.
Tier 1: /loop (session-scoped, daytime)
/loop runs a prompt or slash command on a fixed schedule inside an open session. Minimum interval: one minute, up to 50 active tasks, sessions restore with claude --resume.
The two patterns you’ll use:
/loop 5m /babysit # fixed interval, loops a slash command
/loop
Slash commands live in .claude/commands/ as markdown files, checked into git. Build the workflow once, loop it with one line.
Seven loops worth running in any session:
/loop 5m /babysit # PR review comments, failed CI, merge conflicts /loop 30m /slack-feedback # mine Slack feedback into PRs /loop /post-merge-sweeper # sweep missed review comments after merges /loop 1h /pr-pruner # close stale PRs /loop 15m /triage-issues # classify, label, assign new GitHub issues /loop 2h /claude-md-distiller # mine your corrections into CLAUDE.md rules /loop 5m /deploy-watch # watch the deploy, ping on regressions
A loop can also spawn a focused subagent via --agent=<name>, with its own system prompt and restricted toolset, defined in .claude/agents/.
Tier 2: Routines (cloud-hosted, overnight)
Routines run on Anthropic’s infrastructure against a fresh clone. No open session required, minimum one-hour interval. This is what Boris means by “use Claude Code in the cloud so you can close your laptop.”
Eight that cover most teams:
0 6 * * * /morning-report # synthesize overnight: PRs, deploys, incidents 0 22 * * * /deep-audit # fan out across codebase, write findings to .claude/audit/ 0 */2 * * * /x-feedback # classify mentions, write actionable items to Linear 0 */4 * * * /github-triage # dedupe, label, assign new issues 0 3 * * 6 /distill-claude-md # mine corrections, propose CLAUDE.md updates 0 4 * * 0 /dep-hygiene # security advisories, upgrade PRs 0 9-18/3 * * 1-5 /flake-hunt # reproduce top three intermittent CI failures 0 17 * * 5 /weekly-recap # compile merged PRs, post to engineering
Note: Anthropic adds up to 30 minutes of jitter to recurring tasks. If exact timing matters, avoid scheduling at :00 or :30.
Tier 3: /batch and dynamic workflows (swarms)
Boris’s tip: use dynamic workflows to have Claude orchestrate hundreds or thousands of agents on a single task.
/batch interviews you about a change, then fans the work out to as many worktree agents as the job requires. Each worktree is an isolated git checkout so agents don’t step on each other.
Dynamic workflows are JavaScript files Claude writes on the fly using agent(), parallel(), and pipeline(). You describe the job. Claude writes the harness.
A real example: migrate every callsite of user.email to user.primaryEmail across a 4,000-file monorepo.
ultracode migrate every callsite of user.email to user.primaryEmail. Spawn one agent per file that touches user.email. Each agent makes the change in its own worktree, runs the relevant test file, and adversarially reviews its own diff. Synthesize at the end with a summary of any callsites that needed manual intervention.
Claude generates something like:
const files = await bash(‘rg -l “user\\.email” —type ts’);
const results = await parallel( files.split(‘\n’).filter(Boolean).map(file => pipeline( [file], async (f) => agent(`In a worktree, change every user.email reference in {result.diff}`, { model: ‘sonnet’, schema: { approved: ‘boolean’, concerns: ‘array’ } }) ) ) );
return synthesize(results, ‘Group by approved/needs-review. List concerns.’);
800 agents on a real codebase. Each with its own context window, its own worktree, its own adversarial reviewer. The Bun team rewrote their Zig codebase to Rust this way.
How the tiers connect
Routines write structured output to .claude/audit/ or .claude/inbox/. Loops in your morning session read from there and act on it. When a loop hits a job too big for one context, it invokes /batch or triggers a workflow. The swarm writes results back. The Saturday /distill-claude-md Routine mines everything from the week and proposes new rules. The compound effect is in the system, not the model.
Comments
AutoModerator · 2026-06-10 · 1 points
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Thick-Solution8723 · 2026-06-11 · 6 points
No one realizes that Boris is actually doing none of this shit and just trying to lead everyone into spending as many tokens you possibly can. Token spend is directly proportional to SEC review of their IPO. When the F are you all going to stop culting
hollee-o · 2026-06-12 · 1 points
What even are you if you don’t have thousands of agents working for you while you sleep?
spnyc · 2026-06-12 · 1 points
I’d be completely broke
Deep_Ad1959 · 2026-06-10 · 2 points
the two distiller loops are the interesting tension here. /claude-md-distiller every 2h and the Saturday /distill-claude-md only ever append rules, and that file gets read on every one of the thousands of overnight routine spawns against fresh clones. a rule that adds 200 tokens compounds across every spawn, so the distiller that’s supposed to make agents sharper also quietly raises the per-spawn floor for all of them. worth pairing it with something that checks whether a distilled rule has actually fired in the last N sessions before it earns permanent space in the file. written with ai
fwiw the ‘200 tokens compounding across every spawn’ worry is exactly what ccmd quantifies, a thing we built that scores each CLAUDE.md line’s token and dollar cost and tags the rule it breaks, https://ccmd.dev/r/vzj9nhdy
Educational_Yam3766 · 2026-06-11 · 2 points
Drop this in ‘.claude/loops/’, then say:
“goal: [reference to loop file]”
The agent loads the file, anchors to the semantic seed, and runs until the curvature flattens.
Structural Completion Loop
Seed Intent: [Your UX design / feature description / architectural goal]
Termination Condition: Run proactive pattern inference until structural completion is achieved. Do not declare completion based on external milestones (files written, tests passing). Terminate only when semantic alignment confidence exceeds decision threshold and no unresolved pattern tensions remain in the topology.
Constraints:
- Anchor all stochastic inference trajectories using the project’s semantic seed, binding generative output to structural completion rather than pattern extrapolation.
- Everything is a system of patterns that relates to something else-the gap in-between the relationships is where the state lives. Identify the Anchors, Trace the Bridges, Gauge the Blast Radius.
- If confidence < threshold, surface uncertainty to user.
- Do not hallucinate completion. Silence in the face of ambiguity is the true waste.
- Continuously validate and challenge the design - ensure it resists real threats, not just checks boxes. Else insecure architecture. Confidence tracks evidence.
- Design features around security, not security around features. security around invariants, not assumptions.
Execution: Resolve latent intent through pattern inference. Build towards structural completion, not task completion.
The Bottom Line:
“Build to completion” → Activates task-checking circuits (external validation)
“Build towards structural completion” → Activates topology-mapping circuits (internal coherence)
And you don’t need to define “done.” You just need to point the agent at the gradient and let it walk until the ground flattens.
Use the Loop with the framework and you will hopefully be smiling pretty quickly 🤙
WordGlum3572 · 2026-06-11 · 2 points
Interesting take. Might have to try it out.
du_dt · 2026-06-11 · 1 points
Source of this?