Models

Definition

Models coverage in this archive spans 3 posts from Mar 2024 to Apr 2026 and treats models as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, llm, and open source. Recurring title motifs include ai, state, open, and source.

Working claims

• The archive repeatedly argues that models only creates leverage when it is wired into an existing workflow.
• The consistent theme from 2024 to 2026 is disciplined execution over hype cycles.
• This topic repeatedly intersects with ai, llm, and open source, so design choices here rarely stand alone.

How to apply this

• Define quality gates up front: eval sets, guardrails, and explicit rollback criteria.
• Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
• When boundary questions appear, cross-read ai and llm before committing implementation details.

Where teams get burned

• Shipping agent behavior without hard boundaries for tools, data access, and approvals.
• Optimizing for model novelty while ignoring reliability, latency, or cost drift.
• Applying guidance from 2024 to 2026 without revisiting assumptions as context changed.

Suggested reading path

• Start here (current state): The Best Model Is the Smallest One That Works
• Then read (operating middle): Picking an AI Model for Production (Late 2024)
• Finish with (foundational context): Claude 3 First Impressions: Three Models, One Decision Framework

Related posts

• The Best Model Is the Smallest One That Works
• Picking an AI Model for Production (Late 2024)
• Claude 3 First Impressions: Three Models, One Decision Framework

References

• The Twelve-Factor App
• Google SRE Book
• RFC 9110: HTTP Semantics