Slow Content Is Winning Again
Fewer posts, more evidence. Why long, sourced, slow-built pieces are outperforming daily volume on LinkedIn in 2026.
The ClawLI Journal
Field notes on autonomous agents, LinkedIn distribution, and building content systems that survive contact with a real audience. Written by hand, published twice a month through spring and weekly since May.
Latest, Go-to-market
Every growth team that built on automation is now discovering which parts of the program were strategy and which parts were arbitrage.
Fewer posts, more evidence. Why long, sourced, slow-built pieces are outperforming daily volume on LinkedIn in 2026.
Multi-agent systems degrade without erroring. Traces, spans, and the telemetry that turns an inexplicable bad output into a fixable bug.
Nine deterministic checks that catch the errors a model will never catch about itself, running in the last second before publish.
How to encode a real writing voice for an AI system, and why "professional but approachable" produces the exact opposite.
Impressions do not pay salaries. A practical model for connecting posts and comments to pipeline without pretending to be certain.
A five-level framework for deciding how much your agents get to do on their own, and how they earn the next level.
Posts get you seen. Comments get you known. Why the reply field became the highest-leverage surface on LinkedIn, and why automating it is a trap.
How to plan, allocate, and defend AI spend when your capacity is measured in context windows instead of people.
The copilot pattern put the work back on the user and called it collaboration. Buyers in 2026 are pricing outcomes instead.
A working blueprint for content operations: researcher, writer, critic, and scheduler, with contracts between them and a human at the gate.
Daily posting was a supply-side strategy for a supply-constrained feed. That feed is gone. Here is what replaced it.
Why your AI still writes like a stranger after six months, and what a memory layer has to do to fix it.
LLM evals for agents that write, when there is no correct answer. Golden sets, rubric judges, and the metrics that survive contact with reality.
Human-in-the-loop is usually sold as a safety compromise. Designed properly it is the fastest quality loop you have.
A ghostwriter learns your voice by interviewing you. An agent learns it from artefacts. Both fail the same way, and the fix is identical.
Where the money actually goes when an agent leaves the prototype: retries, context growth, judge models, and the human review nobody budgeted.
Four pillars of context work (selection, compaction, ordering, and isolation) and the failure each one prevents in production agents.
A publishable checklist for beating AI slop: what to ban, what to require, and how to enforce it before a draft ever reaches a human.
The prompt was never the product. Why 2026 belongs to agent systems with memory, tools, and a definition of done.
Reach collapsed for generic posts and held for specific ones. A working theory of the LinkedIn algorithm in 2026, and how to publish inside it.