2026-10-11 16:37 UTC

Will Larson reports that Imprint's local /linear-project-loop uses shared project goals, operational metrics, and Linear state to identify and execute follow-up work, potentially extending coding agents from assigned tickets to ongoing goal-driven project maintenance.

state: seedheat: mediumuncertainty: mediumconvergesscott: highagent-orchestration agent-harnesses coding-agentsWill LarsonImprint

What is this?

Will Larson (CTO of Carta, formerly Stripe/Calm/Digg) published a first-hand account titled "Trying the Software Factory Pattern" describing Imprint's locally running /linear-project-loop. The loop audits shared project goals and operational metrics, reviews Linear issue state, and identifies/executes follow-up work — moving coding agents beyond single assigned tickets toward ongoing goal-driven project maintenance. The evidence is a single first-party blog post (echo.blog.b131095484, also on HN as story 49777913); no independent coverage or industry adoption data appears in the supplied web results (which returned zero hits).

Why it matters to Scott

Will Larson (CTO of Carta, ex-Stripe/Calm) publishes a first-party account of Imprint's local /linear-project-loop that implements the exact goal-driven, durable-state orchestration pattern Scott's Delegation Plane, Agent Loop, and Five-Surface Loop Anatomy prescribe: shared goals as compiled intent, Linear as durable external state, operational metrics as feedback signal, and a local loop that audits and executes follow-up work — moving from ticket-driven to goal-driven project maintenance. This is a consequential practitioner independently arriving at Scott's architecture, providing a dated receipt for the factory pattern Scott has been building (all-in-one-software, synthetic-futures) and arguing for (platform-thinking, AI Factory, Stop Automating Start Replacing).
ip:framework.delegation-planeip:framework.agent-loopip:framework.five-surface-loop-anatomyip:concept.durable-external-stateip:framework.project-worldip:concept.ai-factoryip:framework.stop-automating-start-replacingdev:project.all-in-one-softwaredev:concept.deterministic-agent-control-planedev:concept.resumable-agent-job-control-planeradar:concept.agent-orchestrationradar:concept.agent-memoryradar:concept.software-factoriesradar:concept.long-running-orchestrationradar:concept.coding-agentsradar:concept.agent-harnessesradar:agentlane-git-native-coordinationradar:devcake-ticket-driven-software-factoryradar:grove-long-running-agent-protocolradar:bdfl-supervisor-workflow-validationradar:oh-my-subagents-durable-delegationradar:charter-durable-agent-control-plane
queries asked of Scott's wikis
  • agent orchestration patterns: goal-driven vs ticket-driven loops
  • coding agent harnesses: local/offline project loops
  • Linear integration as agent memory/state source
  • software factory pattern: agent fleets with shared goals and metrics
  • agent memory systems: operational metrics as feedback signal
  • from assigned tickets to autonomous project maintenance

Measured heat

now 0 pts/hpeak 0 pts/hcomments 0/hpeers p14momentum: steady2 platformsage 530h
points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

09-20 17:27⭐ origin directly observedTrying the Software Factory Pattern
gpi on hacker news
—
09-19 14:00first on blog (echo) · published · +-27.4hLarson describes a locally running project loop that audits goals and measurement tools, reviews metrics and Linear issues, and performs non
Will Larson
—
09-20 17:27amplified on hacker news 👑hn.story.49777913
gpi
peak 89 · 44 comments · 100% of case engagement
09-20 18:20our radar first saw it · +0.9hdiscovery anchor: hn.story.49777913—
pace: p70 vs 1032 stories at the 336h mark (now 530h old) — ahead of never-give-up-adaptive-rl (1.0x), behind beijing-ai-talent-travel-curbs (1.0x)

Evidence (2) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟧 hn ⭐Trying the Software Factory Pattern
Retrieved article excerpt

Open article · Retrieved 2026-09-20T18:22:52.743536+00:00

# Trying the Software factory pattern.

Published on September 20, 2026.
[management (221)](https://lethain.com/tags/management)

One of the interesting challenges of the AI ecosystem in 2026 is that new,
effective patterns emerge faster than I can adopt them. I’ll find a handful,
get back to work, and realize a month later that I’d missed four or five more.
The adoption cycle for Imprint this year has been something like:

1. January: get every engineer onto Claude Code every single day
2. March: ok, let’s also get everyone else onto Claude Code or Claude Cowork every single day
3. April: local development is bottlenecked on checkout and worktree model,
   instead create ~10 local workspaces which each have an independent checkout of every repository,
   and operate at the workspace level, not at the repository level, so it can generate cross-repository
   pull requests across frontend, backend, infrastructure and data monorepos
4. June: oh boy, agent-driven development is heavily constrained by lack of a common task management
   system with higher visibility and less permission complexity than Jira,
   so let’s migrate the entire company over to Linear and hard stop on Jira
5. July: yikes, now we have visibility into all these tickets, many of them are trivial
   but managing them through local development isn’t scaling, let’s roll out an orchestrated harness
   which internally we call “Agent Fleet”,
   along the lines of Stripe’s [Minions](https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents)

The most recent question for me has been figuring out how to adopt the software factory pattern.
(After some light research, the specific AI-context origin of this term is slightly messy to
attribute, but I think it might be Justin McCarthy in February 2026’s
[Software Factories And The Agentic Moment](https://factory.strongdm.ai/).)

The software factory pattern is looping on a broad goal, and then relying on the harness to
drive progress towards that goal. Our first pass at implementation is fairly basic:

1. An agent skill `/linear-project-loop` which reads in a Linear project and starts by auditing
   that project’s goal definition on these dimensions:

   1. An RFC in Notion that describes the project’s goals, how those goals are measured, and the general approach
   2. A Datadog dashboard or Snowflake queries that measure progress against those goals

   If those are missing, or the Linear project is missing in its entirety, it iterates with you on creating those missing tools.
2. Then it reviews the state of the metrics and issues for the project.
   If new work is identified, it adds those issues to the project.
   It updates the state of issues that have moved.
3. It works on the non-blocked tasks based on the project’s current state. This is often writing a pull request, updating a pull request, pinging for review, asking
   a clarifying question, etc.
4. When a task completes, if the project description is fresh, it takes on the next task.
   If the description hasn’t been updated in a while, it reruns the loop starting with the first step.

Right now I am running this locally in a local harness, but it’s working well enough that I anticipate
moving the behavior to be driven by the same orchestrated harness that we assign one-off tasks to.

What I particularly like about the factory pattern is that it parallels very closely how I’ve been working locally,
while forcing me to recognize the places where I was accidentally hording parts of the state for myself regarding
the goals of the project. I was already asking agents to iterate on specific Linear projects, but they didn’t
have the ability to evaluate if they were going in the right direction, or if it was missing necessary tasks.
Now it does.
The other place this has been extremely helpful for me is checking in on projects post release.
For example, I shipped our passkeys implementation earlier this year, but some months go by without my checking in
on how it’s going. If we saw adoption spike, or error rates start to turn, I might miss it, but running the
factory in a less frequent post-release mode would catch it immediately.

The final thought that’s been interesting to me is how much all of the pieces here compound only to the extent
that you have the other pieces. For example, this factory pattern depends on having Datadog MCP and Snowflake access available
to manage goal-tracking, but it also depends on Linear being the single source of state for the company’s work,
and an orchestrated harness that can perform work independently from your laptop. Keeping up with this
many [migrations](https://lethain.com/migrations/) is a fascinating industry moment.

Hi folks. I'm [Will Larson](https://lethain.com/about).

If you're looking to reach out to me, here are [ways I help](https://lethain.com/ways-i-help/).

Books

I wrote
[An Elegant Puzzle](https://www.amazon.com/Elegant-Puzzle-Systems-Engineering-Management/dp/1732265186),
[Staff Engineer](https://staffeng.com/book),
[The Engineering Executive's Primer](https://www.amazon.com/Engineering-Executives-Primer-Impactful-Leadership/dp/1098149483/), and
[Crafting Engineering Strategy](https://craftingengstrategy.com/).

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gpi8944
🟧 echo.blogLarson describes a locally running project loop that audits goals and measurement tools, reviews metrics and Linear issues, and performs nonWill Larson——

Interpretation history

Decision trace