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Pull request overview
Adds a new Layer5 blog post explaining how “System 1” vs “System 2” task shapes map to AI workflows and how that impacts model/workflow selection.
Changes:
- Adds a new MDX post covering System 1 vs System 2 task categories, selection tradeoffs, and a brief “System 3” framing.
- Uses standard blog MDX structure with wrappers/components (BlogWrapper, Callout, Blockquote, CTA) and internal links.
- Introduces a task-fit comparison table inside the post.
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| - Engineering | ||
| - Platform Engineering | ||
| - Open Source |
| | Task shape | System 1 fit | System 2 fit | | ||
| | ---------- | ------------ | ------------ | | ||
| | Email or ticket triage | Excellent | Usually unnecessary | | ||
| | First-pass summarization | Excellent | Useful only for high-stakes summaries | | ||
| | Multi-file refactors | Weak alone | Strong | | ||
| | Root-cause analysis | Risky alone | Strong | | ||
| | Tool orchestration with safety checks | Limited | Strong | |
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| For many teams, the best design is not one super-model for everything. It is a pipeline. Let a fast model handle retrieval, routing, and first-pass synthesis. Escalate only the ambiguous or high-risk cases to a slower model that can reason, verify, and explain. That is usually a better systems design than paying a premium for deep reasoning on every prompt. It is also why teams building AI-enabled platforms, including the <Link to="/projects">Layer5 projects</Link> and the broader <Link to="/community">Layer5 community</Link>, care about predictable interfaces that help models know when to think and when to act. | ||
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| ## Is there a System 3 |
This adds the requested Layer5 blog post on how AI System 1 and System 2 task categories differ, why that distinction matters in practice, and when a model optimized for one or both is the better fit.
Content
src/collections/blog/2026/09-16-ai-system-1-versus-system-2-tasks/index.mdxStructure
Assets
src/collections/blog/2026/09-16-ai-system-1-versus-system-2-tasks/hero-image.jpgExample